How Data Analytics Bolsters Business Intelligence

Recently we discussed Business Intelligence (BI), the technology-driven process of analyzing business information and making it actionable. BI can help business leaders make informed business decisions and drive the direction of the company. It provides a historical, current and predictive view of operations. But how is it able to create predictions? With data analytics.

In this blog we’re going to discuss the different ways in which data analytics can bolster business intelligence and, in turn, maximize the overall effectiveness of a business strategy.

Business Intelligence In A Nutshell

As a quick recap, business intelligence is used to influence and improve decision-making abilities with an organization, perform data-mining, analyze information from within a business, create easily-digestible dashboard reports, and enhance business operations. The thing to keep in mind when considering the role of business intelligence is that it is used predominantly with historical data in mind and is able to enhance operations through data management and business performance management. So, then what is the role of data analytics?

The Role Of Data Analytics

Data analytics is used to look at raw data and convert it into a form that is easily understood by the user. The use of data analytics to enhance business intelligence (and therefore business operations) is far and wide, but some of the most notable trends of use of data analytics in business intelligence are discussed below.

Data Modeling

Data modeling is a paramount aspect to effective business intelligence. It begins with a clear and succinct understanding of the business and how data is going to be used to support goals and initiatives. It’s not only possible, but common, that a data model for one initiative is not the most effective model for another initiative or tactic. Ultimately the goal is to understand the data and its role within the business before modeling the data for business intelligence. Here is a basic outline of what effective data modeling within business intelligence looks like:

Be Specific: Having a data model specific to each unique initiative within a business can help to ensure that the data is being analyzed properly and informing business intelligence in the most effective way possible.
Thorough, Big Data: The data modeling should accurately represent the business, data, and decision-making process. It needs to be thorough and exhaustive or it will not be able to inform business intelligence.
Structure and Intelligence: Data that has built in dictionaries, hierarchies, metadata, and inheritances can help reduce the amount of inferences being made and ultimately inform business intelligence in a much more efficient and succinct way.
Implementable: This goes without saying, but each data model needs to be easily implemented either by your organization or by a partner for it to effectively inform your organization’s business intelligence.
Scale: Finally, the chosen data model should be easily and effortlessly scaled to support evolving business needs.

Data Transformation

Another critical element of data analytics and its role within business intelligence is transformation and storytelling. The role of data within organizations is changing, and so the role of data within business intelligence changes alongside it. There is a great conversation being started around using data to tell a story instead of using it to argue a singular, specific point. This allows for data to exist as a larger cloud of influence and bolster full strategies and initiatives instead of singular decisions. Data transformation also creates the opportunity to involve more stakeholders to understand data-driven decisions. Tell a story with your data and stakeholders will listen. Making the case for data literacy and for having a conversation about data, rather than arguing a single point, has allowed for a more modular role of data storytelling in business intelligence.

Data Cleansing

Often times called data cleaning, data cleansing is a simple premise but still an important aspect of the role of data analytics being able to inform business intelligence. Data scrubbing is often-times confused with data cleansing, but the two are very different. Data scrubbing is an error correction technique that uses background tasks to check sets of data for errors. Data cleansing is the process of finding and eradicating corrupt or inaccurate records from a record set, data base, or table. It refers to finding incomplete, wrong, or irrelevant cases within data and then correcting or replacing them.

So How Does Data Cleansing Impact Business Intelligence?

The most clear and direct answer is: business intelligence cannot function properly with skewed or incorrect data. It will make predictions based on distorted facts and therefore be ineffective in informing an organization’s decisions. Having a data analytics strategy that involves data cleansing ensures that you will remove major errors and inconsistencies from your datasets and allow your business intelligence to accurately inform decisions.

Major Impacts

Business intelligence exists to inform decisions and help business leaders make data-driven and strategically-minded decisions for their organization, but business intelligence cannot exist without a robust and well-modeled plan to integrate data analytics.

At the end of the day, there are countless ways in which data analytics can bolster business intelligence. Each organization is going to have different needs, data sets, initiatives, goals, strategies, tactics, and needs, but business intelligence (and data analytics) can always help an organization or business act in a more data-driven and strategic way.

Is your organization using data analytics in the most effective way?

Get Started

There are a few simple things to keep in mind when you’re making the jump to integrate data analytics into your business intelligence. Below are a few key considerations:

Platform Compatibility

When getting started with using data analytics to bolster business intelligence, it’s important to think long-term. Define the business intelligence goals you would like to achieve with the help of data analytics. How possible is it that the data may need to be manipulated in new ways in the future? Choosing a data analytics platform that integrates with your pre-existing business intelligence platform is important. It’s also important to make sure that the data analytics platform you choose will scale as you grow.

User Literacy

Whomever is tasked with integrating data analytics into your business intelligence should understand the scope of the project and can make thoughtful choices to set your organization up for success. Having one person do everything may ensure proper set up, but won’t ensure that all users interacting with the system will have full, unencumbered literacy and understanding of the relationship between data analytics. Having someone able to explain the relationship in lay terms early on can help all users and stakeholders make actionable use of the data and business intelligence.

Key Performance Indicators

Set up the right key performance indicators from the outset to define and more accurately measure success. KPIs can also uncover areas that need improvement and track real ROI.