Module 1
The data legal work produces: sources, structure and what a row stands for
Every legal matter leaves a trail: when it was opened, who worked on it, how many hours were recorded, what was billed, how long it took, whether the client complained. Every contract holds dates, parties, amounts and obligations. Every court case, every regulatory filing and every email adds to the pile. Most of it sits unused. The skill of the legal engineer who works with data is first to see what is there, to know which of it can be counted and to decide what questions it can honestly answer. This module introduces the data that legal work produces, the difference between data that is ready to count and data that is not, the idea that every analysis rests on a choice of what one row stands for and the duties that come with other people’s confidential information.
Hover over an idea to light up what it connects to, click it for its definition, minimum requirements, an example and the usual trap, press Play to watch the map build step by step, or trace a short case through it.
Simplified teaching map, in general terms. Check the law where you work. On a small screen, scroll the map sideways.
By the end of this module you can
- Name the main sources of legal data.
- Tell structured from unstructured data.
- Decide what a row stands for in an analysis.
- Match a question to the data that could answer it.
- Recognise the confidentiality duties that attach to legal data.
Part 1. The lecture
Where legal data comes from
Legal data is not a single thing. It is a set of records, each made for another purpose, that can be put to a second use.
| Source | What it holds | Example questions it can help answer |
|---|---|---|
| Matter management | Matter number, client, practice area, responsible lawyer, open and close dates, status. | How many matters are open? How long do they take? |
| Time and billing | Hours recorded, rates, amounts billed and collected, write-offs. | Which work is profitable? Where does time go? |
| Documents and contracts | The text of agreements and letters, with dates, parties, amounts, clauses. | Which contracts renew this year? How often is the cap negotiated? |
| Requests and tickets | Requests from the business to legal, with dates, type, priority. | How long do requests wait? What do people ask for most? |
| Compliance and risk registers | Obligations, incidents, breaches, audits and their status. | How many incidents by type? How fast are they closed? |
| Court and regulatory records | Cases, decisions, filings, deadlines, outcomes. | How long do cases take? What are the outcomes by court or judge? |
| Client feedback | Surveys, complaints, instructions and referrals. | Who is satisfied? Why do clients leave? |
Each of these was designed for operating the work, not for analysing it. Time records exist to be billed; a contract exists to bind the parties. When you reuse them for analysis, you inherit their quirks: fields that were optional, categories that changed over time and entries made in a hurry.
Structured and unstructured data
The most useful distinction is between data that is ready to count and data that is not.
| Structured | Unstructured | |
|---|---|---|
| Form | Rows and columns with defined fields. | Free text: documents, emails, notes, recordings. |
| Example | A matter table with practice area, hours and status. | The body of a contract, a client’s email, a judgment. |
| How you count it | Directly, with a query or a spreadsheet. | You must first extract the facts, by reading, by rules or by software. |
| Strength | Fast, consistent, easy to check. | Rich: it contains what actually happened. |
| Weakness | Only what someone thought to record. | Hard to count, and extraction can be wrong. |
Most analysis in a legal department starts with the structured data, because it can be counted at once. The unstructured data usually holds the more interesting answers, and converting it into structured data (for example, extracting the termination date and the cap from a thousand contracts) is a project in its own right, which the Contract Analytics course teaches. In practice you move between the two: a surprising number in the structured data sends you to read the documents behind it.
A practical rule
Start with the data you already have in a table. If the answer you need is not there, ask whether it is worth extracting, and how you will check the extraction.
What does a row stand for?
Before you count anything, decide the unit of analysis: what one row of your table represents. It sounds trivial and is the source of many wrong answers.
| If a row is … | You can ask … | But beware … |
|---|---|---|
| A matter | How many matters? How long does a matter take? | A client with five matters appears five times. |
| A client | How many clients? Which clients bring most work? | Matters are hidden inside the client. |
| A time entry | How many hours by lawyer, by task? | A long matter has hundreds of rows. |
| An invoice | How much is billed per month? | One matter may have many invoices. |
| A contract | How many expire this year? | A master agreement and its order forms may be separate rows or one. |
| A person | How many lawyers by seniority? | A person may change role during the year. |
Mistakes arise when two tables with different units are combined carelessly. If you join a matter table to a time-entry table and then count rows, you are counting time entries, not matters. A common error is an average of averages: the average of the partners’ average days to close is not the average days to close across all matters.
