Data Science and Business Intelligence. (Difference, similarities, and interrelationship).
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Data is the new gold. As key to the information revolution, the importance of data and the potential it unleashes has been well acknowledged and emphasized. Companies and institutions, realizing the importance of data, have been collecting and storing data even before they knew what to do with it. However, as the volume of data has tremendously increased in recent years, organisations have sought to extract insights from these data that can help identify and explain past and present trends, and predictions future outcomes. This need has given rise to two related and complementary fields: data science and business intelligence.
Meaning of Business Intelligence
Business intelligence (BI) is the performing of descriptive analysis of data to inform business decisions. It uses technologies and tools to collect, organise, and transform data with the aim of driving the actions of a business. BI technologies and practices are used to support business operations such as :
- Identifying trends in market behaviour and business performance.
- Discovering ways to improve business processes and identify revenue generation opportunities.
- Comparing performance with competitors to identify strengths and weaknesses of the business.
Meaning of Data Science
Data science is the process of using advanced mathematical and statistical techniques to extract valuable information from datasets and create forecast. It employs sophisticated statistical and computer science tools such as machine learning, artificial intelligence, and descriptive analytics to collect, model, summarize and analyse data. By analysing the data, the patterns behind the raw data can be discovered and used to predict future trends.
Data science is applied in a broad range of industries ranging from the health sector to the automobile sector where they are applied in self-driving cars. It also offers a broad range of carrer opportunities [link to Fatimo’s post] across industries. In business, data science can be used to forecast sales, predict market trends, and discover consumer spending patterns.
Differences between data science and business intelligence
Though both data science and business intelligence turns data into valuable information, there are some subtle difference between them. These differences can be grouped into the following categories:
Data science uses predictive analysis of historical data to forecast future events and conditions such as business trends and consumer behaviour. It is more concerned with getting insights on the future outcomes of specific business issues.
Business Intelligence, on the other hand, is more concerned with what is happening or have happened in the past. It uses descriptive analysis to present historical data in a way that is easy to visualise and understand. It is focused on generating reports that clearly communicates the present and past state of the business.
Data science aims at predicting specific events and finding out the validity of specific hypothesis. Hence the result of a particular predictive analysis is limited to the specific hypothesis in question.
On the other hand, business intelligence is general in scope. The results of descriptive analysis must be in a form that any business unit can use it to generate whatsoever report they need. Business intelligence can provide insight on a wide variety of issues and often serve as the foundation for more in-depth analysis.
Data scientists are required to have a variety of technical knowledge such as coding, data mining, machine learning, and other advanced statistical knowledge. They are also required to have a good degree of understanding of the particular domain of the problem they are handling.
Business analyst require mainly basic statistical skills in addition to data transformation and visualisation skills. They however require a deeper and wider knowledge of the business to effectively serve each unit.
Data Collection and Integration.
Data science applies the process of extract/load/transform (ELT) in handling data. In this process, the data is first extracted from one or multiple sources then loaded into the data warehouse. The data is then transformed (sorted and normalized) in the warehouse in line with the needs of the specific analysis being done at a particular time. Thus offers flexibility as the query can be tailored to meet the needs of the specific analysis.
Business intelligence, on the other hand, employs the process of extract/transform/load (ETL). In this process, the data is transformed before it is loaded into the data warehouse. Thus the warehouse schema is clearly defined making it easy for users to carry out analysis and generate reports.
Similarities between data science and business intelligence.
Despite the differences between data science and business intelligence, there are still major similarities between them. The most glaring similarity between them is that they both use data to provide meaningful insight for an organisation. As such the quality of output they produce is largely dependent on the quality and accuracy of data they receive.
Another important similarity between them is that they both require collaboration with other parts of the business to fully function. They require input from the operations of other business units and their output is determined by the needs of the business. Also data scientists and business analysts often collaborate to produce a more complete and useful insight on the organisation’s past, present, and future operations.
Interrelationship between data science and business intelligence
Though either data science and business intelligence can provide meaningful insight for an organisation, combining both of them will provide the full insight of the past, present, and future state of the organisation. With business intelligence, the organisation can discover trends in its past outputs and profitability as well as in market and consumer behaviour. While with data science the organisation can predict the impact of various decisions and market conditions on the output and profitability of the business. Thus the insight from the two fields, when combined together, can be used to accurately guide business decisions to achieve a specific goal.
Getting started with data science and business intelligence
It is clear at this point that both data science and business intelligence, either apart or in combination, are essential in providing data-driven insights that are needed to accurately guide the decision making process. Also, with the recent advances in such technologies as cloud computing, machine learning, and artificial intelligence, the ability of data science to transform business outcomes has greatly increased. Consequently, the demand for qualified data science and business intelligence professionals is steadily rising.
The key to an impactful career in data science goes beyond simple knowledge of statistical or data manipulation procedures. Success in data science or business intelligence requires a deep understanding of how to glean out insights from a business and how such insights can be structured in a way that accurately guides decision making.
At Tongston’s introduction to data science course. we provide the basic knowledge in statistics, data cleaning and manipulation, and programming needed to carry out basic data science and business intelligence processes. Also, our curriculum involves practice with real-world scenarios and entrepreneurial classes that offer first-hand insight on how data science can transform business outcomes. Enroll in the introduction to data science course today.