Sign up for our newsletter and get the latest big data news and analysis.

What is the Difference Between Business Intelligence, Data Warehousing and Data Analytics

In the age of Big Data, you’ll hear a lot of terms tossed around. Three of the most commonly used are “business intelligence,” “data warehousing” and “data analytics.” You may wonder, however, what distinguishes these three concepts from each other so let’s take a look.

Business Intelligence (BI)

What differentiates business intelligence from the other two on the list is the idea of presentation. Business intelligence is primarily about how you take the insights you’ve developed from the use of analytics to produce action. BI tools include items like:

  • Graphics and charts
  • Written reports
  • Spreadsheets
  • Dashboards
  • Presentations
  • Insights shared at meetings

To put it simply, business intelligence is the final product. It’s the yummy cooked food that comes out of the frying pan when everything is done.

In the flow of things, business intelligence interacts heavily with data warehousing and analytics systems. Information can be fed into analytics packages from warehouses. It then comes out of the analytics software and is routed back into storage and also into BI. Once the BI products have been created, information may yet again be fed back into data storage and warehousing.

Notably, BI doesn’t have to be a finished product in the traditional sense. For example, a BI dashboard for a clothing retailer might include up-to-the-minute trendspotting data from social media, buyers overseas, inventories, store sales, focus group interviews and fashion shows. Come back to the dashboard in a half-hour, and you might see different information being displayed because the trends have shifted within that time frame.

Data Warehousing

This is sometimes grouped together with storage, but many organizations differentiate the two. The difference is largely about data that’s stored for very long periods, warehousing and data that’s stored for immediate use. Some organizations don’t draw this distinction, though.

Warehousing can occur at any step of the process. Data gets warehoused right after it has been acquired so the raw stuff can be rescanned for analytics purposes. This is an excellent safeguard against data being mangled by processes, leaving the original information potentially unrecoverable.

Data will also be warehoused in the middle of projects. For example, it might be warehoused after several runs of analytics have been conducted. This ensures the results of analysis programs are stowed away in case they need to be referred to again. It also avoids possible problems with mangling in business intelligence packages.

Lastly, data often gets warehoused after it has made it to the promised land of being used as BI. Reports, charts, daily states of dashboards and spreadsheets may all go into the warehouse for permanent records-keeping, legal, historical and auditing purposes.

Data Analytics

Analysis is the sexy part of this business for many folks. This is where statistical methods and computer programming techniques are combined to study data and derive possible insights. Much of the toolset comes from the stats world, with common methods applied to data including:

  • Linear regression
  • Bayesian analysis
  • Frequency studies
  • Network analysis
  • Hypothesis testing
  • Clustering
  • Correlation

Performing analysis often involves a lot of prep work. Data may have to be formatted properly for machine-reading. It may also have to be filtered for duplicates, errors and other troublesome flaws. This all has to be done to preserve the integrity of the data as much as possible.

After analysis has been done, there’s still more work to be handled before everything gets fed into warehouses and BI packages. Further analysis should be performed to validate the data. Data scientists often reserve part of a dataset to use for comparison. If there are radical departures between the analysis and what real world data looks like, that might be taken as a clue to go back into the lab and figure out what went wrong with the analysis efforts. Consideration may also be given to whether different forms of analysis might be worth exploring before moving to the BI phase.

Conclusion

Working with data in the modern world is far from a single action or even set of actions. Organizations now break up the process into many pieces because there are numerous responsibilities along the way. Competent data warehousing methods can ensure that information isn’t lost. Skillful analysis will try to avoid problems like social and statistical biases, over- and under-fitting, duplicatability failures and self-reference. Good business intelligence usage can ensure that information gets into the hands of decision-makers and powers a data-driven culture.

About the Author

Christopher Rafter is President and COO at Inzata. Before starting his career, Chris earned a bachelor’s of science in economics and an MBA from New York University. A veteran of innovative technology & startups, Chris then helped launch one of the first cloud applications for Master Data Management at the enterprise level in 2004 – long before Cloud and SaaS were common terms. Chris then spent 14 years at Logicalis/Datatec, a global technology and cloud provider where he ran the global business intelligence practice, and most recently was Chief Technology Officer at Vology. His extensive and impressive experience in the technology industry then earned him his position at Inzata in 2016, where he sets the vision and direction for Inzata, and oversees company strategy, business activities, and operations. He is one of the brains behind Inzata’s long term technology roadmap and adoption of disruptive technologies like artificial intelligence and machine learning.

Sign up for the free insideBIGDATA newsletter.

Leave a Comment

*

Resource Links: