When a new product solving data issues enters the market, companies are often reluctant to adopt it and replace their current data systems, claiming these already do the job. Indeed, the management part is taken care of, and teams can handle their data quite well.
But the majority of such systems don’t address the issue of data quality, nor can they prevent data from being tampered with.
Without quality data, no ease of management makes up for unreliable information.
Existing data systems are useful but not enough
There are several reasons why predominant data management systems only work to some extent but prove deficient in other areas.
- They are centralized. As such, they represent a single point of attack or failure. In case of a data breach, most data management systems do not guarantee that data retains its integrity.
- They are managed by administrators. This makes them prone to human error, mismanagement, negligence, and downright abuse without it being noticed or tracked.
- They are overseen by enterprises. As such, they could be manipulated to align data with company goals and cherry-pick only the information that directly benefits them.
Data manipulation occurs more often than we think
One of the main goals companies naturally pursue is economic growth and increased market share. To achieve that, many businesses play fair and square, while some decide that end justifies the means.
Data edits and manipulation, in general, can be performed to either boost reputation on the market, reach accounting targets, or confirm a hypothesis in favor of the party funding a study or research.
Cases of data manipulation abound, here are just a few of the most notable in recent years:
- In 2015, Volkswagen had deliberately programmed their TDI diesel engines to provide better results during regulatory emissions testing to meet the US standards, while real-world driving produced tens of times more emissions.
- Also in 2015, Toshiba overstated operating profits by $1.2 billion over several years, including booking profits early or pushing back the recording of losses.
- In 2017, Coca-Cola faced a lawsuit alleging the company had influenced contradicting independent scientific data about the adverse health effects of drinking sugar-sweetened beverages.
- In 2019, Novartis submitted a drug application to the FDA with manipulated data causing inaccuracies that the company was aware of but remained silent.
Data management is about processes. Data integrity is about trust.
Data systems that enterprises employ today focus predominantly on handling and organization of data. What they lack is the integrity aspect.
With Authtrail, companies can continue using their data management systems as usual. What they gain, however, is an added security layer on top of their existing data handling systems. This way, Authtrail prevents data from being manipulated or changed without a trace in the history of edits.
The main goal of Authtrail is not to disrupt the current processes – in fact, the platform respects the up-and-running enterprise systems with seamless integration while adding a quality, security, and integrity aspect to all data throughout its lifecycle.
Authtrail is about guaranteeing that the data within an organization remains as reliable as possible for all parties involved – from R&D to the sales department, and from internal teams to auditors, regulators and end-customers.
Why would companies want to lose their ability to alter data?
Of course, it’s quite tempting to alter data a bit here and there if that means (seemingly) faster reach of the end goal. So why would companies give up on this opportunity?
Once a case of data manipulation comes into the spotlight, legal consequences, loss of partnerships and licenses, and mistrust of customer base can heavily outweigh the short-term gains of fudged data. Enterprises are involved in a complex system of suppliers, partners, and consumers, who will, sooner or later, demand transparency, responsibility, and ethical conduct.
The moral aspect of business inevitably translates to a financial one. No embellished yearly financial reports or research results can make up for the loss of the position and reputation in the increasingly demanding market. Whatever the product or service they provide, organizations should always pair it with data transparency, trustworthiness, and accurate representation.
Data integrity is a business issue
With Authtrail, companies gain something that is extremely hard to implement and guarantee otherwise. While data integrity might seem quite abstract, it simply means that a company can be trusted and collaborated with.
Data quality can be a subjective matter when it’s taken into the hands of individual actors. But when integrity is guaranteed by a computer protocol independent of human intervention, it becomes a much more objective and tangible asset that every company should invest in.
With Authtrail, enterprises can do just that. They can continue their business as usual, but with much more reliable data powered by cutting-edge blockchain technology. For their customers, partners, and investors, this means less risk of a brand being involved in data manipulation scandals, higher trust in its offering, and ultimately more business transactions.