Why Your Enterprise Analytics Strategy Needs A Data Hub

Enterprise analytics is a complex task. Unfortunately, many of the individuals involved in doing planning for an enterprise analytics model don’t fully grasp what they should do and what the jargon means. This problem isn’t a failing of their information sources, but rather a complexity that uses many terms interchangeably. Most upper-level executives mention terms like data lakes and data hubs without grasping why these technologies are used. Because data lakes have become so popular lately, it seems everyone wants one to be the source of data for their analytics back end. However, on closer examination, a data lake might be overkill and what a business really needs is a data hub.

Hubs, Lakes, and Use Cases

Data lakes are collections of data, but as a business uses different types of analytics, a data hub may become bogged down with input from various sources. The result is a data lake with bits and bytes that may serve the same purpose but show different results when passed through an analytics engine. In comparison, a data hub consolidates all this data from your sources as input. It then streamlines it, thorough cleaning, ingesting, and categorizing, taking the most common data and storing it for access by any analytics engine that needs it. The result is a far faster data access time, thanks to a data hub’s wheel-and-spoke design. Instead of sifting through data that may come from multiple redundant sources in a data lake, a hub simply provides clean, efficient data on call. This easily accessible, cataloged data, of course, makes it the preferred way to retrieve data for real-time applications and display on a dashboard.

Near Real-Time Delivery

One of the most tedious parts of retrieving data from massive stores is waiting for results. As mentioned before, the data hub uses a wheel-and-spoke topology. In essence, this boils down to the system taking the most accessed data and storing it within easy reach to quickly return that data to users as they demand it. Other information is stored in the “wheel,” which may take longer to find. However, it’s not nearly as long and access time as if the system were consolidated like a data lake. Streamlining data comes from categorization, which typically happens when the data is processed. After the data has been categorized, it makes accessing relevant data much faster.

Implementing Data Hubs May be Complex but Worth It

To say that a data hub is a simple solution is not being honest. Data hubs can be more complex than other systems, and implementing them takes a bit of skill. However, a data hub is necessary if a company wants a single source of reliable data that will help its analytics engines perform more accurately. SAP already has a data hub in its arsenal with SAP business data intelligence data management solutions. However, some companies may prefer a more bespoke approach.