Before we look at them together and compare and contrast them, we will do well to look at these two terms and first define them. They both are essential terminologies in the field of data analytics. Even though these fields have many common threads running through them, they are clear boundaries when studying data science vs business intelligence.
When used in business, as the name suggests, data science relies primarily on data. We use multiple interdisciplinary science streams on a typically large volume of data to gain inferences and insights.
In contrast to this, business intelligence (BI) assists in understanding a business’s current health by taking into account the historical performance of an organization. Thus, to summarize, when we talk of data science vs business intelligence, the former deals with past data analysis to give future projections, whereas the latter uses past data for present inferences. BI mainly encompasses what is known as Descriptive Analytics, whereas data science is employed frequently in Prescriptive Analysis.
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Data Science, Business Intelligence and Their Similarities
Before we go into nitpicking the differences between them, we will do well to understand the similar threads that tie data science and business intelligence. Both of them rely on data, and the outputs that we seek from them are broadly similar in scope. We want them both to help us analyze market opportunities, profit margins, increased revenues, and customer retention, to name a few.
In both of these fields, there is a need to interpret data, for which we need to employ professionals who can analyze a data set and give us insights to secure competitive advantages. Managers and decision-makers rely on them to get accurate analysis so that they can decide based on them at critical junctures. They may not be aware of knowing all the nitty-gritty of these fields.
Thus, we have established that managers and other employees can use both business intelligence and data science at points where a decision needs to be driven by data. But let us reiterate the difference between them once again. BI handles data that generally comes from a single source, is static and is very structured.
On the other hand, data science can take care of data from multiple sources, has various structures and is highly complex. Thus, BI can only work with data that we configure in an acceptable format. Data science technologies do not need such boundaries to be put on the data, and we can gather free-form data from a variety of sources.
In fact, data science was from rudimentary business intelligence. Earlier data analysts used to work on and analyze data only to describe past performances. Businesses realized at that time that the past can predict the future and asked them to prescribe the steps they would need to take to replicate past successes and eliminate mistakes. This is how data science came to be. Data scientists could now find patterns and trends and predict future behavior for increased competitiveness.
Data Science, Business Intelligence and Their Differences
There was a time when data was limited and conventional business intelligence techniques sufficed. However, recent years have seen the advent of Big Data. There are multiple forms of data now coming in from various sources. Therefore, businesses must now rely on data scientists to make sense of it all.
Looking towards the future, it is expected that data science will overwhelm traditional business intelligence models. Data science’s main contribution will be the automatization of intelligence. Instead of human input in business intelligence, algorithms and programs can do most of the work. Where business personnel will come is only at the decision-making stage.
At this point, they should have access to all the processed and analyzed data from a central source, which is automated with the help of tools to help them draw inferences. With this change, data has finally moved into the mainstream of core business operations. Business intelligence earlier used to be almost an exclusive domain of IT professionals. However, data science has made it more accessible to all personnel involved in the business processes.
In the future, it is expected that data scientists will come in to automate the intelligence and take a step back after that and provide assistance only when required. Data scientists and business intelligence professionals can still work together, where the latter provide the insights of the existing data set for the data scientist to build on the future.
But business intelligence cannot do it anymore all on their own. Data has become too complex and multi-layered for it. Business intelligence can only take data and react to old data in the present. Data science has stepped into that breach and proactively suggests solutions to claim increased competence in the future.
Data science itself has progressed massively from when it first began. Technologies have become capable of handling more complicated data in many different formats. Some of the new technologies concern data governance, customer reporting and analyzing in a drilled-down format. The era of static reporting has long passed. Now is the time for instantaneous decision-making based on the best inferences possible from the available data.
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The Contrast of Data Science with Business Intelligence
The biggest distinction that we can draw between data science and business intelligence, even in advanced states, is the size and range of machine learning libraries. Machine learning libraries allow a layperson in the business world to take charge of data that has been automated, either partly or fully, and draw insights from there.
In a way, data science is making the entire field of data analysis less elitist. In the future, we can expect those people with basic qualifications to understand the data to employ business intelligence and engage in analytics at an advanced level. They need not be from the information technology sector particularly.
Data science gives this added advantage that business personnel need not concern themselves anymore on data’s technological operations. They can move over and concentrate on the side of operations, bringing in the profits and focusing on outcomes to increase competitiveness and profitability.
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In currently existing BI platforms, organizations cannot work on the data on their own. They need an expert team of business intelligence professionals who take data and identify the patterns and trends. With data science now getting powered by machine learning, the need for such technical expertise is gradually dwindling. Business stakeholders can extract the necessary information from the data and analyze and draw their inferences, which help them make the best possible decisions.
The four main areas where data science diverges from business intelligence are the size of data, the variety of data, the prescriptive capacities and visualization platforms. It is when we compartmentalize the variances within these areas that the differences become glaring. Even in advanced business intelligence, data discovery tools limit the variety and volume of data that they can process. Data science breaks all these boundaries and can deal with any kind of data and prepare an analysis from there.
Top Data Science Skills to Learn
| SL. No | Top Data Science Skills to Learn | |
| 1 | Data Analysis Programs | Inferential Statistics Programs |
| 2 | Hypothesis Testing Programs | Logistic Regression Programs |
| 3 | Linear Regression Programs | Linear Algebra for Analysis Programs |
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The Complementary Nature of Data Science with Business Intelligence
While we have drawn some contrasts above, we will do well to remember again that because both data science and business intelligence rely on data analysis, there are many complementary parts therein. There are processes and functions like visualizations and algorithms common across both fields, and the inferences from both are likely to affect business potential.
When BI experts and data scientists work together, they can achieve a synergetic output. The analysts working on business intelligence are better at structured data, and therefore, they can help prepare the data for speedy analysis. The data scientists can use those as input for their own models.
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The professionals who have worked with business intelligence for so long can also offer their current purview of analytics, which gives the business’s current status. Using this descriptive analysis, the data scientists can predict the future and provide more accurate projections by making their algorithmic models even more powerful.
Ultimately in the analytics division or team of any business, both will find a place. The BI expert will be in charge of reporting the technical activities. In contrast, the data scientist will be responsible for automating them and providing future solutions directly to the business stakeholders.
With the help of the business intelligence analyst who can tell the data scientist exactly what parameters are required for current analysis of business affairs, the analysis team can build a model that can help business personnel take decisions without going into the detail of the technological operations.
In conclusion, even the most technologically savvy organizations are struggling to keep pace with the evolution and change of technology. They are also struggling to deal with the amount of data coming in. To structure all these technologies into a coherent platform, business intelligence is required. To rein in the data to an extent where the managers and decision-makers can work on them without hitches requires a data scientist.
Thus, what we need in the future are more integrated systems where technology, data and people can work together. Therefore, the need of the hour is to construct strong data analytics teams in every organization. This will help streamline business decision-making, rendering the whole process faster and give such companies a competitive edge in the market.
If you are curious about learning data science to be in the front of fast-paced technological advancements, check out upGrad & IIIT-B’s Executive PG Programme in Data Science.
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