Big Data edge computing IoT ReadWrite

Why the Edge is Key to Unlocking IoT’s Full Potential

edge unlocking IoT

To IoT’s great benefit, edge computing is about to take the spotlight. Consider that each day billions of devices connected to the Internet of Things come online. As they do, they generate mountains of information. One estimate predicts the amount of data will soar to 79.4 zettabyes within five years. Imagine storing 80 zettabytes on DVDs. All those DVDs would circle the Earth more than 100 times.

In other words, a whole lot of data.

Indeed, thanks to the IoT, a dramatic shift is underway. More enterprise-generated data is being created and processed outside of traditional, centralized data centers and clouds. And unless we make a course correction, the forecasts could come unglued. We must make better use of edge computing to deal more effectively with this ocean of data,

Network Latency

If we do this right, our infrastructure should be able to handle this data flow in a way that maximizes efficiency and security. The system would let organizations benefit from instantaneous response times. It would allow them to use the new data at their disposal to make smarter decisions and — most importantly — make them in real-time.

That’s not what we have nowadays.

In fact, when IoT devices ship their data back to the cloud for processing, transmissions are both slow and expensive. Too few devices are taking advantage of the edge.

Traffic Jam: The Cloud

Instead, many route data to the cloud. In that case, you’re going to encounter network latency measuring around 25 milliseconds. And that’s in best-case scenarios. Often, the lag time is a lot worse.  If you have to feed data through a server network and the cloud to get anything done, that’s going to take a long time and a ton of bandwidth.

An IP network can’t guarantee delivery in any particular time frame. Minutes might pass before you realize that something has gone wrong. At that point, you’re at the mercy of the system.

Data Hoarding 

Until now, technologists have approached Big Data from the perspective that the collection and storage of tons of it is a good thing. No surprise, given how the cloud computing model is very oriented toward large data sets.

The default behavior is to want to keep all that data. But think about how you collect and store all that information. There is simply too much data to push it all around the cloud. So why not work at the edge instead?

Cameras Drive Tons of Data – Not All of Which We Need

Consider, for example, what happens to the imagery collected by the millions of cameras in public and private. What happens once that data winds up in transit? In many – and perhaps most – instances, we don’t need to store those images in the cloud.

Let’s say that you measure ambient temperature settings that produce a reading once a second. The temperature reading in a house or office doesn’t usually change on a second-by-second basis. So why keep it?  And why spend all the money to move it somewhere else?

Obviously, there are cases where it will be practical and valuable to store massive amounts of data. A manufacturer might want to retain all the data it collects to tune plant processes. But in the majority of instances where organizations collect tons of data, they actually need very little of it. And that’s where the edge comes in handy.

Use the Edge to Avoid Costly Cloud Bills

The edge also can save you tons of money. We used to work with a company that collected consumption data for power management sites and office buildings. They kept all that data in the cloud. That worked well until they got a bill for hundreds of thousands of dollars from Amazon.

Edge computing and the broader concept of distributed architecture offers a far better solution.

Edge Helps IoT Flourish in the era of Big Data

Some people treat the edge as if it were a foreign, mystical environment. It’s not.

Think of the edge as a commodity compute resource. Better yet, it is located relatively close to the IoT and its devices. Its usefulness is precisely due to its being a “commodity� resource rather than some specialized compute resource. That most likely takes the form of a resource that supports containerized applications. These hide the specific details of the edge environment.

The Edge Environment and Its Benefits

In that sort of edge environment, we can easily imagine a distributed systems architecture where some parts of the system are deployed to the edge. At the edge, they can provide real-time, local data analysis.

Systems architects can dynamically decide which components of the system should run at the edge. Other components would remain deployed in regional or centralized processing locations. By configuring the system dynamically, the system is optimized for execution in edge environments with different topologies.

With this kind of edge environment, we can expect lower latencies. We also achieve better security and privacy with local processing.

Some of this is already getting done now on a one-off basis. But it hasn’t yet been systematized. That means organizations must figure this out on their own by assuming the role of a systems integrator. Instead, they must embrace the edge and help make IoT hum.

The post Why the Edge is Key to Unlocking IoT’s Full Potential appeared first on ReadWrite.

Accurate Data AI artificial intellgence Automation Data Big Data Machine Learning ML

How Can AI and ML Transform the Way We Read and Understand Data?

ai and ml understand data

Today’s business is ruled by data and data-driven understanding. How you understand the data and interpret the data into business decisions has a direct impact on your business conversion and growth. For a more precise understanding of data, today we have artificial intelligence (AI) and Machine Learning (ML) technologies on our side. No doubt, these technologies that mimic human reasoning can positively transform businesses and their strategies.

