Your Data Strategy: How to get it moving

 

Building and executing a data strategy is hard, and if anyone tells you they can do it for you in 3 easy steps, they are lying.  The fact is, there are quite a few steps involved that can seem overwhelming.  Some of these steps can take an organization years to cultivate, like building a culture that not only relies on the data but demands to have it to make any decision. 

A key thing to remember, however, is that a data strategy does not need to be fully baked when you start down the road of implementing it.  When you build using strong data principles, you will be able to move forward providing business value, and your data strategy can flex as you fill in the details. 

The Main Parts of Your Data Strategy:

So what are the parts of a data strategy, which ones do you need right away, and how can your data principles help fill in the gaps?  Below are 10 core parts of a data strategy that you will need to at least consider before starting.  Not all of these need to be fully fleshed out, but your data principles need to be able to address them in one way or another before you begin.  They are:

  • Business Objectives & Strategic Alignment: Ensuring your data strategy serves your overarching business goals.
  • Data Governance: Establishing the rules and standards for managing your data.
  • Data Architecture & Technology Infrastructure: Designing the systems and tools to support your data initiatives.
  • Data Quality & Integrity: Ensuring the accuracy, consistency, and reliability of your data.
  • Data Management: Developing the daily operations of data handling.
  • Analytics Capabilities: Enabling the organization to gain insights from data.
  • Data Culture & Data Literacy: Fostering a data-driven environment within the organization.
  • Data Strategy Roadmap: Creating a plan to implement your data strategy.
  • Security & Privacy: Protecting your data from unauthorized access and misuse.
  • Collaboration: Encouraging cooperation among stakeholders.

The Backbone of Your Data Strategy: Business Objectives and Strategic Alignment

The one thing that is critical for moving forward is having a set of business objectives and making sure you have some type of strategic alignment.  Do these business objectives and our alignment need to be at the enterprise level?  No, they don’t.  In fact, it may be smarter to start at a department level to reduce risk and gain buy-in from the organization.  Having said that, the business objective does need to generate enough ROI to be worthwhile to the organization.  You still want your effort to be worth your time, and the idea when you start at this level is that you continue to grow into an enterprise data strategy.  But why is your business objective and your alignment so important? 

To answer that question, think of your business objectives as the compass guiding your data strategy and strategic alignment as the engine that drives it forward. Without clearly defined business goals, a data strategy lacks direction and purpose, and without aligning to the correct part of the business, you will not be able to move it forward into new areas of business.

What are the areas of your data strategy that are impacted?

  • Defining the Direction: Business objectives dictate what an organization wants to achieve with its data. If a business aims to increase customer acquisition, the data strategy should focus on collecting, analyzing, and leveraging customer data to identify new leads, personalize marketing campaigns, and optimize the customer journey.
  • Evaluating ROI: Aligning a data strategy with business objectives is crucial for measuring its Return on Investment (ROI). Tying data initiatives to specific business outcomes enables you to quantify their value. For example, the ROI of customer acquisition initiatives can be calculated by measuring the increase in revenue generated from new customers compared to the cost of acquiring them through data-driven campaigns.

But remember, we are not working with a data strategy that has been set in stone.  The business landscape is constantly changing, and a data strategy must be able to adapt to these shifts. By focusing on data principles instead of being tied to specific technologies, a data strategy can remain flexible and agile. For example, if a business objective shifts from customer acquisition to customer retention, a data strategy built on sound principles can seamlessly adapt to leverage existing customer data for personalized engagement and proactive retention efforts.

When you start executing your data strategy with just a framework, you will be able to adjust to these needs faster.  That is because, using data principles, you are setting up the groundwork to make sure you can deliver on any objective.  The biggest reason why these objectives are the most important thing to have documented is that they will be how you prove your data strategy is successful.  When you show a positive ROI you will be given the opportunity to grow your foundation into other departments and eventually into an Enterprise strategy.

Over the next few weeks, we will dive deeper into all of these topics.  Some places we will spend more time, but that does not mean some of the less talked about steps can be skipped.  The fact is that all of these parts are important, and that is why data strategies are hard.  So let's roll up our sleeves over the next few weeks and get into the details.  I promise that if we take this step by step, you will find yourself on the road to a successful data strategy sooner than you expect.  


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