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Tony Lopykinski

Founder & Managing Principal

Data strategy: 7 Best Practices to Start Today

Most companies know their data is a valuable corporate asset. Yet many still struggle to advance beyond an IT-driven, technology-focused approach to data and analytics. Here’s how to develop a strategy that will elevate your data and analytics to a transformational level.

Though most companies know they need to look more strategically at the massive amounts of data that are available to them today, it’s much easier said than done. There are many reasons organizations drag their heels when it comes to strategy: cost, lack of data strategy experience, legacy technology, and corporate politics, to name a few.

The challenges are real, but no one can afford to not get strategic about their data. The longer you wait, the more complicated it becomes. Here are 7 best practices to help you move forward.

  1. Program sponsorship and governance: take a top-down approach 
    Data strategy must be driven from the C-level, sponsored by the business, and overseen by a governance organization. This means that the CIO can facilitate or co-sponsor data strategy development, but the business must be actively involved. A successful data strategy requires involvement of champions or sponsors with authority to assign budgets and prioritize competing business needs. These same roles may be involved in ongoing program governance to keep the organization focused on execution and accountability.

  2. Follow a program approach based on business strategy
    To be project-focused is to be tactical. To transform data into a strategic asset for your company, you must take a program view that is rooted in business strategy. This means that you have a vision for a uniform approach to data management that follows common, repeatable methods and structures and addresses major business pain points or objectives.Instead of a random project approach, you build toward the vision purposefully and incrementally. A program approach saves time and money in the long run, since you can build solutions that ultimately benefit the organization as a whole.

  3. Get to know your data 
    Your ability to deliver on a data management vision will be dependent on the state of your data, so it’s critical to assess the quality and availability of the data that you need to access for each project. This will require the business to say what data they use and how they use it.Data quality assessments can be challenging since most people overestimate the quality of their data. We recommend leveraging a framework that uses a combination of qualitative and quantitative assessments. Ultimately, the current state of your data will tell you if a project is feasible. For example, you can’t really start on a customer loyalty program if you can’t access point-of-sale data.

  4. Prioritize projects
    Now that you know your business objectives and the current state of your data, you can begin to prioritize your projects, balancing strategic and tactical initiatives. We typically plot priorities against two dimensions: business impact and ability to deliver. For example, you may have your sights on an initiative with high business impact, such as Customer 360, but your organization’s ability to deliver is low. So while you build the technical, data, or business maturity for that initiative, you may execute against another one with slightly less business impact but stronger ability to deliver.

  5. Identify future-state capabilities and maturity
    As you build out your plans for the coming years, you must look at capabilities and maturity in four key areas: data, technology, people/organization, and process. It’s important to realistically assess your capabilities and maturity in these areas today vs. your vision for some time in the future–perhaps two years from now–and identify the gaps. Then, determine which capabilities you need by project. You won’t need everything at once, so again, build incrementally. This can be applied to hiring, new products, structure changes, and more. Your roadmap identifies when you implement each piece.

  6. Manage change and communicate proactively
    When it comes to project adoption, you really have two goals: 1) secure buy-in from key stakeholders and 2) keep stakeholders from becoming resistors. You are affecting change and change can be hard.Identify the groups that will be affected by the initiative and communicate early – don’t wait until you’re ready to deploy. Explain the change that is coming, why it is necessary, and how it aligns with the overall strategy. Enlist trusted messengers, such as your CEO and individual managers, to evangelize the message. Identify the resistors, understand their objections, and work to overcome them.

  7. Be flexible
    Things change. All the time. Your strategy should be flexible and fluid. We structure our strategy engagements on a rolling 24-month basis, which allows clients to be flexible to evolving market conditions, company change, and so on.  Evaluate your strategy at least once or twice per year, if not quarterly, to assess whether you’re still focused on the right things or if you need to reevaluate priorities.

It’s not too late – time to get started

Whether your organization has a data strategy or not, now is a great time to create or refresh it. Don’t let your executives and organizational decision-making be held back by inaccurate, incomplete or inaccessible data any longer. If you don’t have a strategy, identify what is holding you back. Too busy? Lacking the right resources? Don’t know how to get started? An experienced partner can help you break through these barriers to get started on an achievable data and analytics strategy.

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