Is Big Data Enough in Law Enforcement Investigations?
We live in a world with more data available to investigators than any other time in history so that should result in better investigations, right? Not necessarily according to a recent article in Government Computer News (GCN) which shows a more complete picture of big data’s role in investigations.
The story states that there’s a misconception that big data is the main thing that law enforcement needs for effective decision-making. Many also assume that big data is flawless and that more data means better data. In fact, big data used for law enforcement investigations and intelligence analysis is often contradictory, partial, biased, non-transparent or even manipulated. To make sense of big data, law enforcement agencies often use a decision intelligence platform for data fusion and analytics. But even that’s not enough.
Once the data analysis process begins, analysts must also determine if they have an intelligence gap. And how do they learn what they don’t know? Proven decision intelligence methodologies—based on critical thinking skills—are required to validate assumptions and make the best decisions with the intelligence at hand.
Law enforcement agencies gather big data for an investigation by bringing in more and different sources of data, both structured and unstructured. The amount of data and the process of sifting through it can be overwhelming, so the use of technology is imperative. Agencies can use data fusion to enable entity resolution from disconnected data sources and leverage artificial intelligence and machine learning to enrich unstructured data for more effective, automated analytics that improve decision intelligence. AI (artificial intelligence) and ML (machine learning) can also surface previously hidden relationships and patterns between individuals and/or entities; however, these applications are only part of the solution.
On its own, ML is not useful. ML requires human expertise to learn and generate better insights. While it makes sense to apply ML to big data to accelerate decision-making and to find similarities, commonalities and patterns, analysts must still examine the suspicious patterns or irregularities found and what they may indicate. It is a best practice to apply critical-thinking methodologies after uncovering those unique data points that analysts seek. Machine learning should never replace law enforcement analysts. Rather, analysts benefit from the accelerated insights machine learning provides, and then the ML algorithms learn from the analysts’ know-how and experience as inputs.
There are two concepts in critical thinking that reveal the challenges analysts face: failure of imagination and failure of conception. With failure of imagination, analysts have trouble imagining things that they haven’t previously experienced. One example of this is the United States’ inability to imagine a world without the Soviet Union. Despite all the economic and political indicators that pointed to the Soviet Union’s collapse, it remained inconceivable to most. A more recent example is the United States’ failure to predict the rapid Taliban takeover of Afghanistan prior to the final withdrawal of American troops, along with the (incorrect) assessment that the Afghan military and government would be able to maintain control of the country.
Failure of conception is the inability to accept data that conflicts with beliefs and past experiences. The 9/11 attacks are an example of this because many in the U.S. intelligence community believed that terrorism was not a domestic issue—despite those same intelligence organizations knowing that there were foreign nationals learning to fly planes but uninterested in learning how to land. In addition, the intelligence community’s experience was that all of Osama bin Laden’s previous attacks were outside the U.S.
It can be difficult to decide which methodologies are best suited to an investigation and to then work through them. Experts in decision intelligence methodologies can guide law enforcement agencies to make the most of their big data, data fusion and analytics. They can assist in testing assumptions and looking at the data and insights from different angles, all to ensure the best outcomes in investigations.
So we see that big data can’t solve everything and human skills will still be needed to conduct proper investigations. And as we know, success or failure in the courtroom or during the settlement process depends on having the very best, most detailed information about a case. That’s why great attorneys and law firms across the country work with the worldwide team of Santoni Investigations who will ensure you know everything to make the right decisions and maximize the settlement process or win your case in court!