3 Tips for Uncovering Hidden Insights Through Advanced Analytics

When you think about the sheer volume of information being created in the modern era, it’s easy to see why so much valuable insight is overlooked.

According to one recent study, 90% of all data that exists on Earth has been created in just the last two years. Decades ago, the process of trying to find someone using data was long and arduous. Today, the issue is that there’s so much to wade through, and many aren’t even sure exactly where to begin.

Yet, at the same time, the narrative hidden inside this data is essential to a number of different fields. In healthcare, it could hold the key to diagnosing and treating certain types of diseases earlier than ever. In personal finance, it means understanding where the markets are headed in a way that lets people capitalize on them moving forward.

Advanced analytics help make this all easier by “training” computer systems to look for the insight on our behalf. That way, we can collectively spend less time rummaging through this data and more time acting on what we find.

One key application for advanced analytics is skip tracing. This is an investigative technique that is most commonly used to locate individuals who might not want to be found. This technique is used by many professionals, including real estate investors, debt collectors, and even bounty hunters. Understanding how skip tracing works and where its strengths are in this context is the best way to uncover those hidden insights you’re after.

Integrate Data from Diverse Sources

Skip tracing is only as good as the data you feed into it. Because of that, you’ll want to make sure you’re properly integrating data with as many diverse sources as possible.

That is to say, public records can be a great way to begin by combing over property records, tax assessments, and even court records. But that isn’t where things should end. You’ll also want to look at social media and online profiles, credit reports, financial data, and more.

Only by pulling in information from all these diverse sources can you get a better indication of what you’re looking for and how close you are to trying to find it. This can also help prevent certain data quality issues that many have run up against in the past. If all your data comes from one “pool” and you have a quality issue, you can’t truly trust your findings, either.

Leverage Predictive Analytics to Identify Trends and Opportunities

From a certain perspective, predictive analytics can be a great way to let the past inform the future, so to speak. So long as you are confident in the quality of your data sources, you might be able to predict where something is going based on how far it has come up to this point.

For example, in real estate, predictive analytics can help with trend analysis. Based on historical property transactions and owner behaviors, you can uncover patterns that may help you capitalize on opportunities before your competitors even realize they exist.

The same concept is true of performing a risk assessment – the cornerstone of any successful investment strategy. Based on every available record about a property owner’s history, you can determine the probability that they might have their house foreclosed on or go through bankruptcy, for example.

Think about how skip tracing is employed to find criminals who may be trying to avoid detection. If you have historical data that points towards a certain financial instability or a proclivity to relocate, predictive analytics might be able to use data about where someone has been to give you an indication of where they might be going. That way, you can locate the individual in the most efficient way possible.

Apply Machine Learning to Refine and Enhance Accuracy

Finally, machine learning can be a great way for skip tracers to refine their efforts – particularly as it equates to the sheer volume of data they’re working through.

Machine learning can be used to detect unusual patterns in data, for example, by highlighting whenever conditions deviate from the accepted definition of normal. On the one hand, this might help give you an indication of where someone is headed so that you can locate them. On the other hand, it could also point to issues with data quality – letting you know that you should focus your skip tracing efforts elsewhere.

Natural Language Processing, otherwise known as NLP for short, is also a great way to extract more value from unstructured data. This could be used by skip tracers to go over social media posts, to retrieve text from photographs or emails, and more – all to uncover even more relevant information that will help with their efforts.

In the end, these are just a few of the practical tips that you can use to leverage advanced analytics to enhance decision-making and drive strategic action across your organization. One of the great things about the type of technological tools we’re talking about in the modern era is that they’re nothing if not malleable. The potential of something like machine learning often feels limitless – because it largely is. That is to say, it can be used in a lot of different ways by a lot of different people.

Really, what you need to do is start with a strategy that will point you in the right direction. What do you need something like skip tracing to do that you can’t do right now, and how will advanced analytics make that easier? Then, you’ll be able to work your way backward to the practical tips and tricks needed to get you there.

It’s also important to note that these insights and strategies can be applied beyond any one industry, like real estate. They’re applicable to virtually any field where uncovering hidden data can lead to competitive advantage. But they also almost always create an environment where more informed decision-making is no longer a question of “if” but “when,” which in and of itself is the most important benefit of all.