Last updated on March 4th, 2023 at 01:43 am
In today’s data-driven world, Data Science was only possible because it happened when it did. When one examines the evolution of data analysis over time, it becomes clear that traditional descriptive Business Intelligence would still be primarily a static performance report within the current business operations.
The increasing complexity and volume of data and the growth of data input technologies meant Data Science was born to offer some solutions to the large data volumes that are affecting many modern businesses. Many people who deal with these technologies are interested in the evolution of Data Science and Business Intelligence.
The shift towards Data Science and BI is happening in 2022, 2023, and beyond. Both Data Science and BI are essential roles in business because they both help to analyze and forecast past and future events.
- Definition of the Terms: Data Science and Business Intelligence (BI).
- The Main Similarities between Data Science and Business Intelligence
- What are the Major Differences between Data Science and Business Intelligence?
- What is the difference between Data Scientists and BI Analysts?
- Data Science and BI Together: What's in store for the Future
Definition of the Terms: Data Science and Business Intelligence (BI).
First, it is essential to understand the terms. Data Science is used in business. It is intrinsically data-driven. This means that many sciences from different disciplines are combined to extract meanings and insights out of the available business data.
Business Intelligence, or BI, helps to monitor the state of business data and understand past performance. In a nutshell, BI interprets past data. Data Science analyzes past data (trends and patterns) to make predictions.
BI can be used primarily for reporting or descriptive analysis; Data Science, however, is used more for predictive analytics or prescriptive Analytics.
The Main Similarities between Data Science and Business Intelligence
Data Science and BI allow business users to make smart data decisions. Data Science and BI both focus on “data” with the goal of providing favorable outcomes. In business, this could be profit margins or customer retention.
These two fields have the ability to “interpret data” and often engage technical experts who transform data-enriched results into friendly insight or competitive intelligence. Managers and senior executives are not likely to have the time or desire to learn about the technicalities behind data analytics. However, they will need to be able to quickly access the right information to make the most critical decisions in a short period of time.
BI is often used to answer questions such as why one quarter’s sales were lower than the other three, what product brings in the highest ROI, and who my top customers are. BI is about identifying trends and patterns to develop actionable insights.
Data Science queries deal with the future (unknown). For example, how likely is an employee of losing his job? Which product will be the most popular next quarter? Or how much revenue growth will occur next year? This analysis’s predictive and prescriptive nature allows for testing hypotheses through statistical models.
Both BI and Data Science provide reliable decision-support systems for busy executives, managers, and front-line workers who are experts in their fields. They can expect to receive reliable support and help from data experts to make data-driven decisions.
The main difference between Data Science and Business Intelligence lies in the fact that BI is meant to handle static and highly structured data. Data Science, however, can handle complex, multi-structured, high-speed data from a variety of sources.
While BI cannot understand data that has been “preformatted” in specific formats, advanced Data Science technologies such as big data and IoT can collect, clean, and prepare free-form data from a wide range of operational touch points.
What are the Major Differences between Data Science and Business Intelligence?
Business Intelligence (BI) and Data Science (DS) have some similarities, as both revolve around the use of data. However, there are also key differences between the two disciplines.
Business intelligence is focused on helping businesses to make better decisions and gain insight into their activities. It does this by leveraging structured data from internal sources to generate reports and visualizations for understanding performance over time.
BI focuses on analyzing past data to understand current trends in the business, as well as presenting information in a clear way that allows users to easily spot opportunities or challenges they need to address. This can be done through dashboards and other interactive visualization tools, which give businesses powerful ways to monitor how they are performing.
In contrast, Data Science involves taking complex algorithms and predictive models and using them to make predictions about the future based on data sets. It is much more complex than Business Intelligence, as it requires a much deeper level of analysis and a wide range of tools for advanced statistical analysis, data processing and big data management.
Data scientists use unstructured or big data sets to create insights that can help businesses make informed decisions about their future growth. They use sophisticated machine learning techniques such as neural networks and other predictive analytics tools to identify patterns in the data that can give them greater insight into potential opportunities or risks they may face in future markets.
Whereas Business Intelligence helps businesses create reports based on internal structured data, Data Science helps them generate insights out if unstructured or big data. These insights are generated by using complex predictive analytics models and Machine Learning algorithms which allow them identify patterns in the data that may not be immediately obvious or visible at first glance.
