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Big Data, Make Room for Precision Data

By   /  April 24, 2018  /  No Comments

Click to learn more about author Justin Schweisberger.

In the six years or so since it started to grab headlines, Big Data has made it to the mainstream, and businesses are starting to report tangible results. Most large organizations now have Big Data initiatives in place. Yet for many, there’s still a long way to go to reach the full benefits. A McKinsey survey of execs in various industries last year reported only “mixed success” for Data and Analytics programs; 86 percent said that their organizations had been at best only “somewhat effective” at meeting their primary objectives for these projects.

Clearly the biggest impact of Big Data is yet to come. But in the meantime, companies are looking for quicker ways to pull more value from their data assets. And they’re finding that bigger is not always better when it comes to data. You can deliver great results from tightly focused data sets at high levels of accuracy – Precision Data. It’s a question of leveraging just the right amount of data, focused on a specific, high-return business challenge.

Some of the most data-dense and potentially ROI-rich zones of the enterprise are tough nuts to crack with a hardware-and-Hadoop approach. Take customer relationships, often among a company’s most valuable assets. If you haven’t been involved with the processes around customer contracts, for example – a central pillar of customer relationships – you might be surprised at how convoluted they are. You might think it’s simply a matter of stashing the documents in a repository, perhaps using a bit of OCR to extract the data as needed. If a company needs to know, say, the date that a contract is up for renewal, it should be able to pull that up with ease, right?

The reality is much more complicated, even for something as simple, on the face of it, as finding a renewal date. The date will certainly be present in the original master contract, but it could be encased in legalese that’s hard for computers to parse; it may be tied to data in another document (a delivery date in the order system, for example); or it may be amended by a later contract. Contract data is complex, especially for highly-negotiated deals, and it resists standardization. Companies struggle to handle just about every aspect of customer contracts. They may negotiate discounts into a deal, then fail to terminate them at the agreed time. They’re always looking for opportunities to upsell or cross-sell, but they may not have a clear picture of what the customer has already bought.

A lack of standardized data is only one part of the problem. The information that companies rely on is scattered across an acronymic jumble of tools – CLM (customer lifecycle management), CRM (customer relationship management), CPQ (configure-price-quote) and more – that don’t talk to each other well, or at all. The data is usually far from clean. If it’s entered manually (as it often is), it may be full of errors. Updating is probably sporadic. Information may be incomplete or inaccurate, or even missing – sometimes whole documents are missing and companies don’t even know about it.

So, this is clearly a data issue; but it’s not a Big Data issue. What’s needed is not a full-blown Big Data aggregation and an attempt at Analytics using low-quality data. Instead, you need a tightly focused initiative using smaller, high-quality data sets.

To go back to my earlier example of contract renewal dates, let’s say you want to give your sales team more notice of upcoming contract terminations so they have time to prepare and drive a better deal. You need a limited data set that may include, for example, the contract signature date; a notification period; perhaps the date your product was installed, taken from the order management system; and information on whether the contract is set to auto-renew. Assembling these data points will take some effort, and you need each of them to be accurate – being out by even one day isn’t going to work.

Leveraging narrow, clean data sets is a fast, practical way to zero in on specific business challenges. Maybe you want to rein in overly generous discounting. Or perhaps you see an opportunity for a quick win by tightening up on late penalty payments. Identify the data points would you need to tackle each issue and make sure the data you collect is accurate.

For smaller businesses, managing Precision Data sets in spreadsheets is doable. But larger organizations may require a more scalable approach to help them organize and mobilize their data. That is a task that’s ideally suited to the most powerful type of Artificial Intelligence – human-in-the-loop AI, which combines advanced machine learning technologies and human expertise.

Big Data and Analytics will play an increasingly important role in helping companies understand and influence their environment. But while they continue to leverage those tools, businesses will also be looking to Precision Data to deliver rapid, high-impact results.

About the author

Justin Schweisberger is the chief product officer at Pramata, a company that operationalizes the details of commercial relationships so large organizations can maximize revenue, reduce risk and drive business efficiencies. Justin joined Pramata in 2008 and has played a key role in the evolution of the company’s product innovation. Justin has held a variety of leadership roles at Pramata, including on the product, solution consulting, marketing, and operations teams. He has extensive experience guiding large implementations projects in support of retention, post-merger integration, contract risk assessment and business process initiatives. Previously, he was a project manager at the Husch Blackwell law firm, managing due diligence activities around merger and acquisition activities, including Hindalco’s $6 billion acquisition of Novelis. Justin graduated from Harvard College with an AB in psychology. Follow Justin and Pramata at: Twitter, LinkedIn

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