The integrity of a building rests on its foundation, which holds the structure upright and keeps it from collapsing. In many companies today, data is the foundation of their digital transformations, supporting ways to generate innovations, streamline operations, reduce costs and assist in better business decisions. If this foundation is weak, the digital transformation may collapse.
Trusted decisions are powered by trusted data. If your business embraces a data-driven culture, then every decision you make, and every answer offered by machine learning and analytics, is guided by data. Your success or failure will be determined by having data that you can trust. You can start trusting your data when you understand the integrity and quality of the data, your enterprise is using.
Which brings us to the question, what is Data Integrity?
Data integrity can be defined as the maintenance of, and the assurance of, data with maximum accuracy, consistency and context over its entire life-cycle.
The impact of recent pandemic on the data industry, has caused organizations to rapidly increase the need to integrate data. There was a need to catch up with the new set of requirements and thus fast-tracked their journey to cloud data warehouses as a next generational platform. This allowed the companies to be faster in satisfying data needs, more agile and reduced the operational costs. But in terms of accuracy and consistency this is a double-edged sword, in terms of data governance, data security, data quality and maintaining integrity across environments. There is a need to find the right balance between speed, consistency and accuracy
And now, what is Data Quality?
Data can be called of high quality when it is fit for the intended purpose of use and when it correctly represents the real-world construct that it describes. The new technological advancements have made it easy to gather large amounts of data, Over the last couple of years alone more than 90 percent of data in the world was generated. Hence capturing good quality data, right at the source is critical to data integrity. With such high volumes of data, we cannot ignore the veracity of it.
“Not all data is created equal”, is a term most of us have come across at least once. There is irrelevant data in respect to the context of our business problems, which is generally called as noise. So, Strategic context is very important through-out the data’s lifetime. Thinking about Data quality in context of what is critical to the business, is key.
What role is Third party data playing ?
There is a constant need for data happening but that is also against this real pressure around speed, accuracy and agility, and because of that, the nature and pace of overall business is going through these very profound changes. Companies as a result are transforming to just catch up and meet a new set of requirements and that is where the need for third party data is playing a significant role. Most of the newly started enterprises and small-scale companies cannot afford to source data directly and rely heavily on third party data to meet their data requirements.
The companies are trying to accelerate their efforts to respond faster to the need of their customers and that data, be it third party data, is ever present and is critical to do that effectively. It stands for everything, whether you are dealing with customer service or customer experience or marketing, are just a couple of examples where companies today are relying on data to differentiate themselves in the market.
Data democratization
The key aspect of it, is to make as much of relevant data available to as many consumers within the enterprise as possible. At the same time, with the notion of governance, it puts more limits and controls on how much data can be available, and to who it can be available. As such, finding the right balance is certainly the key.
The way to find that balance is to start with what sort of questions you want to ask the data, and then understanding what data you need to make available to answering the said questions but not the whole data.
The process to improve your data’s integrity and quality is better started at the data acquisition stage and through setting up proper data pipelines, so that all departments in the enterprise would only work the data that you can trust. This process can be overwhelming in the beginning but it will have long term impact on the progression and growth of the enterprise. Our team can be of help along the way!
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