The Long-term Costs of Data Debt
It’s no secret that many of today’s enterprises are experiencing an extreme state of data overload. With the rapid adoption of new technologies to accommodate pandemic-induced shifts like remote work and changing customer expectations, incoming data is flooding businesses.
Due to this information overload, it can be challenging for a business to take the time to hone in and discern what information may be relevant and what may be inaccurate, outdated, or incomplete. Unfortunately, outdated, incomplete, and inaccurate data can accumulate and result in detrimental ramifications to a business. Such data can harm an organization’s approach to internal operations, financial decisions, customer service, partner relationships, and more. It can be especially harmful to those who use ServiceNow, a cloud-based workflow automation platform.
Before the pandemic, the cost of bad data was estimated to be an astounding 15% to 25% of revenue for companies. With the rapid use of new technologies since then, the cost of outdated, inaccurate, and incomplete data is likely to have grown.
Another way to view the culmination of irrelevant information is through the concept “data debt.” The idea of data debt is that the piling up of outdated, inaccurate, and incomplete data will result in long-term negative consequences to a business. Data debt is counterproductive to the overall growth of an organization. It is an issue that will accumulate over time and stay with the company until remedied, similar to other types of debt.
Data debt can be categorized into three different types – incomplete, outdated, and inaccurate. Incomplete data is information that is missing critical information. Old data is information that is no longer current and needs to be updated internally. An example of this could be outdated internal records. Lastly, inaccurate data does not enable a business or an IT team to have a holistic view of a process or workflow. Instead, it has holes and inconsistent metrics, preventing a company from making accurate forecasts or predictions.
There can be benefits to experiencing a surplus of incoming data. An increase in data can help businesses make more informed and strategic decisions. It can provide insight into how successful a new product is doing in comparison to older models. However, when a business is plagued with outdated, inaccurate, and incomplete data or “data debt,” internal decisions or insights are no longer founded on the correct information.
Outdated, Inaccurate, and Incomplete data
Data debt can lead to a business making incorrect decisions in areas like hiring talent, forecasting sales, developing department budgets, monitoring the success of a product, responding to customer feedback, and much more. Data debt creeps into every aspect of a business and impacts its bottom line.
The good news is that there has been an increased industry awareness of the costs of outdated, inaccurate, and incomplete data. And with that heightened awareness, IT teams have learned of ways to address and resolve data inconsistencies. Outlined below are best practices on preventing data debt from occurring in the first place and how to resolve information inconsistencies.
- Consistently update records as soon the new info is available. Some of the best ways to eliminate inconsistencies are to set solid and explicit processes. One of those critical processes is ensuring that IT continually updates records as soon as new information arrives. It’s also good to have internal notifications set to remind IT staff to check in on how current the data is and if it needs to be updated.
- Develop a data warehouse strategy and take snapshots over time. Inaccurate data can be remedied by data warehousing. Once a data warehouse strategy is in place, it’s essential that IT staff take snapshots of that data over time to store in the data warehouse. Taking snapshots of information and then storing those in a data warehouse enables IT staff to get a comprehensive picture. It allows IT staff to be able to analyze the trends and find any anomalies. Similar to data warehousing, taking snapshots over time is also incredibly effective for inaccurate data.
- Frequently sync technology. The process of regularly syncing technology is critical for identifying and solving data inconsistencies. To do this, there is a process of either “pulling” the data from where it currently lives or “pushing” the data from inside. “Pulling” is widely used in the industry, but it requires giving outside individuals permission to access data (i.e., the username, log-in info, etc.) This can expose data to inaccurate modifications, deletion, and other detrimental changes. Push technology, however, is a newer technique. It has more protection protocols and is a much more secure way to snapshot and data warehouse internal information in near real-time.
- Collect and analyze data across multiple applications or workflows to cross-check. IT teams should use various applications or workflows when monitoring and updating data to help cross-reference one another. Using various applications, instead of just one, to scan data and ensure its relevant creates more barriers and makes it is less likely for inaccurate, outdated, or incomplete data to slip past.
The inflow of data is not going away. In fact, it will only significantly increase in the future. According to analyst firm IDC, the amount of data created over the next few years will be more than all the data created over the past 30 years. With businesses already overwhelmed with the current influx of information, it’s imperative to be proactive now and set solid processes to manage and resolve data inconsistencies. Implementing reliable tracking, ensuring consistent snapshotting, and continuously syncing technology are a few ways enterprises and IT professionals can avoid data debt.
By David Loo
David Loo is the Chief Product Officer for BitTitan and is responsible for driving the product organization. A 30-year veteran in systems and applications integration, David founded Perspectium in 2013 and was a founding member of ServiceNow’s development team and instrumental in creating the foundation for integrating and extending the platform.