Today, data is king. Growth in data capture and ‘big data’ thinking is ubiquitous. Yet, at the same time, data can be one of the most undervalued and underutilised assets of any business. The effect of poor data quality can severely impact operations and limit growth and leverage opportunities.

 

Business systems are continually improving their automation, efficiency and productivity offerings, yet we continue to observe law firms operating as if they are simple record keeping and compliance tools. There is opportunity to unlock a great deal of unrealised potential trapped in systems with poor data quality. We like to believe and encourage 4 ways to make data fit for purpose – the four R’s:

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• Rationalise

• Restructure

• Remove

• Recover.

 

Rationalise

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Rationalising data reduces clutter and confusion. This should often include consolidating configuration lists. For example, the use of ‘General’, ‘Miscellaneous’, or ‘Other’ matter types (or worse, all of them!) can be the path of least resistance when opening matters. Valuable analysis opportunities begin to reduce in this example.

 

Rationalising to a comprehensive, yet simple and understandable list will improve efficiency (via intuitive selections), provide better insights (through analysis of ‘real’ data) and enhance productivity (through tailored data sets for automation purposes, workflow and document production).

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Restructure

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Restructuring aligns the system to the productivity and analysis outcomes desired, including correct company, office, department and profit centres.

De-duplication of entities (names) helps users easily find the insights in the firm’s investment, connections and potentially new business. Duplicates inhibit efficiency and complicate reporting, and also limit automation and integration opportunities.

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Remove

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Removing focuses on cleaning out data that does not add value and is hindering analysis and sometimes operations.

 

Matter financials is one such area – the small ‘rats and mice’ balances, aged unrecoverable balances that get ignored on a month-by-month basis, and matter closures encouraging a healthy, paper-light office (or remote office as is the case for many now).

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Cleaner financial data will always be more accurate and provides better opportunities for faster decision-making.

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Recover

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Recovering increases the value and usefulness of the data. This includes accuracy (for example correct categorisation of names as persons or organisations), format, style and consistency (for example correctly and consistently formatted addresses for professional output of documents) and completeness (full capture of email addresses for online campaigns). There are many examples to contemplate to recover and enrich data.

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Prevention and approaches

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Multiple approaches to achieving and maintaining desired outcomes are available. Logistically, two methods of data cleansing are in play – manual and electronic. Manual can be used where there is low volume, case by case decisions are required, or data is to be added at a detailed level. Electronic is necessary for high-volume changes where there are consistent, repeatable rules that can be applied. A mixture of methods is often our experience and the most pragmatic.

 

Policies and procedures should be put in place to reduce poor data being added as the cleansing is undertaken.

 

From a timing perspective, there is no need to think the boat has been missed once data has been converted into a new system. Cleansing data after operating a system for some time is often more important as poor practices have likely crept in.

 

Seeking the right outcomes

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Once data is fit for purpose, it will engender trust. If users trust data and have confidence in it, they will rely on it, and subsequently take care with new data. A positive self-perpetuating outcome!

 

More needs to be considered in businesses today to automate data flow across systems to help improve accuracy, efficiency and productivity. Think not only about the integration of internal systems but also B2B systems with valued clients, which can improve service levels and strengthen relationships.

 

Step change to introduce these measures will prevent onerous cleansing tasks, build trust in systems, provide accurate analysis and slowly change culture.

 

Rationalise, Restructure, Remove and Recover to have data fit for purpose and to ensure the value of your firm’s asset is fully realised.

 

Reach out to learn more about how Harriss Wagner can help.

 

Matthew Arendsen, Associate, Harriss Wagner Consultants & Advisers