We need to understand the impact of AI and ML technologies have in shaping our understanding and capability to interpret data.

Data-Driven Personalization

Any business understands the importance of communicating with customers individually. Yes, thanks to the very nature of digital interfaces that opened up the tremendous scope of individual preferences and choices, your business communication must take into account the preferences of individual customers. The increasing importance of addressing individual choices for business conversion has forced many companies to focus on data-driven personalization measures.

Not only the large businesses but also the startups and small businesses increasingly understand the importance of having access to the relevant data for meeting the needs of visitors. AI can dig the available user data deeper and fetch out relevant patterns and insights that can be further utilized for data-driven decision making personalization. AI can also help to scale up such personalization efforts for every individual user.

Stop the churn rate.

A superb example of how AI can allow personalization in business operations can be found in the case of Starbucks. The global coffee chain brand designed 400,000 different types of emails created based on the data of individual preferences, tastes, and choices. Such well crafted personalized communication can help brands to create more engaging communication and conversation for business brands. The brand actually AI to decipher the volumes of data corresponding to customer preferences and choices.

Data collection and data-centric.

When it comes to smaller businesses and little startups, such as AI-based data collection and data-centric personalization may be a little expensive. But small businesses can embrace similar approaches to create very specific data-oriented marketing campaigns with short duration to boost business conversion and customer engagement. Such AI-powered data-driven campaigns can also help to lift the brand image of any company.

Generating Sales Leads from Data that’s Understood

For the B2B segment, business conversion highly depends on generating new leads. The B2B companies also need to depend heavily on tracking contact data and reaching out to them effectively through lead generation funnel. Most marketers agree to the humongous range of challenges B2B-based businesses face in doing this. This is where AI can play a great role in streamlining the process of lead generation through intelligent automation.

Artificial Intelligence (AI) powered lead generation and contact tracking solutions have the capability to make an analysis of the customer base along with important trends and emerging patterns. These trends, patterns, anomalies, characteristics, and various attributes can deliver important insights for optimizing websites and web apps. Thanks to AI-based optimization insights a website can venture to use better programming language, tools, features, and UI elements to generate more leads.

Analytics and you.

On the other hand, AI-based business data analysis can work hand in hand with big data analytics. This sophisticated and highly incisive approach to data utilization can easily help to discover ideal customers for a business. The interactions of users on web pages and corresponding data can be analyzed by B2B brands with the help of AI tools to produce the most relevant as well as actionable insights.

Analytical activities.

To make things easier for the businesses, AI, and machine learning technology for such analytical activities are now spotted in most of the leading analytics solutions across the spectrum. Simple Google Analytics can also offer highly result-oriented and precision-driven reports. Such technologies can easily know about the shortcomings and loopholes behind the decreasing motivation of traffic and readings of business conversion fallout.

Great analytics tools.

There are also great tools like Finteza that uses AI technology for monitoring website traffic on a continuous basis besides checking other crucial issues and irregularities. These tools can also improve your data security since by detecting bad traffic they automatically point out the vulnerabilities in the web app.

Poor web traffic often results in DDoS attacks, manipulation of website cookies, and hackers or malicious programs impersonating computer bots. An AI-based lead generation solution can also reduce these security vulnerabilities.

Optimizing the User Experience (UX)

AI optimizes the scope of personalization in a data-driven manner and that is portrayed as the principal useless of AI in dealing with data. But AI is also highly effective in optimizing the web design and improving the user experience (UX).

User Behavior

AI achieves this optimization and improvement by analyzing user behavior and interaction data and user feedback. Machine learning programs particularly can play a very effective role in learning from user behavior and adjusting various interactive elements accordingly.

AI and ML programs running behind the scene basically collect a lot of data corresponding to real user behavior so that real-time feedback about shortcomings and improvement needs can be communicated to the business owners. An ML-based program can also bring instant tweaks to the UX attributes for better engagement.

Another important thing in this respect that needs to be explained is the great role of AI in improving the efficiency of A/B tests. In the A/B testing process the AI and machine learning can deliver the most important insights about user demands and preferences to take further enhancement measures for UI and UX.

The most important aspect of AI in making an impact over A/B testing is that it leaves no scope for vague assessment or guessing. The data-driven insights guiding the A/B testing is more possible now as website cookies provide clear insights concerning user behavior.

Based on such insights the landing pages can reduce form fields as per user interest and preferences.

