IT professionals have a first-person understanding of how quickly advancements happen when we’re able to leverage tools and machines to make improvements. The Internet of Things (IoT) has revolutionized everything, from our cars to manufacturing, by providing a stream of data that holds the promise of valuable insights that can make everything we do better, faster, and more productive. However, those who work in IT also know that using technology without an understanding of the correct tools means you’re unlikely to maximize potential benefits and could even open a door to additional risks.
With the incredible increase in data collection over the past few decades, IT organizations that use big data management tools now face more opportunities to gain insights and get ahead of issues than ever before.
Understanding the Past, Present, and Future in Data
Once upon a time, data analytics was a manual process using historic data to understand something that happened in the past and make educated guesses about what that could mean for the future. Afterwards, the user would have to wait and see if those guesses were correct and adjust as needed. That was the previous process of descriptive analytics, and it was, and still is, quite time consuming. It’s often what we think of when we see pictures of pie charts and graphs in a PowerPoint presentation.
Because descriptive analytics is a manual process—which means humans process and understand the data—it’s fundamentally backward-looking. Descriptive analytics is still used in many organizations who have not made the Industry 4.0 transition, but with so much data coming in through IoT and various other technologies, those organizations are rapidly being left behind.
One benefit of IoT is the ability to collect information in real time and then aggregate it in big data management tools, which can also analyze and visualize it in real time. A current view of what’s happening is much better than a historic view. For example, real-time anomaly detection means you can address an issue as it’s happening—not after it’s already created a problem. It also means more secure data by detecting potential malicious actors.
The real gold is when real-time analytics and historic data are combined along with predictive analytics techniques. This gives you an increasingly accurate glimpse into the future.
What is Predictive Analytics and How Does it Work?
Big data management tools can take in the astronomical amount of information that’s produced as part of IoT and very quickly process it into useful information. One category of the tools used in this type of software is predictive analytics. Sightline EDM uses these tools not only to make predictions into what historic and current data indicate will happen, but also to get better at those predictions over time. It does this by using AI and machine learning to add additional data—including the real time data constantly being collected—to its existing data. Then, it automatically learns from it to get a better understanding of what it all means.
Future planning is valuable in all industries. However, with an increasing amount of the world dependent on information, technology infrastructure, and tools, combined with the increasing severity of consequences caused by cyberattacks and capacity issues, it is a requirement for IT.
One issue that can be a challenge when using predictive analytics is an unexpected piece of data. This can feel frustrating if you’re doing manual data analysis. Dealing with outliers in predictive analytics isn’t as simple with a manual process. One huge benefit of big data management tools is that they take the frustration out of encountering an outlier by offering visualization of the data and how it relates to the big picture. Sightline EDM does this easily and efficiently. Predictive analytics tools also help mitigate any impact the outliers could have on your data insights by putting that in context and correcting for any effects that need to be considered.
How to Use Predictive Analytics for Better Business Results
To recap, IIoT is the process of collecting data from physical objects, like manufacturing equipment, You’ve probably experienced the effects of a website crash or a software failure at some point in your career. They can be panic-inducing and stressful and, sometimes, incredibly expensive or harmful. Predictive analytics can help prevent issues like these.
Predictive analytics is especially useful for capacity management. Websites often have peaks and valleys when it comes to traffic. In many cases, those peaks and valleys can be anticipated. For example, we know flower shops are likely to see a spike before Valentine’s Day due to the nature of the holiday.
However, there’s always unpredictability in the world, and the issues brought on recently by a global pandemic is a prime example. Nearly overnight, consumers were suddenly reliant on online shopping to a much bigger degree than before, meaning companies had to adjust. Unanticipated spikes could lead to downtime, which means lost revenue and additional expenses.
In this case study, you can see how Sightine EDM was able to help the Pennsylvania Department of Health with capacity planning, among other things, when launching their Affordable Care Act website. The key ways Sightline prevented downtime that are addressed in this case study were: anomaly detection and alerts, predictive analytics, downtime detection, risk identification, and customized dashboards that offered whole-picture data visualization. DHS was able to use these tools and forecasts to anticipate issues, set goals, and adjust tactics if projections were not where they needed to be.
Sightline Provides Leading Predictive Analytics Capabilities
Sightline EDM is an effective big data management tool that can aggregate data points from a variety of sources, including servers, applications, and network devices. It then unifies them into a single dashboard. The data you’re collecting is coming in real time, and Sightline’s software solution can analyze and visualize that data nearly simultaneously, so you’re getting a big-picture view of up-to-the-minute insight.
While simply being able to see and understand your data as it arrives is hugely beneficial, Sightline EDM is also able to place the new, incoming information into the appropriate context with historical data to project what may happen in the future. With that information, you can determine a solution before a negative event occurs and damages your business, not after.
Using Sightline EDM’s predictive analytics tools throughout your infrastructure means you can:
- Communicate effectively between departments
- Keep accurate records
- Secure your data in real time and
- Create customizable reports, alerts, and predictions for labor availability, equipment capacity, repairs, supplies, logistics, storage and more
- Discover potential bottlenecks
- Avoid downtime across systems
- Automate the capacity planning process
If you’re ready to move from the past of data analysis and into the future of predictive analytics, the Sightline team is ready to help you learn more when you book a free, no-hassle consultation. Contact us today.
FAQs:
The manual process of using historical data to make educated guesses about what that could mean for what is happening now or might happen in the future.
Combining real time and historic data to get an increasingly accurate glimpse into the future.
Predictive analytics is especially useful for capacity management and can help prevent outages and downtime.
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