How To Use Business Analytics To Fuel Your Company Growth

Business Analytics (BA) refers to the practice of exploring a company’s data with a core focus on statistical analysis. Business analytics is used by companies that are committed to data-driven decision-making, which is vital for fueling the growth of your business and gaining a competitive advantage over your market. It requires quantitative methods and evidence-based data for business modeling and decision making, therefore it requires the use for Big Data.

Big data refers to a large amount of data, which can be structured or unstructured. When companies analyze big data, they’re looking to get concrete insights  that will allow them to make better decisions and strategic moves.

We can use BA to gain insights that guide business decisions, allowing you to automate and optimize the processes of your company. Data-driven companies treat their data as a corporate asset (because they are) and leverage it for a competitive advantage. It’s not enough to just collect business data, you need a way to analyze that data so it can be used to optimize your business.

Business analytics can refer to any data field that your company may use to make data-driven decisions, such as POS data.

Successful business analytics has many layers, such as collecting accurate data, having skilled data analysts, the right data tracking software/tools and a full commitment to data-driven decision-making to name a few.

Benefits of Data-Driven Decision Making with Business Analytics

Companies use business analytics (BA) to make data-driven decisions. The insights gained by BA allows these companies to optimize and automate their business processes. Data-driven companies that utilize Business Analytics achieve a competitive advantage because they are able to use these insights to:

  • Conduct Data Mining (Find New Patterns and Relationships)
  • Complete Statistical Analysis and Quantitative Analysis (Explain Why A Result Occurs)
  • Test Current & Previous Decision Making Using A/B Testing and Multivariate Testing
  • Make Use of Predictive Modeling and Predictive Analytics to Forecast Future Results

BA also provides the support needed for companies in the process of making proactive tactical decisions, which makes it possible for those companies to automate decision making in order to support real-time responses.

Examples Of Business Analytics

Most commonly, business analytics focuses on two core areas, one being business intelligence and the other advanced analytics.

Business intelligence focuses on examining historical data to show how a business department or individual performed over a specific time. When we look at the most successful companies, this is often commonplace for them. Without the data, there’s nothing to compare it against, there’s no way we can improve the performance of a specific department or team member.

The second area of business analytics involves deeper statistical analysis, we call this advanced analytics. Advanced analytics may focus on predictive analytics by applying statistical algorithms to historical data to make a prediction about the future performance of a product or service.

This area could also refer to other advanced analytical strategies, such as cluster analysis or grouping customers via multiple data points.

Specific types of business analytics include:

  • Descriptive Analytics – This tracks key performance indicators (KPIs) to understand the current state of a business.
  • Predictive Analytics – This uses trend data collected to assess the likelihood of future outcomes and projections.
  • Prescriptive Analytics –  This uses past performance to generate recommendations about how to handle similar situations in the future.
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While business analytics, business intelligence and advanced analytics are often related, there are areas where they differ.

Business Analytics vs. Data Science

While advanced business analytics can sometimes resemble data science, there is clear difference between the two.

For example, advanced statistical algorithms are applied to data sets, but that doesn’t mean data science was needed. There’s a number of business analytics tools that can perform these types of functions automatically, complex data science skills are not needed.

Data science is complex, it involves more than just custom codes and open-ended questions. Data scientists generally don’t set out to solve a specific question, but business analysts do. Data scientist explores data using advanced statistical methods and allow the features in the data to guide their analysis.

Business Analytics Applications

There’s a number of different business analytics tools, such as:

  • Data Visualization Tools
  • Business Intelligence Reporting Software
  • Self-Service Analytics Platforms
  • Statistical Analysis Tools
  • Big Data Platforms

One of the biggest growing areas among business analytics tools is self-service. In the age of the dot com era, software demand is at all-time highs. Users are looking for a powerful software solution that doesn’t need specialized training to operate. This has fueled the demand for easy to use tools and applications.

Business analytics tools can be installed on single computers for small applications or added to server environments to use throughout the company. Once installed, business analysts can use them to create reports, charts and web portals that track specific metrics in data sets.

Now, once specific data goals are determined, the analysis methodology is selected and data is acquired to support that analysis. Most companies have one or more business systems, this data must be extracted from all systems. Once the data is extracted, data cleansing will begin and the data will be placed into  a data warehouse.

From spreadsheets with statistical functions to complex data mining and predictive modeling applications, there’s many different tools used in business analytics. As data patterns and relationships are found, new questions are asked, and the analytical process iterates until a business goal is accomplished.

Deployment of predictive models involves scoring data records, typically in a database, and using the scores to optimize real-time decisions within applications and business processes. BA also supports tactical decision-making in response to unforeseen events. And, in many cases, the decision-making is automated to support real-time responses.

In Closing

As you’ve learned, business analytics plays a big role in making real-time decisions that can impact your company for years to come. The insights BA can give you lead to competitive advantages in the marketplace. It leads to making the right decisions at the right time.