The value of data — a new structural challenge for data scientists

Data and IT departments can often be fashion victims. Each technological era is marked by one or more trends driven by innovation, societal changes, new requirements or business models. With increasing digitalisation, which has been accelerated in some industries following the Covid-19 pandemic, and with data present in every company aspect, every IT, data and business manager is faced with the same question: how can I get value from my data?

In this quest for value, or even monetisation, data analysis is becoming increasingly important. To achieve this goal, companies are investing in their analytical power with technologies such as artificial intelligence and machine learning. Hence, the discipline of data science has become a “must-have” with data scientists at the helm.

However, many organisations realise that mastering data science is not enough to extract value from their data. So what are organisations missing? Is there an ideal team to better understand business data challenges? Between change management and structural re-organisation, how can we succeed in creating a data driven culture?

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From theory to practice

After a simple Google search for “data scientist job offers”, nearly 3 million results are listed by the search engine. If this is not just a fashion, 2020 has seen a 45% drop in the number of new offers, according to Indeed — a trend that could be explained by cost cutting and decline of investment in technology, as well as the surge in demand for data engineers following the Covid-19 crisis.

This phenomenon can also be explained by not having clear business strategies and needs not clearly identified, as many organisations have just begun their work around data science. The development and use of AI technology is still in its early stages.

Some companies with data scientists in place have difficulty operationalising their skills. If we look at the volumes of data processed by organisations, the different structures and architectures, it is not imperative to have a data scientist in its ranks of data experts. For companies managing an astronomical amount of data, on multiple channels and with a complex structure, the expertise of a data scientist will prove beneficial in modeling data, query it and make predictions. One of the first questions to ask is therefore related to data and business needs and to organise the structure according to an organisation’s structure and its data strategy.

Companies have also realised that having a data scientist was not the answer to their data value problems. This is partly due to a lack of understanding in the environment surrounding data. A data scientist may understand the data, but not its purposes and environments or business applications. Let’s take the example of a marketing department working on implementing AI to accelerate its web ROI. The data scientists will build and implement the algorithm without taking into account this specific environment and its behaviour; this means the website will take much longer loading as it implements the algorithm to offer recommendations, leading to the benefit not being realised.

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A holistic approach to data management

Data has become a fundamental element of any organisation, but remains complex in its life cycle management. Its development is a bit like the quest for the Holy Grail. This Grail really exists but, as in history, you need to surround yourself with the right data knights.

So, what does this new data round table looks like? In order to optimise and leverage data analytics, more and more companies are deciding to refocus the analytics function within a single department and team. Among the 10 data and analytics trends identified by Gartner is the move away from scattering analytics across different business departments to making analytics a central function, with an increasing emphasis on chief data officers.

Centralising analytics solves communication problems that used to exist when this function was scattered throughout an organisation, creating as many silos as departments or stakeholders. With this centralised approach, the management of data and its life cycle and value becomes a collective project with the chief data officer as the leader. From an influential and transverse role with little power, this person becomes a decision-maker. Chief data officers will surround themselves with data scientists, data engineers and more and more business analysts if necessary. The latter will play a key role because they are the missing link between data theory and its practical business application, as the business analyst will understand the business expectations linked to data. They will provide the missing key to unlocking the value of data.

This core team, often reporting to operations, will have the IT, security and compliance teams at their side to ensure maximum alignment during any data transformation or value creation project in line with business objectives and needs.

Written by Felipe Henao Brand, senior product manager at Talend

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