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The Algorithmic Business Analyst

Fortune introduced the term “Algorithmic CEO” in 2015. The trends we have seen in the last two years have only added credibility to that term.

Analysts have been talking about how algorithms will transform enterprises. We have seen several new businesses launched with focus on machine learning, cognitive computing and artificial intelligence. We have also seen several existing businesses going the algorithmic route, by investing heavily in technology that relies on algorithms. These are typically cases where advanced big data analytics solutions are being used to optimize operations, or to improve customer experience.

The algorithmic age is forcing everyone – including the CEOs, CMOs and CIOs – to find a new balance. What about the Business Analyst who has been the bridge between “business” and “technology” all these years? To survive and thrive in this algorithmic age, what skills does a Business Analyst require? One way to answer this is to look at the five different domains that Project Management Institute (PMI) has defined for Business Analysis, and see what additional skills or knowledge are required.

1. Needs Assessment

The Needs Assessment domain relates to understanding a business problem or opportunity, and evaluating various inputs to help develop an effective solution. This assessment is typically crucial to get funding approved and have the project initiated.

The Business Analyst needs to be abreast of the technology trends that are driving the algorithmic age – big data, cloud computing, artificial intelligence, machine learning, internet of things etc. The Business Analyst should be familiar with the nuances of the trends that are most relevant for the project. This is essential to articulate the business opportunity in a relevant manner.

The algorithmic age is heavily dependent on data. The Needs Assessment will most likely require data to provide justification for the initiative, or to showcase how data can be used to solve a problem, or how data can be used to improve the business outcomes. The Business Analyst, hence needs to be proficient at identifying the potential of data, and to tell a story using data. The ability to build a data-driven business narrative to support a business case is crucial.

One of the challenges of algorithmic solutions is to be able to evaluate if the solution met its objectives. This evaluation process often is as complex as building the solution itself. As part of the Needs Assessment, the Business Analyst should be able to cover the expected evaluation process, so that the project team can plan for it.


2. Planning

The Planning domain comprises of all activities related to effectively managing all the business analysis activities such as establishing requirements management, requirements traceability, change control, document control and acceptance criteria.

Establishing acceptance criteria is an area where there could be additional complexities for an algorithmic project. The Business Analyst is expected to translate the business objectives to the goals of a specific algorithm. Defining this goal may need to clearly define the data conditions and boundaries. It may be required to do an extensive study of the business data to be able to come up with these goals. Also, a simulation platform may be required to run an algorithm against data. The Planning should also cover if there is need for such additional platforms, or additional data.

3. Analysis

The Analysis domain focuses on elicitation, analysis, decomposition, acceptance, approval, specification and validation of requirements.

The requirements may need to be defined in the context of the technology trend driving the algorithmic initiative. The Business Analyst’s expertise in the technology trend, hence is relevant in Analysis too.

The Business Analyst needs to be data savvy to be relevant. Depending on the organization and complexity of the project, there may be a Data Analyst on the project. Even if there is a separate Data Analyst role, the Business Analyst needs to have certain level of proficiency in data analysis and data visualization. The Business Analyst will be expected to put in a structure around the analysis done by the Data Analyst. For example, the Business Analyst should be able to drive discovery of data correlations, trends and outliers – to build a data story.

What about user stories, use cases, process flow diagrams and everything else that Business Analysts typically prepare? They are all still important. The Business Analyst will in fact need to go one level deeper as all these standard artifacts will also need to get connected to the data story.

4. Traceability and Monitoring

The Traceability and Monitoring domain related to activities needed to manage the lifecycle of requirements. The requirements lifecycle, and the tasks to manage the lifecycle are no different for an algorithmic project as compared to a standard project. The artifacts used to capture requirements could be different, but the process remains the same as for a standard project.

5. Evaluation

The Evaluation domain relates to activities that assess how well the solution met the requirements and business needs. Typically, a Business Analyst does evaluation by running some tests or by reviewing the test results from a Quality Assurance team or by reviewing a demo of the product or feature. While these still hold good, there are additional tasks required in the case of an algorithmic project.

Once the solution has been built, the Business Analyst should be able to run the algorithm against realistic data, and see whether the algorithm is meeting its goals. Depending on the complexity of the project, this may need additional infrastructure, and involvement from other team members. If the algorithm fails to meet its goals, the Business Analyst may be required to do additional data analysis and identify the conditions under which the failure occurs.


The standard rigor expected for Business Analysis applies to the algorithmic projects as well. However for a Business Analyst to really thrive in this age, it is important to have these additional skills and interests.

  1. Ability to translate business goals to relevant metrics
  2. Passion for data analysis
  3. Deep interest in the technology trends driving the algorithmic age

With these three in place, an already successful Business Analyst is bound to succeed, and take the organization further in its algorithmic journey!