Through data analytics, a number of insights can be gained. Some examples include, but are not limited to:

• Noticing particular times and days where sales spike or crater.
• Uncovering unusual network activity, which could be an indication of hackers.
• Identifying applications that use an inordinate amount of system resources.
•  Noticing changes in customer/consumer traffic due to external influences.

Data analytics is all about better decision making. This complex process is performed primarily by data analysts and data scientists, but in some cases and with the right tools it can be done by non-tech staff as well. The process often starts with raw data, which is data mined, seeking valuable insight – indeed, competitive advantage is the goal of business data analytics.

To better understand data analytics, jump to the following topics:

• The Importance of Data Analytics
  
• Types of Data Analytics
  
• Benefits of Data Analytics
  
• Challenges of Data Analytics
• Data Analytics and AI
  
• Data Analytics Providers
  
• Data Analytics Certifications
  
• Data Analytics vs. Data Science
  
• Bottom Line: The Future of Data Analytics
  

The Importance of Data Analytics

As digital transformation has gained adoption, the practice of data analytics has skyrocketed. No matter what industry you work in, data analytics likely plays a key role in crafting your strategy. Many companies now have data analysts using data mining techniques on raw data – seeking the many actionable insights gained from this process.

In response, the market for data analytics software has climbed rapidly. According to IDC, worldwide spending on big data and business analytics solutions will climb 10.1 percent in 2021 to reach $215.7 billion. Companies are hiring data scientists and data analysts at a steady clip. The mantra now is “data-driven decision making.”

And it’s worth noting that investment in data analytics did not drop off even in the darkest days of the global coronavirus pandemic. “Unlike many other areas of the IT services market, big data and analytics services continued to grow in 2020 as organizations relied on data insights and intelligent automation solutions to survive the COVID-19 pandemic,” said Jennifer Hamel, a research manager at IDC. “The next phase of digital resiliency will spur increased investment in services to address both lingering and new challenges related to enterprise intelligence initiatives.”

These growing importance of data analytics encompasses a wide range of activities that are common in modern enterprises. For example, data analytics can include many of the following:

• Data mining

• Text analytics

• Data visualization

• Business intelligence

• Data Catalogs

• Data warehouses

• Data lakes

• Data fabric

• Data modeling

• Artificial intelligence (AI)

• Machine learning (ML)

• Deep learning

In addition, a wide range of disciplines make use of data analytics and assorted big data trends, from finance to accounting to product management to manufacturing. And an array of related actions and technologies play a role, including data visualization and relational databases, often using large datasets.

Data analytics is integral to research and development, engineering, and strategic planning. And of course it is the very heart of logistics and supply chain management. With every year, analytics plays a larger role in information technology and cybersecurity. In sum, there is hardly an industry that isn’t driven by data analytics.

Today, many organizations have a chief data officer whose job it is to oversee all aspects of data management within the organization, including data analytics and data science.

Types of Data Analytics

Not all data analytics are the same. Most experts divide data analytics into four key types, including descriptive, diagnostic, predictive and prescriptive.

Descriptive analytics describes what happened in the past or what is currently happening. This type of analytics answers questions like who, what, where, when and how. For example, a sales report that shows your monthly sales over the past four quarters is an example of descriptive analytics. This is the easiest type of analysis to perform, but it has only limited value to the organization. You can’t leave it out, however, because descriptive analytics is a necessary foundation for the more advanced types of analytics.

Diagnostic analytics tells you why something happened. For example, if your descriptive analytics informed you that sales dropped last quarter, diagnostic analytics would help you figure out what went wrong. This type of analytics usually involves combining multiple data sets to create a more full and accurate assessment of your situation. Maybe your sales drop happened because of supply chain problems or bad weather or because you lost a key account after hiring a new salesperson. Diagnostic analytics can help you figure that out.

Predictive analytics helps you understand what is likely to happen next. It takes a look at historical trends, looking for patterns that will offer insights into the future. Often predictive analytics tools rely on advanced data models and machine learning technology that can distill the important factors that impacted past performance and apply those to the current situation. This is a much more advanced and speculative form of analytics with a high potential value. It is becoming a very common tool, particularly for large enterprises.

Prescriptive analytics attempts to tell you what you should do about what is likely to happen in the future. For example, if your predictive analytics forecasts lower sales for next quarter, prescriptive analytics can help you see how that might change if you lower your prices or change your marketing strategy or source product from a different supplier. Obviously, the potential benefit with prescriptive analytics is extremely high, but it is also very difficult to do prescriptive analytics well. Currently few organizations have the resources and capabilities to do prescriptive analytics at scale.

Most organizations start their data analytics journey with descriptive analytics. Over time, they expand into diagnostic analytics, then predictive analytics. Many aspire to eventually have a successful prescriptive analytics program to better inform their business decision-making.

A comparison of the various types of data analytics.

Also see: Real Time Data Management Trends

Benefits of Data Analytics

Most experts agree that data analytics is tremendously important for modern organizations because it helps them become more competitive. Organizations undertake data analytics – using using data analysts – for a large number of reasons. Some of the most common things you can do with data analytics include the following:

Better Understand your Customers

Most organizations have access to a wide variety of data about their customers, including demographics, order history, customer service interactions, social media, browsing history, survey responses and more. Employing data analysts to analyzing this data can help companies create a fuller picture of each individual customer as well as an aggregate picture of their customers as a whole. In addition, it might highlight opportunities to better meet customer needs or reach new groups of buyers.

Streamline Business Operations

Many of the processes within your organization, from order taking, to fulfillment, to supply chain management, to customer service, to IT operations and more are measurable. And anything you can measure, you can improve. Data analytics can help you track progress towards key performance indicators (KPIs) and help you identify bottlenecks that might be slowing your organization today.

Identify New Opportunities

One of the more interesting areas of data analytics is the discipline of whitespace analytics. This practice helps organizations identify business that they aren’t doing today that they could be doing. It can help you find new customers, new products and new partnerships to pursue that could increase revenue and margins.

Capitalize on Existing Trends

Even the most basic data visualizations make it easy to see which direction KPIs are moving and at what rate. By identifying these trends – often sifting through raw data – you can do more of the things that are working well and attempt to correct things that are heading the wrong way.

Market More Effectively

Marketing is one of the business disciplines that has been most transformed by data analytics. Because so much marketing takes place digitally, marketing teams have a wealth of data available that can help them identify which targets are most likely to become customers, which customers are likely to buy again, which customers are in danger of defecting to a competitor and much more. They often use data visualization to help data mine for business insight.

Improve your Pricing Strategy

What if improving your prices by just 1 percent can increase your organization’s overall margins by as much as 10 percent? Analytics can help you analyze the variables. Data analytics can help pricing teams identify where they should increase prices (and where they should decrease them) in order to maximize profitability.

Make Better Decisions

Humans are always tempted to make decisions for emotional reasons, often based on preconceived notions that may or may not be true. Data analytics provides a strong check to this instinct so that business leaders can see whether their gut reactions are likely to result in success or not. In a very broad sense, data analytics can help businesses improve their decision-making across the entire organization.

Challenges of Data Analytics

Like every technology solution, data analytics has its challenges for anyone who chooses to embrace it. They include lack of trained staff, and difficulties with data visualization.

Lack of Trained Professionals

Analytics, big data, and AI are all emerging technologies that have only begun to really take hold in the last few years. That means that the training and education behind them is also lack even further. Many universities are putting together undergraduate and graduate programs and technical education institutions like CompTIA and Coursera have also made an effort to get people trained. But there is still a shortage of talent, for a variety of reasons, such as cost for education. The shortage is not seen being alleviated anytime soon.

Getting the Decision-Makers Onboard

Business units within an organizations may all be in the same boat but all too often don’t think that way. According to research from Forbes Insights and Cisco, 70% of leaders say a successful analytics strategy hinges on close collaboration between IT and business units, but only 15% of global executives, overall, rate analytics interactions between these two groups as “excellent,” while 39% rate business-IT collaboration as “fair.”

The consequences of this schism is that investments in analytics won’t give people the information they need, while more than a third surveyed consider this shortfall a deterrent to capitalizing on technology innovation. It’s important, then, to get IT and the business units on the same page with a shared vision.

Messy Data

The way data is received/collected today is very different from before. With manual database entry, everything went into a neat row and column. Now you have the advent of unstructured data, the flood of data from the edge, and new sources like social media.

The result is often data that is siloed and not readily available. The data collected has to be processed, which can take considerable time. Putting data together from disparate sources means multiple challenges, from getting it out of the dirt data stores to putting it all together into a single format. Much work has to be done by data scientists to make data just usable, and that’s time spent away from analyzing it.

Visualization

If you are trying to glean information from a database, your eyes would quickly glaze over staring at rows and columns of numbers and text information. To truly gain knowledge, you have to engage in visualization of the data and display it in forms that reveal information, such as trends, anomalies, and exceptions.

While data visualization tools are incredibly helpful, they are not easy to use. There is however plenty to choose from, from the beginners tool that is Microsoft Excel to a more advanced tool like Tableau. But they all have steep learning curves and will require time and training.

Data Analytics and AI

Data analytics and artificial intelligence have a synergistic relationship. To some degree, they need each other. AI requires a massive amount of data for training and machine learning, but not every bit of data is useful or helpful. You want some method to sift through the data to find relevant, useful data and get rid of data you do not need.

By deploying AI for better data analysis, you can better use the data that you have left and more easily leverage it for advanced analytical capabilities, like predictive analysis. The combination of the two can improve your business in multiple ways:

• Through the anticipation of emerging market trends, you can get ahead of the curve.
• By analyzing consumer behavior, you can spot consumer trends as they are happening rather than after.
• By recognizing customer patterns, you can personalize and optimize digital marketing campaigns for the customer rather than one-size-fits-all.
• Use big data, AI, and predictive analytics to build intelligent decision support systems.

Data Analytics Providers

Many of the largest enterprise IT companies specialize in data analytics. The following is a list of major providers, but is by no means the full list of companies in this space.

IBM: IBM has both on-premises and cloud-based analytics built around its Cognos line of analytics software, starting with IBM Cloud Pak Analytics Cartridges for scalable deployment of Cognos Analytics.

Oracle: Analytics are a part of virtually everything Oracle does, because Oracle is a database company first and foremost. Beyond that, the company focuses on augmented analytics using natural language query. Oracle provides a full-stack of on premises enterprise applications, such as interactive analysis, reports, and dashboards. For cloud users, Oracle has the Cloud Data Science Platform.

Amazon Web Services: AWS offers more than 50 services to store, process, and visualize data, ranging from its S3 for object storage, Glacier for long-term backup and archiving, AWS Glue for data cataloging, Amazon Athena for interactive analytics, Elastic MapRedue (EMR) for big data processing, and Redshift for data warehousing, among many other services.

SAP: SAP is one of the leaders in business intelligence with its SAP HANA in-memory data marts and data warehouses. The company offers a variety of line of business applications, and it’s SAP Analytics Cloud offers integrated analytics with its range of business applications.

Teradata: Teradata got its start in analytic data platforms through its data warehousing and analytic products. It has since expanded to offer real-time, intelligent answers across on-premises, in the cloud, or in a hybrid environment.

Informatica: Informatica specializes in data management solutions for the cloud, on-premises, or in a hybrid environment. The company’s products are entirely built around analytics and providing customers with the information they need to succeed in business.

Data Analytics Certifications

There are many options available for getting formal education in data analytics. Many top-level universities offer courses and degrees, including Cornell and Stanford. Many top vendors that specialize in analytics offer certifications, which are less elaborate than a full college degree but more than enough to get the job done and get you in the door. They are available through online education sites like Coursera. They are:

Google Data Analytics Professional Certificate: Six month program that provides a comprehensive introduction to data analytics for beginners with no prior experience.

IBM Data Analyst Professional certificate: For those with no prior experience, enough to get you an entry-level data analyst positions. Comes with eight online courses that take approximately 11 months to complete.

Microsoft Certified: Power BI Data Analyst Associate: Designed specifically to training users in analytics using Microsoft’s Power BI for visualization of data. Background in data processes and repositories required.

AWS Certified Data Analytics: Designed to train AWS users to build and maintain analytic solutions. Designed for experienced users already familiar with using data lakes on AWS. Amazon recommends potential students have two years of hands-on experience with AWS.

CompTIA Data Analytics Plus certification: For beginners with 1-2 years experience who wish to learn data mining,  manipulation, visualization, and reporting. An exam to take the course is required. CompTIA recommends that those taking this exam have 18 – 24 months of prior experience in an analyst or reporting position.

Data Analytics vs. Data Science

Although they are similar and closely related—and often confused—data science and data analytics are not the same thing.

In a nutshell, data analytics is a business discipline, while data science is a technological discipline. The goal of data analytics is to answer a particular business question, while the goal of data science is to prepare, transform and organize data so that it is useful. Data analytics requires deep knowledge of a particular business domain, like finance or marketing, while data science requires deep knowledge of mathematic and technological disciplines, like statistical modeling and programming.

Harvard Business Review explains, “Data analytics refers to the process and practice of analyzing data to answer questions, extract insights, and identify trends…Data science is centered on building, cleaning, and organizing datasets.”

In practice, data scientists and data analysts often work together very closely and may even be part of the same team within an organization.

Also see: What is Data Visualization

Bottom Line: The Future of Data Analytics

In the next few years, the use of data analytics will almost certainly continue to grow dramatically. However, not all organizations will succeed with their analytics efforts.

In short, analytics are now essential. Gartner warns, “Through 2025, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance.”

In addition to data governance, other key trends to watch include the following:

Cloud computing: Today, most data analytics is happening in the cloud, and that trend is likely to increase. Because organizations store much of their data with cloud providers, it makes sense to analyze where it is stored to minimize costs and take advantage of the scalability and reliability of cloud services.

Artificial intelligence and machine learning: Many of the most complex forms of data analytics, including predictive and prescriptive analytics, rely on artificial intelligence and machine learning capabilities. As these technologies advance, analytics will become even more powerful.

Synthetic data: Privacy regulations often limit the amount of analytics that organizations can perform directly on customer data. One of the ways to get around this is with synthetic data, which is anonymous and usually generated by data models and algorithms.

Multiple analytics solutions and hubs: Most large enterprises find that no single analytics solution meets all their needs across the entire organization. Experts say that the most successful companies are likely to be those who find innovative ways to combine their various analytics solutions and data repositories.

Organizations that stay on top of these trends—and others identified by their data analytics efforts—are likely to be the most successful over the long term.

Also see: Guide to Data Pipelines

The post What Is Data Analytics? Your Guide to Data Analytics appeared first on eWEEK.

AI startups come in many shapes and forms, both hardware and software. While most of the emphasis is on AI software, there are plenty of chip startups and other hardware vendors vying for the AI market. There are also many AI firms serving just one industry, since AI for manufacturing isn’t exactly portable to, say, healthcare.

The following is a list of notable AI startups across a number of sectors. They are in no particular order beyond alphabetical.

Also see: Top AI Software 

Top AI Startups 

Abnormal Security

Abnormal Security is an email security company that uses AI to protect enterprises from targeted email attacks, otherwise known as phishing/spearphishing attacks. The Abnormal Behavior Technology (ABX) models identity of both employees and external senders and through use of behavioral profiling, the software can effectively predict and detect suspicious emails.

Accubits

Accubits develops AI and blockchain solutions for a variety of different industries, and provides web and app consulting for digital transformation. Accubits consultants can help organizations embed AI into their existing operations or develop entirely new applications from scratch.

Akasa

Akasa uses an AI-powered approach to provide hospitals with its Unified Automation services to address administrative waste that ends up costing patients, providers, and payers. Akasa applies AI to automate complex operational tasks such as revenue cycle management. The company says it provides the entire automation process with human judgment and subject matter expertise. 

Argo

Argo is working on a fully-integrated, self-driving system for autonomous vehicles. The company has developed an entire self-driving system, including the software and hardware compute platforms, sensors, cameras, radar, and light detection and ranging radar (LIDAR). The LIDAR system allows the system to develop real-time models of obstacles and roads.

Bearing

Bearing uses AI to aid in tracking ocean-bound cargo shipments, which has traditionally been difficult to do. It’s designed to help cargo ships comply with the carbon intensity indicator, a measure of carbon produced by operation. This provides the shipper with a more accurate performance, reducing fuel consumption in the process.

On a related topic: What is Generative AI?

Cresta

Companies are increasingly relying on chat bots for customer service and there are plenty of AI firms making the bots, but Cresta has a different approach. It uses AI tech on customer service calls to help agents to offer assistance with customer interaction in real-time. The tool integrates directly into chat conversations to offer real-time advice on responses they should give. Cresta also provides real-time visibility for managers to track agent and customer interactions, helping improve every conversation.

Crowd AI

CrowdAI allows non-programmers the ability to create high quality, custom machine learning models to analyze imagery and video without having to know how to program. Models generated can be embedded in a report, used in a production setting, and with real-time data. The solution assists with operational decisions.

GoodVision

GoodVision helps traffic surveyors and modelers get a more accurate picture of traffic flow ro improve safety and relieve congestion, as well as more intelligently route traffic to avoid buildup. Using known traffic patterns, modelers can better predict the most efficient ways of routing traffic in simulations before actually deploying them.

KorrAI

Mining is an expensive process of trial and error, but with rare earth minerals hard to come by, it’s a necessary evil. KorrAI is geospatial and satellite data platform for mining operations that pulls together multiple sources to offer a more efficient way to locate and organize mineral collection. The company says it can determine the deposits in a large area within seconds.

Linguix

Poorly-written text is all too prevalent on message boards and Web sites, but Linguix hopes to fix that problem by providing a browser extension to offer real-time suggestions to improve clarity and style in writing in emails and such. It doesn’t just spellcheck individual words, but it can rewrite complex sentences to make them more coherent.  Not only that, it comes with a personalized writing coach to improve the skills using the topics needed most in everyday writing.

For more information, also see: Top Robotics Startups

Moveworks

Most chatbots are customer-facing, but Moveworks is internally-directed and designed to help a company’s employees. It covers both technical support and HR, the two most commonly addressed departments. It offers automated troubleshooting for common questions and provides a better way to help employees through tech support tickets.

Neatsy

Neatsy serves a very specialized market: shoe ecommerce. It aims to help people make online shoe sales more personalized by using their smartphone to measure and fit their feet. It uses an iPhone camera as a three-dimensional foot scanner to provide a personalized shoe fit by taking highly precise measurements. 

Netra

Netra is a comprehensive video analysis platform using AI to make on-the-fly recommendations or collect video data. Offering tools like safety analysis, metadata analysis, and object detection, users can quickly and easily break down video content or analyze live streams in real-time.

