For all the energy going into Big Data and artificial intelligence, much remains to be said for “little data” and conventional analytics.
A lot of companies can substantially improve their product development, customer support and quality simply by investing in analytics to leverage the product and customer data they’re already collecting.
Good product development practices and backend processes enable data-driven management for faster, more predictable development cycles and more timely, effective customer support. It’s common practice to characterize and monitor the installed base log data to inform product management of customer preferences, adoption rates and priorities for new features. But deeper configuration tracking allows prompt, targeted customer support actions when issues arise. Integration of customer case information with CRM data exposes potential obstacles to new sales campaigns in major accounts. Combining source code changeset information with defect tracking exposes missed integrations before they become regressions in the field.
The key is often in integrating data from independent systems, a sort of one-plus-one-makes-three approach. With hundreds of deployed products, multiple branches of product code, and thousands of bugs (tasks) driving development efforts, automated tools are essential to gleaning these nuggets that drive decision making.
Such engineering management tools help prevent mistakes and improve predictability during product development, and they expose what’s important without relying on individual expertise. In a fast-growth organization with new engineers and new managers constantly entering the picture, the tools help normalize the planning and management processes and enable the team to spend more energy on getting the product to market.
I’ve seen data analytics pay dividends to young and growing organizations, without the complexity of big data infrastructure that is demanded of larger scale analytics challenges. By investing early in this under-the-covers capability, faster and better decisions will lead to faster growth and higher customer satisfaction overall. You still need analytics infrastructure and database specialists to build a robust system with dependable performance. But there’s much untapped value at this simpler end of the analytics spectrum. As the organization grows, big data methods will likely find their place, and the mindset and processes will be in place to fully exploit them.