5 Mantras to Start an Analytics Revolution

You’re amazing and you get it. 

You’ve solved challenges using every data source imaginable. You’ve unleashed the answers that helped your team take action, and you’re sharing outcomes to make everyone more successful and productive. Truly — you’ve rocked it from zero to analytics hero! 

But how do you turn this spark into a full-fledged analytics revolution? How do you change the mindsets and daily practices of analysts and data gurus across your company to empower every team to deliver better, faster, game-changing results? 

Here are five mantras to repeat while you kick-start your analytics revolution and build a sustainable, high-performing analytics culture. 

  1. What Type of Culture Are You Looking to Grow?  

Building out a solid analytic culture is not just about finding the right questions to ask, but understanding your data landscape to know whether you have the data and the expertise to prioritize those questions — before trying to solve the problem with analytic technology. 

In fact, before you even take the first step on the journey to analytic transformation, there’s a fork in the road and an early decision to make: What kind of traveler are you looking to be? 

With analytics, there are (broadly) two ways to change your world. 

First, you can take a process that you work with today and try to improve it by making it faster, cheaper, or more efficient. Author Stephen Covey of Seven Habits of Highly Effective People would call this “Sharpen the Saw.” We’re taking an existing business process and using analytics technology to refine the underlying steps. A nip-and-tuck spreadsheet removal here, a self-service analytics application there, and success! We’ve taken a six-hour-weekly, manual slug-a-thon with your data down to 20 seconds of automated bliss! You’ve won your day back! Rejoice! 

With Covey’s method, you’re helping your best people tackle their problems head-on and freeing their days to work on more valuable activities. Just imagine what a team of analysts could achieve if all that manual spreadsheet munging was history. 

Get inspired by data scientists and analysts who have freed up their time to solve more complex, engaging challenges.  

Covey’s culture is all about winning. It’s about getting rapid, measurable change to existing processes and then banking the winnings to invest in new projects. Winning analytics teams quickly evolve to high-performing units that thrive on the buzz of victory, and this feeling is contagious!  

How good are you at streamlining your day? Learn more about the typical day of an analyst without modern analytics and a list of tactics to help you streamline yours.  

You’ll find yourself quickly surrounded with new recruits as you scale this approach across your company, so use the tips in the rest of this article to manage that growth. 

Second, we have the alternative path: disruptive transformation. Don’t worry, this isn’t the dark side. But it does involve shaking up the status quo and fundamentally changing much of what your company considers “normal.” 

Disruptive transformation often looks to parts of your business where people make decisions: a bank loan approval here, a next best offer there, and so on.  

Analytics (especially advanced analytics) can make a huge impact by automatically driving business actions that lead to faster and more competitive outcomes. 

In fact, prescriptive analytics are enormously valuable — a successful deployment of analytics against a critical business process can often produce gains that pay for entire analytics divisions in a single release. 

Learn how a major soft drink manufacturer uses modern analytics to unlock the value of the deluge of corresponding data. 

Remember — disruptive transformation isn’t better than saw-sharpening — your path is dependent on where you want to end up.  

Before you start down any analytics path, remember to ask yourself what you’re trying to build and then assemble your teams with those skills in mind. Modern analytics has everything you need to play the supporting role whichever direction you choose. 

  1. Analytic Culture Requires Timing 

It’s not just about choosing the right path — a great analytic culture also needs a sense of timing. You need to know where you are in the journey in order to make the right move. 

If you’re just embarking on your journey with self-service analytics, then you need to focus on answering those all-important business questions and learning how to get from data to insights in a fast, easy, and repeatable way. 

Your journey continues as you start to use these same analytics to not only confirm what’s happening in your business, but to start to make models of what might happen next: forecasts, predictions, simulations. This isn’t for the purpose of idle speculation; it’s a means of empowering your analysts to take competitive action. 

Eventually, companies like yours reach that tipping point when analytics needs to jump from beyond a single user, a single desktop, or a single team, and there needs to be a way to take insights and actions and share those outcomes more widely. 

The discipline that comes from walking this path is really what we call analytic culture — to get analytics powering your entire business. 

But it’s not a one-off. You’ll find that successful analytics leaders will find themselves right back at the start with a new project, technology, or department and will need to begin the journey afresh — learning new practices each time. 

