Ten common reasons law firm need data governance according to Iron Carrot
Over the last five years, Iron Carrot has had the privilege of helping many law firms with their data governance journey. Every firm has its own challenges, but I have observed some common themes that make the business case for data governance that much stronger.
1. Duplication of effort
Explain the mistake
The siloed reality of many law firm teams and processes means that data is being captured, edited, enhanced, and used in many separate places around the firm. This often manifests as a cottage industry of spreadsheet creation, where many individuals insist that only their spreadsheet (and not the central systems) is right.
What causes this mistake?
This usually happens because of a lack of trust in the central data due to a lack of a feedback mechanism and poor visibility of cross-functional processes and the steps to get new/different data captured as part of a shared process.
How do you solve this mistake?
Transparency and communication of operational processes and the day-to-day management of the firm’s data are key. Everyone in the firm should have a good understanding of what each central system is for, where and how data goes into it, and how to correct data errors that they see. It’s also important to have a system in place to request that additional data be captured and managed to meet the needs of a client, practice, or business services team.
Case stories/examples
An example of duplication of effort is when two PSLs/KMLs from different practices each keep their own spreadsheets of work done, which doesn’t feed into the firm’s central credentials or knowledge repositories. This can lead to wasted time and resources by people outside their immediate practice group who will often be unaware of the existence of these spreadsheets. This can ultimately impact the quality of pitches and pricing estimates.
Expert tip
To avoid duplication of effort, it is helpful to establish a centralised place that explains what each repository is for and who is accountable and responsible for the data in it.
2. Different teams use the same labels for different concepts
Explain the mistake
This mistake occurs when different teams or individuals use the same label to refer to different concepts. This can lead to confusion, miscommunication, frustration, and errors. It is a contributing factor to a lack of trust in the firm’s data.
What causes this mistake?
A lack of standardisation and cross-functional communication causes this mistake. The nature of law firms is that they have looked at data and reporting in functional silos. Concept labels have been standardised within functions but not across the whole firm.
How do you solve this mistake?
It is important to establish clear definitions for terms and concepts and ensure that all teams use the same terminology. This is usually done by leveraging the collective wisdom of a cross-functional team of SMEs.
Case stories/examples
For example, when both the Finance team and the HR team use the concept ‘headcount’ but the numbers on the respective monthly reports don’t match. This is usually because the Finance team use a different calculation to the HR team. The confusion is solved by clarifying the data labels as something like “headcount (end of month)” and “headcount (at report date)” to explain better what the headcount number means and where it came from.
Expert tip
To avoid this mistake, it is helpful to establish a common vocabulary across the firm. This is usually captured and shared in a business glossary, which everyone in the firm can access, and that explains each term, its definition, and where/how it is used.
3. Lack of ownership
Explain the mistake
Lack of ownership refers to the mistake of not assigning clear accountability for data or data sets. This can lead to confusion, missed deadlines, and, ultimately, failure to meet business goals. It’s hard to make decisions or solve problems quickly and effectively when no one knows who to talk to.
What causes this mistake?
This mistake can be caused by a lack of clarity around roles and responsibilities, so no one understands who is accountable for that data. It’s also common to see mistaken assumptions and a lack of communication at the root of this mistake.
How do you solve this mistake?
Data Owners are the cornerstones of data-related activities, and identifying your data owners is a foundational part of adopting a data governance framework. Every firm will have different expectations of data owners, but their accountabilities do need to be documented and agreed upon.
Case stories/examples
An example of this mistake is when a project is delayed because no one took ownership of improving the quality of a critical piece of data. This can lead to finger-pointing and, ultimately, a failure to deliver the project on time.
Expert tip
To avoid this mistake, it is helpful to establish a culture of accountability by adopting a data governance framework which clarifies and communicates data roles and responsibilities, as well as establishing a data governance centre of excellence to support data owners (and others) to perform the data roles expected of them.
4. Poor or misunderstood data quality
Explain the mistake
Poor or misunderstood data quality refers to the mistake of relying on inaccurate or incomplete data. This can lead to incorrect decisions, missed opportunities, and, ultimately, a failure to meet business goals.
What causes this mistake?
This mistake can be caused by a lack of understanding of data sources, a lack of data governance, or a lack of data quality checks. It can also be solved by lawyers not understanding the importance of inputting the correct data when asked.
How do you solve this mistake?
It is important to establish data governance policies and procedures, perform regular data quality checks, and ensure that everyone understands the importance of data accuracy.
Case stories/examples
An example of this mistake is when a firm tries to report on the kinds of work it is doing in each practice, but the Partners disagree with the report because it doesn’t reflect the work that they know is being done. This further decreases the trust in the firm’s data if the lawyers do not select the correct type of work at matter opening (or correct it later when the scope became clearer).
Expert tip
To avoid this mistake, it is helpful to establish data quality standards and ensure that all data sources are regularly audited and validated. A data fluency programme should also be implemented to help everyone understand the role they play in creating high-quality data for the firm.
5. No visibility or control of downstream usage
Explain the mistake
No visibility or control of downstream usage refers to the mistake of not knowing how data is being used once it leaves a functional system and goes to another system, an operational data store, a data warehouse, or a report or dashboard.
What causes this mistake?
This mistake can be caused by a lack of data governance policies and procedures, visibility into data usage, or a lack of understanding of downstream data consumers. Most often, it is a lack of communication between key stakeholders.
How do you solve this mistake?
To solve this mistake, it is important to establish data governance policies and procedures that include controls around data usage and access. It may also be helpful to establish a system for tracking downstream data usage.
