Why Make Data-driven Decisions?

The aim of data-driven decision-making is to make decisions based on obvious and subtle insights, the trends those insights produce, and other relevant facts, rather than making decisions based on feelings, assumptions, and ideas.

According to Tableau:

Data-driven decision making (DDDM) is defined as using facts, metrics, and data to guide strategic business decisions that align with your goals, objectives, and initiatives. (para 1)[1]

Throughout history, innovative ideas have sprung from human ingenuity and creativity – but now data and algorithms can support, enhance, or even replace human ingenuity in some instances. In the realm of process innovation, data and analytics are helping organizations determine how to structure teams, resources, and workflows. High-performing teams can be many times more productive than low-performing teams, so understanding this variance and how to build more effective collaboration is a huge opportunity for organizations. This involves looking at issues such as the complementarity of skills, optimal team sizes, whether teams need to work together in person, what past experience or training is important, and even how their personalities may mesh. Data and analytics can test hypotheses and find new patterns that may not have even occurred to managers. Vast amounts of email, calendar, locational, and other data are available to understand how people work together and communicate, all of which can lead to new insights about improving performance. In product innovation, data and analytics can transform research and development in areas such as materials science, synthetic biology, and life sciences. Leading pharmaceutical companies are using data and analytics to aid with drug discovery. ( p. 10) [2]

In their article, ‘Big data: The management revolution’, McAfee and Brynjolfsson found that ‘companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors’ (para. 11).[3] The risk to businesses that are not data-driven, or are not shifting to a data-driven decision model fast enough, are evident. The core reported stifler of progress is slow human adoption or obstructive organisational culture.

In their 2018 survey, NewVantage Partners found 77% of respondents reported that ‘business adoption’ of big data and AI initiatives continues to represent a challenge for their organisations. According to those surveys, ‘this issue, and the low percentage of companies that have achieved data-driven organizations and cultures, suggests the need for a new focus. Respondents clearly say that technology isn’t the problem – people and (to a lesser extent) processes are’. (p. 3)[4]

To get the most out of DDDM, it’s vital to embed a data-driven culture and invest in driving change at all levels of the organisation.

Consider this…

Data-driven decision-making seems to have many advantages. Why do you think people might resist adopting data-driven decision-making?

References

1. Data-driven decision making: succeed in the digital era [Internet]. Tableau. Available from: https://www.tableau.com/learn/articles/data-driven-decision-making
2. Henke N, Bughin J, Chui M, Manyika J, Saleh T, Wiseman B, Sethupathy G. The age of analytics: competing in a data-driven world [PDF]. McKinsey Global Institute; 2016 Dec. Available from: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-age-of-analytics-competing-in-a-data-driven-world#
3. McAfee A, Brynjolfsson E. Big data: the management revolution [Internet]. Harvard Business Review; 2012. Available from: https://hbr.org/2012/10/big-data-the-management-revolution
4. Big data and AI executive survey 2019 [PDF]. New Vantage Partners; 2019. Available from: https://www.tcs.com/content/dam/tcs-bts/pdf/insights/Big-Data-Executive-Survey-2019-Findings-Updated-010219-1.pdf