Now that the world is digitized, data is everywhere. Whether it’s the food we ate this morning or our shopping habits online, businesses have more information on consumers than ever before. But how can they manage all of this data effectively?
There are two fields that companies rely on to maintain and analyze all of these numbers and statistics. They are Business Intelligence (BI) and Data Science. While they are related to the same thing (interpreting numbers about consumers and industry), they operate in fundamentally different ways.
Today we’re going to break down the elements of both of these systems and compare how they can be utilized together to create a better business model for anyone.
Business Intelligence Overview
At its core, BI is all about understanding information as it is and as it was. Analysts will look at current and past data to draw conclusions about various elements of the business. For example, you may look at how consumers shopped for particular products around the same time of year for the past five years.
The goal of business intelligence is to provide a comprehensive picture of the marketplace for executives and business owners to better understand their customer base. This information is invaluable because it offers strategic insight based on hard facts, not bias or belief. With the numbers coming in from BI, any company can adjust its marketing strategy to match, allowing them to adapt.
Data Science Overview
While BI is all about analyzing what happened before, Data Science is the next logical step in this process. Once you have a complete picture of your consumers, where do you go from there? That’s the core question driving data scientists and how they work.
Overall, this practice is all about taking the numbers and analyses and formulating projections and models for what’s to come. Using our example from above, if you notice that shoppers are buying certain items around a particular season, you can anticipate their needs and deliver better products or services to meet them.
Comparing the Two
As you can imagine, BI and data science are technically two sides of the same coin. Both are necessary for a business to adapt to a changing marketplace, and both systems are highly valuable in their own way. You can’t do data science without hard numbers, which is why they are mutually beneficial.
To further help understand how both of these operations can work together (as well as how they differ), let’s break down some of the fundamental methods used for data gathering and intelligence.
Managing the Hard Numbers
For the most part, BI is an excellent way to take all of the data you have and present it in a consumable format. Things like graphs, dashboards, spreadsheets, and KPIs are all par for the course when using business intelligence.
However, there are some significant limitations. Usually, this data is transcribed and formatted in a particular way, which doesn’t offer a lot of room for changes and adaptations. Thus, if the method of data collection or presentation is flawed, you may not realize the issue until much later.
With data science, you have the flexibility to make adjustments as necessary so that you can adapt in real-time. Feature engineering may be necessary when analyzing metrics in Data Science. Feature engineering is the process of using domain knowledge to create new attributes that can be used to insert additional context to algorithms. Technology has evolved, and with it comes our ability to monitor, extract, and manage any information related to the marketplace.
Analysis vs. Prediction
When looking at BI graphs, you can get a clear picture of what’s going on right now in your business. Having quantifiable items can offer better insight into the potential of your company based on where you’ve been and what’s happened before.
Data science lives exclusively in the future. When you look at models and graphs with this system, they are all guesses and predictions, which can make them a little hard to master. Because you can’t see the future, you aren’t basing your knowledge off of real data, but simply what could be.
Data Discovery
One critical difference between these two strategies is that data science allows you to discover new elements of the marketplace and your business that may otherwise go unnoticed. BI is based on questions you already know, while data science can help you figure out different questions that you want to have answered. The inherent flexibility of the system allows for greater discovery and adaptation in the company.
Storage and Deployment
Traditionally, businesses that used BI had a very limited number of methods of distributing the data across the company. It was managed and analyzed by the IT sector, which stored it on proprietary servers and hard drives. To get access, you usually had to use legacy systems or wait for a presentation to get the information you needed.
Data science, however, is much more reliant on new technology like the cloud, which means that updates and changes can not only be interpreted in real time but distributed as necessary to keep everyone up-to-date. Since the data is not stored on legacy hardware, it’s much more accessible. And although many organizations still have Data Scientists reporting through the IT function, more and more organizations are embedding them throughout the organizations (i.e., Marketing, Finance, Manufacturing organizations).
To further blur the lines, BI software vendors are introducing Data Science elements into their BI solutions. Tableau has built integrations to allow users to execute R and Python code to make models more accessible to organizations. Microsoft PowerBI has also included visualizations in their marketplace that allow users to quickly incorporate K-Means clustering, an unsupervised machine learning algorithm, into their dashboards.
Conclusion
While business intelligence is a core component of any analysis procedure you may have, data science is the key to unlocking your company’s future. Rather than waiting for numbers to come in and be analyzed, you can get a real-time understanding of what’s happening and how it can change in the near future.
Data science will become more and more critical for businesses to stay ahead of their competitors, which is why so many top-tier companies are investing heavily in the technology and the people with this skillset.
