Data warehouses are like garages. They store all kinds of random things that are organized in a structured way. In a garage, you’re likely to have boxes of old clothing or kids’ drawings, wedding gifts, toolboxes, and all the other knick-knacks that build up over time. Unlike in a house, when these items are in the garage they’re no longer random and unstructured but rather set into an organized environment (or at least that’s the goal).
In the business world, all those unorganized knick-knacks can include customer data, payroll information, operational data, sales numbers, and much more. They all likely come from disparate sources and need to be cleaned and structured for storage in a data warehouse through an ETL process.
For years data warehouses have been the primary way of storing large amounts of cleansed data, and many companies have made large monetary investments in them. Although new technologies can often render an old one obsolete, data warehouses have retained their value, though often with the help of added tools to increase their total functionality.
Using BI to Extract Insights
If you never need to do heavy analyzation of the contents of your garage, then simply having space would be enough. But, let’s say you have a new baby on the way and you want to take an inventory of the baby clothes available in the garage to establish what new items you need to buy. What do you do? You go in, dust off the right boxes, and search them to analyze what’s inside. This is when having a system for analyzing what you’ve got, sorting through what you’re keeping and giving away, and finding the stuff you need quickly becomes important.
Like the garage, a data warehouse can collect all kinds of things over ongoing periods of time. And while most people don’t require massive record keeping systems for maintaining their garages, consider the systems in place to maintain a large business: there’s likely inventory management systems, a CRM, an ERP, marketing data, financial data…the list can go on forever. A data warehouse becomes more than just a place to store all that data; it becomes the hub from which information is extracted, manipulated, and used for insights and predictive analysis.
That’s where business intelligence comes in.
The Hybrid Approach
Until recently, the first step in an organization’s BI efforts was typically to invest in a single data warehouse. Now, however, it’s expected that information comes from many places. To adjust, some BI products on the market today no longer require a data warehouse and can connect to original data sources directly. However, for companies that have already made expensive investments in data warehouses, have no fear, your data warehouse is not obsolete.
Let’s think about the garage again. What would happen if you had so much stuff that you needed to rent a storage unit? With your stuff now spread across different locations, it would be even more difficult to get an integrated picture of everything. You might never know the extent of what you have or are missing, how those items could be used or repurposed, and how much wasted space there is.
While it might be okay if you’re unaware that your significant other rented an entire storage unit for their still-unused exercise equipment, a business cannot function optimally without combining all of its data sources. So, how can you leverage your data warehouse and all of the other information your business stores?
A hybrid model will allow you to get the best of both worlds. With the ability to combine all of the data stored in your data warehouse with other sources, such as real-time transactional data, you’ll produce a continuous mix of data to make informed decisions.
The Future of Data Warehouses
Businesses are constantly investigating better ways to optimize their operations, increase revenue, and keep track of KPIs. Companies that operate off legacy data warehouse systems are in a position to benefit from a hybrid model that utilizes all the advantages of a data warehouse while incorporating other data sources and live data streams by way of a BI tool.
Legacy data warehouse systems are still an integral part of the business world; however, as times change, it’s important to take a new inventory of the big data environment to determine the best tools going forward for the growth and wellbeing of your business.