Ocrolus

Ocrolus is a document automation platform that uses AI to automate the underwriting process for loans by automating credit decisions across fintech, mortgage, and banking. It has a rather narrow focus on the digital lending ecosystem, automating credit decisions across small business, mortgage, and consumer lending, so it is specialized for that market. It analyzes documents and data to help financial institutions make a decision about a loan candidate.

Also see: The History of Artificial Intelligence 

Observe.ai

Observe.ai is an intelligent workplace platform that operates in a company’s contact centers by embedding AI into customer conversations, optimizing agent performance, and automating repetitive processes. It provides call center workers with actionable feedback and evaluation workflows after every customer touchpoint.

Osaro

Osaro brings smart AI automation into industrial environments at scale, while maintaining quality and safety. It focuses on the piece-picking solutions for e-commerce in the goods-to-robot (G2R) function. This is where the greatest gains in efficiency and accuracy can be achieved while solving labor challenges.

Powerhouse AI

This is an automated inventory solution for warehouses that allows workers to walk through the warehouse and take photos of the inventory. It then recognizes the individual boxes and products and builds an inventory list much faster than human staffers. It performs such mundane tasks as counting pallets and boxes, supporting cycle counting, reading bar- and qr-codes, checking correct placement, supporting stocktakes and audits, and providing audit reports.

Reality Defender

Deepfake content is more than a problem for a celebrity in a fake picture or video. It can lead to potentially damaging misinformation. Reality Defender is a tool with which companies can scan media for deepfake content, get alerts, report cards and other ways to visualize and take action against fake content. It offers onboarding protection through identifying fraudulent users and materials, content moderation and disinformation detection.

Riskified

Using an AI-powered fraud prevention platform, Riskified helps ecommerce sites streamline the checkout process and identify fake shoppers from real. It has machine learning models that pull from over one billion transactions to identify the individual behind each online interaction, stop fraud attacks and help merchants eliminate risk and uncertainty from their business.

Starling

More and more, medical testing reserved for a doctor’s office or lab is coming into the home, such as the well-hyped Kardia Mobile. Now there’s Starling, an AI-powered home urinalysis using an in-bathroom spectrometer. Starling aims to catch problems like bladder infections and other urological issues early.

Strong Compute

The training portion of machine learning is the most compute-intensive and time-consuming portion of the process. Strong Compute is about removing these bottlenecks in the training process and speeding up the process by as much as 1,000x or more, according to the company. It uses AI to speed up AI by performing optimizations based on a model.

Tempus

Tempus uses AI and deep research data to perform precision medicine, particularly in the area of cancer research. Precision medicine is medicine customized to the patient rather than the usual one-size-fits-all approach. Its goal is to use the data to provide better treatment decisions, drug developments, and patient therapy.

Toggle AI

Predicting the stock market is never easy, not for lack of trying. Toggle is an artificial intelligence startup that uses data and machine learning to predict what startups are likely to be successful and provide investors with better access to financial issues and market insights.

Uizard

Uizard is a rapid prototyping and design tool for building a mockup of an app or web page with no coding or development experience required. In minutes, designers can put together a prototype of how they want the app to look and work. 

Virtual Sapiens

Virtual Sapiens is an AI startup designed to help professionals improve their virtual communication skills. Think of it as an AI Toastmasters. It provides in-call coaching and insights after your video conference call or meeting in order to help you improve your performance on video calls.

Viz.AI

Another medical company looking to speed things up, Viz.AI alerts healthcare teams as soon as information about patient is ready, rather than forcing the medical team to repeatedly manually check. With Viz, providers saved 22.5 minutes per patient during the transfer of care, and patients spent an average 2.5 fewer days in the hospital.

Voize

Voize is a digital voice assistant for non-desk workers, like medical personnel and field workers who have to document their work. The user dictates their notes or memo into a smartphone and the Voize AI automatically generates structured documentation records. The documentation is then transferred into the existing enterprise software systems of the organization.

Bottom Line: AI Startups

While ambitious and forward-looking, AI startups – like startups in any tech sector – have a high failure rate. Furthermore, AI is a particularly competitive sector, with a vast level of investment by deep-pocketed firms, making life even more difficult for AI startups.

Still, the AI startups on this list reflect future directions in artificial intelligence, as well as machine learning, deep learning, and automation. As much as any list of promising companies, this list of AI startups reveals the future of the tech sector in the years ahead.

Also see: Best Machine Learning Platforms 

The post Top AI Startups appeared first on eWEEK.

On the hardware side, the data security market encompasses backup, storage, and recovery as well as physical policies such as remote backups and “air breaks,” where hardware is physically disconnected and not networked.

On the software side, the data security market comprises best practices that govern data protection techniques such as encryption, key management, data redaction, and data masking as well as privileged user access controls and auditing and monitoring. This includes data security for cloud computing.

The result is a booming data security market. The data security market is expected to grow from an estimated $31.0 billion in 2022 to $55.3 billion by 2027, at a compound annual growth rate of 12.3%, according to research firm MarketsandMarkets. Factors driving market growth include digital transformation and associated security problems, adherence to strict regulatory guidelines, and increased usage of cloud technology and edge computing devices in enterprises.

For more information, also see: The Successful CISO: How to Build Stakeholder Trust

What are Key Strategies in the Data Security Market?

Best practices in data security usually call for a multilayered strategy designed to apply multiple layers of defense. Given that foundation, here are the leading strategies that managers are using to protect data security.

1. Protect the Data Itself

There is a tendency to focus security on just the perimeter of the network and put all the emphasis on the firewall. But what happens when an intruder gets past the firewall? If the data is unprotected and unencrypted, they will have full access to it. So, engage in good data protection inside the firewall as well.

2. Adopt a Zero-Trust Network Design

Zero-trust networks are just that; users cannot move around inside a zero-trust network freely. They must validate their credentials and show they have the right to access that data at every move. Zero-trust networks are highly effective at stopping an intruder in their tracks, but they require significant reengineering of a network.

3. Encrypt All Data

Data should not sit unencrypted and readable by everyone in the network or in the cloud. Make sure all data is stored in an encrypted format and remains encrypted during migrations because significant data loss can occur when data is in movement.

4. Establish Strong Passwords and Policies

Too many organizations still have lackadaisical password policies, letting employees use simple, generic, and easily guessed passwords and not requiring them to change them regularly. No doubt, employees will complain about using strong passwords, but it is a necessary step. So is requiring them to change their password at least every 90 days and not allowing them to reuse old passwords.

5. Test Security

Don’t just assume the network is safe and secure because a firewall and antivirus programs have been installed. Security programs are fallible and are prone to bugs and exploits, and hackers are determined. Just as software is tested while it’s in development for functionality, you must test your firewall and security systems to make sure they work.

6. Invest More Money and Time on Cybersecurity

Security is a tough sell, because the real return on investment (ROI) relies on “nothing happening.” Businesses spend thousands, if not millions, on security measures and short of reports on attacks, they may never really know if it’s working or not or if it is keeping out criminals. Many big companies with sensitive business data are appointing chief security officers (CSOs) or chief information and security officers (CISOs) to implement policies and make them a priority to the C-suite.

7. Update Systems Regularly

Patching is a never-ending process. Most hardware and software firms have regular updates to vulnerabilities, either monthly, quarterly, or when necessity dictates it. In a big enterprise, that’s a lot to patch: the hardware firmware, the operating system, the applications, the firewall, and more. But it’s a necessary evil that must be done.

To learn more, also see: Secure Access Service Edge: Big Benefits, Big Challenges

8. Clean Up Data Stores

Many enterprises are sloppy with how they manage their data storage. They leave redundant copies lying around in multiple locations. Most decent storage systems come with data duplication applications to find and remove redundant data and to keep data stores clean and pruned.

9. Back Up Data Regularly

Regular backups of both on-premises and cloud data is a must. On-prem backup allows businesses to store data in another physical location in case of disaster in the data center, while cloud backup safely stores data in the cloud. In both cases, disaster recovery software helps businesses recover lost data quickly.

10. Implement a Company-Wide Security Mindset

Data security is not just the job of the CSO and IT department, it is a job for everyone who logs into the network. Their responsibility includes safe, secure passwords and embracing best practices. That means not opening email attachments from unknown senders, not sharing login and password information, and not leaving login and password information written down on a sticky paper in the office.

11. Consider the Physical Security of the Data Center

Wherever data is stored on-premises should be the hardest location to get into, and it should be the most physically secure location. This means granting database, network, and administrative account access to as few people as possible, and only those who absolutely need it to get their jobs done. Further, it should have full disaster prevention equipment like fire suppression and climate control.

12. Use Comprehensive Network Monitoring

Networking security and monitoring tools have a tendency to be highly specialized and not broad. It is rare to find one product or tool that does every aspect of network monitoring. So it is necessary to implement a comprehensive suite of threat management, detection, and response tools across on-premises environments.

13. Consider Data Security and BYOD Policies

The bring-your-own-device (BYOD) trend is never going away, so companies might as well adjust to it. That may mean installing proper security software and services for employees who wish to use their personal computers, tablets, and mobile devices. If it will work for a company issued device, it should work for a BYOD device.

14. Use Encryption

Far too many datasets are completely unencrypted and unprotected from theft. Encryption keys scramble data, so only authorized users can read it. There are also file and database encryption solutions that serve as the last line of defense by obscuring their contents through encryption or tokenization. Databases come with encryption and other protections; they just need to be turned on.

