Processing and analyzing vast amounts of data is an innate part of projects related to digital transformation, especially in banking. Therefore, it requires appropriate skills from specialists working with data.
Over a year ago Gartner warned that organizations with actual maturity in BI and analytics are quite scarce. Data from other sources are also far from optimistic: Big Data, AI and advanced analytics projects flounder because organizations are unable to determine precisely what role the introduced technology is to play for end users, or for the business itself.
Perhaps the root of the matter is not the technologies themselves, but the appropriate skill set of the project team? The knowledge and experience of individual team members need to complement each other, especially in IT projects. Choosing the right skills and the right proportions is crucial.
Data Science: foundation or fad?
The Data Science sector is booming, and Data Scientists are in demand on the labor market. So much so, that they were dubbed “the unicorns of the labor market”. Due to their unique combination of skills, these new-generation specialists support AI, ML or NLP teams. In one Scandinavian bank, the Chief Data Officer is building a team of 100 Data Scientists, supported by 50 Data Students. The sheer scale of Data-Driven Product operations in banking indicates that the area will grow, challenging companies such as Amazon in exploiting the newly discovered data potential.
However, there are some critical voices given the massive demand for Data Scientists, coming not only from HR departments. It seems that the demand for these highly-specialized team members is only partially justified, stemming from a kind of fad caused by inaccurate understanding of the demand for specific skills in the transformation project team.
For example, a Data Scientist is not a synonym for Analyst; his task is to delve deep into the analyzed data. However, he often ends up with a job description of Analyst, Statistician or Data Engineer. Each of these roles is associated with the need for a slightly different set of skills. Their inadequate selection for the project may end up with frustration for both sides, the company and the employee himself, as the various roles required for IT projects have very different job descriptions:
> Data Scientist
With the combined skills in mathematics, statistics and computer science, Data Scientist adds value to data, to create new services or optimize existing ones. Due to the demand, it is the highest-paid role in the team.
> Chief Data Officer
CDO oversees Data Scientists and Data Miners, and is responsible for the company’s information resources and adequate management. He liaises between the IT department and business but also ensures the proper exchange of data between clients, partners and suppliers.
Statistician delivers well where traditional analytical techniques are involved, e.g. regression models and significance testing. The role is especially appreciated in the business context, e.g. improved marketing targeting or business analytics. In banking IT, statisticians with previous experience in economic analysis departments are a genuine asset.
> Data Engineer
Usually focused on back-end solutions, he designs how the architecture of all relationships between different data sources come together to tell one story. He deals with data modelling (ERD), ETL structures and frameworks, and integration of many data sources into one model.
> Data Miner
His task is to search for information in various company data sources, as well as to sort and qualify them for use by Data Scientists. Data storage and management structures, as well as platforms related to large data, e.g. Hadoop, are their daily bread.
> Cybersecurity Specialist
Defines technical and organizational measures to combat more and more sophisticated threats, even before the data is processed. The area of cybersecurity includes many specializations: technical, auditing and testing.
Roles and functions as a step to BI maturity
Data-Driven Banking model allows offering complementing services based on the “by-products” of these organizations, i.e. data. It is also an opportunity to build the image of the organization as an employer: innovative projects attract the greatest talents in the field of Data Science.
However, when dealing with such a demanding area as Data-Driven Banking, the right balance of skills and experience of team members vs role demand is very important. As a software manufacturer, in our proprietary project Payres, we are not excluded from the considerations of the optimal combination of roles in the Payres project team.
What is your experience with Data Scientists? Do you think the high demand for this role is justified? Looking forward to learning your thoughts on this, feel free to drop me a comment.