Plotting Your Data Strategy
How to get started
In my last post, “21 Questions You Should Be Asking of Your Data Strategy,” I intentionally seeded a sense of urgency…and it worked! Since then, I’ve received numerous inquiries about how to get started developing an organization-wide data strategy, so here’s the skinny…
Developing a comprehensive Data Strategy is relatively new to business, yet it’s become table stakes for competing in today’s competitive marketplace. Interestingly, recent survey results published in the Harvard Business Review demonstrate that large enterprises who don’t have a strategy in place are increasingly worried. Disruptions from startups that have nurtured data-driven cultures from their beginning and innovative stalwarts who are rewriting culture are leading the way. Whether your enterprise is established or burgeoning, large or small, there has never been a better time to ensure that you’re programmatically working to harness the power of your data.
Step One: Determine Who Owns Your Data Strategy
Several common challenges must be navigated in order to build a durable data strategy that will accomplish your desired business outcomes. Chiefly, different roles within any company have different perspectives and decidedly different needs. Those clever creatives in your Marketing department already have a plethora of tools and systems in place. Your Finance wizards and your agile Sales Force both hammer spreadsheets and likely have homegrown systems to manage all their moving parts. HR, too, and merchandising use specialized tools tucked into the corners of your technology stack. And then there’s your Analytics and Insights teams that come with their own means and measures for plying their craft. Pretty much every technology in your stack and every individual in your organization has their very own methods for accomplishing their aims. And (almost) everyone also has their own data silo. While each technology is capable of collecting data aligned to their vision of the world, it takes effort and planning to pull together all those vistas to see the whole ecosystem.
And someone needs to see your whole world.
So, find that hero. And support them with a consistent and unified view of data. While this doesn’t necessarily need to be a fully federated data lake, the perspective into aggregated data must take into consideration a modern multiplatform data architecture. This setup must enable your Chief Data Officer (or whatever title you bequeath) to manage data, govern it, and make it accessible for myriad forms of analysis and utilization. Lots has been written about the role of the CDO, here and here…so I won’t write the job description. I’ll just leave it at this: ownership and accountability over data are critical, so make sure someone is securely holding the bag.
Step Two: Assess Your Data Strategy Readiness
Readiness is a funny thing. Are you ever fully ready to plunge into the deep end? As the aphorism goes, Perfect is the enemy of good. This is true in data strategy as well. However, it is important to understand where your strengths are and what needs improvement. Take stock of your data and understand what you have and how you can use it. This is particularly important today because of GDPR and upcoming CCPA regulations that will impede you from using data any old way you choose. So understand what’s available in customer data, across your proprietary systems, and what you can access from 3rd parties. Your data strategy must encompass all sources of data with clear cut policies about how you can use it.
Of course, using data assumes that it’s accessible. Having a comprehensive data strategy means that data is available to users across your business. Gone are the days of hoarding data and only providing access to a precious few, satisfying the rest with Excel reports or (please no) PDF printouts. Leverage the power of your people and elevate data literacy to assure that your data will be used conscientiously.
This is yet another reason that data architecture is so very important. By linking requirements to solution documentation to outputs, you have a better chance of maintaining data integrity. And most BI aggregation technologies will offer governance protocols that help you manage and certify datasets, so that they are used for their intended purposes.
Being ready also means using your data with purpose. To do this effectively, prioritize time for data quality and literacy, because you will want to automate things. Automating data is a ninja-level standard for activating your data. But it doesn’t have to be complicated. Simply using alerts, triggers, and rules-based events can be the first stage in automation. Then, you can get fancy and start building models and employing machine learning and artificial intelligence. As you delve into questions about your data strategy readiness, assure that you know exactly what data you have, how it’s held together, and what opportunities and risks you face when it comes to automation.
Step Three: Build an Executable Data Strategy
You’ve probably heard of (or experienced) consulting firms offering huge, monolithic Data Strategy rollouts that span years and require massive enterprise heavy lifting. But here at Search Discovery, that’s not us. We build bite-sized strategies that get you off the blocks running. Each component of our data strategies delivers results and adheres to a plan to help you achieve business impact. Executable data strategies are dependent upon a data and analytics ecosystem (we can help with that too), which provides the infrastructure, the people, and the processes to move at an agile pace. By starting with executability, we are able to identify what is important to you and then build 3–6 month strategies that “ladder up” to a north star strategy that we collectively define.
What Getting Started Looks Like:
The data strategy projects we’re delivering, whether the client is a non-profit corporation, large regional healthcare organization, or multinational Fortune Global 500 company, all have a similar workflow process. We begin by understanding what the organization is working towards. Since our strategies are all bespoke, they’re built upon a solid understanding of each clients’ goals and objectives. These are specific to the group/department/business unit that brings us in. But they also consider the larger corporate culture, IT infrastructure, and resources. We align the strategy to this context and proceed to define specific purpose for their data.
Once that phase is complete, we work to develop desired outcomes that are key components of the strategy. These outcomes are the results that have a measurable impact on the business. We work tirelessly to deliver. Yet, doing so typically requires developing foundational elements like data governance practices, measurement frameworks, standard operating procedures, and activation protocols. We strive for alignment. So, throughout our engagements, communication is key.
Finally, we advise, support, or deliver on the actual implementation of the strategic plan. This may include passing off a detailed plan for execution that you, the client, will own, or something that we support through training and roadshows across business units to drive adoption. In many cases, we’re deploying technology or configuring applications. But in all cases, we’re setting the business up for success as defined by the executable data strategy that we’ve created. Change management almost always plays a role in the adoption and implementation of our projects, and it’s something that we love to do for you OR to coach you to take on independently. But the bottom line is that we set you up to succeed. Our strategies are stress tested and proven on the basis of what works for your organization.
Need help building out your data strategy? Drop me a line at email@example.com. I’d love to chat and help you get started.