15. Implement Data Masking

Data masking is the process of removing bits of data from an entry and replacing them with an asterisk or other character. You’ve probably seen it used in secure logins and passwords. By incorporating data masking, an organization can develop applications using the real data without worrying about exposure.

16. Employ an Elaborate Backup Policy

It goes without saying that businesses must maintain usable, thoroughly tested backup copies of all critical data. All backups should be subject to the same physical and policy security controls that are applied to the primary databases and core systems. Backing up to a regulatory-compliant cloud provider can save a lot of headache because they are responsible for the physical and virtual security of the data.

17. Invest in Employee Education

Training employees in the importance of good security practices is vital and all too often overlooked or just assumed. There is a reason phishing attacks are so successful; far too many employees don’t know enough to not click on a link or attachment from an unknown sender, and this problem has dogged IT for decades.

Putting together a handbook of practices and policies is not enough. Time needs to be spent training and educating employees on what is expected of them.

For more information, also see: Best Website Scanners 

What is the Future of the Data Security Market?

Business is undergoing a digital transformation revolution as data recorded in physical formats is converted into a digital format. This means there is a great deal more data to protect and heavily regulated industries, such as healthcare and finance, are especially sensitive to this issue.

The sheer volume of data that enterprises generate and store both on-premises and in the cloud is growing at a tremendous rate and drives a greater need for data governance. This is made even more complex through the advent of edge computing and the Internet of Things producing data outside of the data center.

Businesses create and manage data like never before, and their loss exposure has never been higher. From trade secrets and intellectual property (IP) to customer information, a company’s data is its lifeblood, and confidence in the ability to protect the data has never been more important. IBM states that 75% of consumers surveyed said they will not purchase from companies they don’t trust to protect their data.

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AI as a platform spans hardware, software, and on-demand services. All three categories have very different players, although there is some overlap between hardware and software players.

The number of U.S. AI companies has doubled since 2017. According to Tracxn Technologies, which tracks startup businesses, as of the third quarter of 2022, there are 13,398 artificial intelligence startups in the United States.

IDC predicts the worldwide AI market, including software, hardware, and services, will grow from $327.5 billion in 2021 to $554.3 billion in 2024 with a five-year compound annual growth rate (CAGR) of 17.5%.

Also see: What is Artificial Intelligence 

What is AI?

Because it is so widely used, AI has become tricky to define. Ask ten people to define AI and you will likely get ten different variations. IT consultancy Gartner defines it as “applying advanced analysis and logic-based techniques, including machine learning (ML), to interpret events, support and automate decisions and to take action.”

This definition coincides with the current state of AI technologies, which is to say that AI performs data analysis and conducts actions based on the findings of that analysis. Analytics has been around for quite some time but it tends to use smaller data sets and offers smaller, less elaborate results. AI handles larger datasets and offers more probabilistic outcomes.

Clearly, there is a wide range of ways in which artificial intelligence can be used. Not to mention the many subdivisions of AI, such as machine learning, deep learning, and natural language processing (NLP).

Therefore, it is incumbent on customers of AI vendors to ask them their definition of AI, and how their offerings will meet your expectations. You have to make sure that their vision of AI aligns with your business needs.

Also see: Top AI Software 

AI Hardware

According to the market research firm Tractica, the global AI-driven hardware market is in the process of growing from a mere $19.63 billion back in 2018 to an expected $234.6 billion by 2025. The AI-driven hardware market includes categories such as CPU, GPU network products and storage devices.

When it comes to AI hardware, the real players are chipmakers, because AI processing is vastly different from typical application processing using CPUs. For the most part, that involves GPU makers, but in recent years there have been startups using new chip designs specifically geared toward AI processing in the hopes of being more efficient and faster than GPUs.

The leading vendor in AI hardware is GPU maker Nvidia. It has repurposed chips normally used to accelerate video games as AI processors, working much faster than an x86 CPU. AMD was not much of a player in this field for a long time because it was struggling to survive, but it has made a remarkable comeback in recent years and is now making serious inroads in the AI and high-performance computing (HPC) market.

Intel is also finally finding its footing in the AI space. It has an inference processor, called Goya, and a processor specifically for self driving cars called Mobileye, as well as its Altera FPGA line for training processing. But it never could quite get the GPU product right until now. Its Xe architecture will be sold under the Arc brand name for consumer GPUs while the AI/HPC product will be known as Ponte Vecchio.

All of the major server vendors – the top brand names like HPE, Dell, and Lenovo as well as vendors such as Supermicro, Wiwynn, and Inspur – all have AI-oriented hardware using chips from Intel, AMD, and Nvidia.

Also see: AI vs. ML: Artificial Intelligence and Machine Learning

AI Software

Getting an accurate measure of the overall AI software market is challenging, because many general purpose applications have AI in them – this leads to the question of whether they should be considered AI software or merely software with AI capabilities.

If we go with the latter, the overall market is massive because it breaks down into so many different categories, each with multiple competitors.

And it’s not always who you might think is a leader. One of the biggest AI software vendors is Nvidia, which we’ve already mentioned in the hardware category. The company often boasts that it has more software engineers than hardware engineers, making AI software to run on their GPUs.

But there are many other vendors. Gartner estimates worldwide AI software revenue was $62.5 billion in 2022, an increase of 21.3% from 2021.

Also see: The Future of Artificial Intelligence

AI-as-a-Service

AI is also being made available as a service, just like software, infrastructure, platform, and other on-demand services through cloud service providers. AI-as-a-service has an appeal to many midsized and smaller enterprises because it means that they don’t have to make the massive investment in AI hardware.

AI hardware is extremely powerful. It’s also extremely expensive. The only real need for horse power is in the training segment. The inference portion of AI, which is where it will mostly be used, does not require high performance computing. A company may perform algorithm training just a few times a year, but then run inferencing against those algorithms as part of business.

That means a company’s expensive AI training hardware, which can easily run into the six and seven figures, will sit idle for long periods because it’s not needed. So why buy when you can rent for the short period you need it? Using AI-as-a-Service, a company on a budget can do the expensive training portion through a cloud provider for much less than the cost of investing in the hardware.

AI-as-a-Service is provided by the top cloud hyperscalers: AWS, Microsoft, Google, and in particular IBM. IBM has lagged behind the other major cloud vendors in overall cloud market share, but it has made a significant AI effort with IBM Watson Cloud. First, it allows companies to make AI a part of their existing applications to make more accurate predictions, automate the decision making processes, and get optimized solutions.

Watson has a number of pre-built applications, such as Watson Assistant, Watson Speech to Text, and Watson Natural Language Understanding. IBM Watson Cloud also provides AI solutions for specific markets such as AI for Customer Service, AI for Financial Services, and AI for Cybersecurity.

Also see: How AI is Altering Software Development with AI-Augmentation 

Toward an AI Strategy

For a business to truly gain the benefits of AI, it should be deployed enterprise-wide, because the benefits of AI can be most fully realized across virtually every department in the company. Gartner says a proper AI strategy identifies use cases, quantifies benefits and risks, aligns business and technology teams and changes organizational competencies to support AI adoption.

The first step is to focus on what your organization is trying to accomplish and the business problems you’re working to solve. AI does not have to be for new applications only. It can be made part of your existing suite of applications, as IBM is trying to do with Watson.

However, it should be done slowly with great deliberation, for two reasons: first, there’s a learning curve inherent in every new technology. No matter how talented the IT staff, they are still going to need time to grasp all of the fundamentals of AI programming and integration in with their applications.

The second reason is that Gartner has noted that organizations experiment with AI but often struggle to make the technology a part of their standard operations. That’s because AI is still in its early stages and the maturation process cannot be rushed. Gartner predicts that it will take until 2025 for half of organizations worldwide to reach what Gartner’s AI maturity model describes as the “stabilization stage” of AI maturity or beyond.

The Future of AI 

The future of looks to be more, faster, and larger investments. Clearly there will be many more use cases for AI, many more applications. The hardware will get faster, leading to more powerful AI systems. And the data sets will get bigger, meaning more complex AI applications in the future.

Beyond the usual speeds and feeds will be the next big step in AI, known as artificial general intelligence, or AGI. Whereas AI does what is programmed to do, AGI has initiative. It asks questions that were not part of its programming and acts upon the.

That may frighten some people. From “2001: A Space Odyssey” to “The Matrix,” fear of AI coming to life and turning on humanity has been around for decades. Hopefully, the benefits will be obvious and overcome any fears induced by popular culture.

The post The AI Market: An Overview appeared first on eWEEK.

According to Statista, the global robotics industry reached a combined value of $43.8 billion in 2021. And according to the Boston Consulting Group, the robotics industry will hit $87 billion by the year 2025. Clearly this is a growth industry with many startups.

As AI has advanced, so has robotics. The robotics devices are adding machine learning (ML) and artificial intelligence along with improved mechanics, navigations skills, advanced sensors, and better dexterity. This growth has expanded their use and rate of adoption.

Also see: What is Artificial Intelligence 

30 Notable Robotics Startups

The following is a list of 30 notable robotics startups, in alphabetical order.

Alert Innovation

Curbside pickup and delivery is a new normal in retail, but for now, customer purchases require an employee to run around the store collecting the order. Alert Innovation’s Alphabot Automated Storage and Retrieval System (ASRS) and Automated Each-Picking System (AEPS) creates a new kind of automated supermarket where goods the customer ordered are collected and delivered to a customer in the store, at curbside, or delivered to their home. Walmart is among the customers for Alphabot.

