There is a very thin line that separates data analytics from business intelligence. In both the cases, we make use of data to analyze and interpret results. While data analytics is all about asking questions and setting up predictive models and forecasts, business intelligence is all about using these processes to make business decisions.
In essence, data analytics comes before you can perform business intelligence tasks. Bringing together data analytics with business intelligence is a crucial first step towards generating meaningful information from the terrabytes of business data.
Executing data analytics tasks for business intelligence is useful in a wide number of industries. For instance, data analytics could be used in the customer service industry to study the response time of support tickets with various degrees of priority, analyze the customer satisfaction metrics and then use this data for business intelligence like deciding on the number of agents dealing with each of these different categories of support.
High Frequency Trading (HFT), which involves analysis of millions of data points each second to make buy/sell calls, is another wonderful example of how data analytics and business intelligence can come together to make profitable business decisions.
How To Bring Together Data Analytics With Business Intelligence
By necessity, data analytics precedes business intelligence. Before you can make smart business decisions based on data, it is important to set up a data analytics system that can ask the right questions, monitor the right data and make the right inferences.
Business intelligence is merely the process of using these inferences to make business decisions. According to Eran Levy from business intelligence software firm Sisense, vendors routinely fail to place emphasis on the most crucial aspects of data analytics namely, data preparation. Unless you have a solution in place for cleansing, structuring and integrating information records before they are made ready for analysis, these under the hood activities can often take between 50 to 80 percent of a data scientists time.
The business intelligence part of the equation is mostly about using the right tools and models to derive meaningful information from the data being analyzed. However, the success of a BI initiative depends on the effectiveness of the data analytics work that precedes it.
A KBRG whitepaper notes that there are essentially six data rules that govern the successful implementation of a BI project. For BI to provide useful insights, you need to make sure you ask the right questions, work with well-scrubbed numbers, emphasize seamless inter-departmental communication, decentralize your big data, build your BI program out incrementally and keep asking more questions.
Picking The Right BI Tool
While the success of BI itself may be incumbent upon the six data rules, it is also equally important to pick a BI tool that seamlessly engages with your data analytics and produces the right output.
If you handle business intelligence in one of the more common industries like retail, banking, healthcare or transport, there are already a number of products that offer out-of-the-box solutions to your most common business requirements, like customer transaction data, information on profitability, streamlining and increasing operational efficiency. However, for more niche business requirements, custom business intelligence is the way to go and cloud-based ITES (Information Technology Enabled Services) providers usually come in handy here.
The solution here is thus basically a right mix of features requirements and a companys available budget.
