Author: Anne-Flore Elard, Jan 6 2021
This post gathers three elements that are critical to Data and Analytics in a company: 1. A data-driven culture 2. The management of data as an asset and 3. the definition of Data and Analytics roles and responsibilities
The term Data and Analytics encompasses a variety of roles and qualifications, for which there is little clarity. Some Data and Analytics roles focus on statistics, some on data science, some on business analysis, some on client solutions and some on reporting to mention a few. Some might not have defined objectives.
There is not only a need for a definition of overall standards in this fairly recent function but also a required effort at company level to describe the outcomes wanted out of Data and Analytics and tie those back to clearly defined roles. Anything less may lead to a lack of coordination between parts of the organization, overlaps, inefficiencies and lost talent.
The fundamental question before getting to the creation and standardization of Data and Analytics roles is whether the company is ready to be data-driven. It goes without saying that the impact of Data and Analytics will be minimal if the company is not ready to be data-driven. A maturity assessment is always a good starting point and in this case specifically to understand the leadership reflexes and the company culture. Culture defines actions: the type of employees hired, the way decisions are taken, the way results are measured, the priorities where money goes. Depending on the assessment, intermediate steps may be needed before launching a successful and fruitful Data and Analytics function.
Sometimes it happens that a Data and Analytics function was created in an organization that was not ready for it. This can be a difficult case where the company may need to step back, reassess and identify areas of work that will help shift towards a data-driven culture. The difficult part of that exercise resides in the coordinated cross-functional effort of most of the company leaders to make it happen
The second question to address is the state of data in the company. This is a more technical aspect of the problem where Data and Analytics improve operations and digitalisation by leveraging first party data. That first party data needs to exist and needs to be in good shape to derive good decisions
Finally comes the question about nurturing talent. Talent attraction, retention and management relies on good planning. Planning relies on a good understanding of the current state and the desired future state. So let’s start by getting a clear picture of the Data and Analytics talent landscape in the company. From there, it becomes possible to create talent categories, map those to a know-how and highlight the gaps. Talent categories are groups of jobs or qualifications, for instance: data science, reporting, business analysis.
For each category, roles can be described and with them responsibilities and career paths. The number of roles depends on the size and objectives of the company but each role should have its main purpose loud and clear with the required level of qualification or experience. Above all, the roles need to be mutually exclusive and collectively exhaustive (hint to the good old MECE principle) for the framework to survive over time