Power to the people! Encouraging an enterprise analytics competency

Matthew Madden, Director of Product Marketing at Alteryx, shares how businesses can unlock the real value hidden in their data

Matthew Madden is the Director of Product Marketing at Alteryx.

Opinions expressed by EM360 contributors are their own.

There are some questions that the more evidence-based colleagues might ask depressingly frequently, if they can’t use data to make good decisions. How can we unlock the real value hidden in our data? Why aren’t we doing more with analytics? How can we get more people empowered to solve our business challenges?

Forward-thinking, fact-based executives and business leaders at every level of modern organisations are asking these questions right now. The promise of analytics has been pitched before, but without the right culture and solutions, initiatives flounder and fail to produce results. A data science team may be in place, but their tools are too complex for the rest of the business and their bandwidth is limited. Things fizzle rather than sizzle.

Despite these common challenges, the promise of analytics has not been oversold. Companies that get it right build sustained advantages and outpace their competition. So how do they do this? What must happen within an organisation to harness the power of its data?

Freedom
Companies that want to win using analytics must develop a top to bottom enterprise analytics competency. This competency is the organisation’s ability to perform meaningful data analysis by department and by individual in a manner that provides analytic freedom to any employee who wishes to participate. It is the enterprise-wide proliferation of self-service analytic proficiency beyond the Information Technology department or specialised data science teams.

Developing an analytics competency across the enterprise is no small undertaking. It requires a great deal of self-awareness from a broad cross-section of business units and individual employees. The organisation must step outside itself and evaluate how it goes about achieving its mission on a daily basis.

This will be challenging. Some would ask, ‘why not leave analytics to the data scientists?’ It’s because the people who understand the problem should be the ones solving it.

The hardest part of the analytics process is figuring out the right questions to ask. And functional knowledge is required to get at those really meaningful questions. It’s largely ineffective to expect a small analytics team to understand the pressing issues within each department across an organisation.

Yet the experienced analysts in the business reside in the departments, and these are the people who have the functional knowledge of the business and the way it ticks. Understanding so much, they will find that answers lead to new questions, and the hunt for excellence continues.

The surrender of no longer asking questions is one of the biggest fundamental losses within a company when expertise is siloed and people no longer have the understanding on the context of what they’re working on.

Specialised analytics teams don’t scale
No matter how talented data scientists are, there simply aren’t enough of them to support the number of requests that come from across the business. Under traditional models, business analysts get frustrated with long report queues, and data scientists themselves aren’t generating real value when working on requests that don’t require specialisation to solve.

There’s a secret that some might not want the wider business to know. There’s an entire set of analytic tasks that can be offloaded to the line of business. This frees up the data scientists to work on the difficult things they’ve been hired to do, and it gives business analysts the power to ‘fish for themselves’.

There is a legacy belief that analytics are best left to IT personnel or someone with a technical background. This belief persists because early analytics tools were not user-friendly and they required advanced coding knowledge to be of any use. The thought was that people on the business side can’t be trusted with the data because they’re untrained with the tools and analytic methodologies. They don’t know what they’re doing, they don’t understand the output, and they’re going to interpret things the wrong way.

Today’s analytics tools no longer require deep coding knowledge or special skill sets. With a small amount of training, business users can explore the data and search for their own answers, feeling empowered. Trusting them with the data creates liberation, and this will free them up to do the job they were hired to do.

Perhaps most importantly, when front-line staff are trusted and given the tools they need to do their jobs, a culture of engagement is created. The closer people get to their jobs the more engaged they become. The closer they are to being part of the solution, the more value they are going to take from their job.

Businesses hire smart people who want to make a difference. They invest in top-tier talent and then equip them with the tools that empower them to do great. But if they are sequestered inside an isolated data science vault, the business-side will disengage, stop asking questions, and eventually leave to find a more fulfilling job elsewhere.

Give power to the people – putting analytics in the hands of the people gives them the thrill of solving their own problems, their way. And they likely know the best way to do it.