The digital era resulted in consumers gradually moving towards online transacting. The advent of COVID-19 further accelerated the move to online thereby curbing the number of physical (in store) transactions and the subsequent growth of online activities such as banking, shopping, and general communication. This came with advantages for businesses as this move “ to digital, allows for rapid data creation. Data, in this instance, is the raw information collected from the consumers™ digital activities which is turned into insights that are used to drive business decisions. This evolution has thus turned data resources from vanity capabilities to becoming critical parts of business operations that are able to drive the businesses™ bottom-lines.

Introduction of data roles in businesses

Data has become the new currency in businesses and so, it is important for businesses to put the correct resources in place to ensure the most accurate and useful data collection and analysis. Some of the key things to consider in putting together the resources for the purpose of data analysis include the size of the business and the industry for which the data is collected. It is also critical to clearly state the problem, that the collection of this data aims to address, outright. By predetermining these three key factors the business will know what capabilities are key in the resources that must be put in place but also, the size of the team needed to successfully deliver on this.

Capabilities in data analysis

To efficiently put resources together for data analysis, it would be prudent to understand the most critical competencies that can help with the problem at hand but also, it is important to clearly distinguish between the specialist roles in this space. Guided by the three key factors, the resources require a combination and varied focus in the following competencies (shown in Figure 1):

• Business or domain knowledge: Domain knowledge is an in-depth understanding of the business, process, and industry. This can be attained through experience and directly engaging with stakeholders to better unpack requirements and fully understand the problem that is intended to be solved by the data.
• Computer science: This is a technical competency that is vital to extract and process data into the necessary formats. The extracted data is then used for modelling solution which are packaged as a data product “ for ease of consumption by non-technical users. This technical competency encompasses software development (front and back-end), data warehousing and systems design.
• Mathematics and statistics: digital algorithms are rooted in basic mathematical and statistical principles. To be able to apply existing algorithms to various problem, it is important to have a broad understanding of the underlying principles. It is also important to understand nuances in the data in the context of these principles. For example, when scaling features to be used in a model, the distribution behind the data is important.

Roles in data analysis

Businesses must carefully consider the resources they employ for the purpose of data analysis. As highlighted in Figure 1 some of the competencies in data analysis overlap, with Data Science being the intersection of these. This suggests that data science is critical in data analysis. Through understanding the problem, a business can consider one or more of the following resources for the data analysis purposes:

• Data analysts these, have a strong understanding of the business domain. They perform analysis on structured data and present findings to stakeholders.
• Business intelligence (BI) analysts can extract (query) and analyse data. This data is then turned into actionable insights for the business. BI analysts are often sitting closer to the data management and dashboarding functions.
• Data scientists have overlapping responsibilities with data analysts but use more technical approaches to design and implement predictive solutions for typically unstructured datasets. Data scientists work with machine learning and programming, but also play the critical role of engaging with stakeholders directly.
• Data engineers design and build systems for collecting, storing, and analysing data at scale.
• Machine learning engineers work alongside data scientists. They craft the AI (Artificial Intelligence) / ML (Machine Learning) models as informed by the task at hand. They are also tasked with the maintenance of these solutions and ensuring that there is consistent retraining, as the composition of the data changes over time.
• Decision scientists are responsible for finding insights from the collected / available data. Through data interpretation, they unlock value from the data to solve business requirements, thereby driving key-business decisions.

Conclusion

Data analysis resources are critical to progressing businesses to the next level. This, thus, makes data careers the careers of the future. The current era demands that nothing can be done without data and so, to improve the performance of a business, data and the resultant insights are important. To fully enjoy the benefits of data, it is critical to clearly define the problem to solve, which in turn predetermines the resources that must be employed. A business should match its objectives to the competencies in data analysis, and this will guide them to the correct resource that they require for the set project. In putting together a team, the collaboration amongst the various roles in the data team should always ensure that simplicity is maintained without compromising the quality or accuracy of the work. There is no single fit for what makes a data team function better than others however, whether you are looking to expand your team or make a career change, it is important to understand the intricacies of the various specialists™ roles discussed alongside the client problem as these can serve as a barometer for you being the current ˜fit™.