Improving Business Intelligence with Change Data Capture

In today’s hypercompetitive business world, business owners and managers need to react and adjust quickly to the ever-changing trends of commerce and industry—especially with the Internet and the Digital Age making those changes happen not in a span of months or weeks but days, hours, or even minutes.

To be able to respond to changes appropriately, they need to constantly be at the top of their game where business information is concerned. This means they need to be at the pulse of all data that are relevant—not just to themselves and their customers, but also to their competitors as well as the industry they’re in as a whole. It’s here where good business intelligence, or BI, comes into play.

What is business intelligence? Business intelligence is defined as technologies and methods that have to do with the gathering, examination, presentation, and integration of information related to business and business-dealing. Its main purpose is to help business owners and managers make better business decisions by giving them the information they need, the moment they need it, and in the format they require. With good business intelligence, business owners or managers will be much better informed and equipped to make difficult business decisions that could make or break a company.

But how can organizations improve their business intelligence? One sure-fire way is through the adoption of software solutions with change data capture capabilities.

How would change data capture technology affect business intelligence as a whole? Again, we look first at its exact definition, i.e. the process of capturing changes made in a data source or database, and applying them throughout the enterprise or business. Simplified further, change data capture, or CDC, ensures data synchronicity—that everyone in the enterprise deals with only the most up-to-date version of the data. This helps decision makers make the right business call with the right information on hand.

What CDC essentially does—ensuring data synchronicity—is nothing new. ETL – Extract, Transform, Load – tools do the same thing, but the process takes up a lot more time, effort, and resources. This is due to the fact that ETL tools extract the data in their entirety, transform them into a format usable by the systems in the company, and then loads them. This process can take hours or days depending on how big the requested data are, thus causing delays in decision making and business strategizing processes.

CDC, however, only processes the changes made to the data itself, and sends out those changes to all the systems that access that specific data set, so that their version of the data is complete and up-to-date in real time. In this way, not only is the burden on company resources lessened, but also the amount of time it takes to have the required data set updated across the entire company.

With that explained, here are the four main methods of change data capture:

  1. Date_Modified: As its name suggests, this method relies mainly on keeping track of when the changes were made, and which pieces of data were modified during that period. The entire database is then run on a filter based on those parameters, and the results are what is replicated through the enterprise.
  2. Diff: This method compares the current version of the data to the previous version to identify what has changed. This approach takes a lot of resources as it is a 1:1 comparison every single instance, and resource consumption is linear to the growth in the volume of data being checked.
  3. Triggers: This method involves the use of triggers that are set up to ‘set off’ whenever a particular or specified data set is modified. The changes on that data set are recorded and then applied to the versions of that specific data set across the enterprise. This method, while efficient, can be affected by many variables (such as multiple changes being made on the same data set, which may cause change loss) and could result in an increase in the overhead of resources rather than reduce it.
  4. Log-based: this method involves the use of the transaction logging feature inherent in all transactional databases, wherein changes made to data in that database are automatically logged in case of a system crash or failure. Log-based CDC takes those changes, interprets them, and then applies them to the entire enterprise.

Whichever method of CDC a business manager or owner applies to their enterprise—as it depends not just on their industry but also the type and volume of the data they use—their business intelligence will surely improve enough such that they are able to keep up with today’s business demands.

John Morris
John Morrishttps://www.tenoblog.com
John Morris is a self-motivated person, a blogging enthusiast who loves to peek into the minds of innovative entrepreneurs. He's inspired by emerging tech & business trends and is dedicated to sharing his passion with readers.


Related Stories