Biometric Data Pushing for Enhancements

Biometrics data corresponding to direct interactions with a web app can help developers and marketers with a lot of actionable insights. There are many advanced online services right now available in the market that can help to understand and decipher website data.

Biometrics data coupled up with AI and machine learning technology opened up new possibilities for improved user experience.

Among these available services for data interpretation mostly take the help of a combination of both artificial intelligence and machine learning. These sophisticated solutions can easily track the eye movements of the users.

In addition, some of these services can also track facial expressions to assess user responses in different contexts. These services can extract the most organic kind of user data and generate the most valuable insights that can be used for UX design and performance optimization of websites.


As the trends stand, from this year onward the AI and ML-based data analytics and data-centric optimization of business apps will have more dominance. Thanks to these two technologies, there will be the least guesswork for all design, development, and optimization decisions.

The post How Can AI and ML Transform the Way We Read and Understand Data? appeared first on ReadWrite.

Big Data hyperlocal data IoT Tech

The Increasing Need for Hyperlocal Data Collection

hyperlocal data collection

The ability to collect and harness big data has transformed businesses worldwide. This process, which is continually improved by data scientists and companies that heavily invest in big data, has allowed for dramatic changes to numerous industries. The benefits of using big data include improved processes, data-driven business decisions, and better resource allocation. Here is the increasing need for hyperlocal data collection.

While conversation often surrounds how to use big data, the use of hyperlocal data has also gained attention.

Hyperlocal data refers to a more niche version of local data. A zip code, for example, is an example of local data, but a street address is hyperlocal data. It’s more specific and tends to surround a very particular, defined geographic area. This data is more difficult to collect, but its uses in business are growing.

Hyperlocal data benefits businesses

There are more obvious uses for hyperlocal data for businesses that rely on Google Maps or location-based services. A food delivery company, for example, would benefit from having more addresses and exact locations of restaurants in a particular area because they can then offer customers more unique choices, gaining an edge over their competition.

Google Maps itself also benefits from this data because the company can offer more accurate, up-to-date information to users, including directions and nearby locations.

Both marketers and advertisers use hyperlocal data to hone in on local customers. Near-me searches have risen in popularity, and marketers are capitalizing on these searches to get customers into local stores and restaurants.

Instead of focusing on a potential customer that may come to the area later, these campaigns focus on customers that are already in the vicinity. Of course, marketers must also focus on strong Google My Business listings and SEO to beat out the competition fighting for the top spots on search engines.

Hyperlocal data has a place in a traditional business or finance setting as well. A company applying for a loan may overstate its value. Hyperlocal data can capture accurate information such as credit cards accepted, hours of operation, number of employees, etc. All of this information is necessary for financial institutions that want an accurate portrayal of a business. However, there are far more uses for hyperlocal data than just this.

Hyperlocal data and Covid-19

With the world battling a pandemic, many have turned to hyperlocal data to track the spread of Covid-19. By mapping out global cases, experts have been attempting to take a worldwide view of how the virus has spread and its impact on businesses.

More importantly, they want to look at areas that have begun to flatten the curve so that other affected areas can mimic their efforts. Of course, none of this is possible without hyperlocal data that tracks infected populations. This data will play a key role in both curbing the spread and creating lessons to draw from in the future.

Hyperlocal and IoT use cases

What is particularly interesting is how hyperlocal data can benefit the Internet of Things. With improved mobile technology, people are constantly creating data. Now, more than ever, people are sharing this data for the benefit of others. Take traffic reports, for example. There are apps where users can report accidents, and delays, which helps others traveling in the vicinity.

The report is given to a GPS application, which can then reroute drivers to avoid sitting in long delays. It’s even expected that self-reporting will soon become unnecessary as AI will become sophisticated enough to collect hyperlocal data without user input.

Connected cars have grown in popularity and will soon play a substantial role in hyperlocal data collection and distribution. Equipped with smart sensors, this type of car could report weather information to a cloud service, which could then alert others in the area.

If there is a severe storm or ice on the road, other travelers would know about it, assuming they are in connected vehicles themselves. Hyperlocal data can power IoT simply by offering larger amounts of accurate data.

Hyperlocal data moving forward

The challenge of data collection, particularly in emerging markets, is one hindrance to this movement. While data is readily available in developed cities, data in underdeveloped locations is not as easily accessible. This data, however, is growing in its value as more global companies seek to enter these markets.

There are solutions to this problem of gathering data in underdeveloped markets, and big brands are starting to vie for this data as it directly impacts expansion plans.

Image Credit: Anna Shvets; Pexels

The post The Increasing Need for Hyperlocal Data Collection appeared first on ReadWrite.