27 Feb Revenue Loss Because of Poor Data Quality is Real – How to Prevent it?
Ever since computers became mainstream, there has been a steady growth in the amount of digital data that was created by humans. The rise of the internet and the transformation of mobile devices into smartphones created an explosive growth for data generation, especially during the last decade of 2010 to 2020. During this period it is estimated that the amount of data that was handled by digital systems worldwide in a combination of data creation, copying, and consumption scenarios witnessed a 5000% growth in volume from 1.2 Trillion gigabytes to 59 Trillion gigabytes. It is rightly said that “Data is the new oil of the 21st Century”.
The digital economy, which is rapidly growing across the world, relies on leveraging data generated by consumers to help businesses deliver more personalized experiences at every interaction they have with each other. But having just a raw collection of data isn’t going to add a reasonable value for any organization. Data quality is of utmost importance. By quality, we mean the right data that can be used to generate accurate insights for organizations to make decisions on their business. Getting this criterion wrong can prove to be a huge loss scenario and has the potential to lead to even revenue loss for an organization.
In fact, Gartner estimates that organizations can incur an average yearly loss of around USD 15 Million due to poor data quality.
Basically, the revenue loss due to poor data quality can occur in 3 ways:
- Damage to Brand Value: Any irrational transactions or business operations carried out on the wrong data set can create lasting damage to the reputation a business has in the market. For example, failure to validate data authenticity by a bank could lead to it enabling seamless access to financial instruments by illegal entities such as black-market dealers, banned terrorist organizations, etc. A single event like this could lead to the bank suffering years of damage to their goodwill and can even lead to the closing of the business due to poor customer patronage since the incident.
- Loss of Opportunity: Failure to capture and process the right data may prevent a business from utilizing a crucial market opportunity and ultimately lose out to their competitors who were more alert in managing such critical data-driven opportunities.
- Actual Revenue Loss: Poor data quality can cause direct revenue loss to organizations when departments or sub-functions within the business fail to supply the right information for transactions and operational activities. For example, the failure of the marketing team to analyze the worth of a customer segment due to poor data quality can result in investments in marketing programs going in vain without any results thereby causing loss to the organization directly without any ROI.
Now comes the important part – How can organizations prevent revenue loss due to poor quality data?
Create a culture for data quality
Human error can often be the biggest driver of quality issues as far as data management is concerned. The entire organization needs to cultivate a culture that supports quality data generation and management. Leaders must ensure that employees are well aware of the priority the business has for data quality and the issues that can come up due to poor data quality. There should be regular sessions and events within the business that involves all stakeholders, and such events should focus on collaborative thinking and execution strategies by all team members to ensure quality data is put to use.
Invest in Data Standardization
Getting a grip on data quality requires organizations to first create and implement a standardized data collection and recording mechanism. This is where standardization of data comes into play. Through standardization, only the right data with the appropriate filters are recorded in the organization’s digital stream in formats that are required for further analytical processing.
It is important to know that a business whose core expertise is not data management may not be self-sufficient to deal with their data quality problems as they come in all sizes. For this, organizations require expert advice to ensure that all digital channels and platforms they utilize within the business are supplied with only the right data after rigorous quality checks and validation using a combination of best practices, tools, and automation.
Encourage Continuous Quality Management
Steps and practices put in place for ensuring data quality need to be continuously encouraged within the organization and not perceived as a one-time investment or exercise. New data streams may be generated within the organization as they begin to rely on new digital platforms for various business operations. These streams need to be synced with existing quality workflows to ensure that overall data quality within the organization is in line with expectations.
Poor data quality can cause severe damages to a business. As illustrated above, it can be a huge source of revenue loss as well. By leveraging the guidance and services of data management specialists to manage quality, organizations can ensure that their digital ecosystem functions efficiently with a consistent supply of accurate data.