Today’s businesses are more reliant than ever before on data. Very little data “speaks for itself” – most data is unstructured, providing few clues about how it should be read or what it means. To make meaning from raw data, business professionals must apply a wide range of methodologies. They must be prepared to deal with a “fire hose” of data that is continuously increasing in two ways:
- Volume: The total amount of data to be processed, understood, and stored.
- Velocity: The speed at which new data enters and expands the total “pool.”
With all this in mind, data analytics has driven the conversation on business process improvement in recent years. However, not all data is created equal. Data provided by consumers to businesses and data that arise from day-to-day operations require entirely different perspectives. The end “use” of these data pools is also different. That has led to sharp distinctions between two key fields that make meaning from data:
- Data Analytics: Applies statistical methods to identify meaningful patterns in Big Data.
- Business Analytics: Uses similar methods specifically for executive decision support.
Both disciplines interact with data – sometimes, though not exclusively, the same data – but their intended outcomes are quite distinct. Their colleagues and supervisors need to understand these differences, so lessons learned from data can be applied effectively.
The Business Impact of Data Analytics
A data analyst is a type of data scientist who typically has a thorough education in statistical methods. He or she performs sophisticated operations directly on the data itself. By “processing” data in certain ways, it becomes more meaningful and accessible to business decision-makers.
Some of these processes can include:
- Cleaning: Eliminating corrupt, false, or misleading data from the data pool.
- Mining: Finding the “right” type of required data within a far larger data set.
- Transformation: Applying statistical methods to clarify the data’s meaning.
Once data has been cleaned and clarified, data analysts can make it available to the various other stakeholders who might find it useful. Analysts can also create databases for the finished data or develop automated programs that will perform a particular sequence of tasks on new data.
The value the data analyst brings to the organization is in his or her extensive understanding of the types and sources of enterprise data. This is a person who works with data in its “raw” and unprocessed form before it becomes a complete resource with a broad spectrum of uses.
By accelerating and improving the processes by which raw data is shaped, the data analyst makes it less likely that inaccurate conclusions will be drawn from that data down the line. It also becomes easier to define, identify, and use new sources of data in the future.
The Business Impact of Business Analytics
As data analysts perform their craft, data is transformed from its original form into a structured “human-readable” format. This is where business analytics comes in. By examining vast quantities of this “polished” data, the business analyst seeks answers to current business issues.
You can think of the business analyst as a sort of consultant or strategist. These analysts are not interested in the format or origin of the data. They look at data synergistically and try to develop a “big picture” understanding of a specific business challenge based on what the data tells them.
For example, consider a business planning a merger or acquisition.
To explore the implications of a merger or acquisition, business analytics must evaluate several factors. They must account for the financial valuation of the target enterprise, its revenue performance, the size of its market, and the various factors that could influence its future growth.
Each of these individual factors may have thousands of data points that combine to describe it. The data may come from investor calls, quarterly reports, articles in business periodicals, internal memos, recorded interviews, and many other sources.
The business analyst is not genuinely interested in the data itself except to the extent that it is accurate and credible. He or she may use statistical methods like the data analyst does, but can also incorporate analyses from finance or management science.
While the data analyst ensures the data is sound and suitable for a particular purpose, business analytics must strive to determine its business meaning. After researching the data, a business analytics professional often needs to distill it down even further into reports or presentations.
Data Analytics and Business Analytics Can Work Together
Some enterprises – especially in fields like math and sciences – focus almost exclusively on data analytics. Most enterprises, however, benefit when the disciplines work hand in hand. Data analysts ensure that data is clear, consistent, and credible. Business analysts use data to make a case for specific actions, and analysts propose that case to decision-makers.
