What is Business Analytics?

What is the Difference Between Business Analytics and Data Analytics?

To more clearly define Business Analytics, it is helpful to understand how it differs from Data Analytics. There is no clear delineation between Business Analytics and Data Analytics within and across industries. In fact, the roles of Business Analysts and Data Analysts will likely slightly differ from one organization to another. In addition, there may be overlap in their roles and responsibilities both in definition and in practice. Organization size may be a factor in the degree of overlap between roles, with smaller organizations using the titles interchangeably more often than larger ones.

One difference between Business Analytics and Data Analytics is the data tasks involved. Both fields include hands-on data work. However, Data Analysts more often work on data processing and data manipulation relative to Business Analysts. Data Analytics involves more adhoc data work, while Business Analytics encompasses a more focused approached with predefined data sources.  This approach involves dealing with the data on a more conceptual level compared to Data Analytics.

While using a more focused approach with the data, Business Analytics still must begin with a generally broad organizational view to achieve the desired goals. As Business Analysts start working with others in the company, the focus is narrowed to specific strategic decisions to be made. On the other hand, Data Analysts more often work individually or within their department and less with other groups in the company.

Ultimately, Business Analysts aim to implement these strategic changes efficiently and effectively. In most cases, Data Analysts are not involved with the application of data learnings or strategic decisions.

Why is Business Analytics important?

A 2019 Microstrategy Report, found most companies (94%) believe data and analytics play an important role in their current operations and their overall future. However, this same report found many of these companies are not promoting a “data-driven culture”. Business Analytics can play an important role in involving an entire organization in data-based strategic thinking.

1. Business Analytics decreases risk associated with strategic decision making.
   Basing decisions on historical data mitigates some of the risk involved with new or revised strategies. Business Analytics promotes informed decision making and avoids the need to implement changes blindly.
2. Business Analytics can reveal growth opportunities.
   Increased revenue is an attractive potential outcome of growth. Business Analytics can identify and evaluate new business opportunities for a company. Because Business Analysts work with different departments across an organization, potential new opportunities are approached using a holistic view. This allows for a thorough evaluation of the overall viability of these business opportunities.
3. Using Business Analytics, strategies can be built to increase operational efficiencies.
   Studying a company’s historical data can give Business Analysts the ability to identify operational inefficiencies. These may be found in various areas of the company, including Production, Marketing, and Information Systems. After identification, the inefficiencies can be prioritized, strategies can be formulated, and resources can be assigned to address them. A KPMG study predicts companies’ reliance on data to address these inefficiencies will continue to increase over time, with more of them devoting more resources to analytics.
4. Business Analytics can aid in effective Human Resources decisions.
   Whether it is related to the hiring of qualified candidates, promotion decisions, or human resource allocation, Business Analysts can help manage an organization’s human capital. Using past performance evaluation data, Business Analysts can build models and methodologies to help place employees in the most appropriate positions within the company. Placement factors would likely include their skills, strengths, and weaknesses.

The effectiveness of Business Analytics is largely dependent on the availability of quality data. Without it, Business Analytics’ positive impact on a company’s future diminishes.

Examples of Business Analytics in Different Industries                

Retail Companies Can Employ Business Analytics to Increase Marketing Productivity

Business Analytics can facilitate a more efficient use of a company’s marketing dollars. A study conducted by McKinsey & Company with 400 of its clients revealed that an integrated marketing analytics approach “can free up 15% to 20% of marketing spending”. This savings can be reallocated to other parts of the company or go straight to total revenue.

Using their vast repository of customer transaction data, retail companies can build models to predict their customers’ future purchasing behavior. This allows them to send highly targeted messages and offers to customers when purchase intent is high. For example, analysts at Target Corporation built a model to assign “pregnancy prediction scores” to its customers. These scores are used to create marketing communication and promotion strategies that more effectively target pregnant customers. Historical purchase behavior would identify customers with a higher likelihood of being pregnant. Similar models and scoring methods can be used to identify customers in other life stages. 

Using Business Analytics, Organizations Can Improve Productivity and Increase Operational Efficiencies.

Boston Consulting Group (BCG) has helped a variety of clients make operations decisions that positively affected their bottom line. Supply chain models are one example of how BCG has utilized Business Analytics to help their clients. These models helped a mining industry client avoid a $500 million planned capital expenditure by determining the most efficient and cost-effective way to revise their operations.

In addition, BCG has used Business Analytics to develop optimization techniques for clients in a wide range of industries. Optimization techniques involve algorithms that determine optimal decisions based on the objectives and business rules or constraints involved.  Using optimization techniques, BCG assisted a national broadband network company with an internet rollout schedule. The plan also included the sequence of the rollout and the technologies to be used in each location. The end result was a $2 billion decrease in the required budget.