The era of Big Data gives rise to “data-driven” organizations that are very effective in collecting and processing large amounts of data as a means of optimizing their business processes and their managerial decision making. Data-driven organizations are therefore provided with opportunities to improve their competitiveness and gain strategic advantages that set them apart from their competitors. To this end, data-driven enterprises need to build powerful and effective data science teams, which enable them to fully leverage Big Data in their operations. However, building a powerful data science team is a very challenging task, since it asks for attracting and bringing together several people with different profiles and skillsets, which is quite difficult given the known talent gap in data science technologies and skills.
The advent of Big Data has drastically changed the data science landscape and the skillsets required from the members of data science teams. In the near past, data science was mostly about traditional business intelligence in enterprise environments. Data scientists had to deal with conventional data types and very common data analysis techniques, including standard and ad-hoc reporting, the development of dashboards, as well as the formulation of queries over enterprise databases. Likewise, they had to deal with structured datasets, which comprised manageable data volumes that usually resided in data warehouses. In this context, data scientists were usually offered with tools for querying and exploring these warehouses as a means of answering questions about what happened and whether things keep up or deviate from a plan (e.g., planned sales). The latter tools could also support data mining tasks towards forecasting future activities and creating future plans.
In the Big Data era, this conventional data processing is no longer the case. Data scientists have to deal with much larger and diverse datasets in order to dynamically predict future business scenarios and evaluate different alternatives. Data are no longer structured, but rather stem from a variety of heterogeneous sources that include unstructured data sets such as data from social media sources. Likewise, data may arrive in databases with very high ingestion rates, as is, for example, the case of data stemming from sensors and internet-of-things devices. Most important, the processing of the data is no longer confined to reporting and the use of simple data models. Rather, an enterprise data science team needs to be competent in formulating and solving complex optimization problems, which are typically based on predictive modeling, forecasting, and statistical analysis. This is because businesses are not only concerned about finding out whether things are working well. On the contrary, they want to identify optimal business scenarios and hidden trends, while also explaining why something happens in a certain way. Furthermore, the availability of very large amounts of data gives rise to advanced data analysis techniques (e.g., deep neural networks) that were hardly used in the past. Such techniques are at the heart of Artificial Intelligence (AI), which is nowadays trending.
When considering the assembly of a data science team, enterprises must have in mind the contemporary Big Data environment, rather than the traditional business intelligence one.
In this context, a data science team needs to bring together individuals with knowledge and skills in the following areas:
The members of a data science team are likely to possess more than one of the above-listed skills. For example, it quite common for programmers and software engineers to have a very good knowledge of databases as well. Likewise, the machine learning and deep learning experts are usually competent on statistics as well. However, it’s highly unlikely for a member of the team to be proficient in all of the above areas, which makes evident the complexity of the team assembly task.
Beyond their skills, the members of a data science team should be characterized by the following general characteristics:
While looking for the above profiles and properties, enterprises can take advantage of the following best practices:
In the coming years, more and more organizations will be trying to create highly effective data science teams. The task is challenging, but there are best practices and solution guidelines for putting it on the right track.
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