12/27/2017 | Chuck Leddy

Mohan Sawhney is a business author, management consultant, and the McCormick Tribune Professor of Technology at the Kellogg School of Management at Northwestern University. Sawhney’s new book, “The Sentient Enterprise” blends practical experience and a firm theoretical foundation to explain how organizations can become fully mature in how they leverage data and pursue digital transformation. The NCMM spoke to him recently about how middle market companies can implement what he calls “the 5 steps” to digital maturity.

How might the book apply in particular to middle market companies? Does organizational size matter?

Sawhney: Size does matter. When it comes to your data, as well as your IT systems and infrastructure, complexity is typically correlated with size. You might have legacy systems and heterogeneous IT, you may have complexity coming out of separate geographies, subsidiaries, business units, functions, and so on. A start-up firm probably doesn’t have many of these problems because silos are a consequence of scale. Middle market companies are sort of in the middle, so their problems aren’t as intractable as Fortune 50 companies, but they still have challenges in being able to effectively manage and leverage the data assets they have.

How might middle market companies gain competitive advantage in how they leverage their data versus less data driven rivals?

: You have to first get your data house in order. The problem for many companies in leveraging data is that it's in silos, it's in different systems and those systems are on top of each other. In the pursuit of, “getting something moving quickly,” companies tend to proliferate tools and platforms and end up with an ungovernable mass of “data spaghetti.” 

This creates problems. One is that you can't see 360 degrees across your business. You can't see a customer in totality. You can't see any of the relationships in totality. Another problem is data drift, the idea that if I make a copy of the data and start working on it, it becomes stale. The data is outdated if it’s not continuously being synchronized. So, that would be the starting point: to create an agile data platform, which balances agility and decentralization with governance and centralization of the data assets. You need a single source of truth, but done in a way that allows people to extract slices of that single version of truth and work in agile ways that scale.

You’ve explained some of the wrong ways to build digital capability. What are the right ways?

Sawhney: If we were starting from scratch, you’d start with an integrated single data lake where your data resides and you’d never create silos. But most mid-market companies already have systems, they already have data warehouses. They already have data access that’s spread out. So, everybody’s got spaghetti. You need to start stitching together all of your data marts [silos] through the creation of an enterprise data lake. You basically throw all your data into one place, with tagging and indexing so that it can be retrieved. You either physically move the data or virtually connect to the legacy system through APIs [that connect systems]. Then you have all your data in one place. So, that’s your agile data foundation.

Once they’ve put their “data house in order,” what should middle market companies do next?

: Step two is to augment your agile data foundation with behavioral data. Customer insights may come from data in your systems. But do you have access to location insights, regional insights, social insights, what your customers are saying online? The data around customers is much broader now, more comprehensive.

When you’ve finally connected all the data sources and you've built the behavioral data platform, you’ve created other problems. “How do I analyze all this data?” “How do I actually scale my analytics capability?” The problem with analytics capability, even in mid-market companies, is that it’s the preserve of the few. At step three, we say “Can we crowdsource analytics inside the company, can we get a whole bunch of people to collaborate on data and analysis?” Basically we build communities around questions of interest, around passion points. We call this third step, “the collaborative ideation platform.”

What’s the next step for digital maturity?

Sawhney: We call step 4 “the analytical App platform.” A 13 year-old kid can write an app today, and put it in the App store, so why can’t we do that inside companies? Why do we have to go to IT and beg? The concept is a really light-weight, zero touch, zero service application that can be built by anybody who is a business decision-maker. You don’t need to be an IT expert or coder. 

You bring more humans into the problem and you collaborate, you build a community to solve the problem, because analytics has to be done by people and we have to find ways for people to scale. It’s democratization of data.

The 5th and last step is “autonomous decision-making at scale.” What is that and what does it enable for middle market companies?

Sawhney: Let me give you an example of a real business case of autonomous decision-making at scale from a telecom company I’ve worked with. There’s lots of promotions being offered in the telecom space. Customers will call and say, “I have this better deal from a competing provider. What can you do for me?” The way you would typically handle the situation today is for a human to say, “Tell me about the offer you have. Where did you find this ad?” Imagine if 10,000 customers a day are calling you.

This telecom company created an end-to-end automated process where customers are asked to take a photograph of the offer or send a text message. The system basically reads the offer through the machine and, since we maintain an ongoing database of all offers on the market, it checks whether it’s a legitimate offer. Then it looks at the customer's lifetime value and history and, based on all that, computes a response: “Here is the deal we have for you today.” All this happens in real time, with no human intervention.

That is autonomous decisioning, where we eliminate humans from the whole decision-making process. The only involvement of people is to set up the data system. Of course, humans will still be making some decisions, but autonomous decision-making will be increasing.

What else would you like to say to the middle market leaders about growing their digital capabilities?

Sawhney: This is not a technology transformation, it’s a business transformation. The culture and mindsets of people are as important as technology. In fact, the technology isn’t the issue -- it’s really about you mobilizing your organization to create a data-centric culture, a customer-centric culture. 

The only way to to embark on this journey is to start from the top. It has to be led by the CEO and the Board. It’s probably a multi-year journey and you’ll probably never become a fully “sentient enterprise” because it’s an ideal to aspire to. I would suggest starting somewhere, but starting now. Think big, but start small and scale what works. Find the low-hanging fruit and attack it.

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