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When J.B. Wood and Thomas Lah first introduced the concept of the “data handshake” in their book, B4B, it mainly focused on how it occurs between the technology supplier and customer. The basic premise of the data handshake is that in order for your company to provide data-driven, value-add services to your customers, you need their data. They give you some of their data, and in return, you give them enriched data insights. Depending on the vertical and geographical region, the handshake might be chock full of rich user-level information, or it might be mitigated to metadata and aggregated information. Either way, the supplier had to provide a compelling value proposition to gain the trust of its customers, and then deliver.
The data handshake represents a set of mutual agreements between a supplier and its customers for data sharing, monitoring, and usage intervention with either employees or machines.
As Dr. Andreas Weigend, former Chief Scientist at Amazon.com, said in his address at a past TSW conference, “You have to give data to get data.” This analogy resonated well with my past experience in performing analytics projects for a wide range of customers. I would often get pulled into sales calls as the “geek with people skills” to demonstrate the value of predictive modeling.
Going into the call armed with success stories of how we were able to help former clients avoid poor investments, and capitalize on sure bets, the value proposition was an easy sell to a small roomful of like-minded leaders. Little did I know that the most challenging data handshakes would occur inside of the large corporate customers themselves.
My colleague, Maria Manning-Chapman, often speaks to her Education Services members about the “Holy Grail” of education services: showing the impact of training on customer adoption and success. The reason it's the “Holy Grail” is that education services organizations historically have failed to connect their rich, user-level data in learning management systems to other data within their corporate organizations about product usage, upgrades, and other services. We ran a survey together not too long ago that reinforced this observation.
In other words, the data silos that exist within one company’s wall can be just as difficult to break down as the walls between two different companies. But, when your competitors are providing data-driven, value-added services to your customers, and your customers start demanding similar services from you, the value proposition for the internal data handshake stands much taller than your data silos.
It can be just as difficult to break data silos within a single company as it is between different companies.
Cue the nimble, cross-functional analytics team. They're armed with tools that enable them to get data from anywhere, process it, analyze it, and find insights. If you pair them with a strategic leader that has a handful of powerful customer initiatives in need of data and a development team to quickly productize the insights, they'll churn through project after project. It doesn’t matter if that strategic leader is in a products team, professional services, support, or customer success. The team will track down data from your CRM system, accounting, case history, product logs, etc. In doing so, they'll make bridges between departments and the resulting data driven services will cross historically separate lines in your organization.
What burning customer initiative could be addressed with a data driven solution from your company? How can you help make the necessary data handshakes to create this value? To find answers these questions and more, I hope to see you at our upcoming Technology Services World conference. This year’s theme is Organizational Convergence in a Recurring Revenue World, where we’ll be discussing how better collaboration and communication across the various functions within your organization can help keep your business moving forward.
Post Date: March 23, 2017
Jeremy DalleTezze, PhD, is the vice president of analytics for TSIA. His professional background includes positions as a senior analyst, analytics consultant, and assistant professor of business, working with both small and large corporations on topics such as revenue forecasting, retail chain optimization, web analytics, text mining, and customer analytics.
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