AMP Robotics

Virtually every city has a recycling effort, but sorting the trash remains a task where humans stand at a conveyor line and sort the trash into different containers and remove non-recyclables. AMP Robotics has developed AI-powered robots that are able to analyze waste material based on shape, color, and other criteria and automatically separate the different recyclable materials.

AvaWatz

Existing commercial robots are designed to perform a single task, and they work individually. The AvaWatz platform of “Cobots” is designed to work in collaboration to tackle more complex, multistep assignments requiring detection, response, and real-time decision-making. For example, one drone will scan a runway for hazardous debris and dispatch a second set of drones to clean it up.

Also see: AI vs. ML: Artificial Intelligence and Machine Learning

Bear Robotics

Bear Robotics develops the Servi line of intelligent robots designed to deliver food to diners in restaurants. It doesn’t completely eliminate the need for human delivery, but it does let servers spend more time with guests or tending to other duties. The robots have smart navigation to avoid objects and humans in the ever-chaotic restaurant setting.

Bedrock Ocean Exploration

Bedrock provides smart robots to map the ocean floor and to collect and manage seafloor data, which is important to shipping companies. Its portable autonomous underwater vehicles (AUVs) provide high-resolution marine surveys, which are shared in the cloud. The old way of gathering seafloor data required human involvement and shipping data to other companies on hard drives.

CleanRobotics

CleanRobotics makes TrashBot, a “smart” trash can that uses robotics and sensor technology to differentiate between recyclable materials and non-recyclable waste. After placing garbage in a TrashBot, it sorts recyclables, such as metals, plastics, and glass, from landfill waste, while simultaneously gathering information about the type of waste generated.

Company Six

Company Six creates reconnaissance drones for first responders and other staff working in high-risk environments to thoroughly scan an emergency scene, terrain, or structure for potential dangers before they enter the area themselves. Its first product, ReadySight, is a “throwable” robot the size of a soda can filled with cameras and sensors that can be deployed in places humans can’t go, or act as an extra set of eyes in places they can go.

Cybernetics Laboratory — CynLr

Robots have been on many manufacturing and assembly lines for years, but their work is limited and often requires human presence. They can weld joints but lack the dexterity to place a screw. CynLr is working to make robotic arms object-aware so as to manipulate objects of varying shapes, orientations, and weights with superior agility.

Diligent Robotics

Diligent Robotics manufactures collaborative robots designed to work together with humans to ease the workload of professionals in everyday environments. These technologically advanced robots come with social intelligence, mobile manipulation, and human-guided ML capabilities. The startup’s first product is a medical robot named Moxi that assists clinical staff in daily logical tasks, so the majority of work duties focus on giving quality care to patients.

Also see: The Future of Artificial Intelligence

Emancro Robotics

Emancro is developing a general-purpose robotic system to address a range of logistics tasks in hospitals, such as restocking storage rooms, delivering supplies and lab samples, serving food, and more. This frees up a significant portion of nurses’ time, allowing them to focus on patient care.

Energy Robotics

Even robots suffer wear and tear. Energy Robotics provides robots to improve inspection of robot fleets. The robots are designed to offer predictive maintenance and are used primarily for remote inspection and monitoring, especially in industries with environments that are harsh and dangerous to humans, such as the oil and gas and petrochemical industries.

Exotec Solutions

Exotec is a warehouse version of Alert Innovation, in that it performs automated order-picking through its Skypod robots. These mobile robots work in three dimensions, transporting and storing bins containing items in racks up to 12 meters high, and can autonomously navigate warehouses without guidance infrastructure. They can carry a standardized box weighing up to 30 kilograms (66 pounds).

Exyn Technologies

Exyn Technologies is developing software for aerial and ground-based robots to autonomously navigate and collect data from dangerous and inaccessible places and environments to humans where there are no maps or GPS coverage. The platform doesn’t need human control, prior information, or knowledge of the infrastructure. The drones are guided by the company’s software and rely on multiple redundant sensors, mapping, and independent autonomy.

FarmWise

FarmWise is building adaptive robots for agriculture to give farmers greater yield, more profits, and a healthier environment. The company is starting with an autonomous weeding robot that can cleanly pick weeds from fields, reducing or eliminating the need for chemical pesticides. It also recognizes and differentiates between vegetables, giving farmers projection on crop yields.

Flexiv

Flexiv is the creator of Rizon, the world’s first adaptive robotic arm, that integrates force control, computer vision, and AI technologies for industries such as automotive, consumer electronics, aerospace, agriculture, logistics, health care, and retailing. The AI enables the robot to adapt to the complicated environments, and accomplish better hand-eye coordination similar to humans.

FlytBase

FlytBase helps enterprises automate and scale drone operations to help firms deploy a fleet of drones, not just a few, through what it calls the Internet of Drones. It is platform-agnostic, working with all of the major drone vendors. Its software development kit (SDK) handles remote drone operations, emergency response, automated inspections, security and surveillance, and more.

Harvest Automation

Harvest Automation builds robots to serve the material-handling challenges in the agricultural industry. Its robots are used in nurseries and greenhouses and for various e-commerce fulfillment applications. The company’s robots are designed to automate labor-intensive and physically demanding tasks, with little need for human input.

HEBI Robotics

HEBI Robotics creates hardware and software tools designed to make it easier to develop customized robots faster. HEBI Robotics’ X-Series Actuators come with sensors and other features, like a brushless motor, gear train, spring, encoders, and control electronics.

iUNU

iUNU is an agriculture technology company designed to make indoor farming and commercial greenhouses more efficient, autonomous, and scalable. It manages crop pests and pathogens, monitors crops, and sends reports to a mobile device, and it has harvesting robots.

Knightscope

Knightscope builds Autonomous Security Robots (ASRs), which utilize a combination of self-driving technology, robotics, AI, and electric vehicles to patrol facilities or help officers and security guards. It can patrol multiple locations at the same time, gathering audio and video data and even speak to people.

Magazino

Magazino develops autonomous mobile robots (AMR) logistics, which enable companies to improve the supply chain, from procurement to returns. In addition, the company provides autonomous picking robots for e-commerce fulfillment companies. The robots adapt and learn from their environment and are networked, so as one learns, it shares what it learns with others.

Miko

Miko is a robot designed to talk to and interact with kids. It has a personality and tells jokes if it detects the child’s mood is down. Its maker claims it understands and responds to a kid’s world, instilling feelings of companionship to help build confidence and encourage creative interactions that are individual to every kid.

Refraction AI

Refraction’s robots serve the last mile of delivery, putting them in competition with the likes of DoorDash and Postmates. Its robots cost less than delivery vehicles used by the likes of DoorDash; can operate on streets, in bike lanes, and sidewalks; and are weather durable, operating in rain and snow.

Robust.AI

Robust.AI is a robotics technology designed to reduce the time it takes to set up robotics systems. It uses a cognitive engine, along with AI, to develop robot software more efficiently. This helps the robotic systems become more reliable and robust in unpredictable environments.

Skydio

Most if not all drones require human guidance, but Skydio is looking to change that by utilizing AI software to give its drones the “skills of an expert pilot.” The software makes drones more intelligent and cognizant of their surroundings.

Simbe Robotics

Simbe Robotics is another robot vendor using automation of mundane, repetitive tasks in the retail industry. Simbe’s Tally robot performs common tasks such as shelf auditing for misplaced items, out-of-stock items, and pricing errors. Although it looks like an ironing board, Tally is a humanoid robot, interacting with employees and shoppers.

Southie Autonomy

Southie Autonomy is developing intelligent assembly robot arms that can be reconfigured quickly with AI software and a device called “the Wand,” which lets users tell robots what to do by using gestures and voice commands. The Wand is a handheld pointer that uses AI and augmented reality to retrain an arm for things like packaging and assembly, with no computer skills required.

ViaBot

ViaBot makes RUNO, an autonomous mobile robot for property maintenance designed to perform outdoor commercial tasks, such as sweeping and grass cutting, without requiring human intervention. RUNO has the ability to swap its own tools and charge itself when not in use.

Voliro

Voliro produces autonomous flying drones for the construction industry. These drones are designed to access dangerous or hard-to-reach locations within a construction site to perform safety and integrity inspections.

Zipline

Zipline is aiming to deliver global shipments of medical supplies using drones. The company provides inventory management, fulfillment, and customer management services and has a fleet of autonomous vehicles that deliver blood and other medical supplies to needy patients. It’s also moving into the retail and e-commerce space, thanks to a partnership with Walmart.

The post 30 Robotics Startups to Watch appeared first on eWEEK.

An edge location – which supports data processing – can literally be the size of the proverbial breadbox, but they are often much larger, from phone booth-sized to shipping containers. Edge computing units are placed in and around major cities, often around retail locations, to gather local data.

A subset of the term edge computing is Internet of Things, or IoT, which is a network that typically comprises hundreds or thousands (or more) of edge computing units, which hold small sensors. IoT is intended to gather data, such as telemetry, remove the unnecessary or superfluous data, and send the relevant data up to a data center for processing. Or, increasingly, process the data right at the edge. IoT supports bringing enterprise applications closer to vast array of data sources.

What Are the Benefits of Edge Computing?

