Data Science is a fairly young discipline, having only really developed in the last few years - enabled by the technological advances that have helped firms to deal with the massive amounts of data available to them. Business Intelligence (BI), on the other hand, has been popular for several decades now. But are they mutually exclusive, and do they have to compete with one another?
There are a number of points at which BI and Data Science intersect, yet there are also many differences between the two. Both are a form of analytics and both use historical and present data. However, BI is retrospective while Data Science is predictive. BI uses purely internal data sources generated from inside the business - such as customer service data, sales data, operational data employee performance data - to gain insights about what has happened and gain a greater understanding of the business. It is a static and comparative process. Data Science, on the other hand, is exploratory, experimental, and visual. It uses both internal and external sources, taking the same data that BI looks at, but also incorporating data from things such as social, machine data, audio data, and video data.
They use different technologies. BI uses OLAP, ETL and data warehousing, while Data Science uses cloud platforms, machine learning, and Python, among others. The two also have different focuses. BI looks at reports, KPIs, and trends, whereas Data Science looks for patterns, correlations, and models. There is subsequently a big difference in the expertise required for each discipline. For BI, practitioners must be skilled in Information Technology and Business Technology. Data Scientists, meanwhile, need to be more experienced in maths, coding and statistics.
They also differ in respect to their outputs. BI manifests itself for interrogation in the form of reports, data tables, and dashboards. Data scientists produce interactive analytics application programs, and data visualizations. Data Science takes a number of complex ideas, including Big Data and unstructured data, and arrange it in such a way so that non-technical audiences can understand it. It is often the case that BI information is not presented in such a way as for the layman to understand, but equally it is not always the case that BI must be presented in tables, and it can also be put in more understandable formats.
In terms of the quality of the data, BI produces a single version of truth, whereas with Data Science, the data needs to just be good enough. There is less precision required with Data Science because it is quantitive. However, organizations today are looking for more dynamic predictive analysis and the ability to iteratively perform data discovery. The simple matter of it is that BI is good in so far as it can tell you what happened, but Data Science can tell you what will likely happen in the future, fulfilling the brief for most companies. Ultimately, whether you need BI or Data Science comes down to the requirements of your organization. Ideally, a company would use both in conjunction with one another, but some companies simply don’t need expensive advanced analytics. It is not the case that going with BI is simply settling for second best. BI tools are constantly expanding to include greater access to more forms of data in intuitive, interactive ways that favor non-technical users, and there is still much you can learn about future events by looking back at the past.