Data Reconciliation for Data Oriented Enterprises
In a world that is increasingly dependent on how data impacts the enterprise’s bottom lines, we are constantly collecting data, collating it, cleaning it and finally merging it with our own collateral to create competitive advantage. This evolution has happened due to the fact that enterprises are becoming increasingly data oriented . What does this mean exactly? Here are the implications:
- To be able to enable better business outcomes with data;
- To ensure accurate and timely data that comes to the fore;
- To address the changing technological landscape which also requires a system that would deliver a certain quality when it comes to extraction, merging, reconciliation, observability and finally, security;
- To deal with the shortage of talent in data monitoring and reconciliation as well as analytics by automating tools that can deliver accuracy and timely data on a platter;
- To find any errors and gaps before it hampers any major business decisions and one’s authority in the market as well;
- To be able to deal with the sheer bulk and quantum of data with tools that check for discrepancies and gaps while the data moves from source to target.
- Freshness and Relevance: With the help of data reconciliation, you will always have a keen sense of how data moves within a particular data stack. This would help the analytics team in submitting fresh and relevant data that would further contribute to efficient decision making processes.
- Accuracy: Accuracy, as we have covered before, is the name of the game. The point is to always have the right kind of data from the right sources so that you build a credible name for yourself and you command an authoritative standing in your consumer segment. Reliable data mining from raw measurement data is something you can achieve when you turn to data reconciliation. This would ensure that you are not only extracting accurate data, but you are also portraying the right information.
- Redundancies: Missing records as well as missing values and duplicated data or broken links are all signs of a badly run enterprise – is that the enterprise you want to be? Clearly, not! This is where data reconciliation also comes to the fore since it gives you alerts and allows you the chance to rectify before your business users or end users catch any discrepancies. The cost of using bad data could reflect through a fast shrinking customer or user base, which is not the ideal scenario for any business..
- Observability: When you observe how each cog in the machine works, you would know which cog and wheel to keep and which one to relocate or let go of, entirely. This is the same way in which data forks for the data driven or data optimized organization. Enterprise control becomes a reality with a tool like data reconciliation since you would be able to keep an automated watch on your enterprise with parameters that are unique to you, your functions and your customer, who is the end user.
- Integrations: When you have a single set of data that represents the overall process and functionality of your enterprise or a certain data catalogue (for individual products or services), it is easy to build integrations so that one set serves the other. This can be done with the help of data reconciliation that allows you to build data sets to represent many data functions.
- Anomalies: With better observability, you would be able to spot and define gross errors with the help of variance. This will lend credibility in terms of a more professional front – and a more professional way of running operations within as well. This is important since the cost of bad data or bad data quality can cost you much prospective revenue and even existing customers.
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