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Data Analytics Expert Witness

Vident Partners provides vetted data analytics expert witnesses for cases involving large transaction records, payroll and claims data, database reconstruction, summary exhibits and the reliability of analytic methods. Request a referral today.

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About Data Analytics Expert Witnesses

A data analytics expert decides what a large set of records actually shows: how many transactions fit a claim, which ones do not, and whether the method used to count them can be repeated by someone else. The value is in turning millions of rows into a defensible answer. This page covers the handling and analysis of business records in litigation. Inference from samples and regression belongs to a statistician, pricing a loss belongs to an economist or forensic accountant, and collecting data from devices belongs to a digital forensics examiner.

The usual material is payroll and timekeeping exports, insurance claims, billing systems, trading records, customer logs and point-of-sale data. The expert also needs the data dictionary, the system's change history and someone who can explain what each field really meant at the time.

Getting the Data

The first fight is over form. Under Rule 34, if a request does not specify a form for electronically stored information, a party must produce it in a form in which it is ordinarily maintained or in a reasonably usable form 1. A printed report or locked PDF of a database is often not much use, so an analyst should help counsel write the request: which tables, which fields, which dates, and in what format. Scope is limited by proportionality, measured against the needs of the case 2, and an analyst can often give the court something concrete on that point, such as the actual cost of an extraction or the size of a test run.

Privacy can shape the work. Health data offers a defined route: under HIPAA's expert determination method, a person with appropriate knowledge of statistical and scientific principles may determine that the risk of identification is very small and document the methods 3. That is a real assignment in its own right, separate from the analysis that follows.

Cleaning and Matching: Where Results Are Decided

Most of an analyst's judgment is exercised before any result appears. Choices made here can swing a number as much as the analysis itself:

  • Duplicates. Whether two near-identical rows are one event or two
  • Joins. How records from separate systems were matched, and what share failed to match
  • Exclusions. Which rows were dropped, by what rule, and whether the rule was set before the result was known
  • Definitions. Whether a field meant the same thing across system migrations
  • Time. Whether stamps are local or UTC, and whether a day boundary moved events

A report that says the data were cleaned tells the reader very little. A strong one lists each step, reports the row counts before and after, and hands over the code. An opposing expert should be able to run it and reach the same figure.

Reliability and Rule 702

The rule asks the proponent to show it is more likely than not that the testimony reflects a reliable application of the method, and each opinion must stay within what that application supports 4. For analytics, that points to two questions: was the pipeline tested, and do the results survive reasonable changes in the assumptions? A sensitivity table showing how the answer moves when a judgment call changes is often the most persuasive page in a report.

When the analyst works from a sample, the statistical questions come in. The Reference Manual on Scientific Evidence warns that selection bias is acute when attorneys choose cases for trial 5, and the Supreme Court said in Tyson Foods v. Bouaphakeo that representative evidence that is statistically inadequate or based on implausible assumptions could not lead to a fair estimate 6. A statistician may be needed for that step, though many analysts can run the same work on the full population and avoid it.

Predictive models raise a further point. A black-box score is hard to explain and hard to test, so an expert should be ready to show what went into a model, how it was validated and how it performs on data it did not see.

Getting the Numbers Before a Jury

Rule 1006 lets a court admit a summary, chart or calculation offered to prove the content of voluminous records that cannot be conveniently examined in court, whether or not the underlying records come into evidence 7. The proponent must make the originals or duplicates available for examination 7. Rule 107 treats an illustrative aid differently: it is not evidence and generally does not go to the jury during deliberations 8. So the expert and counsel should decide early which charts are evidence and which only explain, because the foundation for each is different.

Who Is Qualified

No license exists for data analysts. Look for practical experience with the system or industry at issue, such as payroll, claims or trading data, and strong tooling in SQL, Python or R. Ask whether the expert wrote the code personally, or supervised those who did, and whether the work can be reproduced. A strong witness explains a join to a jury in plain words. Pair this expert with a statistician for inference and with an economist or accountant for damages.

Rule 1006 lets a court admit a summary, chart or calculation to prove the content of voluminous records, and the proponent must make the originals or duplicates available for examination.

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Common Case Types

Wage and hour class actions built on timekeeping and payroll data

Insurance claims and overpayment audits

False claims and billing pattern analysis

Securities and trading record reconstruction

Consumer class actions over transaction records

Database migration and data loss disputes

Breach notification population counts

Summary exhibits under Rule 1006

Qualifications to Look For

  • Hands-on experience with the type of data at issue, such as payroll, claims or trading records
  • Strong working skill in SQL, Python or R
  • Code and steps that another analyst can reproduce
  • Personal authorship or direct supervision of the analysis
  • Experience explaining cleaning and matching choices to non-technical readers
  • No license or board covers data analysts, so the work record is the credential
  • Prior report or testimony experience, including Rule 1006 summaries

Frequently Asked Questions

What qualifications should a data analytics expert witness have?

Look for practical experience with the system or industry at issue and strong skill in SQL, Python or R. No license exists. Ask whether the expert wrote the code or directly supervised it, and whether another analyst can reproduce the result from the same data.

How is a data analyst different from a statistician?

A data analyst extracts, cleans, matches and counts records. A statistician decides what can be inferred about a larger group from a sample or a model. Many cases need the count, and some need both. If the analyst works from the whole population, sampling questions may not arise.

What form should data be produced in?

Rule 34 says that if a request does not specify a form, a party must produce electronically stored information in a form in which it is ordinarily maintained or in a reasonably usable form. Requests should name the tables, fields and format. A locked report is usually much less useful than the underlying data.

Can a jury see a summary instead of millions of records?

Yes, under Rule 1006, if the records are voluminous and cannot be conveniently examined in court. The summary may come in whether or not the underlying records do, but the proponent must make originals or duplicates available for examination.

What is the difference between a summary and an illustrative aid?

A Rule 1006 summary is evidence of the content of records. A Rule 107 illustrative aid helps the jury understand evidence or argument, but it is not evidence and generally does not go to the jury room. The foundation for each differs, so counsel should decide early which is which.

How much does a data analytics expert witness cost?

In general, data analytics expert fees are determined by the expert themselves, based on a variety of criteria. Among those criteria are experience, the size of the data, the systems involved, the need for code review and the amount of testimony. Vident does have some influence over expert fees by comparing experts within a specialty, but ultimately it is a personal decision by the expert.

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