How will the ‘sentient enterprise’ spark shifts in company culture?

Nov 15, 2016
Michael Day

Retailers consistently rank analytics as a strategic priority because they know that data management is the core foundation of getting things right. Likewise, they know that uncovering and acting on data-driven consumer insights is essential to creating differentiation in the marketplace.

However, harnessing Big Data remains a major challenge for the retail industry due to the proliferation of data from structured and unstructured sources, like social media and self-service, real-time interaction — services which, of course, consumers now expect from retailers.

Despite retail’s slow start in operationalizing new understandings from the data, at scale, the industry is in the midst of an analytics revolution. Disparate silos of information are being unified, analytics are becoming automated, and actionable insights are more readily available thanks to the unprecedented collaboration between humans and machines called the sentient enterprise.

The sentient enterprise refers to a culture shift around data in which a retailer democratizes access to information, invites experimentation and repositions IT as collaborator instead of gatekeeper. It’s also a road map that retail leaders can use to unlock the full potential of data analytics.

Originally introduced at eBay, the sentient enterprise improves data agility, adopts a behavior-centric mindset, fosters collaboration, builds repeatable processes and uses algorithms at a scale that leverages connected data and analytics across the retail enterprise.

Retail giant Albertsons used the precepts of the sentient enterprise to build a massive data encyclopedia with managers, reporting: “Our analyst teams are now able to coordinate and collaborate across the globe using centralized process documentation while directly connected to a crowd-annotated data catalog.”

Above all, the sentient enterprise delivers a “LinkedIn for Analytics” social environment to connect data and insights and send the best solutions, ideas and analytics virally throughout the organization.

DISCUSSION QUESTIONS: What factors are keeping retailers from more fully adopting analytics and becoming data-driven organizations? What will it take to create a culture shift around data within retailing?

"Those that only want to dip their toes into this will not see the true value that integrated data sets can provide."
"Here’s the problem: The jury is still out on the benefits and ROI of all of this data analysis."
"If you collect the right data, it can almost be like your retail customers are making reservations to shop your stores..."

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13 Comments on "How will the ‘sentient enterprise’ spark shifts in company culture?"

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Lyle Bunn (Ph.D. Hon)

The labour category for “statistician” in retail reflects under 300 professionals employed. Therein is the clue. Granted every discipline in the enterprise has its inherent expertise toward better management but the big picture is fuzzy. Even as the customer experience group may be charged with adding clarity and advising on solutions, these small departments are still struggling for internal credibility and being very cautious with their internal politics.

Mark Ryski

Retailers talk data, but many struggle to act data. Retailers have more data than ever, and it seems new forms and sources of data appear daily. It’s easy to collect data and retailers have become especially adept at doing so, but the real challenge is in turning data into meaningful, actionable insights. There are several reasons for this including: 1.) lack of internal resources and expertise to effectively transform data into insight, 2.) inability to set data priorities and 3.) lack of commitment on the part of senior management to create and value a data-driven culture.

In my view, the most important element for creating a data-driven culture is executive sponsorship and commitment — without a sincere commitment from leadership, culture cannot change.

Kim Garretson

Here’s the problem: The jury is still out on the benefits and ROI of all of this data analysis. One reason in my opinion is that it is all backwards-looking analysis and the future often is not guided by history. That’s why as VentureBeat reported last year on a survey of 1,000 marketing data analytics professionals: “It turns out that marketers are spending well over a third of their budgets (on average) on analytics. This in spite of the report finding that levels of confidence in analytics’ ability to generate insight are mediocre, at best.”

Lyle Bunn (Ph.D. Hon)

Analytics always remind me of Hollywood movies where the nasty beast turns out to a best friend and help. Commerce is at that point where analytics are being drawn from their historic relegation to being more reliable for prediction. But their key value is in driving creative strategy, tactics and composition where cause and effect reigns. The critical success factor lies in executing communications based on current consumer context and sentiment as an overlay to historic data.

Doug Garnett

Direct marketers have used “big data” for decades. What their experience teaches is there is incremental value in big data — but it’s incremental and not leaps and bounds. And many, many things found by data are mis-directions or insignificant.

I caution retailers against getting too caught up in the myths. It should be used at reasonable investments. Done right, that can help tidy up operational effectiveness, merchandising incremental advantages, etc. But the weight on investment is often far higher than the reward.

My good friend Shahin Khan is a brilliant tech strategist. One of his rules of big data relates to this potential for cost to far outweigh the value:

“The ratio of relevant data to irrelevant data will asymptotically approach zero. One way to say this is: there’s only one needle, and lots of haystack. The more data you collect, they more haystack you’re adding. But the real point here is that for a given context, irrelevant data accumulates faster than relevant data.”

