min read
May 6, 2021



Business analytics can’t be fully productive if a company’s technology infrastructure doesn’t support its initiatives. Fortunately, the most common issues are often alleviated by effective business analytic governance.

Business analytics refers to the skills, tech and practices for exploring an organization’s data to make data-driven decisions. It leverages data and statistical modeling in order to gain the insights necessary to drive reliable strategies and knowledgeable decisions.

The importance of collecting and analyzing data has given companies a competitive advantage. With success now being measured by data quality, a good analyst who understand the technologies used can translate the findings into better business decisions. In doing so, businesses gain a competitive advantage when:

  • They’re established an iterative and organized way to explore their organization’s data
  • Continue to explore new relationships and patterns
  • Are comfortable having their data explain an event
  • And lastly use historical data to predict future outcomes.

All of which make a positive impact where it counts most: the bottom line.


While data is a key component to healthy analytics, the most important component is the quality and accuracy of this information. After this is established, storage, analytic capabilities, and reporting can be addressed in several ways. The following will detail these 4 core areas.


Descriptive statistics provide real-time snapshots of the company by summarizing and describing data. It answers what, when, and how things occur based on the available data points. Business analytics brings this to the next level transforming this information from what happened into why, will it happen again and how a change in one thing can impact another.

In short, descriptive analytics provide real-time snapshots of the company’s current circumstances.


Diagnostic analysis delves into past performance and pulls out information about which plans and actions were effective, essentially answering the questions “why”. This form of analytics, for example, can tell businesses the parts of a marketing campaign that didn’t go over well with customers.


Predictive analytics is another form of advanced analytics which is the use of data, statistical analytics and machine learning to assess the probability of future outcomes based on data from the past. “What is likely to happen” is measured by way of data, statistical modeling and machine learning to forecast actionable outcomes. These results can then be used to reinforce sales, marketing, budgetary and growth strategies.


This analytical method can answer specific questions, such as “where in the sales process most customers fall out” and “what can we do to make X happen”. Prescriptive analysis reveals specific weaknesses and provides a better idea of what can be done. It’s a growing discipline, with evidence that half of all business analytics software will incorporate some form of prescriptive analytics by 2020.


Beyond enhanced decision-making, business analytics imparts several significant advantages that can push companies ahead of their less data-savvy counterparts.


The insights gained from business analytics empower companies to discover considerations and patterns that may have otherwise gone unnoticed. Businesses can then test previous decisions against real outcomes to see how history stacks up to forecasts. This, in turn, paves the way for more informed planning and strategic direction of business functions.


Organizations can identify inefficient processes and outdated methodologies throughout the enterprise that are dragging operations down.


Analytical insights facilitate automation of everyday processes such as data entry by allowing the system to automatically put data in front of the people who need it most and to default to the best course of action in any situation.


Business analytics can’t be fully productive if a company’s technology infrastructure doesn’t support its initiatives. Fortunately, the most common issues are often alleviated by effective business analytic governance, two of which are detailed below.


The vast amounts of data that flow through an organization can easily get lost within disparate data silos owned by various departments. This makes it difficult for people to access a particular piece of data when they need it, if they’re even aware that the information exists at all. Many times people don’t know of the existence of the data, let alone have the ability to access it in a timely manner for analysis. A data analytics system that classifies data and stores it in a central location can eliminate this bottleneck.


Related to the data silo conundrum, many current data systems lack the ability to link departments and individuals. This makes it difficult to perform analysis with data that spans across the company and hinders collaboration. There needs to be a seamless connection, preferably within a single data system.


The final hindrance to data analytics is the tendency of enterprises to utilize multiple solutions to deliver data and derive insights. Each of these tools may specialize in a single aspect of data analysis, such as storage, extraction, recoding , loading, processing and data visualization. Synchronization of these systems can sometime be difficult, however having the right team to analyze and report it, is most important to any business if they’re to take full advantage of their information.

Analytics software should provide both breadth and depth to the insights that are gathered through data evaluation, uncovering overlooked opportunities and enhancing decision-making abilities.


Properly visualized data provides organizations with the ability to represent the various realities of large-scale data collections in the most coherent manner, and different uses for the data require different visualizations techniques. Where a pie chart can provide information about the percentage of customers that use a particular sales channel, a matrix can show the relationship between different metrics, such as how customers are arriving at that channel, whether they’re using it to complete transactions and where they most often drop out of the process or switch to another channel to complete tasks. Each visualization method serves a particular purpose, and companies should be able to utilize them all.


The best models are accurate, but they should also be easy to create with an intuitive interface, decreasing the amount of time and effort spent on development and interpretation.

And since the business world changes in the blink of eye, real-time capabilities are a must to ensure that the insights gleaned from a model aren’t built on last month’s information.


Staff, decision-makers and other stakeholders should be able to search for and report on data from a single platform, eliminating data bottlenecks and spreading ownership of data assets across the organization.


Business analytics isn’t a one-size-fits-all undertaking, with numerous factors influencing companies in various industries. Centric Digital can provide broad expertise as well as specialized focus to harness business analytics successfully.