By placing the apps closer to data at its source, edge computing companies can deliver multiple business benefits, including faster insights, improved response times and better bandwidth performance. Because of this, more and more business processing is moving out of the data centers to the edge. Gartner estimates that by 2025, 75% of data will be processed outside the traditional data center or cloud.

Also see:

• Why the Future of Computing is at the Edge
• Will Edge Computing Devour the Cloud?

Top 10 Edge Computing Companies

• • How to Select the Best Edge Provider
  • AWS Edge
  • EdgeConneX
  • ADLINK Technology
  • Vapor IO
  • Mutable
  • Microsoft Azure
  • MobiledgeX
  • Schneider Electric
  • Equinix
  • ClearBlade

How Do You Select the Best Edge Computing Provider?

The market for edge computing providers is in rapid flux – the deals and and offers of today could be markedly different tomorrow. As you settle on a couple of possible vendors, it’s good to have an extensive conversation with that company’s sales reps to ensure all variables are clearly laid out. Not only do you need to select a pricing model, but you also have to evaluate service, scalability and data policy.

Choose a Pricing Model

There are a few different pricing models and structures offered for both hardware and edge providers.

• Consumption-based pricing, or pay for what you use. This is usually used by IaaS and PaaS providers. Hardly any offer a flat rate. This allows the customer to scale up and down as needed, although if need stays high, your budget can easily run over.
• Subscription-based pricing. This is the flat rate method and is used primarily by SaaS companies. You pay per month and have to pay a license for each user, but the trade-off is you can use unlimited resources.
• Market-based pricing. This is less common because business runs 24/7, but market-based pricing tends to limit when you can use resources. You might be limited to running resource-intensive programs only during off-peak hours, for example.

Evaluate Service, Scalability, Data Policy 

Some edge providers may offer development platforms, while others are purely hardware providers. Additional features to ask for when speaking with the sales rep:

• Scalability and self-service provisioning.
• Automatic backup and restore/disaster recovery.
• Automatic maintenance and patching.
• Servicing remote sites vs. staff must travel to the site.

Additionally, some customers have gotten a rude surprise when they went to take their data back from a remote provider. For starters, data retrieval rates can be exorbitant if you have a lot of data in a remote location. The costs and terms will be spelled out in the service agreement – which you should check very carefully.

Consider the Potential for Lock-In

Few companies have a single edge computing provider, they usually draw from multiple providers. But the vendors would like you to think otherwise. This leads to a tendency for vendor lock-in, where hardware, protocols, apps, and other software are completely proprietary and don’t allow you to migrate edge computing workloads from different platforms, especially from on-premises servers to the edge.

Again, in your due diligence, check the details to ensure that any vendor you work with supports interoperability standards and will not hamstring you should you decide to move or at least interoperate with another edge company.

Top Edge Computing Companies 

Our list of the leading edge computing companies covers both hardware and services providers, since both are integral to the edge. It is in no particular order.

AWS Edge

Edge computing value proposition: As the leader in cloud computing, AWS is investing heavily in edge computing as well, which means they offer an extensive toolset. So AWS has offers for both SMB and large enterprise – but the user interface won’t be simple.

AWS may not have edge locations scattered around big cities but AWS offers a considerable amount of cloud-edge hybrid services for a uniform experience across the edge environment. AWS includes services and solutions that include hybrid cloud, IoT, AI, industrial machine learning, robotics, analytics, and compute services. If you can imagine it, AWS probably has it.

AWS claims to have more than 200 integrated device services to choose from, and sells its own: Alexa and Echo. It also provides solutions like its Connected Vehicle solution, IoT Device Simulator, and AWS IoT Camera Connector.

AWS’s interface is known to be complex, so those companies that will handle their edge infrastructure themselves will need considerable in-house expertise.

EdgeConnex

Edge computing value proposition: The emerging concept of “observability,” which refers to the ability to closely monitor a far-flung platform, is quickly becoming a must-have for enterprise customers; this is a strength for EdgeConnex.

EdgeConnex’s business model is to place data facilities where they’re needed the most for better network and IT connectivity. It works with customers to ensure tailored scalability, power, and connectivity.

Through its Far Edge services, it offers more than 4,000 points of presence outside of its hundreds of data centers worldwide. EdgeConnex’s Far Edge use cases include artificial intelligence and machine learning, fast media streaming, and (of course) IoT devices.

EdgeConnex offers EdgeOS, a self-service management OS for data center infrastructure management (DCIM) providing customers with a single, secure view into their infrastructure deployed in any location across its global footprint.

ADLINK Technology

Edge computing value proposition: ADLINK’s core focus is embedded computing; this and its international presence make it ideal for an edge project that spans global borders.

Taiwan-based ADLINK has a network that spans from the US to Germany to China, encompassing some 40 countries in total. Unlike some companies on this list ADLINK that offer edge services in addition to cloud and other IT services, ADLINK is specifically focused on the embedded computing sector.

In addition to its widespread operational presence, the company has design centers in Asia, the US, and Germany. ADLINK speciality within edge computing includes IoT hardware, software, AI software, and robotics solutions. Its product line includes computer-on-modules, industrial motherboards, data acquisition modules and complete systems, with emphasis on the aerospace, manufacturing, healthcare, networking, communications, and the military sector.

The company touts its focus on artificial intelligence, which is an exceptionally important tool for monitoring and managing the edge; managing the edge is essentially impossible using human staff alone.

Vapor IO

Edge computing value proposition: An edge computing company with a “1+1=3” strategy, meaning that they focus on interoperation with other tech firms – a particularly significant strategy in the edge world, in which cooperative networking is so essential.

Founded in 2014, and arguably a leader among the new cohort of edge startups, Vapor IO develops hardware and software, and has edge-to-edge solutions called Kinetic Grid platform and Kinetic Edge architecture, which are designed to enable customer data delivery and processing across global borders. The company is based in Austin, Texas.

Vapor IO develops a collection of edge colocation and interconnection facilities colocated with wireless network. The company is actively building fiber backbones in numerous markets.

The company builds portable data centers about the size of a shipping container, “micromodular data centers,” that are placed at wireless base stations or wherever they are needed. Vapor IO serves wireless carriers, cloud providers, web-scale companies, and other enterprises.

Mutable

Edge computing value proposition: Mutable’s mission is to get edge computing infrastructure close to remote processors – very close. It uses “micro” data centers to support applications on its platform.

Closer to a start-up – launched in 2013 – the company is a compelling example of the future of edge companies. Mutable is a public edge cloud platform that patterns itself as an Airbnb for servers. If you have some underutilized servers sitting idle, you can loan them out to businesses in the area that need extra capacity, so long as they are within 40 kilometers or less, and turn your idle servers into a new revenue stream. This is done through its Mutable OS edge computing software solution.

Because of the close distance requirement, Mutable’s Public Edge Cloud ensures latency rates of less than 20 milliseconds. It offers a 5G network in addition to wired connectivity. All stacks, snapshots, containers and related services operate in an isolated environment. Mutable’s other edge computing tools include Mutable Node, Mutable Mesh and Mutable k8s Platform.

This concept of hyper-low latency is definitely a vision of where the future of edge is headed. If edge is fast and highly responsive, it grows and succeeds.

Microsoft Azure

Edge computing value proposition: With perhaps the widest infrastructure base in the tech industry, from cloud to data to AI, Microsoft is focused on winning big marketshare in edge, and is investing accordingly.

Microsoft’s Azure IoT is second only to Amazon in terms of market size, but there is more to it than that. It offers Azure SQL Edge, an edge version of its powerful SQL Server database, offering data streaming, time series, and database machine learning. Its IoT Plug and Play lets users connect IoT edge technologies to the cloud without having to write a single line of embedded code.

Microsoft recently launched Windows 10 IoT Core, a derivative of Windows 10 designed for compact devices such as a Raspberry Pi board. Many of Microsoft’s edge computing capabilities are extensions of the Azure cloud platform and it offers Azure Stack Edge to facilitate development and migration.

Azure Stack is an on-premises version of Azure meant to be run internally in a company data center. Azure Stack Edge lets companies develop and upgrade their edge apps on-prem, and when they are ready, deploy them to the edge. Microsoft is a player to watch in the edge sector, clearly.

MobiledgeX

Edge computing value proposition: Edge is an environment that prizes managing and monitoring application workloads across regions; MobiledgeX’s cloud platform offers the cross-interface functionality with an abstraction layer; you might think of it as virtualization for the edge.

Definitely geared for large enterprise customers, MobiledgeX was launched by Deutsche Telekom AG in 2016, and offers automation and orchestration in a multicloud environment.

MobiledgeX offers a marketplace of edge computing services and resources that will connect developers with telecom operators like British Telecom, Telefonica, and Deutsche Telekom. MobiledgeX Edge-Cloud platform helps simplify deployment, management and analytics for developers of their apps to run on telco edge clouds.

MobiledgeX Edge-Cloud Platform allows developers to autonomously manage software deployment across the distributed edge network infrastructure from a number of operators, using a unified console.

Schneider Electric

Edge computing value proposition: A large player with the expertise and personnel for heavy duty edge computing projects, Schneider offers an extensive menu of enterprise IT services to support edge deployments.

A giant in Europe – it’s a French multinational – Schneider Electric is making a big push into the U.S. market with edge data center products, including ruggedized racks and storage units, purpose-built all-in-one units, and wall mounted units where floor space is at a premium.

It also offers EcoStruxure, a DCIM software package for managing servers remotely. Schneider owns UPS specialist American Power Conversion and APC products are frequently a part of its offerings.