  1. Analytic Literacy Means Discovery, Enablement, Enlightenment, and Building Bridges 

W. Edwards Deming, the famous statistician, once said, “Without data you’re just another person with an opinion,” and I’d agree. One of the main reasons companies need an analytic culture is to step away from random gut feelings and the opinions of the HiPPO, aka the highest-paid person in the room. 

But data-driven opinions? Developing analytic literacy is the best approach to developing stronger insights with your most valuable resource: data. 

A huge enabler for analytic literacy is the widespread access of governed and trusted company-wide data, along with technology that makes it easy to discover what’s available from databases, local files, reports, dashboards, and workflows. 

Enable anyone who wants to learn by running office hours — fixed times every week, where you show that your experts are there to answer anything that’s on your community’s mind. Identify missing skills or connections and build out coaching plans for those that want to improve, and use certification as a way of assessing improvement as your rock star analysts walk the path.  

A mixture of core skills, collaboration, and curiosity leads to much bigger impacts for your company. 

Call out where your analytics tribal knowledge has led to a breakthrough and celebrate your analytic champions — inspire everyone on your teams to become THIS good! 

Finally, build bridges wherever you go: subject matter experts, data scientists, and especially IT. You want everyone with you on this journey. 

  1. Harness Soft and Hard Skills to Find Balance in Your Analytics Culture 

As you build a culture of analytics, you’ll be dealing with new data and new technologies, but you won’t be successful if you don’t understand the people who are on the journey with you. 

People don’t neatly fall into boxes for classification, which means that you’ll need to deal with a spectrum of different behaviors if you want your analytics teams to truly perform. 

Personalities range from highly empathic and compassionate to results-driven and focused on hard skills. 

People are complex in how they take actions too — for some it’s all about having conversations at the water cooler or understanding how people feel, whereas for others, it’s about letting the mathematics do the talking in complex machine-learning models. 

Finally, there are the analytic outcomes themselves, which range from pure intuition and educated guesses to a reliance on models and algorithms. 

Your analytic culture needs to be a balance of these extremes. Too much weight on the softer side and you risk building a hit-or-miss analytic culture that won’t deliver sustainable results. Too much weight on the hard side and you risk building a complex house of cards that’s equally unsustainable (unless perhaps you’re Google or Facebook who can afford those Silicon Valley salaries!). 

The middle ground: a balance towards curiosity, blending code-free, approachable analytics with code-friendly building blocks, and working towards actionable insights helps generate both team satisfaction and sustainable analytic performance. 

  1. Getting Analytics Across the Line 

Remember how we talked earlier about how a strong analytic culture gets addicted to winning? You “win” at analytics by getting your model, your report, your actionable insights, into the hands of your audience, be they other employees, customers, or even other applications. Without that delivery, you’re not giving your team's credit for their hard work behind the scenes. 

Getting analytics over the line and making it actionable is often the hardest barrier for teams to cross — according to Rexer analytics, data scientists report that only 13% of their models ever get deployed, but the problem can be just as serious for analysts in the line-of-business. 

Building an analytics team that’s obsessed with winning means that you’re looking to deploy early and often — crossing the last mile of analytics often doesn’t need perfection, but every release should produce new value and remove waste from your business processes. 

With analysts, there’s waste every time they run a manual process that could be automated. Most spreadsheet power users spend north of 28 hours a week in that tool — nine of which are simply reworking sheets and macros to fit new incoming data every single week.  

Most spreadsheet power users spend north of 28 hours a week in that tool — nine of which are simply reworking sheets and macros to fit new incoming data every single week.  

To remove the waste and make that work widely accessible, consider wrapping the process into an analytic app and letting users self-serve without needing to call you everyday for a newly-cut report. 

For data scientists and IT? Take an R or Python model into production without having to recode in other languages such as Java or C++. Get a model operational and serving real-time results inside your business applications, products, or services. 

Key Takeaways

Deploy often. Validate requirements. Fail fast. Get feedback. Rinse and repeat these mantras. The relationship between a data-driven organization and a corporate culture of analytics is strong. Keep this core loop alive and create a winning analytics culture

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