We work with CEOs and the companies they manage on a daily basis. Data is an integral part of the cooperation to withdraw insights and to support top management’s decision-making. When we start the cooperation, we commonly end up discussing current data sources and the availability of data. Whether it exists too much or too little, whether the structure is in shape or not, whether the employees keep the data in shape in a consistent manner or not…
Basically, we discuss about the quality of data. It is an important discussion to have but from the perspective of the company’s performance or to answer in detail where the company is going and how to walk the path, data quality is one of the least meaningful areas to focus on first. Below, we elaborate why we believe so and what we want to focus on in discussions with our customers’ CEOs.
Common data-driven decision-making definitions are misleading
We googled data-driven decision-making and picked the first definitions that popped up – as those are most likely the links everyone else opens when in need of the same definition. What we found out?
Data-driven decision management (DDDM) is an approach to business governance that values decisions that can be backed up with verifiable data. The success of the data-driven approach is reliant upon the quality of the data gathered and the effectiveness of its analysis and interpretation. (1)
Data-driven decision-making (DDDM) involves making decisions that are backed up by hard data rather than making decisions that are intuitive or based on observation alone. As business technology has advanced exponentially in recent years, data-driven decision-making has become a much more fundamental part of all sorts of industries, including important fields like medicine, transportation and equipment manufacturing. (2)
The definitions above are correct. Decisions should be backed up with verifiable data. Quality of the data is a key factor to succeed. Technology has advanced exponentially in recent years – for sure and data-driven decision-making is fundamental across industries.
But those definitions have a fundamental pitfall built in.
Those do not take into account what the company wants to achieve nor where the company is going. If taking an initiative to start developing the data quality without knowing exactly what would be most feasible KPIs to track, the results achieved will not be as high as desired. What should be done is to get going from the purpose, from the bigger picture and strategic point of view and from the metrics that actually are crucially important to keep tracking on a regular basis.
Criticizing existing definitions without providing our own would be unfair
Purpose first, data second. That is the short version of our point of view about data-driven decision-making. But let’s elaborate it a bit more, in a bit more profound way.
Data-driven decision-making (DDDM) is the modern backbone in running and managing a business. It harnesses a limited quantity of the most relevant data points into regular tracking per level across organization – based on the company’s strategy and business logic that determine the data needed for timely, data-driven decisions.
We do not want to be too corny when looking at the definitions word-by-word but the mindset behind any definition is crucially important. It is way more beneficial to change data collection approaches based on clear business drivers and digested need stating that we truly need certain data.
The most common starting point for our cooperation with our clients is to break down the business logic. It is a bullet-proof approach to identify root causes and causalities between true drivers of the business and to identify the most relevant KPIs for a company to start tracking on. When done right, it helps in seeing the forest for the trees. Only then are we eager to discuss where and which data will be integrated into our Clarity software. And only then data quality becomes important.
(1) WhatIs / Techtarget