Additionally, Schneider supports projects for everything for data centers to corporate headquarters to homes, and has strength in services and automation.

Equinix

Edge computing value proposition: As the leading name in the colocation sector, Equinix has a deep legacy of expertise in enterprise IT – and it has the physical infrastructure to support big ambitions in edge computing.

Equinix is the largest American data center provider, as well as the largest around the world. Its primary focus is colocation, where a customer puts their compute systems in Equinix data centers, so the customer doesn’t have to maintain a data center facility.

Its edge strategy dovetails from this, in that Equinix’s goal is to help large enterprises quickly shift their IT infrastructure to colocations in major cities as needed without having to build their own infrastructure. Equinix also offers a variety of virtual network services to improve edge performance and reduce latency.

ClearBlade

Edge computing value proposition: In a world in which many large tech companies are adding edge capability to an already large feature set, ClearBlade offers an image of the future in which companies launch to focus deeply on the edge itself.

ClearBlade is another company purely focused on IoT and the edge. ClearBlade Edge allows customers to develop compute services and solve business problems from a single platform. It also offers real-time location and asset tracking, and its middleware platform helps build and connect systems to IoT without coding.

This no code focus is a fascinating take on the union of edge and trend within digital transformation to allow all users to build and upgrade applications. The company’s no code IoT Application edge-native computing.

Its primary products include its ClearBlade Enterprise IoT Platform, ClearBlade Edge IoT Software, and ClearBlade Secure IoT Cloud. It is particularly focused on the transportation, energy, and health care sectors.

The post Top 10 Edge Computing Companies of 2022 appeared first on eWEEK.

Digital transformation is the process of replacing or enhancing traditional business processes with digital technologies. The goal is to improve and streamline processes at all levels. In a now common example, many medical offices are paper-free, or are moving that way. Patients fill out medical documents on a tablet and records are migrated through the system digitally, replacing the highly inefficient paper folders from yesteryear.

Even with a relatively modest example like shifting from paper to electronic records, digital transformation can dramatically improve how companies serve their customers. However, as you’ll see in the examples below, digital transformation can require a seismic shift in process, which means considerable investment of time and money in technology and staffing.

Virtually every example of digital transformation requires some form of cloud computing, and often uses more than one cloud provider. Certainly most examples require machine learning, a significant element of data analytics, and – increasingly – support from artificial intelligence software.

Needless to say, most businesses can’t achieve this transformation on their own; they’ll need to partner with digital transformation companies to enhance their odds of success. But because the advantages are so great, even deep investment is typically justified. In the long run, many digital transformation deployments become examples of success. 

Also see: Top 10 Digital Transformation Companies

Examples of Digital Transformation

Digital Transformation in Retail

In 2017, Domino’s Pizza adopted artificial intelligence and chatbot technology to create a conversational interface with Facebook Messenger. Customers who have a Domino’s Pizza account can use Facebook Messenger to quickly reorder a previously ordered pizza combination or a new series of toppings.

Ordering is streamlined and interactive, and since it’s tied into social media (as opposed to requiring a separate web site or a phone call), the frequency of orders have increased. The chatbot uses a data-based logic system to offer upsells like strategic discounts and related food items. Additionally, the AI-based system saves labor costs for the pizza chain.

This example of digital transformation reflects the strength of AI in driving sales. One of AI’s main strengths is human-like chat, in which an algorithm responds to customers in real time using NLP (natural language processing), even if the customer is using idiosyncratic language. Additionally, AI excels at performing tedious tasks that would stretch the limits of human performance. This includes sifting through vast amounts of data to search for useful patterns and other insights – in this case, patterns in how humans order food delivery, to ease friction in the ordering process.

Given the many successful examples, it’s no surprise that the market for digital transformation services is forecast to top $800 billion by 2025. Source: MarketResearchFuture. 

Digital Transformation in Marketing

In 2012, toy and game manufacturer Hasbro saw its marketshare slipping and had an epiphany. Rather than market to children, it targeted their parents, who actually make the purchases. They began a new digital and data strategy, which took a number of years to reach full implementation.

The company began gathering and mining customer data to create more targeted marketing campaigns, promoting both nostalgic and newer brands. It made a major push into online media, targeting web sites with high cohorts of parents. This data-based online approach gave Hasbro better and more immediate feedback. In 2016 the company broke the $5 billion sales mark for the year and is on track for $6 billion in 2021.

One of the main tools for digital transformation in marketing is data. Mining electronic data gathered in massive quantities in data warehouses and data lakes greatly increases the ability of sales leaders to hone campaigns. This usually involves business intelligence software – a core tool in digital transformation – which allows pinpoint targeting by demographics.

Digital Transformation in Banking

Institutions across the financial sector, such as Wells Fargo and Bank of America, have automated transactions, even as this has required handling major security and compliance issues. The fascinating aspect of this example is that instead of one limited instance, retail banks have undergone a rolling adoption of digital transformation over the last three decades.

The major milestone in this process was the adoption of Automated Teller Machines (ATMs); by allowing customers to get cash without human assistance, the banking sector has been a leader in using automation to save on staff costs. For a contrasting example, look at the grocery sector, which didn’t move heavily to self-checkout (SCO) until the last few years; globally, less than 200,000 units existed in 2013, a figure forecast to rise to 1.2 million units in 2025.

The banking sector is also a leader in digital transformation in its use of mobile. Its adoption of mobile banking uses a combination of edge computing (its network of security gateways) and IoT devices (cell phones) to provide convenience to customers. Going a step further, allowing customers to create automatic payments means that – at a very basic level – users are deploying automation themselves. Furthermore, third party payment systems like Google Pay and Apple Pay let consumers pay for everyday purchases with a wave of their smartphones; this is yet another example of deploying technology, Near Field Communication, to enable an easier process.

Digital Transformation in Transportation   

There is a good reason Uber grabbed so much marketshare from traditional taxi companies over the last decade. Uber took an in-depth, data-driven look at the taxi industry and identified all the pain points of customers, from locating a nearby taxi to offering non-cash payments. Using a key element of digital transformation strategy – active listening using data – the company catered to customers in ways they’d never been responded to before.

All of this logistics and data-centered strategy was built into the smartphone app, which facilitates convenient and rapid customer feedback. Again, this is close listening to the customer.

This example of real time, interactive listening is echoed in a foundational practice in digital transformation: using social media to form a close bond with customers. The interactive smartphone app is a click away from major social media platforms and, in the minds of many consumers, is the same world. Clearly, social media has become the new customer service desk. LinkedIn, Twitter, and Facebook have replaced 800 lines and email for instant communication between sellers and customers, handling sales, disputes, and measuring brand sentiment.

In its “State of the Connected Customer” report, Salesforce found 84% of high-performing companies are managing and responding to social inquiries and issues in real time, while just 37% of under-performers say the same. As an additional advantage, this interactive response is often accompanied by a marketing element.

Digital Transformation in Healthcare

Telehealth services like Teladoc, MeMD, MDLive and a host of other providers enable users to receive care from healthcare professionals using a Webcam, lessening the need for physical infrastructure, including fewer staff hours spent supporting waiting rooms.

An advantage of digital transformation is better focus on the customer, and that’s certainly true with telehealth, which allows better care for rural and other isolated patients. Not to mention enabling an on-demand format that’s more flexible for all patients. The telehealth market is forecast to expand from $144 billion in 2020 to $636 billion by 2028.

Additionally, the healthcare sector is migrating to electronic data management at all levels, from patient sign-in on electronic devices to record keeping with blockchain and data warehouses. This helps create a centralized data repository for each patient known as an EHR, or electronic health record, which facilitates better care by pooling data from disparate sources.

Data management and data mining are rapidly growing trends in healthcare, easily rivaling telehealth as examples of digital transformation. Using data analytics on large number of anonymized patient records enables lower rates of medication problems by flagging consistent problems across many patients. Data is also used for preventative care, which involves analyzing patient data to find links between current and potential future medical problems, then informing the patient far in advance.

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Data visualization tools incorporate support for real-time and streaming data, artificial intelligence integration, collaboration, and interactive exploration to facilitate the visual representation of data. Many data visualization applications are cloud-based.

Data visualization supports data mining – indeed, some experts consider it to be a key data mining technique. Many of the leading data mining tools incorporate a data visualization feature.

Also see: Top Data Analytics Tools 

Data visualizations present complex metrics in the context of visual relationships, enabling a faster and more intuitive approach to data mining. 

How to Select the Best Data Visualization Tool

The market for data visualization tools is growing rapidly, which is driving the growth in solutions. Because of the complexity, businesses should speak with sales reps to gain a better understanding of how a given application meets their needs. Before the final decision is made, consider the following strategies when selecting data visualization software.

Research Pricing 

Many data visualization tools are priced on a per user per month basis. Make sure to consider how many of your employees truly need access to these solutions, as every additional user typically costs more.

Alternatively, many solutions offer plans specifically tailored for larger enterprises. Be sure to consult various sales representatives to inquire about these options.

Decide on Your Use Cases 

Once you’ve found solutions within your budget, consider what each solution can bring to the table in regards to your needs. There are three critical points to consider when selecting data visualization software:

1. Big Data

Data quantities are only growing. Moving forward, different approaches to business operations such as AIOps are available to manage these vast amounts of data. Until then, businesses should gain an understanding of how each data visualization software can help them aggregate and translate mass Big Data sets.

