What is Happening?
ISVs can, should, and do profit from the use of advanced data analytics – not only by integrating them within software and services offerings, but more importantly, by integrating an increasing range and scope of data (including Big Data) and analytics into their own business operations and decision making. Data regarding user behavior, operational efficiencies, and relationship management can and should be analyzed to help determine and take advantage of customer / buyer desires and needs, as well as competitive abilities, solution improvements, development strategy, upsell / cross-sell opportunities, pricing, business models, and hiring / retaining the most useful employees.
These were among the lessons reported by Saugatuck Research Fellow Bruce Guptill, who had the pleasure of attending and participating in this week’s “Deciphering the Data Storm” event, presented in Boston by the Software & Services division of the Software and Information Industry Association (SIIA).
Key lessons learned and reported by ISVs regarding the analysis and application of a wide range of business data (including Big Data) include the following:
- Data needs “gravity” in order to be useful; i.e., data needs varying combinations of human business context, situational relevance, and environmental semantics (i.e., “the voice of the author”) in order to be qualified, let alone be useful in analysis.
- Don’t always focus on reducing / limiting the “bigness” of data. Adding to / augmenting data with similar, complementary, and relevant data can provide and improve the “gravity” of that data. The key information sought may not be found completely in your own data. That being said, don’t be afraid to apply a variety of filters to screen Big Data; just be willing to accept failure and move on quickly when the filtering doesn’t work as expected.
- Share data in common to improve collaboration. “Success” is defined differently everywhere, even within small ISVs. Utilizing common sets of data has more beneficial impact, and enables more and better business collaboration, than trying to develop and focus on a “single version of the truth.” Different groups will always have different perspectives, and use data in different ways; ensuring that the data used is common rather than simply absolute will enable better understanding, and foster more (and more useful) interaction.
- Know what the next step is. In other words, set realistic business goals beyond simply analyzing data. Once deeply into the analysis, it’s easy to lose sight of business reasons behind the analysis. And as more data becomes more readily available from more sources, it becomes more and more easy to become overwhelmed.