Ask each time
“What is one row?” and “If I count rows, what am I counting?” These two questions prevent most counting errors.
Questions data can and cannot answer
Data answers what happened well, why poorly and what should we do not at all, unless people supply the reasoning.
| Kind of question | Example | What data can do |
|---|---|---|
| How many / how much | How many matters opened last year? | Answer exactly, if the data is complete. |
| How long | How many days does a matter take to close? | Answer, with care about open matters. |
| Which | Which practice area has most complaints? | Rank, and show how sure the ranking is. |
| Compare | Do fixed fees take longer than hourly? | Compare, but cannot say that the fee type caused the difference. |
| Why | Why do some matters run late? | Suggest patterns; the reasons need people to explain. |
| What should we do | Should we change our fee structure? | Inform the decision; it is a judgement. |
Be wary of the temptation to read causes into patterns. If matters handled by one partner close faster, it may be that the partner is good, or that the partner is given easier matters. The data does not say which. Good analysts state what the data shows, what it does not and what they would need to check next.
Whose data is it?
Legal data is almost always about other people: clients, counterparties, employees, individuals in a dispute. It is held under duties.
- Confidentiality and privilege. Client information is confidential, and some of it is privileged. Using it for internal analysis is generally permitted, but disclosing it, even in aggregate, may not be.
- Data protection law. If the data identifies living people, rules on lawful use, minimisation and security apply. Anonymised and aggregated data is safer, but re-identification is possible, especially with small numbers.
- Contract terms. A client’s terms of engagement may restrict how its data may be used, and some prohibit use in benchmarks.
- Employment duties. Data about the performance of colleagues is sensitive, and measuring people changes how they behave.
- Professional rules. Duties of competence include handling client information properly.
The habits are simple. Collect the minimum, use it for the purpose it was gathered for, aggregate where you can, hide small numbers and keep it secure. A dashboard that shows ‘one client in Disputes complained’ may identify the client. The Law and Cybersecurity and GDPR and AI courses of this site go further on these duties.
Invented data in this course
Every dataset you use in this course is invented. When you work with real data, check with the owner of the data, and with the firm’s rules, before you start.
Part 2. Lab: Structured or Not?
Decide whether each item is ready to count (structured) or needs work first (unstructured).
Situation 1 of 6
A matter table with practice area, partner, hours and status.
Where does this belong?
Part 3. Lab: What Does a Row Stand For?
For each question, choose the table whose rows let you answer it directly.
Situation 1 of 3
You want to know how many different clients the firm had last year.
What should one row stand for?
Part 4. Check your understanding
Ten questions drawn from a larger pool. Sign in to have your score saved towards your certificate.
You have finished the free module
Here is what you can do now, and where the course goes next
Module 1 gave you
- Name the main sources of legal data.
- Tell structured from unstructured data.
- Decide what a row stands for in an analysis.
- Match a question to the data that could answer it.
- Recognise the confidentiality duties that attach to legal data.
Next, 6 more modules and a capstone
- Module 2: Structuring data: tables, columns, consistent values and the tidy tableYou will learn to recognise a tidy table.
- Module 3: Asking questions of data: select, filter, count, group and sortYou will learn to write a query with SELECT, FROM and WHERE.
- Module 4: Data quality and bias: finding what is wrong before you trust a numberYou will learn to name the six dimensions of data quality.
- Module 5: Metrics that mean something: definitions, rates, averages and the traps of targetsYou will learn to write a metric card with a precise definition.
- Module 6: Charts and dashboards: showing the answer so that people act on it
- Module 7: Governing and using the numbers: ownership, access, ethics and decisions
- Capstone: Capstone: a dashboard and a report for the managing partner
Each module has its own practice labs and a graded quiz, and a Certificate of Completion follows when you pass them all. The whole course is $15 once, or included in an All-Access Membership.
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General education on legal data and analytics, using invented organisations, datasets and figures. It is not legal advice. Duties about client confidentiality, data protection and the use of data about employees differ between countries and employers: check the rules that apply to you.
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