Data is today the most valuable resource, but it is going to be an easy resource. Unlike oil, which needs high investment to procure it and can be delivered post-processing to the consumers, data is tricky.
Not only does it require an organizational and financial commitment, even after putting everything in place, making the right decisions from data is a complex process and one that requires constant improvements.
The Global Big Data Analytics Market was US$ 37.34 billion in 2018 and set to reach US$ 105.08 billion by 2027 at a CAGR of 12.3%. Such levels of investment and growth hint at the growing significance of data and analytics.
A survey of C-executives points out that even big players have to fake the data culture and have not been entirely effective on data. Even the biggest names in the corporate world are struggling on these fronts:
- Cultivating a data-driven culture
- Half of the surveyed companies are not treating data as an asset
- Half admit that they are not competing on data and analytics
Let us explore the reasons for the failure of large companies to achieve the goal of data-driven organization; when so much investment is being poured for its achievement.
- Short Term Vs. Long Term: Each company big or small faces a choice; pursuit of short-term financial goals pushes longer-term objectives like data-based cultures to the back burner.
- People Challenge: The sternest challenge faced for DX as well as data-driven culture is the people challenge. When not managed properly, it can assure failure to change.
- Hybrid organizations: Many companies have gone with the approach of establishing centers or excellence, analytic sandboxes, and innovation labs to reap returns from digital investments. They are charting new roles and functions with a team that includes CXOs. These efforts and many more have also failed to catalyze change as they miss something fundamental.
The team in your company may be acting on data, but their findings are different, they are bound to disagree. Here are the 6 symptoms that can tell if your organization is using data just for the sake of it:
- Tyranny of Averages: Data-driven decisions must capture the nuances. If your company has a global supply chain, then the global average of delivery time and payment schedule will be nonoptimal. Averages are easy, but lose the true picture, is your company doing so?
- Siloed Data: Can cause each team to see a section of data and conjure their slice of reality. Data must be available more uniformly to all, and each team must have access to the same information.
- Different Data Set: Lack of agreement on which data should drive a particular decision.
- Decisions Preceding Data: As humans, we may tend to have already made a decision and then find the data to support it, thus defeating the entire purpose of being genuinely digital.
- Injudicious Incentives: Data-driven bonuses and other rewards must be refined over time, else it will remunerate behavior that benefits the people, but no gain for the organization. A customer representative may take a little more time to resolve a call, but he may also contribute to better NPS and bring more business. False data-led incentives may encourage wrong behavior. It would just give the impression of data-led decisions to reward, but in fact, it is not so.
- Human Bias: Data may show findings that do not gel with C-suite experience. It is easy to harbor mistrust on data. Decisions may be taken on experience with finding and manipulating data to say the same story. Trust is vital.
The Way Forward
Data is a treasure trove that holds the key to improve customer service, innovation, and efficiency. But a significant amount of dedication and the environment is needed to harness its potential.
Companies must ensure that data should be available widely and homogeneously so that employees have a shared source of truth. Employees on the frontline must be empowered to act on data, there must be channels where they can share their ideas and put them into action quickly.
A data-driven culture is different, it’s not top-down per se, and its newness creates problems. On the other hand, the opportunity is enormous for a truly data-driven organization.