Precision Data Is The New Big Data
Businesses, especially big enterprise, are always looking to grow revenue by leveraging their most valuable asset, their installed bases. They know that leveraging their commercial (customer, partners and vendors) relationship data inside their agreements and contracts goes a long way to improving their bottom lines. That’s why they’re willing to employ big-ticket, long-term big data initiatives that often require hiring an in-house team of data scientists or experts, commissioning the infrastructure, and waiting for the data lakes to fill up.
For these businesses, the promise of corralling commercial relationship data into data lakes in order to manage their supply chains more rigorously and improve and expand deal negotiations is worth the big price tags and huge in-house teams of data experts. In fact, according to a 2017 study from NewVantage Partners, covered recently in Harvard Business Review, 85.5 percent of executives report that their organization has taken steps to create a data-driven culture, but only 37.7 percent report that these efforts have been successful to date. Further, while 60.7 percent of executives report that their firms have developed an enterprise big data strategy, 18 percent report that their firms lack “a coherent data strategy.”
The harsh reality is that big data initiatives and their big promises don’t often get the desired results, especially in the short-term of a year or less. Then, the areas where data could have the most immediate impact on bottom lines are the areas that turn out to be the most impervious to big data approaches. That’s because the most valuable commercial relationship data is almost always the most complex.
The surprising news is that businesses actually don’t need the all-encompassing, time-intensive big data technologies and methodologies to access the value that lies within their commercial relationship data. They can crack that data nut with a smarter, lighter and more timely approach that leverages smaller sets of highly targeted, highly accurate data – what could be called “precise data”.
B2B Relationships Are Not Standard
Among the notable big data success stories are those from online retailers, such as Amazon, where they are continually creating ever-more accurate marketing by collecting, curating, and mobilizing their customer data. The advantages these online retail businesses enjoy is that their customer data is organized and formatted in the ways they control in their online stores, so it’s ever-clean and up-to-date, enabling their analytics and AI systems to use it in the way they need and want.
However, for most B2B businesses, their commercial relationship data doesn’t enjoy that same cleanliness because it’s buried in customer contracts and agreements, with the highest-value contracts tending to be the most heavily negotiated and amended.
The data exists inside a complex web of documents, including master agreements, exhibits, amendments, and statements of work. Key, useful information is usually missing – in fact, entire documents are often missing, and the company may not even know it. Updating tends to be manual, sporadic, and error-prone, if at all. Standardization is usually minimal with similar language written in different ways in different contracts. While some simpler types of contracts may permit a degree of standardization through contract templates, it’s not unusual for companies to end up using multiple templates for the same purpose. And, of course, a large percentage of contracts are created on the other party’s templates or systems, so they’re essentially custom.
Commercial relationship data isn’t just non-standard – it’s scattered everywhere. While the file cabinets and stacks of binders have largely disappeared, they’ve largely been replaced by their electronic near-equivalents: simple stand-alone contract repositories. These pseudo-systems offer limited functionality and can’t check for data accuracy or ensure that agreements are updated regularly. In addition, data often reside in an acronymic jungle of tools such as CPQ (configure price quote) and CLM (contract lifecycle management). Or it may exist only as a kind of tribal knowledge in the memories or personal hard drives of individual team members.
The biggest challenge is to keep these commercial records current as relationships evolve and expand. And this is where the enterprise data warehouse/big data approach really gets stretched. It’s one thing to build a structure system of record, but it’s quite another to maintain it. Data warehouse systems are only as good as their source data, and the high-quality data that companies need to shift the revenue needle doesn’t exist at most organizations today.
Building A Usable Data Set
To significantly impact revenue and improve corporate performance, companies need a bit of a mind-shift away from the usual data-first approach that often drives big data initiatives aka “just grab the data, we’ll figure it out later.” A superior approach is to first determine what you need to know to deliver the best business results; then pinpoint the right, key data to achieve those outcomes; then convert it into accurate, up-to-date information that’s accessible and useful for anyone who needs it.
Let’s say your company wants to address revenue leakage, which in many businesses can account for anything from one to five percent of revenue. You could start by segmenting your customer contracts, ranking them by revenue tier. Or you may want to simply separate your standard, template-based agreements from your higher-value, more heavily negotiated “key” contracts. Next, find the sources of revenue leakage across segments. Do you have unrealized opportunities for price increases? Are you losing money through over-discounting? Billing errors? Missed upsell opportunities? Assess the risks and impacts by leakage source for each segment, and assign a dollar value to them.
Now you’re ready to hone in on the specific business challenge of revenue leakage. Let’s say you’ve identified poorly executed contract renewals as a major source of revenue leakage, with a high potential for improvement across several of your segments. The next step is to identify the factors that impact renewal execution, such as early termination penalties that can be leveraged if a customer wants to exit a contract early; post-termination obligations for both parties; and expiration/renewal dates.
Now you need to analyze each of these categories into tightly defined data sets. To calculate contract renewal dates, for example, your data set may include the contract signature date; a notification period; perhaps an install date, taken from the order management system; and information on whether the contract is set to auto-renew.
Now, figuring out all this is by no means is a trivial task; indeed, just knowing which variables are needed to approach it in a methodical way is a big step forward. And if you try to manage it all with a spreadsheet, it can feel like hundreds of steps. What you need is a limited number of data items at 99 percent accuracy. If you can identify and assemble those pieces of clean, current data, you can ensure that you don’t miss any more renewal opportunities, and you can reap the maximum revenue yield possible for each renewal event.
Stacking Up Fast Wins
Leveraging narrow, clean data sets is a fast, practical way to pull more value from your commercial relationship data. You can stack up impressive results quickly by zeroing in on the data that’s most relevant to the business issue at hand, whether it’s reining in overly generous discounting, implementing agreed price increases based on the consumer price index, or tightening up on late penalty payments. By plugging specific holes in the revenue pipeline, you can quickly realize savings of up to 4 percent of revenue.
Simply put, most big data initiatives are too blunt and heavy a tool to be used on a task as delicate – and potentially rewarding – as maximizing the value of commercial relationships. That said, it’s also true that the precision-data route may be too demanding for many organizations to tackle as a home-grown project. The good news is that solutions are available to help businesses organize and mobilize their customer relationship data and put it in the hands of the people who can best use it to drive new revenue. Look for solutions that combine systematic application of best practices in finding and fixing revenue holes; rigorous, tested methodologies; and cloud delivery.
Bigger Data Is Not Always Better
To be truly data-driven, businesses need to determine what they need to know, then pinpoint the right, precise data and make it easily accessible and useful for anyone in the organization who needs it.
About the author: Justin Schweisberger is the chief product officer at Pramata. 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.