In the data-oriented age, compiling, processing, and utilizing data has become quite a nuisance if not dealt with the right way. But knowing why and how to process data without jeopardizing its veracity can bring great value for enterprises on many levels. Actually, on all levels imaginable.
Unreliable data and its inefficient management could cost companies up to 15% to 25% of yearly revenue. And still, many digitized companies and organizations face several issues in the context of achieving enterprise-grade data integrity.
How data integrity differs from data quality?
On a daily basis, enterprises deal with an immense amount of processes and exchanges among different stakeholders – managers deciding about long-term business plans, R&D department and sales department exchanging essential information, orders being sent to customers, etc. Dealing with bad data in situations like these can trigger impactful consequences and some of them may not be detected for years to come. So why not prevent errors from happening and start securing your data’s veracity already by checking it into the process?
Data quality refers to the accuracy of the information gathered and it testifies to its compliance with the organization’s standards. In practice, it merely guarantees the correctness of the information values. But data integrity takes it a step further – it means overseeing how said data is entered into systems, transferred, used, and made transparent.
Does your data provide the most value for the company?
The answer should be yes. In theory, organizations are well aware of the new business opportunities that well-structured and trustworthy data could bring. But in reality, either they don’t grasp the importance of the whole process or they believe tackling the amassed data is a minor detail that sorts itself out. And how can one be even sure what enterprise-level data should be like to actually bring value?
First of all, data should be traced to its provenance easily and it should enable tracking it to its source, issuer, date, location, and other metadata. At every step and every level of business transactions, it should be trusted enough to serve as a base for any kind of enterprise decision-making – from its raw format to more structured forms of data. Not only should it facilitate internal processes but it should also streamline business dealings outward. Data should work with you and for you when promoting the authenticity of the product to the customer, proving a standard-compliant case for the auditors, etc. Sharing pertinent information to various stakeholders, carried out in a controlled, safe, and easy way, is a solid basis for doing business without any shortcomings.
If data integrity is good for business, why the hesitation?
Businesses are mindful about implementing proper data strategies that could ensure more success by solidifying their position on the market. But they are often left with legacy data management systems that are inherently quite hard to upheave towards increased data integrity management. And this makes it hard to achieve enterprise-grade data integrity. More specifically, the main obstacles are:
- lack of trust and veracity of the data already compiled, and a reliable means of verification,
- inadequacy or even lack of the resources and knowledge to adopt blockchain technology efficiently,
- costly and unscalable blockchain transactions in terms of quantity and speed.
Ideally, to ensure and inspect data integrity, organizations should employ a tool that provides the advanced features of blockchain technology in a user-friendly and intuitive way and at the best price-performance ratio possible.
Authtrail is a universal platform for data integrity and verification that adds value to enterprises through blockchain technology. With a simple add-on to their existing management tools, enterprises can proceed with their business as usual, while harnessing much more reliable data. Authtrail’s support offers a base so the enterprises can expand to new business opportunities, bring down costs, and gain a competitive edge.