2. Data Tracking

When consulting with a sales representative, inquire about the various ways in which your desired software can track flaws and anomalies in data. Tracking links and similarities between data is also critical.

3. Scalability

Gaining an understanding of how each data visualization application can help with data sets and data tracking will generally reveal each solution’s potential for scalability. Still, be sure to dig into just how scalable a product truly is, and how providers can work with your business cost-wise as it continues to grow.

Best Data Visualization Tools

What follows is our top ten best data visualization tools, in no particular order:

• Tableau
• Microsoft Power BI
• Google Cloud
• Excel
• Sisense
• Zoho Analytics
• Google Charts
• FusionCharts
• Infogram
• Looker
• IBM Cognos Analytics
• Qlik

Tableau

Data visualization value proposition: Tableau is, without question, an industry leader. The team at Tableau emphasizes its usability for any and all users. This means that it is incredibly user friendly and built for a diverse amount of teams.

Tableau is by far the most popular data visualization tool, so much so that Salesforce bought it. Boosting its popularity, Tableau is accessible to everyone from college students to data scientists.

It’s an easy-to-use tool that provides results quickly, in a wide variety of formats. It provides integration with all of the major advanced databases, including Teradata, SAP, My SQL, Amazon AWS, and Hadoop.

Although Tableau is built for a wide range of users, it is still used for scalable, large enterprises. Some of its customers include Verizon, the WFP, and JPMorgan Chase & Co. If you’re planning on connecting with a sales representative at Tableau, be sure to inquire about their security as well as their integration capabilities.

Microsoft Power BI

Data visualization value proposition: Microsoft Power BI has been recognized as a leader in the 2021 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms for the past fourteen years. Much like Tableau, Power BI prides itself on its data-driven culture that’s open for all user types. One of the biggest advantages Power BI can bring for businesses is in its industry-leading AI.

Microsoft has two major visualization tools, with Power BI on the high end. It provides classic data visualization tool elements like interactive dashboards and APIs for integration and is tightly integrated with the Microsoft data platforms like SQL Server and Sharepoint.

It operates a lot like Excel, so if you have cut your teeth on Excel the learning curve is shorter. Still, Power BI comes with a slew of features that set it apart from its more ubiquitous counterpart. The standout point of Power BI is in its AI and machine learning (ML) capabilities. Microsoft AI is incorporated to allow non-data scientists to plan and build full machine learning models and quickly track insights from their data sets.

Plus, for teams working with Excel as well, Power BI integrates with Excel to publish data in more user-friendly, digestible formats.

Excel

Data visualization value proposition: Excel is one of the most affordable and no-frills data visualization tools available today. This makes it one of the best beginning tools for businesses, especially SMBs.

Beyond its primary use as a spreadsheet tool, Excel comes with very good basic data visualization tools and functions. It comes with 20 or more built-in charts, including pie charts, radar charts, histograms, scatter plots and more.

It’s a great beginner tool you probably already have, but it doesn’t scale well and if your data sets grow, you will likely want to upgrade to Power BI. Price-wise, Excel offers business plans for scaling companies, but at a monthly fee that also features tools like Microsoft Word, Powerpoint, and OneDrive.

Sisense

Data visualization value proposition: Sisense works with scaling businesses to integrate and track analytics in both their workflows and analytics products. It is primarily built for larger businesses and enterprises looking to sort through very dense and large data sets.

Sisense is a data visualization tool based on a business intelligence model that offers multiple tools for data analysis. The tool is pretty easy to set up and use, it can be installed in minutes and provides instant results, yet it has the advanced functionality seen in mature, high-end software like Tableau.

One of these features is in-chip processing which allows for faster, more efficient queries. It allows the users to export files in common formats like PDF, Word, Excel, and PowerPoint.

Sisense is no-code friendly. This means that insights can be extrapolated and complex models can be built by non-data scientists. Still, Sisense is built to be scalable across all skill levels and use cases. This radically opens up its usage for virtually an entire business. Again, this is yet another reason why enterprises should consider Sisense over SMBs or individuals looking for data visualization tools.

Zoho Analytics

Data visualization value proposition: Consider Zoho Analytics a similarly affordable alternative to Microsoft Power BI. Use cases are broad and scalability is very similar. Businesses should, however, inquire about Zoho Analytics’ AI and ML capabilities before moving forward.

Zoho Analytics also specializes in business intelligence visualizations, offering a number of different ways to chart your data and a variety of dashboards. It supports a broad number of different data sources and lets you prep your data within the platform.

Its real strength is an artificial intelligence assistant called Zia that lets you ask questions in natural language. Through conversational AI, users can ask Zia questions about insights and gain results instantaneously. Zia even provides smart suggestions as users type out their questions. Still, this use case is primarily built for non-data scientists and individuals in need of quick data. Zoho Analytics provides a number of advanced solutions for larger enterprises.

In fact, Zoho Analytics offers a large set of APIs for developers looking to tweak data integration, authorization, custom styling and more. Zoho Analytics has been used by companies such as HP, Hyundai, LaLiga, and Ikea for insight gathering and data visualization.

Google Charts

Data visualization value proposition: If you are an individual looking for a free charting solution and have coding experience, consider Google Charts. Other than this use case, we recommend businesses looking for data visualization software to look toward more scalable and AI-based solutions.

Google Charts lets you create interactive charts which include maps, bar charts, histograms and more. You can then embed these online and run them live. This is ideal for people who already use Google Workspace and want to integrate with many data sources.

Google Charts works with a wide variety of SQL databases and also has a number of data connectors for you to collect your data. On the downside, you need to know how to code to use it, which is likely a barrier for most beginners. And it isn’t as well supported as other products.

FusionCharts

Data visualization value proposition: FusionCharts is the much more flexible and scalable alternative to Google Charts. Still, solutions like Tableau or Power BI will be of more interest for enterprises in need of advanced, AI-driven data visualization and business intelligence.

FusionCharts is a JavaScript-based tool that offers more than 100 interactive charts and more than 2,000 maps, making it one of the most flexible tools out there. It also integrates with a number of other data visualization platforms, such as Angular, React, Vue, JQuery, PHP, Ruby on Rails, ASP.NET, Django.

While the software offers a large number of pre-set templates, it’s based in JavaScript, so you do have to know the language to make the most out of it. This means that it is not the most scalable solution skill-wise.

One of FusionCharts’ most helpful features is its accessibility when publishing charts. Charts work across all platforms, including desktops, tablets, and mobile phones. Charts are also touch optimized with no additional effort on the user-end.

Infogram

Data visualization value proposition: Infogram is a more user-friendly, graphic-design forward alternative to Google Charts and FusionCharts. It is not an advanced solution built for enterprises like Power BI and Sisense.

Infogram is popular for creating reports, charts and maps. Its strength is in generating infographics and comes with more than 550 maps and 35 interactive charts. It has an easy-to-use interface and the data visualizations are considered easy to learn.

Another point in its favor: it comes with many different templates with aesthetically pleasing designs. Although Infogram has been used by a multitude of industry leaders, its use cases are ultimately rather simple. If you’re in marketing, media, or in the nonprofit sector, Infogram is a good solution for simple social media engagement and graphic design solutions.

Looker

Data visualization value proposition: Looker pushes its flexibility as a standout point. Looker can integrate and adjust to any business workflow and is embeddable in a number of third-party systems. It also offers low-code solutions that allow non-data scientists to build their own applications.

Looker is a comprehensive business intelligence tool with links to Snowflake, Redshift, and BigQuery along with over 50 SQL dialects, so you can connect with several databases at once, then export your results in any format.

It offers a real-time dashboard for data analysis to help you make business decisions based on the data visualization. Furthermore, distribution of data reports, insights, sets, and query results can be scheduled and automated. Looker is used by a multitude of different business verticals, including eCommerce, media, ad-tech, SaaS, and healthcare.

IBM Cognos Analytics

Data visualization value proposition: Highly affordable and incredibly advanced, IBM Cognos Analytics is very effective for a multitude of enterprise uses. It also offers the same user-friendliness that alternatives like Power BI and Tableau offer for SMBs.

IBM Cognos Analytics is a cloud and on-premise-based business intelligence solution that uses an augmented intelligence-infused AI assistant that allows users to ask questions and get answers in natural language. It also recommends new visualizations and potential connections in data, allowing users to unearth connections they might not anticipate.

It offers users a wide range of business analytics functionality and a full complement of essential analytics functions, including advanced dashboarding, data integration, reporting, exploration and data modeling.

IBM Cognos can import data from CSV files and spreadsheets, and connect to a variety of data sources. This includes SQL databases, Google BigQuery, Amazon, and Redshift. Cognos also offers a mobile app where users and stakeholders can access data and get instantaneous alerts.

Qlik

Data visualization value proposition: Qlik’s approach to BI is to go beyond the idea of passive BI. Qlik’s primary approach utilizes the Qlik Active Intelligence Platform, which delivers real-time and up-to-date information that trigger immediate actions.

Qlik Sense is Qlik’s proprietary software, built primarily for users of all skill levels to build data models and track insights from their data sets. This no-code approach does not detract from its AI capabilities, though. Qlik features AI-generated analysis, automated data prep, and predictive analytics fueled through machine learning.

Qlik’s products are built to scale. This is true for its security and governance as well. Its platform can be deployed on a single server or scale to larger enterprise frameworks. This is true for on-premise or cloud networks. This means that Qlik is highly optimizable and will deliver high performance for growing data sets, all while maintaining a company’s security model.

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