What does it mean to be a truly data-driven organisation? Does it have anything to do with the infrastructure? Or does it mean that data has to play an important role in the functioning and decision-making within the enterprise?
Questions like these and (obviously) many more are always a part of boardroom discussions — be it in small startups or buttoned-up MNCs.
AIM Daily XO
Join our editors every weekday evening as they steer you through the most significant news of the day, introduce you to fresh perspectives, and provide unexpected moments of joy
Your newsletter subscriptions are subject to AIM Privacy Policy and Terms and Conditions.
Most companies in this age are striving to become data-driven. But as we saw from the myriad of questions above, it is impossible to have one single definition. More than just installing the right tools and applications, for an organisation to be truly data-driven, data needs to be integrated into its strategies, systems, processes and culture. It’s about creating a mindset in which analytics forms the basis of all fact-based business decisions, and are embraced by all levels of the organisation.
At the recently-concluded event Cypher 2019, hosted by Analytics India Magazine, Ram Kumar, the Executive Head at Quantium Analytics, posed this question in his session: Does inculcating data science mean that the organisation is data-driven? In what was an enlightening session which captivated the audiences, Kumar broke down the characteristics of a data-driven culture in an organisation.
Download our Mobile App
He raised the following excellent points:
1 | A data-driven culture is championed from the top-down with accountability. An organisation which thrives on data has its C-suite championing its usage and reliance in the organisation, not sponsoring it.
2| Another interesting thing about a data-driven organisation is that it treats data as a strategic and a competitive asset. The organisation governs and manages its lifecycle in a way which has been illuminated by insights garnered from data.
3 | Interestingly enough, every successful organisation who wants to gather insights from data, needs to give a certain amount of leeway for the data scientists to access and play with it.
Only when the analysts and data scientists are comfortable working with the numbers, then they can get actionable insights from it.
4 | An enterprise which wants to embrace data in a full-fledged way, needs to start by launching small-scale pilot projects. Inculcating data in a big way needs time, but to understand how it can be done needs smaller projects and experimentation. Test, fail and learn — that’s the mantra.
It is important to have a continuous testing and continuous improvement mindset.
5 | In this day and age of fast-paced upskilling and instant gratification, it is a great motivation if data-related projects are made a part of the employees’ key performance areas (KPIs). Enterprises who have understood the importance of data have already started embedding data-related KPIs in balance scorecards of its employees.
6 | Before implementing a data-based culture in the company, the C-suite has to understand and clearly define the business use cases for data exploitation or exploration. Doing this will have clear goals and no overlaps.
7 | To successfully implement data usage in an enterprise, an organisation has to start using modelling errors and similar learnings to improve models.
8 | Successful implementation of data science in an organisation also means adhering to the slated budget and ROIs. “HiPPOs” or “highest-paid person/s’ opinion” can mess up that balance. So instead of relying solely on HiPPOs, it is always wiser to follow data-supported decision making.
9 | A data-driven organisation uses data as critical evidence to help inform and influence strategy. This is basically, encouraging an evidence-based culture in the company.
Encourage evidence-based culture in the organisation
10 | And last, but not the least, enterprises who have been using data successfully, have found the right balance between data monetisation and acceptable use of data.
Watch the complete session here:
