Skills of a Business Analyst for Business Analytics

There are a number of key tasks for a business analytics to perform including:

  • Identifying the research questions within the business that can be (fully or partially) be solved through the use of Analytics. It should be noted that these questions might either be clearly stated by relevant stakeholders (eg. business managers), or might require the analytics team to identify and clarify these research questions.
  • Understanding what data might be available to use within an Analytics Engine (an environment for analysing these data). This includes both data collected internally within an organization, and data collected externally by other organizations. This process includes assessing the appropriateness and quality of these data (does the data address the research questions or is it on a similar topic but does not completely answer the research question?  Are there biases in the data due to errors or missing data?). This process also includes any efforts needed for access to the datasets (who owns the data?  Do we have permission to use these data to answer these research questions?)
  • Data analysis (including both the initial analysis to obtain key metrics such as means and ranges of variables and levels of missing data, and a full analysis to understand the patterns and relationships between variables)
  • Reporting the results of analytics back to stakeholders (knowing that different stakeholders will be interested in different research questions, different levels of expertise in interpreting analytics results, different amounts of time available for reading through analytics results, potentially different levels of privacy and confidentiality and hence different types of results they have permission to view, and different needs in terms of timeliness – do they need to view results in real-time or would they prefer a monthly/annual report).

 

Let’s start with an over-simplified description of the different members of a business analytics team:

  • Data scientists – people with formal training in data science (computer science and data mining, statistics and/or operations research). These people will be responsible for data management (including receiving data from stakeholders), data analysis (eg. statistical analysis), and producing data visiualizations.
  • Business analysts – people with training in business but no formal training in data science. These people will be responsible for identifying the research questions, sourcing data and assessing its value, providing data to data scientists for analysis, interpreting the results generated by data scientists, and communicating results back to relevant stakeholders.
  • Application matter specialists – an analytics team possibly might also contain people with relevant application matter knowledge (eg. knowledge of different business tasks such as marketing, accounting, medicine, natural resource management, etc).

In such a description we might define the data scientists to possess the technical skills and the business analysts to possess the functional skills).

 

While we might provide this over-simplified description of these different roles, the lines between these three tasks will be blurred and different for every business environment. Some environments might define roles as above, others might require their business analysts to also perform data analysis (eg. statistics) themselves (either with the support of data scientists or as independent staff members), and others might require business analysts to also have some application matter knowledge.

 

There are many reasons for why different business environment will use different breakdowns of these three tasks a few are:

  • The size of the analytics team – does the team consist of one single person who is expected to do the full range of analytics tasks, or is a large multi-disciplinary team.
  • Does the amount of analytics work vary from month to month – if there are busy and quiet times for an analytics team throughout a year then a business analyst might need to perform some of the data science work during the busy times (to support the data scientists).
  • Does the organization have one single, central analytics team or are the people with analytics expertise divided and allocated to different functional units within the organizations (eg. some analytics people sitting in the marketing department, while others are sitting over in accounting).

 

Given this huge variation in roles there are opportunities and challenges for business analysts working in analytics. There are jobs where business analysts will be working alongside data scientists (and indeed analytics might only be one part of an analysts job), as such this means that business analysts do have opportunities for slowly developing the perspective and skills needed within an analytics environment. On the other hand for business analysts to provide a central, crucial role within an analytics team they need to both possess knowledge of business analysis but also core skills in data science (and indeed not just to have these skills but to be able to clearly demonstrate that they have these skills, such as through formal learning in data science).