How can Data Quality Make or Break Your Business Success?

Whether you are running a business, or you are heading a division or business arm of a larger enterprise, you know that you need more than an MBA to run a data team and prosper from it. Today’s show is a production that has a number of data driven and digital elements run by data teams that require many tools. When we run a data team, we use various assets in order to create revenue and profit. But before that we need to understand that bad data can cost you dearly when you are running the data or analytics team within an enterprise. There are a number of ways in which assets add up to a profitable bottom line. Building a worthwhile presence is one of these things. In today’s data driven world, running a business is about making use of the right data in the right way so as to earn from the following two things:

Your data, which is used as your asset;
Your data driven business presence, which validates your standing and grants you authority in your market segment.

When we look at both of the above parameters, we realize that data truly is at the very core of all business activities in this day and age. When we look at data, we also find that there are a number of vulnerabilities that we can fall prey to. Let us look at these threats and weaknesses before we talk about strengthening our standing with high quality data:

Is it Complete? This is one of the foremost questions to ask. Your data quality will depend to a large extent on whether or not the set is complete and whether or not it answers questions and caters to all the right functions. This would also depend on where the data is sourced from and whether or not it has been reconciled while it has changed stacks and sources.
Is it Valid? The validity of the data or the data set is of great importance when we are looking at using it to build our authority in our niche. Every enterprise would set a number of values and parameters for the data and the validity of the data would be determined by whether or not the data matches these values. The metrics for each data field must be valid so that there is an address only in the address line, rather than the line provided for the phone number!
Is it Consistent? Do you collect data enthusiastically and then let it go later? Do you scramble to find the latest updated data at a particular point of time? Do you struggle to get timely insights due to lack of timely data extraction and analysis? Is your observability suffering due to lack of consistent data extraction? A ‘yes’ to even one of these questions points to a bigger problem of data consistency, or rather – lack of it.
Is it Timely? Many enterprises slip from their A game when it comes to data quality. In the course of running functions and operations that would eventually contribute to a certain level of revenue and profitability as well as growth. But when we speak of growth, there are many data sets that would grow obsolete with the demand for new data that would fuel new growth in new avenues. In such a case, the data too is time bound.
Is it High Quality? The quality of the data would also therefore, depend on whether or not it serves the purpose at the right time. For example, the new billing address of a client, or updated system checks for a new process and other such time bound data sets would fall into this category of data quality assessment.

While these are the main areas where your data quality can suffer, there are a number of ways in which these areas can be satiated so that your data quality is on point. Here are the simple ways in which you can control your data quality:

Data Reconciliation: Simply put, this should be at the very foundation of your enterprise and its data optimized presence or operations. The reason we are using the word data optimized is quite simple. When you use data in keeping with the four things to avoid as enumerated above, you have to turn to a system that would keep all four things in check even as you roll out error free, data driven operations. This is called data reconciliation and it would reflect in an optimized approach that makes good use of data. This is what would contribute to a high data quality in the long run.
Data Monitoring: When you work analysis and observability into your system and its operations or functions, you can be sure that you have to rely on data monitoring in order to keep tabs on the data quality. Observability and analysis come from data monitoring and reconciliation, applied in equal parts for a well oiled system. This system would not only help you spot errors, but also help you build links between data sets for better data integrity and intelligent systems. Related tables would be worked into each new data set or field so that the connectivity and flow is built to avoid either deleting new information (new orders and such information), or duplicating information, or committing random mistakes.
Data Security: This is a big one that many of us end up ignoring. When our data is going from one stack to the other, it can be easy to make it even more vulnerable, while it changes data stacks. In doing so, if there is a compromise, it would be difficult to find the exact stack or source where the same happened. At the same time, there are a number of enterprises that find it difficult to do since this kind of a function has to be coded into each line of the data monitoring function at play. Yet, with an automated service, data security can be easily applied to fortify your data and ensure that it is not compromised at any time, thus also ensuring that your customers trust you that much more.

In order to carry out data assessments, one must first know what makes data quality high or low. These are important things to keep in mind even as you then shop around for services like data reconciliation, data monitoring and data security. In this way, it can easily be said that data quality is the new disruptor that easily make or break the customer experience and thus, make or break your business success.

Data Quality Success

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