“You also have to articulate your vision and strategy and where you’re headed very clearly and often... This means speaking in plain terms to demystify data and analytics and make it something that everyone understands.”
We then looked at how this might apply to our journey, while recognizing that a cookie-cutter approach cannot achieve successful analytics transformations. We are operating in a ‘build the plane while you fly it’ mode, so we aren’t waiting for our data to be in a perfect state before we move forward. This is where some companies stumble – by prioritizing perfection over progress, there’s a high probability that you’ll never get started. We’re also adopting a use-case-driven approach rather than a tech-first strategy. That means we
focus on the different domains across the business where analytics can drive the most value. For example, that might be revenue growth, pricing, trade promotions or marketing and media effectiveness. We choose a couple of priorities and then focus on building, embedding, and scaling. How do you organize analytics teams for success? What are the skill sets typically needed? The first t hing I’d say is that the partnership with the IT organization is critical. For us, that means having folks working on data strategy and data operations that act as
a day-to-day link with our IT experts. They make sure that we have the data we need to do the type of analytics work that we want to do. Of course, you cannot underestimate the importance of roles like data engineers and data scientists, and they need to work together in an ecosystem. We also need analytics translators for adoption to serve as a link between data scientists and the business teams. To succeed, we need to articulate what data means, how to use it, and how it solves key problems. We’re also looking to foster important attributes, such as curiosity, across our entire team. Curiosity is important
in terms of problem-solving and around best-practice approaches to learning, understanding what’s new and evolving in the analytics space, and how different approaches might set us apart. Storytelling is also crucial. It’s never been more important for everyone within an organization to connect the dots in their work and weave it together to tell a compelling story that spurs action. The most successful analytics teams can clearly articulate what their work means and how it can impact the business.
The greatest successes within analytics come from embedding that work into the rhythm of day-to-day business operations. Another is data storytelling. Of all the case studies I’ve seen around analytics transformation, as well as the transformations I’ve led myself in prior roles, the ones that are most successful are those that succeed at changing behaviors and getting those behaviors to stick. It’s about fostering a business-first approach that can be enabled through storytelling – especially as we start to link many different types of data. It’s not just about syndicated data or
What competitive advantage will your focus on analytics provide? What potential will this open for the business? There are two areas where we are focused that I believe will set us apart. The first i s that business link. Everything we do within analytics starts with a business question - rather than a tech-first approach. We have to deeply understand what decisions our business partners need to make, when they need to make them, and what priorities are most important to their success. Then, we can help to solve some of those through analytics.
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