The resulting insights can then be used by companies to inform their strategy moving forward and evaluate potential risk versus reward scenarios before making any major decisions. Ultimately, both Business Intelligence and Data Science play important roles in helping businesses leverage the power of their own data for greater success.
What is the difference between Data Scientists and BI Analysts?
Both fields are aimed at helping to derive business insight from the data available. Data Science Enhances Business Intelligence. Data Science projects often require collaboration between various experts such as business experts, statisticians, data engineers, and software developers.
Although data scientists might have a deep understanding of statistics, they don’t understand the business or software-development side of things. This is where BI experts come in. They can help Data Scientists forecast the future using their historical wisdom (analyzing past records).
BI experts work with fixed assumptions and simple data points to create “metrics” that can then be shared with others in the data team for predicting the future of their business. Data scientists and BI professionals share a passion for data analysis. Both algorithms are used to varying degrees. Now both can use advanced visualization tools for the nuggets that will make or break a business.
Data Science is different from traditional BI in three key areas: the volume and variety of data, predictive capabilities and visualization platforms. This article, Business Intelligence & Data Science – Same, But Different, offers a fascinating contrast between these two analytics methods. Advanced BI systems have “data discovery tools”, but they are often limited in the quality and quantity that they can process.
Data Science breaks down the glass ceiling on “data” and allows any type of structured, semi-structured or unstructured data to been collected, cleaned, and prepared for analysis.
While BI teams provide decision support to executives and managers, Data Science allows them to become analytics experts. A BI team should oversee Operational Analytics. Data scientists should, if necessary, spend more time improving the existing Analytics and BI footprints and automating the system to make it easier for everyday business users.
In reality, BI experts can work with data scientists to prepare data that they can feed into their algorithmic models. BI specialists can share their knowledge and current understanding of the analytics needs of a company and help data scientists create powerful models to predict future trends and patterns.
The Enterprise Analytics team includes both the data scientist and the BI expert. BI collects historical data to understand past events, while DS generates data that models events that have yet to occur.
The BI expert and data scientist can work together to build a powerful in-house analytics platform that business users can use with minimal technical assistance.
Data Science and BI Together: What’s in store for the Future
Look at the global retail industry to see how traditional BI has evolved to Data Science in order to transform just-in-time insight into profitable business outcomes. The author of the article entitled Retail Minded: from Business Intelligence and Data Science observes that while most businesses are seeing the benefits, there are still some businesses struggling with Data Science.
This article provides some tips for creating successful Data Science frameworks that yield profitable results. The blog post How McKinsey’s 2016 Analytics Study Defines Machine Learning’s Future is a Look at McKinsey’s Conclusions about Machine Learning in Minimum 12 Industries.
McKinsey provides convincing data to show that Data Science with its rich data (big) and advanced analytics capabilities(machine learning) is superior to traditional BI. Traditional BI was not able to provide enough justifications to “predict” or “prescribe” future events.
Machine learning and data science have greatly benefited the enterprise IT team and allowed them to quickly and accurately predict existing data patterns. McKinsey states that for an enterprise analytics platform, it is necessary to have a solid support structure, good architecture, and senior management involvement.
McKinsey reports that over five years, businesses that invest in Analytics and BI infrastructures see a 19 percent increase in margins. Data Science vs. business intelligence: Final thoughts One of the greatest stumbling blocks for technologically capable enterprises is the rapid growth in allied technologies that can be used together to make business transformation possible.
Many enterprises today are unsure how to keep up with technological changes and how to incorporate newer and more powerful capabilities.
Advanced technologies like big data, IoT, and machine learning can all transform your business. But how many businesses know how to combine these solutions to create a powerful analytics platform.
There are two important things that you can take away from this article: Data Science is an equally important part of the same team. Although their individual roles are different, they all serve the larger business analytics world.
Although there are some differences between Data Science and BI in how they handle data, tools, data, and deliverables, the ultimate goal is the same – winning data. Technology, tools, processes, talented manpower, and processes – all of these must work together to reap the full benefits of data and analytics.
Dani Lehmer is the Founder and Head Honcho of Dani Digs In.
She is a Quality Assurance Analyst and blogger whose natural curiosity allows her
to dig in (pun intended) to help people build their businesses and satiate curiosity
in regard to data science, analysis, and crypto.
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