What is a data strategy

Season 1 Episode 8

In this episode, CJ Anderson begins at the beginning by explaining what a data strategy is.

She’ll explain why having a data strategy is critical for data governance and for achieving the firm’s strategic goals. She’ll talk a little about the four crucial elements of a data strategy and who should be holding the pen.

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Episode Transcript

Welcome to the law firm Data Governance Podcast, the Data Governance Companion for law firm leaders who want to know more about implementing and improving data governance.

Each week I’ll help you with your law firm’s data governance initiative by sharing something I’ve learned in my 20 plus years of working with information and data in law firms.

In this episode, I’ll begin at the beginning by talking about data strategies, I’ll explain why having a data strategy is critical for data governance and for achieving the firm’s strategic goals.

I’ll talk a little about the four crucial elements of a data strategy, and also think about who should be holding the pen.

Jack Welch, the former CEO of GE, said that strategy is simply resource allocation.

You cannot be everything to everybody.

A data strategy helps the firm manage and use data as an asset.

Helps you allocate your resources to manage data.

It provides a standard set of goals and objectives across projects and teams to help you make sure that data is being used effectively and efficiently.

Data strategy refers to the tools, processes and rules that define managing, analysing and acting on business data.

Your data strategy will help you make informed decisions based on your data, and it also helps you keep your data safe and compliant.

Now, of course you can run a firm without a data strategy.

However, most firms or any business really only thrive when they have a systematic approach to collecting, storing, analysing and managing their data.

That’s just the reality of the modern world, and that requires a data strategy that serves the entire firm.

When setting your data objectives, the order in which they’re tackled are going to be influenced by regulatory reporting requirements, professional practice rules, strategic objectives, strategic goals and operational priorities.

For that reason, it’s usually a key output from a data governance team or a centre of excellence.

And once your data governance foundations are in place, people start thinking about cross-functional data strategies, usually about year three.

Although no two data strategies are the same, all successful data strategies include four key elements, and these elements are what play the essential role in putting your data strategy into practice.

First up is the firm strategy.

Any data strategy should reinforce and advance the overall firm strategy that refers to the processes that you use to operate and improve your firm.

It’s helpful to establish clear goals and measurable objectives for your data strategy that serve the larger firms business strategy and set both long term and short term goals to improve continuously.

For example, suppose you set a short term goal of performing data quality reviews once every month.

You can still set yourself the long term goal of achieving continuous data quality, by which I mean that you continually identify and address data quality problems rather than relying on periodic checks.

And you should regularly check-in and review your data strategy to assess whether it still aligns with your current firm goals.

Is your firm strategy flexing, and your data strategy should flex to make sure it’s supporting it.

The second element is organisational roles.

You need to think about documenting who does what with the data to facilitate collaboration and avoid duplication.

Not everyone in the firm will use data in the same way, and their roles in the data collection, data management and data analytics is going to vary.

This is where you start seeing methodologies like a RACI matrix or a UPIG matrix come into play.

It makes it really easy to see where you have overlaps and responsibilities and gaps in coverage.

The third essential element is data architecture.

Your data architecture will consist of the tools and processes that allow you to work with and to analyse your data, and these elements may include various kinds of on-premises and cloud-based hardware and software.

Data identification, data ingestion, data storage, data analysis.

These are all parts of data architecture, but may exist in disparate and different parts of your firm.

Documenting and implementing the data architecture in a cross functional way is essential for consistent, predictable data strategy.

It also makes it easier to scale your data operations as your needs change.

We talked about the importance of having a data dictionary in episode four of season one ‘Begin at the beginning’, but still an output of your strategy would involve creating and maintaining data dictionaries for all of the critical systems if you haven’t got them already.

The final essential pillar is data management.

Data management encourages all team members to think of data as a business asset rather than a byproduct of business operations.

It encourages everyone in the firm to follow best practices when working with data and helps increase the data literacy across the firm.

The Foundation for effective data management is data governance.

Data governance, of course, establishes the processes and responsibilities that ensure the quality and security of the data that you’re using across the firm.

It can also help drive and support data literacy in a role based type way.

Data is increasingly becoming the key to competitive advantage, meaning that your firm’s ability to compete will be increasingly driven by how well it can leverage data and apply analytics and implement new data technologies.

Without a coherent data strategy that supports the firm strategy your firm doesn’t have those identifiable data objectives that help you reach that competitive advantage.

It means that your firm doesn’t have the focus it needs to achieve data goals or develop data plans that will move the firm’s use and management of data forward.

A lack of data objectives means that your firm doesn’t have a clear vision of how data can support the firm’s objectives and competitive advantage.

A good data strategy, supported by a robust data governance process, allows firms to realise a return on their investment well into the future.

In short, the right data strategy breeds opportunities to enhance existing services, improve decision making, mitigate and minimise risks, and produce valuable insights about operations and client sentiments.

The data governance team is usually the team that develops the data strategy cross-functionally and it includes elements of the firms strategy, data architecture and data management capabilities.

It is absolutely a cross functional road map to help prioritise data efforts to best support the firms strategic objectives.

We’ll begin at the beginning in the next episode by answering the question: “What is a data governance framework?”

In the meantime, if you have any questions about data strategies, data governance, or any of the ‘beginning at the beginning’ series.

Hop over to IronCarrot.com and let me know.