“What matters now is not so much the quantity of data a firm can amass but its ability to connect the dots and extract value from the information.”

That’s the central point of a recent Harvard Business Review piece on “Building an Insights Engine,” in which authors Frank van den Driest, Stan Sthanunathan and Keith Weed recount the success of Unilever’s Consumer Markets and Insight Group (CMI).

As both and engineer and marketer -- and a big fan of Tom Davenport’s seminal work “Competing on Analytics” -- I locked right into the concept of building a customer insight engine as a “new source of competitive advantage ... deeply understanding your customers’ needs and fulfilling them better than anyone else.”

This is dead on imho (in my humble opinion). Understand and fulfill customer need better than anyone, and no matter what business you’re in, you’re in good shape. Do it deeply, repeatably and scalably and you have a sustainable competitive advantage.

Which, of course, is where the engine comes in...building a repeatable way to “transform data into insights about consumers’ motivations and to turn those insights into strategy.” To implement an insights engine, the authors lead with the idea of “Data Synthesis.” I call it “Data Preparation and Unification.”

Either way, the point is the same. Better fuel means better insight engine performance. In this case, better means not only cleaner data that avoids the classic “garbage in, garbage out” problem, but relevant data scattered across organizational silos that has been identified, unified and prepared for analysis within the analytics app or tool of choice.

In arguing that data unification “differentiates successful organizations from less successful ones,” the authors cite recent Kantar Vermeer survey research of 10,000+ business practitioners worldwide showing that “67% of the executives at over performing firms (those that outpaced competitors in revenue growth) said that their company was skilled at linking disparate data sources, whereas only 34% of the executives at underperformers made the same claim." Referring to Unilever’s CMI group specifically, the authors point out the complex challenge of unifying and preparing data from across the modern enterprise:

For any insights group that serves as a data aggregator, interpreter, and disseminator, the first challenge is to integrate massive and disparate sets of both structured and unstructured data from such sources as product sales figures, spending on media, call-center records, and social media monitoring. This may amount to tens of millions of pieces of data. The data sets are customarily owned by different teams—sales data by sales, media spending by marketing, customer interactions by customer service, and so on.

As the authors suggest, data unification at scale poses technical and organizational challenges for today’s enterprises. For the sake of argument, say you’re a Chief Data Officer (CDO) at a large enterprise responsible for fueling its insight engines continuously with clean, unified data.

Technically, and in the simplest of terms, you have lacked the ability to understand three basic but crucial things about your data: 1. Where it comes from, 
 2. What you have, and 
 3. Who uses it. 


Identifying and unifying tens of millions -- or billions -- of pieces of data across disparate sources is not achievable through traditional (e.g., ETL), manual, homegrown or self-service preparation approaches. This sort of radical data volume and variety now serves as your fundamental roadblock to effectively using all of those state-of-the-art analytics and visualization tools meant to surface insights and drive decisions.

Organizationally, as CDO you need to use data as a means of surfacing and delivering highly valuable, trusted institutional knowledge to drive decision-making. But vast amounts of domain expertise is trapped in the heads of people in the organization who are not technical – who can’t write SQL. In short, you need processes and tools built for liberating institutional knowledge across the organization and delivering it through high-quality data to key decision makers.

Enterprise-scale data unification systems like Tamr have been built to meet precisely the technical and organizational challenges central to a CDO’s job: organizing and preparing data from disparate sources – quickly, accurately and repeatably – to fuel today’s powerful insights engines and other analytic tools. Tamr, for example, uses a machine-driven, expert-guided approach to automate the collection, organization and preparation of enterprise-wide data (supplier, customer, product, financial, etc.) for the full range of spend, cost service and revenue insights and analytics.

Ultimately, these new automated approaches are for CDOs and their operational business counterparts (including CEOs, CIOs and line-of-business executives) who want to get more out of their investments in analytics -- and who know that even the best engines are only as good as the data that fuels them.

Continuous, clean, unified data from relevant sources available to the enterprise. That brings me back to the HBR piece, and how the authors characterized the highest order benefit of a data synthesis system like Unilever’s CMI that “integrates data and presents it in consistent formats” throughout the company. Such a system ensures, they write, “that all users, wherever they reside in the firm, see the same information in the same way—what CMI calls ‘one version of the truth.’”

One version of the truth -- Derived not from some master top-down model, but from the bottom up, using machine-driven, human-tuned models. It is a noble and valuable purpose for engineers, marketers and CDOs alike.

(About the author: Andy Palmer is chief executive officer at Tamr)

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