Companies Must Develop New Data Muscles to Pivot to Profitable Growth
As the economic environment becomes more somber, companies are focusing on achieving profitable growth and not just growth. Data-driven decisions can accelerate this goal, but organizations must develop new data muscles to get them there.
The last few months have been a whirlwind for the business world: slowing growth, bleaker capital markets, layoffs, the possibility of a recession, reduced investor risk appetite, and an overall uncertain economic climate. These have forced companies to rethink their strategies and course-correct their paths. One of the biggest corrections has been to balance growth with profitability . Although this has been most prominent in high-growth tech companies, the implications are broad.
As new realities emerge, the mindset must evolve, and new data muscles must be developed. The frenzied pace of growth in the last decade meant that the primary focus of data analytics was in the areas such as demand generation, upsell cross-sell models, growth marketing, etc. While these will stay important, maybe even more so, new data analytics muscles must be built to complement existing ones – muscles that help drive efficiency and maximize return on investment. Many things were not areas of focus for a long time but now need to be. Specifically, there are five areas that companies would do well to focus on now:
Lean technology and operations models to weed out inefficiencies: There are several areas where inefficient models have become commonplace. A prime example is the technology model of having multiple software and vendors do very similar things or do things that are not needed. In the data world, it has manifested in the forms of a company having multiple “data platforms,” data warehousing technologies, BI tools, etc.
The genesis was that teams had funding to buy and deploy what they felt was the most convenient technology for them. And this has left organizations with multiple stacks that don’t interoperate. Similar themes exist on the resourcing side, where, for example, data engineers and analysts across teams are doing overlapping work. And companies are ending up with multiple versions of “similar but different” data products that then cause confusion. A hard look is needed at what rationalized tech stack and resource deployment optimizes at the company level and not the team level.
Optimization focus to complement the maximization focus. A cultural change of valuing and celebrating optimization wins is necessary to sharpen this focus.
