Data Transformation: Crafting a Data Strategy


It’s been a whirlwind of a month! I started a new job as the Chief Data Officer at CSpring consulting, so I can spend more time working on data problems with clients! To go with that theme, I thought we would explore the topic of Data Strategy today. Specifically, what is a good data strategy, and how can you use it to help your business?

The first thing to consider about strategy is that it is an aspirational model. Many leaders get hung up on the how, but that’s more tactical. The second thing to understand, which is hard for many technologists, is that the business has strategic imperatives; you’re not generating new ones in most cases. You’re trying to align the technology or data strategy to the company. Aligning plans to those goals is more about strategic alignment than new strategies. You may need to think entrepreneurial or strategically, but your aim shouldn’t be to change the business’s strategy unless you’re a technology or data product company.

In most cases, technology and data are enabling capabilities. Therefore, you must think strategically about aligning them to the business. For instance, if your company’s strategic plan is to help mid-market companies develop data capabilities that allow them to grow, then your data strategy is more of a tributary of that. Your endeavor should be to create a way to align data to deliver against that business strategy. A small company’s data strategy might be some accounting in QuickBooks, and a larger company may need full analytics and a predictive modeling platform.

Why do we need data strategies, though? Can’t we take the company’s big goals and deal with issues as they come? The answer is complicated. Some companies can leverage a more reactive approach; for others, that’s a recipe to fail. Companies that fail to recognize the importance of an aligning data strategy will ultimately find issues with data silos, poor quality, or inefficiencies. Data strategies help depict the alignment and call out these challenges as hurdles they mean to address. Data strategy, on the whole, is a function of your business strategy, business model, business goals, and technological capabilities.

Any MBA knows the basics for developing a strategic roadmap. Define the goals, map the current state, propose a future state, and implement it. Data strategy, on the whole, is the same. Once done, you should be able to put the strategy on a page.

Here are some thought-provoking questions as you build your strategy:
How do data usage and needs align with your company’s strategic goals?
Is the primary goal of the company over the next year enabled by a data strategy?
Do you need an offensive or defensive approach to handle the company’s objectives?
What data would be valuable to the organization?
Is there public data we can make use of in our data strategy?
What data could we possibly purchase that would help drive value?
Are we ethically handling our data (AI and ML specifically)?
How can we monetize the use of our data?
Does our architecture make it easy to work with data?
Have we done a data maturity assessment? What are the gaps?

Elements of Data Strategy
Business Context and Alignment - Define the business objectives and how a data strategy will enable it.
Maturity Assessment - Assess the organization’s maturity with a current and target state model.
Business Capabilities - Define the business’s capabilities to effectively capitalize on the data strategy.
Data Principles - Develop 3-10 guidelines that drive the decision process for your organization when using data. Example: We will treat data as a strategic asset.
Data Governance - Develop a framework for monitoring data for ethics, quality, lineage, and use.
Data Architecture - Develop a technological vision of how the organization’s systems and tools interact with data.
Data Management - Develop a plan for how data will be acquired, stored, shared, and used.
BI/Analytics - Develop a plan for using BI and analytics across the company. If your plan involves ML or AI include it as a vision in the analytics approach.
Culture and Adoption - Develop a plan to bring the organization forward into the new data-centric world.
Roadmap - Last, build a 3-5 year plan to get there.

What’s next?
As you continue to form your strategy, keeping these four core tactical questions in mind is essential.
How do we acquire the data?
How do we store the data?
How do we share the data?
How do we use the data?
These questions are the foundation of your architecture (a topic for another day). Your architecture enables the implementation of your strategy and is critical to the ability to bring any of your plan to life.

This topic is easy to continue to expand on and grow. I’ll post more about it in the future, but for now, here are some examples and links to help:
Developing a Data Strategy Template - DATAVERSITY
Your-organization-needs-a-proprietary-data-strategy - HBR