The data scientist is not a new professional profile that’s being defined from scratch, or in parallel to the development of data analysis’ techniques. Companies have long been resorting to in-depth data analysis as a valuable tool that helps meet or exceed their goals. What’s changed now is the dimension of this analysis, as in a greater volume of data calls for a different approach, with regard both to procedures and the purpose of the analysis.
Many experts stress the idea of rediscovering data, or rather, discovering its value contribution to the company. The person who used to manage data, target customers or detect products with the greatest turnover quite clearly added value to the company. But the data scientist’s role goes much further.
The data was already in-house
It’s true that the figure of data manager has existed in companies for some time now. Data Analytics has been used in the telecommunications industry for at least 20 years. Banking also has been using Business Intelligence for several years, as have – somewhat more mutedly – all major companies at the helm of their respective industries. However, far from being a cross-disciplinary practice, Data Analysis has often only been applied in specific departments, mainly in Marketing, Communication and Customer Insights. A form of pigeonholing which has to a certain extent jeopardised its importance within the hierarchy of company priorities.
The main problem in companies without a data-focussed corporate culture is that they were often run in a decentralised and disorganised way. As a result of this siloed management, each corporate department has been taking technology-related decisions it deemed the most appropriate at any given time.
Now that the time has come to deal with data, experts are encountering barriers and incompatibilities that hugely complicate their work. In institutions with enormous historical repositories, grouping together and processing data files is a colossal effort, but once this path of self-learning has been completed, the work translates into improvements in internal processes, people management and/or customer service.
The difference when compared to the situation in recent years is that data analytics specialists now have much more powerful and effective technological resources, allowing them to extract greater value from the information. Computing costs are lower, data availability is higher and connectivity between both is greater, so this raises the chances of finding patterns or potential case-based reasoning, helping to update the practice of using data to improve management.
In the process of recognising the status of the data scientists, it’s vital to mention a fundamental advancement in their professional acknowledgement: they have taken on the crucial responsibility of improving company results. Their mission is no longer limited to guiding or advising the actions of other departments, nor to crunch data to later present it to managers responsible for decision-making. The data scientist’s work culminates with the delivery of new business opportunities founded on the comprehensive inspection and translation of data.
Is the company ready to listen to the data scientist?
The data scientist in many cases faces another crucial battle to make sure that their new status within the company is acknowledged: overcoming resistance to change. Digital inertia is pushing many companies towards the culture of data, but in more traditional or larger organisations, where digital natives are not often part of the management, this can become a costly journey if it is long, or traumatic if it is short.
The first leg of the company’s journey towards big data must receive firm support from senior management. There are so many departments involved (IT, Business Intelligence, e-Commerce, Marketing, etc.), and so much coordination is necessary for data to flow, be shared and properly used, that only by providing resources from the top will it be possible for change to take place. Without agility and cooperation, there can be no results.
In companies where there’s a tendency towards convenience or resistance to change, the data scientist might even be seen as a gatecrasher who has turned up to lecture experts on how to run the business. Executives who have established the rules of the game long ago are wary of the mathematician, who seems to use a language foreign to the company.
This is a cultural issue: the scientific basis behind the data scientist’s recommendations must tap into traditional decision-making processes, based on experience or other types of indicators, sometimes as simple as a spreadsheet. There may even be those who ignore the Data Scientist’s contributions, as they may imply a commitment to improving results: meeting new KPIs can be a painful goal.
A phenomenon that is repeated in all kinds of organisations, including startups, because ultimately each person tends to protect their own teams and projects. That’s why, as we shall see later on, entropy and communication are two of the essential non-technical qualities of the data scientist.
This article is part of the study “Data Scientists: Who are they? What do they do? How do they work?“, available on Rebel Thinking.
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