Data Science and Business Intelligence | differences?

/, DS knowledge/Data Science and Business Intelligence | differences?

Data Science and Business Intelligence | differences?

Data science and business intelligence are both techniques to help grow and increase the profitability of your business, with the difference that in data science the focus is on predicting the future based on artificial intelligence techniques and business intelligence on analysis and Conclusions from present and past data are focused without being able to make accurate calculations for the future.

In fact, business intelligence and data analysts can be put on the same level, and of course, considering that a business intelligence specialist feels the need to have more knowledge in the field of economics and business.

Business intelligence is a more general field and more specialized data science, and in a way, it can be said that business intelligence or data analyst is a subset of it. In data science, the use of artificial intelligence methods, especially machine learning, leads to computational predictions based on statistical models for the future of business.

In business intelligence, these predictions based on data are made only through the knowledge of the individual, which may be associated with errors, and in data science, these errors are much less because they use mathematical models such as regression that have been proven in science. To be.

Differences in working on data

Differences in working on data

Data science experts can work on structured and unstructured data, but business intelligence experts perform analysis on structured data.

The data integration process of Extract, Transform, Load (ETL) works well for business intelligence. The data is converted before loading into the data warehouse. This means that the data warehousing scheme is known, which makes it easy for business intelligence users to use analytics tools.

Another option is to load the data into the data warehouse before conversion, ie the steps are extraction, loading, conversion (ELT). Using this integration method, data can be converted to the required structure at the time of query or so-called query.

Because queries can be designed to meet the needs of specific analytics without being locked into a specific design, ELT is suitable for data science applications.

How do data science and business intelligence work together?

How do data science and business intelligence work together?

Although organizations can gain meaningful insights from data science or business intelligence, using the two together provides the greatest insight into strategic decisions.

Consider a situation in which a professional service company is trying to obtain offers. They have limited resources to respond to RFPs’ Request For Proposal designs, so they decide on a data-driven process to decide which RFPs will win the most.

The company decides to use business intelligence to review past RFP results and create an index of high-yield customers and projects. Then, using this insight, the company can create different hypotheses and scenarios and use data science with machine learning to predict the likelihood of winning future projects.

So, using business intelligence and data science together, the company now has the profile of customers and projects that are at their sweet spot to win the work. It is easy to see how BI and data science help to create the right insight, but the combination of the two is what works best.


Conclusion Data science and business intelligence

The two roles may seem very similar or even very different at first, however, it is important to break down the inside and outside of each situation and what is expected in the day-to-day work or projects related to each role.

Objectives may be most similar in that data, insights, and results are shared and discussed with stakeholders. Methods like SQL are more different from Python / R-focused skills, in addition to the fact that data science focuses heavily on machine learning in all areas.

In summary, here are some key expectations for each role:

* Data Science: Data acquisition, Python, as well as machine learning algorithms and scientific prediction of future events

* Business Intelligence: Excel or Google Sheets SQL, data analysis and forecasting

Certainly, the presence of a business expert and data scientist together in a company will bring the best economic efficiency and decision making, which results in calculated and rapid growth, because having and being ready to analyze past data by business intelligence and Predicting the future using artificial intelligence by data science is the best and fastest choice for any business.


By |2021-09-06T15:07:25+04:306th September, 2021|Categories: Business, DS knowledge|0 Comments

About the Author:

An interested and active person in the field of data science and molecular dynamics simulation

Leave A Comment

Hi, welcome to Simulatoran
Send via WhatsApp