What Is Analytics Ops?

THE Way to continuously Leverage Data for Decision-Making

A lot of people get excited talking about the power of data and how it can empower businesses to make optimal decisions in real time. I’m one of those true believers because I’ve seen firsthand how data can be used to achieve amazing things. 

Unfortunately, in reality, data analytics isn’t like that at most organizations. It’s usually a few steps (or even a few miles) behind. Employees can’t easily access their company’s data, and if they can, it’s probably not in a format that makes it easy to make decisions.  

In a lot of cases, the data is overseen by a single team that, when it interacts with other departments, is usually laser-focused on a single, clearly defined project with a defined scope that leads up to a hard deadline. 

If we want to change that — if we really want to unleash the full potential of our data — we need to think about data differently. 

Making Data a Central Part of Company Culture

What if measuring and analyzing performance was a key part of your company’s culture? 

What if analytics was naturally included in every new project from product development to supply chain management to sales planning to HR retention efforts? 

And what if you could make your organization’s data accessible to almost everyone inside the organization, even nontechnical team members? After all, analytics is about using data to make the best possible decisions. Why not use that power everywhere you possibly can? 

To make that vision a reality, you need “analytics ops” — an analytics-first version of dev ops. 

In the same way that dev ops applies the culture and workflow of software development to operational requests, analytics ops puts an emphasis on data and measurement, taking it as seriously as the design and development of solutions, and using that data to guide decision-making. 

That means: 

Including analytics upfront with clear, measurable criteria for success. 

Every time you start a new project, no matter how big or small, you include a plan for measuring your results and learning from them. 

How many projects have consumed tremendous amounts of an organization’s time and budget — and once they’re launched, it’s almost impossible to determine if they failed or succeeded? By identifying your success criteria, you create a target to work toward, and that can help hold your team accountable for the results they produce. 

(What if you can’t find easily measurable criteria? In some cases, it might still be worth moving forward with the project — an inability to measure shouldn’t necessarily derail everything. A secondary measure might work instead.)

Having a plan to measure results from the beginning. 

Identifying success criteria is good, but from the project’s launch, you should also be collecting and storing performance data. In a perfect world, you would have an automated process that handles this. 

The benefit of measuring from the start is that you’ll have the ability to assess results in the middle of a project and course correct as necessary.

Making analytics as adaptable and responsive as any other part of your business. 
Analytics shouldn’t be a project with a firm end date. Rather, you should be using data to make business decisions, which generates more data that you can analyze to make better decisions, which generates more data, and on and on. 

Automating decisions where appropriate. 

A lot of analytics projects end up as a reporting dashboard, and like the dashboard of your car, they’re very good at updating KPIs in real time, so your team can make decisions in the moment. 

But let’s take the car metaphor a little further. Inside your car, there are other computers and processes making decisions without your direct involvement. You might choose how fast to go, but your car is deciding the right mix of oxygen and gasoline for whatever altitude you happen to be at. 

It’s possible to design analytics solutions that do that, too. Maybe it’s a tool that automatically identifies and flags customer accounts that, based on trends in your customer base, are most likely to churn or fail to pay, so your team can intervene early. Or maybe it’s a tool that segments your best-selling SKUs by geography and recommends order numbers to meet projected demand.

Making Analytics Ops a Reality in Your Business

There are some technical resources that have to be put in place, too, such as:

• Building cross-functional teams where data scientists and engineers work alongside domain experts, so that data science isn’t hidden away in an operational silo.

• Creating a process for operationalizing data — that is, automating the creation of data models and machine learning at scale, so they’re readily available and accessible for everyone across the organization. 

But the first step is thinking about measurement upfront. Whatever you're choosing to do, whatever decision you've just made as a business, you have a plan and a process for how you’re going to measure the results of that decision. That change in perspective has to start at the to but should eventually be adopted by everyone in the organization. 

Depending on your company, you may already have the data you need. You might be tracking that in your CRM right now, and you can use it to empower better decisions almost immediately. 

Once you’ve decided to adopt an analytics ops mindset, our team at Stacked Analytics can assist you with designing a strategy and implementing the necessary technical requirements. If you’d like to learn how we help companies harness their data more effectively, reach out today.