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.
This means that now, more than ever, the data team’s job is to gainfully employ this data to build a presence that pays off in terms of leads and revenue.
If we look at the changing digital landscape, we will find that data is a major player that can trigger the way we take decisions and whether or not we are taking decisions that pay off in the long run. More often than not, an analytics team is mindful of the parameters set above. This is because the quality of the data would enable the senior leadership to also take critical decisions with the help of the data scientist who is busy building analytical models. In such cases, the base data has to be timely and accurate even if it comes from a number of source systems, external providers and internal ERPs. How can we ensure that the base data in your analytics platform is accurate and relevant?
Data reconciliation is the name of the game here – and also a game changer that can keep you at the forefront of your data enabled presence and purpose. Now, before we go any further to understand what data reconciliation really is, let us understand the data driven enterprise and its predicament!
If we look around us, every enterprise is more or less entirely data driven in today’s day and age. There are actually a number of enterprises and niches that depend on data and for whom, data reconciliation is a reality that many are not yet ready to or equipped to face.
If we were to take a scenario, we would be looking directly at the analytics team of a company. When they are moving the data from source to target on a daily basis – sometimes, multiple times a day – what happens when some data is deleted or duplicated? That would lead to a lopsided view of the accounts or any other data, at the end of the day. When we fail to release an accurate tally between the source and the target, we are left with data that does not match up and goals that are far from being met. This would leave decision makers with a problem on their hands and the analytics team would have a hard time tracking down where exactly the mismatch happened and where the data was lost or duplicated.
This brings us to the next scenario. This would be followed by a number of calls and complaints and maybe even a few irate updates – because, let’s face it, those updates are far easier to write than trying to track down someone in a company where the team is not equipped to immediately handle such a problem.
This is where data reconciliation comes to the fore. While you run your operations (whatever those operations may be in terms of type and scale), a data reconciliation service makes sure that your data – when it travels through sources and data stacks – remains intact and you receive an error message as soon as a shift is seen. This would help you instantly rectify the same rather than having to speed down the blackhole of going through each layer of the data stack to find out what the customer may complain about later.
Sounds like a fair plan? Well, that is what data reconciliation can do for your business!
Scenario one is about a smaller and upcoming enterprise. This could be applied to an even smaller entity or a far larger one. We are talking about the analytics and data teams at a manufacturing company or at a larger banking or payment portal, a large E Commerce portal, an app that delivers services at the touch of a button, an edutech app or portal – the list can go on and on. The ways your data can let you down when it is being shifted around are numerous. But the way you can prevent this is singular – data reconciliation.
Now that we have covered the scenarios and understood what can go wrong and how data reconciliation can prevent the same, it is time to go forth and understand how data reconciliation can help a data driven (or virtually, any modern day) enterprise.
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.
With the help of data reconciliation, you would be able to take your data driven business to an optimized peak. This would lend credibility as well as functionally sturdy enterprise.