What is the Difference between Business Intelligence and Data Science?
Previous posts have differentiated between data scientists and other tech-related positions including business analysts. Here, we delve deeper into the business world in order to distinguish between data science and business intelligence. A major distinction between the two fields is that while data science is statistics focused, data handling is at the center stage in the discipline of business intelligence.
In terms of data handling, business intelligence starts from the collection and storage stages and ends at the distribution and analysis stages. It is solution focused and its core goal is to facilitate informed business decisions by making data accessible and easily interpretable. These solutions are dependent on database development and software engineering.
The Data Warehouse is the Foundation of Business Intelligence
Business intelligence technologies facilitate the retrieval of existing data to generate reports in order to make informed business decisions. It is a broad field that has many different layers, but the foundational entity in business intelligence is the data warehouse. The purpose of a data warehouse is to harness all business-related data in one place. In a data warehouse, instead of having disjointed departmental electronic-records that are jargon-filled and department-specific, there is a single location for all business data that has a uniform language. This allows for an integrated data infrastructure that has many benefits. For one, data retrieval is easier because there is fluidity between different departments. Also, this enables comprehensive inter-departmental business analysis to facilitate decision-making.
The larger a company, the more complex the data warehouse architecture, and the more complex the data warehouse architecture, the more computationally expensive it is to search through data. Part of business intelligence includes developing algorithms that streamline data retrieval. It is essential that the complex architecture is maintained while connections are made and information is retrieved as quickly as possible. There is an entire subfield in the database world called “data structures”, with the main goal of designing efficient ways to organize and store data while facilitating for fast data retrieval. Such data structures are then incorporated in database software packages. Using algorithms that efficiently construct, update, and query these data structures greatly facilitates extraction of business information.
Once a data warehouse has been established, the entire company’s data infrastructure becomes unified. A cornerstone of business intelligence is to keep the warehouse clean, which is of paramount importance. Measures need to be in place to not only keep data sorted correctly, but also there should be checks and balances to ensure that additional entries are accurate and any removals are authorized. Not only is it important to have a solid framework, but it is also absolutely crucial to maintain the framework’s integrity.
For a fictional and extremely simplistic example, imagine a medical practice that has two different data storage systems. One is a simple spreadsheet used by the front office and the other an advanced electronic record system to record patients’ medical data. The two have been historically kept as different systems to protect patient confidentiality due to HIPAA regulations. Envision that the front office spreadsheet has only two fields:
Next, envision that the existing e-record system of the same medical practice uses a database with the following fields:
- Date of Visit
- Family History
- Visit summary
- Treatment plan
Note that the two different systems share the “name” field and nothing else. Applying the principles of business intelligence to this basic and hypothetical model, the first implemented change would be to create a data warehouse to integrate the two separate systems. The merged database would have all fields accessible to clinicians while code could be written to require passwords in order to access patient medical information so that the front office could only access patient names and addresses. Such specific programming is of paramount importance to business intelligence because it achieves the overall goal of integrating business data while implementing essential functionality simultaneously.
Continuing with the medical practice example, creating the data warehouse would set the foundation for more advanced business intelligence technologies. For example, by integrating the two separate systems into one overarching system, patient address and diagnosis would then be linked whereas historically they were kept separate. Advanced business intelligence could potentially result in an examination that may reveal that patients in a specific zip code are more likely to suffer from a particular allergy.
Stepping Outside of the Data Warehouse
As data handling is at the crux of business intelligence, anyone involved needs to be highly proficient with databases and fluent in SQL. They must not only be able to create and modify databases, but they must also be able to generate algorithms to increase speed and efficiency of data retrieval.
But business intelligence is far more than just creating and maintaining a data warehouse. Equally important is developing software solutions that allow for user-friendly data access and interpretation. Think of systems that have dashboards with business diagnostics including profits, losses, and current activity. Envision reports including pie charts, bar graphs, and trend lines being retrievable with the click of a button. Designing, programming, and displaying those dashboards and reports is the job of a business intelligence specialist. Advanced business intelligence systems would be capable of generating custom reports using data based on specific queries involving specific timelines. These reports may even be available on mobile devices with easily interpretable graphics to illustrate business trends. This sort of technology is designed to aid in data retrieval, analysis, and visualization in order to increase business fluency is dependent on software developers.
Predicting the Future verses Reporting the Past and Present
Previously we have contrasted data scientists to computer scientists, data engineers, and data analysts to illustrate the role of a data scientist specifically. As described previously, data scientists are statistical investigators with a focus on future business. They utilize programming languages like R and python to evaluate big data by applying statistical programming strategies that fit the existing data to math models. These math models can then be used as a backbone to support business predictions and future activity. Data scientists not only generate these prediction models through model fitting and advanced statistics, they also develop visual presentations in order to explain their findings. Visual presentations are generated to make such intense mathematical principles easily understandable and can be generated via excel or packages in statistical programming tools like R. Companies hire data scientists in order to be informed about future business trends so that they can plan accordingly.
The lines between business intelligence and data science may seem blurry because both are dependent on transforming numbers into meaning and both are visualization-centric. There are obvious differences, though, because business intelligence is not as concerned about the future as it is about maintaining and optimizing the data infrastructure of a business by making present and past data easily understandable. Companies hire business intelligence professionals for several different purposes. They might need database specialists to build and optimize data warehouses. Alternatively, they might need software engineers to develop dashboards or visualization tools. They might even hire a business intelligence consultant to evaluate the current business intelligence technologies within the corporation and make suggestions for improvements.