Business analysts focus on data base design (database modeling, at a high level, including defining metrics, dashboard design, retrieving and producing executive reports and designing alarm systems), ROI assessment on various business projects and expenditures, and budget issues. Some work on marketing or finance planning and optimization, and risk management. Many work on high-level project management, reporting directly to executives.

Some of these tasks are sometimes performed by data scientists as well, particularly in smaller companies: metric creation and definition, high-level data base design (which data should be collected, and how), or computational marketing, even growth hacking (a word recently coined to describe the art of growing Internet traffic exponentially fast, which can involve engineering and analytic skills).

There is also room for data scientists to help the business analyst’s job, for instance by helping automate the production of reports, and make data extraction much faster. You can teach a business analyst FTP and fundamental UNIX commands: ls -l, rm -i, head, tail, cat, cp, mv, sort, grep, uniq -c, and the pipe and redirect operators (|, >). Then you write and install a piece of code on the database server (the server accessed by the business analyst traditionally via a browser or via tools such as Toad or Brio), to retrieve data.  Then, all the business analyst will have to do is

  • to create a SQL query (even with visual tools) and save it as a SQL text file,
  • upload it on the server, and run your program (for instance a Python script, which reads the SQL file and execute it, retrieve the data, and store the results in a CSV file),
  • then transfer the output (CSV file)  to his machine for further analysis.

Such collaboration is win-win for the business analyst and the data scientist. In practice it has helped business analysts extract data 100 times bigger than what they are used to, and 10 times faster than they are.

Conclusion: Data scientists are not business analysts, but they can greatly help them, including automating the business analyst’s tasks. Also, data scientists might find easier get a job, especially in a company where there is a budget for one position only, and the employer is unsure whether hiring a business analyst (carrying over all analytic and data tasks) or a data scientist (who is business savvy and can perform some of the tasks traditionally assigned to business analysts) if he/she can bring the extra value and experience described here. In general, business analysts are hired first, and if data and algorithms become too complex, a data scientist is brought in. If you create your own startup, you need to wear both hats: data scientist and business analyst.

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Comment by Henrique Renck on April 1, 2014 at 4:48am

In your conclusion, do you mean the data scientist is the business-savvy one of the two, or that the data scientist can become as business-savvy as a business analyst if conditions so require? If the former, would you please elaborate?

Comment by The Stratalytigist on February 13, 2014 at 8:41am

In the Analytics Age, the lines between business analyst and data scientist roles are blurred into a single new role requiring knowledge and expertise of both disciplines.  There’s a subtle distinction between the following data activities:

  • Analytics
  • Business Intelligence
  • Business Analytics
  • Strategy Analytics 

The discussion around roles is tied to the data activities.  Analytics has traditionally been performed by data scientists whereas Business Intelligence has generally been the domain of IT and the business analyst.

But when we start talking about Business Analytics, we’re diving into an Analytics Age activity requiring a merged set of skills and a new role.  Business Analytics takes analytics and combines to purposes it to create some sort of new value.  If no new business value is created it just BI reporting or Analytics. 

Taking things to the next level, we begin using the business analytics to innovate.  Once innovation occurs, a strategy is developed to place a bet on how and where the business can create new customer value.  Strategy is all about revenue and is focused on creating customer value.  So Strategy Analytics is the science and activity and analytics needed to develop, support and track strategy innovation.

In the new age of Analytics, there is a new role – what we call it, I’m not sure.  The focus of big data and analytics has got to be on generation new value, or it’s pointless.  We all need to pivot our thinking about what it will take to survive the Analytics Age and lead our organizations to through this merging of roles.

Comment by Roderick Sprattling on February 7, 2014 at 8:47pm

When I think "business analyst", the activities that come to mind are those involved in characterizing current business operations (through business process modeling and data modeling) to inform the design of supporting IT systems. BAs' work is also used to improve business processes through suitability analyses and subsequent design of revised and new business operations (aka business reengineering, or whatever it's called nowadays.) BAs get to do the business requirements analyses; model existing data sources and stores to discover what data the business generates, keeps and has available to it; unearth and document business rules; model the roles individuals play as agents in business processes and as providers and consumers of information; and so on. The purview of some BAs does extend into that of the technical analyst role, where they implement some business intelligence tools; but in my experience business analysts aren't responsible for operations issues such as marketing, budgeting, and financial planning. I have run across other role names suffixed "analyst" that do have such responsibilities.

ETL tools like Talend and Pentaho Data Intergration make it easy to pull data - even integrating from multiple sources -, run a script or program or even a custom transformation against that data, and then deliver the data in whatever format is needed. I know PDI comes stock with jobs and transforms supporting WEKA for ML and statistical operations, Hadoop integration, sourcing and sinking SQL, NoSQL, XML, CSV, web services, and so forth. Also, since the soup-to-nuts of an entire process is expressed and managed in a single interface that provides a large number of easily-interfaced components, ETL tools contribute to high cohesion while maintaining low coupling, making both creation and maintenance of the processing chain faster and less error-prone. They are really helpful tools.

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