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Structured Data, Structured Data Strategy, document-based discovery, QDMA Framework, QDMA, data sources, collection strategy, data extraction, Strategy

Structured data is everywhere these days. The World Economic Forum estimates that by the end of 2025, the amount of data generated around the world each day will reach 463 exabytes. To put that number in perspective, all the words ever spoken by humans would fit into approximately 5 exabytes. This massive data revolution has infiltrated nearly every facet of our lives – including the courtroom. 

In many cases, such as Fact Crashing™, we have found that prioritizing structured data can provide a significant procedural, tactical, and even strategic advantage.  However, it can be difficult to determine an appropriate structured data strategy for a legal investigation. The reasons for this difficulty are numerous and may include any combination of the following challenges:

  • You know data can be helpful, but you don’t know the sources in which they’re located.
  • You know which sources you need, but you don’t know how to access them.
  • You know how to access the data, but you don’t know how to strategically and effectively analyze it.

Some legal teams are reluctant to include any type of structured data in their case strategy, opting to rely on the more comfortable avenues of document-based discovery.

However, structured data can provide the insight your case needs, oftentimes at a lower cost with less time.

The QDMA Framework

At iDS, we pride ourselves in creating unique solutions to complex challenges – it’s what we do. The iDS team of consultative experts has developed the QDMA framework to empower clients with the full value of structured data. It’s a complex task broken down into four simple steps: Questions, Data, Model, and Analysis.

Step 1: Questions

The best place to begin involves developing a list of case questions:  that–if you knew the answers–would provide a compelling narrative to support or defend against the allegations of a case. In other words: do I have a problem and if so how big is it? In a typical wage and hour case for example, questions may include:

  • How often are my employees working evening and weekends?
  • Do I see evidence of activity before/after punch outs?
  • How many days did my employees not have a lunch break?

Documenting these questions helps define the case objectives and provides the foundation needed to continue the QDMA journey.

Step 2: Data

Once you’ve defined the relevant questions, the next step is to figure out which data sources contain the information you need to answer them – usually by aligning each question with a relevant data source(s). Sticking with our wage and hour example, we may identify our target data sources to include:

  • Time clock in/out data
  • Payroll compensation data
  • Badge swipe records
  • Computer login/logout

From here, we develop a collection strategy for each source to include:

  • IT points of contact
  • Known nuances/limitations
  • Transfer mechanism
  • Collection priority

Once data has been collected, it’s tempting to jump in and begin analyzing it. But not so fast – we need to prepare it for analysis.

Step 3: Model

In a perfect world, every data source is nicely formatted, normalized, and easy to compare with other sources. However, this is never the case.  Modelling and preparing data sets us up for downstream analysis. In other words, we need to take apples and oranges and turn them into apples and apples.

This type of modeling can take many forms and includes processes like:

  • General data clean up and data type formatting
  • Standardizing time zones and units
  • Normalizing entities and aliases across sources (e.g., John Smith, Jsmith, and Smith.John)

Once data has been appropriately modeled, iDS’ team of consultative experts can analyze with greater confidence and improved efficiency.

Step 4: Analysis

Finally, the fun part: analysis. This is where we actually get our hands dirty and explore our ingested data sources, looking for things like trends, patterns, and anomalies.  Like the iDS STEPS™ framework, we reference processes we’ve accomplished earlier to drive success on every engagement. We begin by  evaluating the extent to which our data supports or refutes each question/claim. Once we’ve performed this analysis, we can now go back to our clients with an organized report or visualization that can answer:

  • Do I have a problem?
  • How big is it?
  • Who is involved?
  • Which time periods does it encompass?
  • Is it isolated or systemic?

The QDMA framework is iDS’ maximizes efficiency for timely, consistent data extraction – ensuring your data is an asset and not a liability.

Optimal Outcomes from QDMA

Introducing structured data into your legal strategy is a complex process – and one that can lead the discovery of crucial data for your case. Within iDS’ QDMA framework, we break down everything you need  to lead you to a data-driven successful outcome in the courtroom. Further, iDS has developed a proprietary data analysis platform – xIOT – which aligns perfectly with our QDMA approach.

To learn more about how the consultative experts at iDS can provide custom solutions for all of your structured data needs, contact us today.

iDS provides consultative data solutions to corporations and law firms around the world, giving them a decisive advantage – both in and out of the courtroom. Our subject matter experts and data strategists specialize in finding solutions to complex data problems – ensuring data can be leveraged as an asset and not a liability.