November 1, 2019 | All About Data | No Comments
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 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.
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.
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.
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.
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.