BI and Data Science are distinctively different aspects. To
survive and prosper in the increasingly competitive market as well as to be
able to resolve complex business problems, drive innovation and growth,
companies must shift their focus from traditional BI to data science.
Business Intelligence offers a useful approach
that describes what happened in the past, enables data to be understood in
business roles not specialized in analytics using powerful visualizations and
serves to make decisions based on global trends.
Business analysts use data visualization
techniques to explore data stored in structured databases. With this type of
tool they create visual panels (or dashboards) to make information accessible
to non-data specialists. The panels help to analyze and understand past
performance and is used to adapt future strategy to improve KPIs (Key Business
Indicators).
Machine Learning, on the other hand, is a
technique that can detect patterns «at a low level» in thousands of individual
data. The development of predictive applications is one of the most important
strengths, as they facilitate process automation, decision making and
continuous learning based on data. In addition, they are systems that learn
automatically over time, integrate into company developments and adapt to
changing environments when constantly fed with new data.
Visualization panels or dashboards are replaced
by Business intelligence helps you understand the past and better manage
the present. Data science helps you to increase the profitability and
probability of success in future.
Data Science changes the game for virtually all
industries. When used in conjunction with predictive analytics, it allows
organizations to achieve real-time insights and make future predictions
that increase understanding of customer behavior, improve response to customers
and deliver a tangible competitive advantage.
|
Traditional
BI |
Machine
learning |
|
Designed to look backwards based on real data from real
events. |
Looks forward, interpreting the information to predict what
might happen in the future. |
|
Delivers detailed reports, KPIs and trends |
Give insight about future in the form of patterns and
experimentation. |
|
BI
Analysts Focus More on the ‘What’ than the ‘Why’ or ‘How’ |
Have
a toolkit of algorithms that they use to understand and predict a business’s
performance. (how and why). |
|
Use
aggregated data |
Uses numerical data, categorical data, time series data and
text data. |
|
Tend to be static and approximate. |
Offer room for exploration and experimentation |
|
Helps you answer the questions you know |
Helps you to discover new questions because of the way it
encourages companies to apply insights to new data. |
|
Measurement
of data quality is limited |
Various
technique can be used to measure the data quality |
|
Owned and operated by the IT department |
Owned
by business team |
