Top Differences Between BI Analyst and Data Scientist

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Thomas Davenport’s book, “Competing on Analysis: The New Science of Winning ” was the first of its kind to spread awareness among organizations about the business potential of the analytics. And, today competitive business, data science and business analysis are very closely linked. They form the base of the organization integrally and help in business development together. They are interdependent and hence, complementary. While the major responsibilities of a data scientist and BI analyst match to a great extent, there is a massive difference between their skill-sets and that paves way for new responsibilities and opportunities.

Role of Business Intelligence Analysts

The major role of a BI analyst is to look for patterns and trends in the historical data of the business and use them in a way that benefits the organization in every possible aspect. Thus, BI is majorly an exploration of past trends. Ideally, a BI analyst works on software like BI dashboards , which eases visualizing business performance. However, dashboards lack the flexibility of coding. BI analysts use programming languages like SQL to manipulate query and databases. Using these tools, BI analysts calculate the impact of a certain event on the bottom-line of a business, and also compare the performance of the company with that of others which share the same marketplace.

However, their role does not demand forecasting business performance which definitely requires more advanced skill-sets.

Role of Data Scientists

The role of data Scientists is to apply an algorithmic approach. They have to find the significance and predictors behind the trends put forth by BI analysts and use their toolkit of algorithms to understand and predict the performance of a business. In addition to SQL used by a BI analyst, a data scientist is expected to know languages optimized for mathematical analysis like R, Python or Scala. The data scientist then uses those programming languages for creating a framework that leverages historical data. They use the data that is currently created to predict how much money a customer has to spend over a certain period and other important metrics.

Areas that Separate Business Analysts from Data Analysts
 Areas Business Analyst Data Analyst
Focus  Reports, KPIs, Trends  Patterns, Correlations, Models
Process  Static, Comparative  Exploratory, Experimentation, Visual
Data Sources  Preplanned, Added Slowly  As Needed, Immediate
Transform  Carefully Planned  In-database, Enrichment
Data Quality  Truth  “”Fair Enough””, Probabilities
Data Model  Schema on Load Schema on Query
Analysis  Description, Retrospection  Prescription, Prediction

BI Analyst and Data scientist together boost business

As discussed earlier, it is very important to know that both the roles play a vital part together in the business development of an organization.

While both the groups are working together to understand the business and market trends, and their correlation hid in a very large amount of data, BI is the first step for companies to enter into big data. It plays a logical role to simplify the complications involved in the big data.

However, if an organization already has a BI team set, which is collecting a significant amount of data, then the role of data science comes into play. It helps to predict the future of the business and also to refine the products and scale operations.

No matter what the market standards are, business intelligence and data science are together important for the betterment of the company. One cannot replace the other. All they can do is, cooperate each other.

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