Making Analytics Work — You Can Get Started with Less Than a Moonshot

People   |   Gib Bassett   |   Aug 18, 2022

As you look through our INPUT by Alteryx Blog, you’ll see plenty of stories and examples of companies that are seeing big success with analytics. We make a point of showcasing the way those companies have implemented advanced analytics and data science, and the way they’re able to make better, more timely decisions.


But what if your company isn’t seeing growth in your analytics efforts, with big payoffs? Is it because you don’t have the right tools and expertise? Is it because your efforts are underfunded? Is analytics the right solution at the wrong time for your company?


Believe it or not, you’re not the only one scratching your head. Using recent content from a conference, a research study, and a webinar, I’ll describe how there is room for improvement in the way entire industries are moving toward analytics. I’ll also point to reasons why this is a good time to change focus and take advantage of those opportunities.


Don’t let the perfect storm become the enemy of the good.


The conference — Still talking about organizing for analytics

It was a pleasure attending 2022 Analytics Unite Summit in person again, after the pandemic hiatus. The conference includes presentations aimed at analytics professionals in retail and consumer goods. But I couldn’t shake the feeling that some of the dominant themes hadn’t evolved much over the last couple of years, let alone since Analytics Unite in 2015, when I attended presentations on topics like “Organizing for Analytics.”


In fact, the same topics were on the bill this year, and I wondered why leaders are still talking about how to structure their teams for analytics. One presenter this year conducted a live poll on top-of-mind topics, and 46% of attendees claimed they had 10-25% open headcount for data science positions.


“Why are so many people in the industry still focused on organizing and staffing?” I wondered. “That made sense when big data was first a thing. But now?”


Data science talent is going to be scarce and expensive for some time to come. That’s why nimble companies have been switching their focus to upskilling non-data scientists, empowering them to use self-service tools and techniques.


Another presenter this year pointed out that it’s difficult to reconcile the charter of data science with IT and the business. I think that too will be true for a long time to come. Meanwhile, which is more important to your company: developing a department for data science, or developing a data-driven enterprise?
My takeaway from the conference is that companies still wrapping their head around analytics should think less about headcount and more about use cases they want to enable. They should look at people internally who understand the business and give the quantitatively oriented ones accessible tools to automate their work with analytics.


Otherwise, things will take too long, people will exit, new people will come in, and corporate inertia will set in. That won’t do anybody any good.


The research study — Retailers still lagging

Consumer Goods Technology and Retail Info Systems have released “A Future Redefined,” their 2022 study based on a survey of analytics professionals in retail and consumer goods. (Alteryx is a sponsor of the study.) It’s hard to imagine two more competitive sectors than retail and consumer goods (CG), which is why I think the study’s insights are so indicative.


Forecasting demand is a priority for both retail (40%) and CGs (61%). But both retailers and CGs report that their biggest obstacles to progress with analytics are in staffing, strategic approach, and company culture. (Sound familiar? See the previous section.)


Retailers report that most analytics execution takes place at the department level. Many retailers wish it was actually owned by a shared department or a center of excellence, but more than half say they’re not taking any steps to address their need for additional analytics resources. That’s up from the 38% who said the same last year. Retailers are more likely than CGs to say that their use of analytics resources has changed either not at all or very little in the past 12 months.


Most telling of all, though, is how retailers look outward and rank their analytics capabilities comparatively. Against their direct competition, 45% of retailers rank themselves as lagging in analytics tools. When they stack themselves up against the gold standard of analytics — companies like Amazon and Kroger — more than half consider themselves lagging in analytics strategy, data management, data quality, and tools.


The research makes me wonder, once again, about focus. When your industry is dominated by the likes of Amazon and Kroger, what’s more important: organization and strategy, or digging in as deeply with analytics tools as the giants do?


The webinar — Empowerment through third-party tools

In a webinar early this year, I discussed some of the use cases for automating analytics in manufacturing with Howard Dresner of Dresner Advisory Services. Some highlights:


  • Digital twins — This is a unique and valuable role for analytics, representing in digital form the physical characteristics of an object being manufactured. The twin represents the object in real time at its current point in the manufacturing process. It could even represent the manufacturing process itself or an entire plant, offering an opportunity to control an entire system.
  • Citing research from Lora Cecere, Dresner, pointed out that the manufacturing sector has become more conservative in recent years with regard to analytics. “Over sixty percent of manufacturers consider themselves laggards.” (And you retailers thought you were lagging.)
  • Do you think your industry depends too much on spreadsheets as its analytic backbone? Then you haven’t worked in supply chains. More research from Lora Cecere suggests nine out of ten supply chain decisions are based on analysis of a spreadsheet. “The problem? It is impossible to model the details of variability, constraints, and cross-functional trade-offs in a spreadsheet.”

Manufacturing companies have learned tough lessons in the pandemic. Maybe the toughest one has been that they should have started investing sooner in analytics tools for insight into their supply chains. Moreover, smart organizations go beyond the analytics built into their enterprise software; they embrace third-party tools for analytics. The tools empower them to do more interesting things that are better tailored to their business.


Next steps — Start with less than a moonshot

I hope you can see, from the missed opportunities and misplaced focus I’ve described in this post, how and where you can improve.


You can spend cycles on data science organizational models for the long run. Or you can get started with something small, by developing and rolling out analytics use cases with the potential to demonstrate value in the short term. I think the latter is more prudent amid the current talk of an impending recession when investments will undergo greater scrutiny and customer spending may fall off.


Start by determining where your company lies on the spectrum of analytics maturity. How data-driven are you? What will it take to be more data-driven? Understanding your current stage will help you move to the next one. Take our short assessment to find out.



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