10 Differences Between Business Intelligence (BI) and Data Science?
There was a time when Business Intelligence (BI) was only used by big companies as per the gurus over at runrex.com, mainly because employing analytics software was an expensive venture requiring the building of data centers and hiring of IT specialists. Over time though, BI systems have become less expensive which has made them a useful way of gathering corporate data and correlating said data in a way that produces useful observations to the business. It is also worth pointing out that times have also changed and data is now getting bigger every day, both in terms of volume and variety. Businesses, therefore, need to utilize data science if they are to capitalize on market opportunities faster than their competitors. This has put BI and data science at loggerheads, with these two being distinctively different from each other. This article will look to highlight 10 key differences between Business Intelligence and data science.
- Difference in concept
The first difference we are going to highlight between the two is going to touch on the concept of each. As is outlined over at guttulus.com, data science is a field that uses statistics, mathematics, machine learning, and other tools to discover hidden patterns in data sets. On the other hand, Business Intelligence is a set of technologies, applications, and processes used by enterprises for purposes of business data analysis.
- Difference in perspective
When it comes to perspective, while Business Intelligence systems are designed to look backward based on real data from real events as explained over at runrex.com, data science looks forward, interpreting the information at hand to predict what might happen in the future.
- Difference in process
The two also differ when it comes to matters process according to the subject matter experts over at guttulus.com. This is because traditional Business Intelligence systems tend to be static and comparative. This means that they don’t offer room for exploration and experimentation in terms of how the data is collected and managed, which is not the case as far as data science is concerned.
- Difference in focus
There is also a major difference in focus as far as the two are concerned since while Business Intelligence delivers detailed reports, KPIs, and trends, it doesn’t tell you what this data may look like in the future by revealing patterns and experimentation. Data science on the other and does reveal patterns, helping you predict what the data may look like in the future.
- Difference in data sources
As is revealed in discussions on the same over at runrex.com, Business Intelligence is static in nature which means that its data sources tend to be pre-planned and added slowly. On the other hand, data science is a lot more flexible which, therefore, means that its data sources can be added on the go and as needed.
- Difference in storage
The subject matter experts over at guttulus.com also point out a difference between the two based on how data is stored, while also pointing out that data, just like any business asset, need to be flexible. Here, while Business Intelligence systems tend to be siloed and warehoused, making it difficult to deploy across the business, data science can be distributed in real-time.
- Difference in how the data delivers a difference
The differences don’t stop at how the data is stored as there is also a notable difference in how the data delivers a difference to the business. Here, while Business Intelligence helps you answer the questions you know, data science helps you to discover new questions, helping you answer “what if” types of questions. This is because of the way data science encourages organizations and companies to apply insights to new data.
- Difference in findings
There is also a major difference in results, because, as captured in discussions on the same over at runrex.com, any data analysis is only as good as the quality of the data captured. This is why while Business Intelligence provides a single version of the truth, data science offers precision, a higher confidence level, and much wider probabilities with its findings.
- Difference in ownership
In the past, as covered over at guttulus.com, Business Intelligence Systems were often owned and operated by the IT department where they would pass on intelligence to analysts who interpreted it. However, with data science, the analysts are the ones who are in charge as new Big Data solutions are designed to be owned by analysts, who spend most of their time analyzing data and making predictions upon which to base business decisions, and little time on IT housekeeping.
- Difference in business value
Of all the above differences between the two, the biggest one is on business value. This is important since the analysis of data should inform business decisions in the best interest of the company, which means demonstrating value in the present and predicting it in the future. In this regard, data science is much better placed to do this as compared to Business Intelligence, which means that it provides more business value.
As always, if you are looking for more information on this and other related topics, then look no further than the highly-rated runrex.com and guttulus.com.