We’ve all heard the phrase “data-driven” — after all, we live in the age of data.
But what does it actually mean? How does an organization transform from a gut-based, instinct-driven culture to one based on data and insights? I’m not talking about using data to support decisions that have already been made, but rather using data to guide the decision-making process and inform areas for improvement.
Sounds simple enough, but if you’re reading this, it’s probably because you know it’s much easier said than done. Here at Rackspace, we’ve been working diligently to create a true data-driven culture. As the director of our Marketing Business Intelligence team, I’m responsible for leading a team of analysts and data scientists who partner with business leaders to determine an objective-focused measurement strategy and develop actionable analytic solutions to key business challenges.
One of the steps we’ve taken recently is to centralize our analytics functions. Not every organization will necessarily need to do this; there are many different types of data org structures — centralized, decentralized, matrixed, center of excellence, etc. Centralization made the most sense for Rackspace, but if companies are intentional about their data strategy up front, they should be able to use any model. It’s also important to remember that it’s okay, and even smart, to course correct a chosen data strategy as business needs change — or if the data suggests such changes!
Follow our journey
As Rackspace’s data journey progresses, I want to share what has worked for us and offer additional suggestions; for this first post, I want to go over three steps I believe every company should take if they’re serious about creating a true a data-driven culture.
Before I do that, however, I want to take a moment to note that while accountability and adoption are required at all levels of management to create a sustainable data-driven organization, it really must start with executive leadership.
Why? Simple — it boils down to accountability. Executive leaders are the best positioned to hold their teams accountable and expect reliable and regular performance updates. While some frontline teams or functional organizations may use data to run their day-to-day operations, it is localized and may not be relevant to drive the overall corporate business objectives, and therefore, have meaningful impact.
Once you have buy-in from the top, here are three key elements to get started:
- Establish the goal. Data for the sake of data may be interesting, but it’s not actionable. Leadership must share your organization’s objectives and strategies. Without this, the business is essentially flying blind;” data can’t influence decisions if it’s not linked to broader goals. Here at Rackspace, my team kicked off 2019 with a download on this year’s objectives. Once everyone is on the same page, functional teams can determine how their work contributes to the overall strategy.
- Ask the right questions. They say getting the right answer to the wrong question is still the wrong answer. Asking the critical questions about the business is key to knowing the best way to measure performance. For example, is it really critical to know how many leads were generated last month, or is lead quality more important? Maybe both! Asking the right questions helps to narrow down the actionable key performance indicators. The Global Business Intelligence team here at Rackspace regularly challenges (lovingly of course!) the rationale for a KPI. It’s good to pressure-test metrics and get real about what questions they’re answering and decisions they’re influencing.
- Create a common language around data. What happens when three teams show up to an operational review each with a different number for same metric? Confusion and frustration, that’s what. When leadership begins requesting data to be shared in key meetings, a common and standard definition of that data is key, as well as an established source of truth. For Rackspace, centralizing our data and analytics teams into a new global organization facilitated this standardization and governance across the business. This was a critical move from our leadership to establish a data-driven culture and common language for our data.
Creating a data driven culture, like any cultural change, does not happen overnight. Leadership at all levels needs to make a conscious decision to go down this path and be willing to invest time and money towards developing reliable analytics. The passion for data must come from the top to ensure company-wide buy-in. Key analytic talent and the right technology stack also play key roles — topics I’ll tackle in future blog posts!
It can be time consuming and daunting to create a data-driven culture from the ground up, but the results are worth it. Our 2019 data programs are estimated to decrease the time spent gathering and reporting data by 20 to 30 percent, by reducing confusion around discrepancies with metric standardization and making data more accessible to decision makers. This translates into hundreds of work hours saved, which can then be re-allocated to revenue-driving work, versus searching for and second-guessing data.
With the right goals, questions and common language, an organization will be well on its way to becoming data-driven.
What obstacles is your organization facing as it works to become a data-driven culture? Put your questions in the comments and I’ll either answer directly or make your question the topic of a future post.
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