Research: 20.04.22

Insights on the digital future of investigations for modern analysts

From policymaking to banking and national security to corporations, decision-makers across sectors struggle with the same dilemma: Running investigations is more challenging than ever.

Not only is there more data, processed in more formats and coming from more places than there ever has been, but dissemination of misinformation means analysts can’t always trust the data they find. Add to that the breakneck speed at which information is shared and decisions need to be made, and you can see why analysts are re-evaluating the way they do things.

The modern analyst doesn’t go by their gut; instead, they leverage the power of technology and data to inform their analysis, establishing themselves as the center of gravity for their organization’s intelligence cycle. And their role will only become even more important.

Here is a deep dive into the role of the modern analyst, including their current challenges, solutions, and future outlook.

What makes a successful modern analyst?

Increased complexity and the deluge of data have upended the workflow for analysts. To succeed today, the modern analyst must be:

Analytical and discerning.

Not all data is created equal. In fact, some data may be detrimental. The modern analyst must be able to objectively filter data to support their goal, parsing out ineffective, redundant, or emotionally influenced sources.

Adaptable to a variety of tools and sources.

The pace of analysis and the proliferation of data mean the analyst must keep up, or risk falling behind their workflow and failing to achieve their goals. The modern analyst must be able to adapt to emerging tools and sources to stay ahead of their dynamic role.

Strong at communication and collaboration.

Digital investigations succeed or fail based on the analyst’s ability to communicate and collaborate. From accepting tasks from their supervisor to sharing findings with team members, the modern analyst must be able to work and share with others.

But while these characteristics seem easily achievable on the surface, a wave of new, difficult changes are emerging — and making the modern analyst’s role harder and harder.

Challenges facing modern analysts

The work of a modern analyst revolves around the intelligence cycle – the process of developing raw information into finished intelligence that can be put to use. The steps of the intelligence cycle run in a continuous loop through five stages:

Direction and planning (where goals are set and strategies created)
Data collection
Data processing (where data is cleaned, formatted and transformed)
Data analysis and production (where data is evaluated, conclusions are drawn, and reports are created)
Dissemination of conclusions and collection of feedback to integrate into the next round of the intelligence cycle

The intelligence cycle is a simple way of summarizing a very complex process. A process that doesn’t always run smoothly. A number of challenges can interfere with the intelligence cycle at any stage:

The sheer volume of data is overwhelming. It’s not just new sources. Existing and established sources produce larger quantities of more granular data too. For example, a single social media profile can provide data on job title, geolocation, relationships, and more. It’s easy to see how quickly data for a digital investigation can accumulate. That makes data collection time-consuming, and even the simplest analysis requires analysts to search through enormous amounts of data to uncover what’s relevant.

“Investigation amnesia” is increasingly common. Between the stress of the job, and the abundance of sources, leads, and directions, the modern analyst is more likely to lose track of where they are or where they’re going in an investigation. The result is that some parts of the data are processed more than once, while some parts of it are missed entirely. It’s also very difficult to keep track of areas to improve upon when they begin working on the next case.

Manual data collection and collation is tedious and at risk of human error. Instead of focusing on momentum-building activities, many modern analysts are still forced to manage outdated, difficult, and mistake-prone manual tasks, taking up time and reducing quality.

More options often means more incompatibility.

Most analysts develop their own ad hoc workflow from different, often incompatible technology. This complicates processes and creates unnecessary obstacles at every stage of the intelligence cycle.

Disinformation and misinformation are on the rise.

Recognizing the value of data in shaping public perception, bad actors are manipulating data to spread false information. And because creating a shareable data set is as easy as opening a Google sheet, low-quality data – even from a well-meaning source – can alter and disrupt the analyst’s evaluation.

The modern appetite for fast decision-making is out of step with legacy technology.

We have instant access to more information than ever before, readily available from our phones and computers. But while this has increased our expectations and shortened our attention spans, many analysts still grapple with outdated, and often incompatible, technology that can’t deliver answers fast enough.

Red tape interrupts effective data flow.

Despite the value of shared data and collaborative workflows for analysts and their teams, data is still frequently siloed into different departments. And permissions and protocols overcomplicate the safe exchange of data.

Ease of access can open the door to security risks.

In an attempt to improve collaboration and data sharing, many systems for analysts expose organizations to security risks and effortless access to sensitive information. Instead of modernizing permissions and protections, many systems overlook them entirely.

Clean, structured, and up-to-date data is far from guaranteed.

With so much information available, it’s inevitable that some of the data that analysts collect will be out-of-date or irrelevant. And not only are analysts at risk of relying on outdated data, they’re also prone to collecting incomplete or poorly structured data requiring substantial cleaning and processing.

Many current conditions complicate accountability, traceability, and logic for analysts.

An analyst must be able to present and defend their conclusions, but most analyst tools can’t track when and why data was accessed or analyzed, how hypotheses were made and confirmed, how conclusions were drawn, and who drew them. That can undermine the conclusions analysts make based on the data– and the credibility of the analysts themselves.

The digital investigation solution

The right technology can help solve many of the challenges that are currently disrupting the intelligence cycle. Of course, you can’t replace an analyst with software and expect equal or better results. The modern analyst serves two key roles.

First, they’re responsible for collecting, evaluating, and processing large volumes of data. Second, they apply strategy and creativity to those large volumes of data to come to the right conclusion. The first role should be taken over by AI to improve productivity, but the second needs a human in the driver’s seat.

That’s why the modern analyst benefits from a balance of AI technology and human ingenuity – plus an operating system that can help both humans and AI integrate seamlessly and perform to the best of their ability.

The best platforms for the modern analyst will:

Save analysts time by using AI to extract relevant data from various databases and file types, such as phone records, social media information, vehicle registration records, bank records etc.
Simplify individual and collaborative workflows through a clean and effective user interface and user experience
Automate filters to spot, flag, and prioritize data items by relevance, preventing analysts from drowning in irrelevant data
Streamline the reporting process by helping analysts put conclusions together quickly, then share them easily and safely.
Automate data collection and searches based on various predefined criteria, so analysts can find the right data faster. Platforms should also allow analysts to create custom alerts that surfaces important data as soon as it’s collected.

Introducing Falkor: the first-ever operating system for analysts

The modern analyst works within a confusing and overwhelming landscape, but their role is more important than ever. Technology should play an important part in that role, but AI hasn’t replaced the modern analyst. If anything it has shone a light on the irreplaceable value of the analyst; the combination of human creativity and technology can lead to more effective, better-informed, and more efficient digital investigations.