To achieve data-driven decision-making, you need all relevant data. The power behind prediction is the past. In any type of predictive AI modeling, you can’t derive insights and forecast the future of what will likely happen without including historical and current data.

But what is the easiest way to use this past data, and what considerations should you keep top of mind?


How Predictive Analytics Works and Why It Requires Past Data

Predictive analytics is an approach that organizations use to understand and identify patterns and trends from historical and current data. It leverages the data itself along with statistical modeling, machine learning, and AI. 

From this analysis, you can detect both risks and opportunities, depending on the questions you’re asking. You may also find relationships between variables. For example, a popular use case for predictive analytics is consumer behavior patter analysis. 

By including past and current data, companies can better predict what factors influence purchases and then personalize promotions to elicit the expected outcomes. 

Without the knowledge of past consumer behavior data, it would be impossible to make such predictions. Garnering these insights is a smart tactic for businesses. A study by the Aberdeen Group as reported by Emerj found that companies that use predictive analytics were twice as likely to be able to target high-value customers and market their products to them exceptionally. 

Although predictive analytics isn’t accurate 100 percent of the time, it is much more reliable than a hunch or an assumption. It paves the way for making critical decisions based on data. But how do you effectively group all your datasets?

How Does Data Ingestion Work?

To make sense of the data and make predictions, you have to feed the models data. That’s not always easy. Data can live in many places and be in various formats. The best way to approach such a task is to use a predictive modeling platform

If your tool does these things, it will be relatively easy to move your historical data into a new environment where it can deliver actionable insights.

Predictive Analytics Platforms Allow You to Gather Data from Unexpected Sources

You may have third-party systems that hold lots of valuable past data, but exporting the data is not necessarily a perfect solution. In some cases, you may not have export access at all. What do you do then? In these situations, an integration will be necessary so that the two systems can exchange data. 

If this is a top concern and has been holding you back from using predictive analytics, you’ll need to ask the right questions of both application owners. It is typically possible to integrate, and we’ve been able to do that for our users, including those who adopt our predictive maintenance solution.

Another concern may be integrating a data source that’s constantly changing, such as weather information. This kind of complex integration is critical in predictive maintenance because it can be a factor for assets in challenging environments. However, this concern shouldn’t limit you. 

The bottom line is that most companies need to incorporate many data streams, and they won’t all behave in the same way. The key is to find a platform that can make this happen efficiently and effectively.

Discover the Power of Predictive Analytics with Diverse Data Sources

Predictive analytics can be a game changer for organizations. It can increase sales, reduce costs, mitigate risk, and much more. 

To see the powerful results such a model can have, read our aviation company use case. The company used Virtualitics Predict to save $6-15 million in annual maintenance costs. 

Data isn’t just for business intelligence anymore; it’s a highly valuable corporate asset. In today’s enterprises, data is being used to advance the business and drive decision-making in many different ways.

For example, marketing and sales teams use data to target their marketing efforts and identify new opportunities for growth. In the logistics and transportation industries, data saves businesses money by allowing them to predict maintenance needs and optimize routes and fuel usage.

Even utility companies are harnessing the power of data analytics to forecast load and make cost-saving adjustments to power generation.


Why Effective Data Management Is Critical

Because data has become such an essential business commodity, how your organization manages its data matters more than ever.

The ubiquity of smart devices for business and personal use paired with increased adoption of the Internet of Things (IoT) and accelerated Industry 4.0 initiatives, there is a historically large amount of data being generated worldwide every day.

Although this data has the potential to provide high value to the enterprise, the data is really only useful if the business can extract meaningful, actionable information from it. All of this data must also be stored and backed up securely, which adds expense and complexity to data management.


Unstructured Data Advantages and Data Management Challenges

Many of today’s data management challenges stem from the fact that up to 90 percent of the world’s data is unstructured, and that number is only going up. According to some predictions, the amount of unstructured data will increase to 175 billion zettabytes by 2025.

To fully appreciate how unstructured data is impacting and changing the future of data management, it’s important to understand where the flood of unstructured data is coming from.

There is no question that we live in a connected society. Almost everything we do—from collaborating with coworkers to shipping inventory to heating and cooling our offices—is enabled and improved through the analysis of unstructured data.

Enterprises across many industries rely on unstructured data generated through:

  • Analytics from AI and machine learning algorithms
  • Sensor data
  • Functional data from IoT devices
  • Geospatial data
  • Weather data
  • Surveillance data
  • Productivity applications
  • Text files (e.g., emails, spreadsheets, chatbots) 

In fact, it is almost impossible to run a successful business today without accessing, analyzing, and visualizing the insights found within your unstructured data.

Main Challenges of Unstructured Data Management

Along with providing highly valuable business intelligence, unstructured data also presents some significant data management challenges.



Although the cloud is often touted as a low-cost alternative to traditional, in-house data storage solutions, the massive quantity of data that organizations are managing today can significantly increase costs for cloud-based storage.

To keep storage expenses in check, it’s helpful to evaluate all of your organization’s data and create separate storage strategies for data that is largely unchanging and data that is accessed and modified frequently.

The unchanging or “cold” data can be stored in unmanaged cloud-based storage, freeing up your budget for storing the “hot” data that requires regular backup and replication.

Legacy data management systems are another potential source of extra outlay. Legacy systems often do not play well with modern unstructured data management solutions, which can require custom-building a solution to effectively process and manage high volumes of unstructured data.



Unstructured data also introduces additional complexity into enterprise data analytics. With a large amount of raw, unorganized data flowing in from many disparate sources, indexing is difficult and error-prone due to unclear structure and lack of predefined attributes.

This disorganization and lack of well-defined attributes makes it difficult for analysts to determine which datasets are relevant to a particular use case and whether the data is high-quality and trustworthy.

How Investing in Unstructured Data Management Will Improve Your Analytics

Despite the challenges inherent in managing unstructured data, the positive impact a solid strategy will have on the quality of your analytics—and, by extension, your business outcomes—makes the investment worthwhile.

At the highest level, an effective unstructured data management strategy provides all users, not just data scientists, with a comprehensive data story that can be used to drive decision-making throughout the enterprise.

When managed well, unstructured data adds flexibility to the types of insights that can be gathered from the data because it isn’t constrained by a fixed schema. This also makes it easier to collect, distill, and correlate information from heterogeneous sources.

From a practical standpoint, unstructured data management makes data portable and scalable throughout the organization so users can confidently take action on relevant insights, from enabling predictive analytics to improving operational decision-making to increasing marketing intelligence.


What’s Next for Unstructured Data Management?

As rapid technology adoption continues to define and shape the enterprise business landscape, many organizations are looking for ways to manage high volumes of unstructured data efficiently and profitably.

Virtualitics AI Platform leads the industry in AI-based data analytics and multi-dimensional visualizations to empower organizations in every industry to take control of their complex data management and put that data to work for them.

Don’t waste more effort trying to decipher your unstructured data. Contact Virtualitics to request a demo of Virtualitics AI Platform.