Lyle Bunn (Ph.D. Hon)

Analytics are about truth. I note the comment made that “the truth will make them set you free!” as reflecting the political and career exposure of being the messenger. Enter the analytics provider with the magical ability to deliver the same messages as internal staff and not get fired. The companion talent to analytics is tact and credibility — that is the currency of the truth-telling. Those involved in change based on analytics will be well-served in learning to have crucial conversations. The bestseller crucial conversations may be one of the best instruments to add in analytics adoption.

Meaghan Brophy

Cost and risk. Any type of massive shift in business operations or software is expensive, and not just in terms of overhead. For independent retailers, having the upfront cash to implement something like this is a struggle, especially when there are likely other more prioritized investments for them to make.

Larger retailers who have the cash flow to make this change also have to factor in the implementation process and learning curve on an individual store level. Especially this time of year, it’s very risky to make any massive changes that have the potential to impact the consumer experience (longer wait times, slower moving lines, etc.). Any change from the normal and expected shopping experience could have a negative impact this time of year when shoppers and sales associates are already on edge due to holiday pressure.

Since a “LinkedIn for Analytics” is a relatively new idea for retailers, the investment is probably seen as not worth the costs and risks associated, especially since the benefits and return on investment are not yet clear.

Zel Bianco

The most challenging hurdle we are seeing in this area is normalizing and integrating disparate data sets, both structured and unstructured. Aside from syndicated data which also has its issues, panel, POS, financial, primary research and other shopper and social media data is hard to bring together in one cohesive “data lake” if you will.

We have been able to accomplish this for those that have the commitment to doing so. Those that only want to dip their toes into this will not see the true value that integrated data sets can provide whether it be to measure what has happened or to predict what will happen.

Many CPGs and retailers are not even using the data they already have access to let alone those data sets that should be added to the mix. It is not impossible, but again it takes commitment and the skill sets to be able to use this data to arrive at actionable recommendations — not just insights.

Tom Redd
From our view (and mine) making the shift from the analytics of the past to real-time analytics is the bump or mountain for some retailers. It is a change that the internal side of a retail operation has a problem accepting. Why? First, they wonder if that is really possible. I remember back in the early-’90s when I was with Steve Beck talking about planning data on the fly — flying through real time data. I labeled that the “Single Version of the Truth” (SVT). We settled on the fact that that was impossible at the time, but someday doable. Today retailers find it hard to believe that it really is doable. Next, the change factor impacts analytics in a huge way — having a real-time data core with a real-time SVT data repository and related forecasting and decision formulas seems like a huge, huge change from their known turf. But when they do step away from the comforts of the past and into real-time they can never go back. When they watch the real-time… Read more »
Dan Raftery

Retail is operations-driven, which relies heavily on data, but in a supporting role. Don’t get me wrong, I’m all for connecting the dots inside an organization so each function can see the result of their actions and decisions on the enterprise — especially on their attempts to sell as much stuff as possible, to as many people as possible, as often as possible (the reason retailers exist). Oh and at a profit.

Ralph Jacobson

The continuing lack of data responding directly to perennial business challenges remains the root cause of retailers not embracing the capabilities in the marketplace today. So, overstock, out-of-stock, labor expense, demand forecasting, etc. are still basic, but daunting challenges today for most retailers. If you do a bit of investigation, you will find emerging AI, machine learning technologies that are capturing the data that was literally invisible to most existing systems and responding to these challenges. I have seen a real retailer improve their demand forecast accuracy by double digits, which is almost unheard of. If you collect the right data, it can almost be like your retail customers are making reservations to shop your stores … which we know is not possible today.

Bottom line, take a look at the newer technologies out there, and you’ll see myriad ways to connect data, both internal and external, with the human challenges in your business.

Shep Hyken

Too much data creates analysis paralysis. That’s what holds companies from taking control and using the data. The smart companies (and smart executives in those companies), know what data to take action on.

Ken Morris

Retailers realize the value of analytics, as improving analytics is retailers’ top merchandise planning priority according to BRP’s 2016 Merchandise Planning Survey that will be published later this week. The challenge is how to make sense of the explosion of data. With visibility to real-time retail data across the enterprise, contextual data like weather, natural disasters, etc. combined with the availability of social media insights, the quantity of data is massive.

The challenge with social media is how to quantify social media data/insights into product decisions. How does a “like,” an impression or even a discussion translate to product selection and anticipated sales?

While sentient data provides anecdotal direction, the science is still in development.

"Those that only want to dip their toes into this will not see the true value that integrated data sets can provide."
"Here’s the problem: The jury is still out on the benefits and ROI of all of this data analysis."
"If you collect the right data, it can almost be like your retail customers are making reservations to shop your stores..."

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