Case stories/examples
No visibility of downstream usage is most often a problem when an existing system is swapped out or upgraded. For example, when a new business system was changed, it changed the codes of the matter types. The finance team were made aware, but the reporting team wasn’t as nobody knew they received a direct data feed from that system. All the partner dashboards broke, and it was a mad scramble to find out what had gone wrong and get it fixed!
Expert tip
Data lineage draws a picture of where your data starts and where it ends up. This picture should contain different levels of detail depending on the audience that needs it. You can do data lineage manually or with software – a key objective is to do it somehow.
6. Rules and guidance not written down (if they exist)
Explain the mistake
This refers to the mistake of relying on informal or verbal guidance instead of written policies and procedures. This can lead to confusion, inconsistency, and ultimately, data quality errors and data reuse mistakes.
What causes this mistake?
This mistake can be caused by a lack of agreement that this kind of documentation is necessary for a modern law firm to operate effectively. It can also be due to a lack of enforcement.
How do you solve this mistake?
To solve this mistake, it is important to establish clear policies and procedures and ensure that they are documented and easily accessible. It may also be helpful to provide training and regular reminders to ensure that everyone understands the policies and procedures.
Case stories/examples
An example of this mistake is when a new employee is not given proper guidance on how to handle sensitive data, leading to a breach of confidentiality.
Expert tip
To avoid this mistake, it is helpful to establish a culture of documentation and ensure that data policies and procedures are regularly reviewed and updated. This is often the responsibility of a Data Governance Centre of Excellence.
7. Invisible data query management processes
Explain the mistake
Invisible data query management processes refers to the mistake of either not having any processes, or having processes that no-one knows about. This can lead to delays in response times, missed opportunities, and ultimately, a failure to meet business goals.
What causes this mistake?
Every team makes assumptions about how much other teams know about what they do and why. They don’t always understand or appreciate the need to make it easier for everyone in the firm to ask them questions or request corrections to data.
How do you solve this mistake?
To solve this mistake, it is important to establish clear data query management processes that include documentation and tracking of requests. It may also be helpful to establish response time goals and regularly review and update the processes.
Case stories/examples
An example of this mistake is when a lawyer requests a report of the matters they have worked on, and some matters that they have never heard of are listed in the report. It is often the case that the person providing the report to the lawyer is not the same person who can remove incorrect matters from the report.
Expert tip
To avoid this mistake, it is helpful to establish clear communication channels and response time goals for data query requests. This can be done through the intranet or a ticketing tool like ServiceNow.
8. Data literacy below operational requirements
Explain the mistake
Data literacy below operational requirements refers to the mistake of the firm’s people not having the necessary knowledge and skills to work with data effectively. This can lead to errors, misunderstandings, and wasted time.
What causes this mistake?
This mistake can be caused by a lack of training and education on data literacy (or data fluency), a lack of understanding of data sources and systems, or a lack of access to the necessary tools and resources.
How do you solve this mistake?
To solve this mistake, it is important to provide training and education on data. You should also ensure that everyone has access to the necessary data tools and resources, and regularly review and update training programs to add data fluence/literacy examples.
Case stories/examples
An example of this mistake is when a team member makes an error in a report due to a lack of understanding of data sources.
Expert tip
To avoid this mistake, it is helpful to establish a culture of continuous learning and provide regular training and education on data literacy/fluency.
9. Inconsistent decisions about data
Explain the mistake
Inconsistent decisions about data refer to the mistake of making decisions based on subjective opinions instead of objective rules and guidelines. This leads to inconsistent usage of data in the firm’s systems, processes, and reporting.
What causes this mistake?
This is caused by a lack of understanding of data sources, a lack of clear data ownership, and a missing process for consulting on, taking, logging, and sharing that decision.
How do you solve this mistake?
It is important to establish data and policy-driven decision-making processes, ensure that everyone understands the importance of data, and regularly review and update decision-making criteria.
Case stories/examples
An example of this is when an intranet page for Client A is linked to the DMS because it got approval from the DMS manager, but Client B’s intranet page doesn’t have a DMS link because the DMS manager made the opposite decision a few months later.
Expert tip
To avoid this mistake, it is helpful to establish clear decision-making criteria and have a simple process for documenting and sharing these decisions and the rationale behind them so that they can be replicated later.
10. Low confidence in data
Explain the mistake
Low confidence in data refers to the mistake of not trusting the accuracy or completeness of data. This can lead to stakeholder disengagement with reports or the creation of a cottage industry of ‘personal’ spreadsheets.
What causes this mistake?
This mistake can be caused by a lack of data governance policies and procedures, a lack of understanding of data sources and systems, or a lack of transparency and feedback loops for the accuracy of data.
How do you solve this mistake?
To solve this mistake, it is important to establish data governance policies and procedures, regularly audit and validate data sources, and provide training and education on data accuracy.
Case stories/examples
An example of this mistake is when a marketing manager gets a report from the CRM system and then has one of their team members manually update that report with ‘shadow’ data that they keep in a separate spreadsheet before it gets sent out to the partners (Instead of correcting the data at source in the CRM system).
Expert tip
To avoid this mistake, it is important to establish a culture of data accuracy being everyone’s responsibility. It is also helpful to regularly audit and validate data sources (including tracking down and closing those cottage industries).
Conclusion
Data Governance is a behavioural and cultural change activity supported by processes and rules. All these mistakes can be solved by having the right people, processes, and education in place.
