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How Business Intelligence is different from Data Science

Data is omnipresent. It exists to be consolidated and yearns to be understood. Data capture the history of a business, and they hold the capability to answer the what, how, why, and therefore of operations. 

While discussing data, it is important to define the two commonly interchanged terms in this field: Business Intelligence (BI) and Data Science. Businesses from e-commerce to financial services employ BI and Data Science to gather data that can explain past performance and predict the path forward. 

BI and Data Science

BI and Data Science are the full stack of data analysis. Let’s explain how:

Business Intelligence refers to the conversion of raw data into actionable information. Data Science concerns obtaining information from raw data to forecast future performance and strategize business-critical operations accordingly. 

So, how do BI and Data Science contribute to a business’ success? 

Let’s break this down with the help of an example. Consider an e-commerce business that has been selling men’s clothing for 10 years. Their offerings range from formal shirts to casual jeans, anything that comes under the broad category of men’s apparel.

The business is at its 10-year mark, and it is looking to rapidly increase sales. How would they go about it?

Understand the present

Firstly, they need to understand the business’s performance to date. What’s the best way to do that? Take the help and expertise of BI specialists to capture all sales and website data for the past 10 years. This process includes collecting, integrating, analyzing, and presenting the available data. The BI team is responsible for the business’ data management, dashboards, data arrangement, and information display. 

Data Science

Performance metrics such as onsite activity (clicks, time spent, bounce rate), e-commerce activity (categories and products visited, searches), etc. are captured and stored in the form of charts and summaries. 

Data converted into information sources such as charts measure performance and quantify the business’s progress. The BI team performs quantitative analysis with the assistance of predictive analytics and modeling. 

Once the data can be visualized, it is stored in data warehouses. The knowledge that such data offers can be used to develop effective strategies to gain business insights. The data can also warn employees about operational red-flags and suggest improvements.

Strategizing the future

Now that the data is available to be analyzed and understood, here’s where data scientists come in. While the work of data scientists can overlap with that of BI teams, the former functions along the lines of the future. The job of a data scientist is to understand the data at hand, locate opportunities for improvement, and back them with a combination of a logical understanding of trends, and the data at hand. 

Examples of business strategies this men’s clothing e-commerce retailer could use include changing product pricing, improving site design to reduce bounce rates and last stage of sale drop-offs, the introduction of new products and cancellation of underperforming ones, etc. 

Data scientists recommend such solutions, backed by the data resource accumulated and organized by Business Intelligence specialists.

Important differences between BI and Data Science

BI involves answering questions that may not seem straightforward at first glance. It answers the “what” of a business’ activity. Data science relies on predictive analytics and a creative dissection of the data that’s available. It answers the “how” and “why” of the data’s findings. 

Career 

To work in Business Intelligence, you could survive with a basic qualification in a science-related degree. Companies tend to be flexible with BI applicants as their main objective would be to understand the data collected and support business decisions. 

On the other hand, working in data science is a little more complicated. Companies opt for aspirants that have a background in Data Science. They might also require a thorough understanding of topics such as statistics, machine learning, and programming, to decipher the collected data to create future predictions. 

Tools

BI teams use tools such as Microsoft Excel, SAS BI, Power BI, Sisense, and Microstrategy to organize and consolidate data. Data scientists use tools such as Python, R, Hadoop/Spark, SAS, and TensorFlow to study past data, discover trends, spot patterns, and predict future business behavior. 

A combination of the two equips businesses with reports that provide powerful insights into the present and help draw the plans for the future.

Conclusion

Growing and established businesses collect a lot of data, and this data can provide insights for improving growth and staying on top of their game. There is no debate that business intelligence and data science are crucial to this process. Business intelligence explains what has happened, and Data Science answers why those events took place. Business intelligence can handle static and highly-structured data, while data science can deal with high-speed, high-volume, complex data.

7 Ways Data Has Transformed Business

October 23, 2019 | All About Data | No Comments

Data in Business

Today’s businesses aren’t new to data. For decades we’ve seen them keep track of their expenses, sales, customer base, etc. But, only until recently has data moved from being a source of bare information to a haven of actionable insights.

What is Data

Credit for popularizing the usage of data and the coinage of the term “Big Data” arguable goes to McKinsey Global Institute’s May 2011 report. The report cites Big Data as “the next frontier for innovation, competition, and productivity. “

Businesses today understand data, and they’re quickly exploring creative ways to make the most of what’s at hand. Data has transformed businesses to the extent where ignoring its importance is a regretful strategy.

Let’s take a look at how various businesses are benefitting from the power of data:

Retail

The first evident benefactors of data are the retail industry, online retail in particular. E-commerce sites harness their data’s capabilities to understand customers better and employ a strategy that improves their retail experience, thus increasing their odds of spending more and increasing profits.

For example, retailers can keep track of their product shelf, and differentiate the successful products from their loss-making counterparts. With this knowledge, retailers can plan to replace unsuccessful products with new additions, and zero-in on the types of products that are making the business the most money.

Financial services

Financial institutions can use data for use cases beyond stock market analysis and large ticket trading. Banks are using big data to create credit scores that reflect the card holder’s behavior in the most accurate fashion possible. Fraudulent transactions can be identified by understanding the data-backed trends of similar earlier transactions. Employing data in their operations allows financial services firms to make the business of money efficient and safer than ever.

Education

Educational institutions are using data to identify areas of learning difficulty, research better learning strategies, and adjusting syllabi based on what’s trending in the industry.

Students can be understood in a way that objectively provides a road-map to their success in academia. Courses can be planned by online education aggregators using data on each course’s adoption, and they can zero-in on the successful courses and eliminate or replace sub-par ones.

Healthcare

Hospitals and drug manufacturers are using big data to track patients’ symptoms, find new medicines, and avoid preventable deaths. Most recently, data enthusiasts have been using data to track the spread of pandemics such as the coronavirus. Drug manufacturers are also using data to discover new medicines by guiding scientists to potential organic raw materials and sources.

Agriculture

Data helps traditional industries such as agriculture too! Farmers can use data to monitor crop growth and predict the influence of factors such as weather, pesticides, and the market for selling their crops. Also, online forums exist wherein farmers across the globe can show source data on agricultural activities to improve information reach and insight-based decision making.

Sports

For decisions wherein referees could go wrong, data can stay right. Sports are using data to ensure gameplay stays fair, by understanding the trends and the movements behind fouls and violations. 

Public safety

Governments, more law-enforcement, have started using data to analyze trends in crime. It also has the potential to be used for identifying missing individuals and victims of criminal activities such as human trafficking, drug dealing, etc.

Conclusion

With growing use cases for data in business, there is no excuse for businesses not taking advantage of this information revolution. Gone are the days where business leaders had to rely on their gut feelings. By harnessing data’s capabilities, businesses can understand the past, evaluate the present, and hack the future a lot closer to their favor.