Data Downtime: The Hidden Cost of Inaccurate Data
Have you ever presented a report to your CEO or manager, only to be told that the numbers don’t seem to add up? Have you ever received feedback from a customer that the data in one of your product dashboards is inaccurate? Have you ever had to spend hours troubleshooting data issues instead of completing your work?If so, then you know firsthand the importance of preventing data downtime in your organization.
Data Downtime refers to periods of time when your data is partial, erroneous, missing or otherwise inaccurate. This can be due to a variety of factors, including technical issues, human error, or problems with data sources. Data downtime can have a significant impact on organizations, leading to lost revenue, decreased productivity, and damaged reputations.
In this article, we will touch base on how crucial data downtime is and an example of what our team faced and how we overcome the data downtime
The Cost of Data Downtime
The cost of data downtime can be difficult to quantify, but it is estimated that it can cost organizations millions of dollars per year. Some of the costs associated with data downtime include:
Lost Revenue:
When data is unavailable or inaccurate, organizations may not be able to make informed decisions about pricing, marketing, or product development, which can lead to lost revenue.
Decreased Productivity:
Data downtime can also decrease productivity, as employees may spend more time troubleshooting data issues instead of completing their work.
Increased Risk:
Inaccurate data can also increase risk, as organizations may make decisions based on faulty information.
Damaged Reputation:
Finally, data downtime can damage an organization’s reputation, particularly if customers or stakeholders are affected by the inaccurate data.
The cost of downtime is so huge !
Imagine a scenario where a company relies heavily on data to drive its business decisions. One day, a critical data source becomes temporarily unavailable due to maintenance or upgrades, causing data downtime. As a result, the company is unable to make informed decisions about pricing, marketing, or product development, leading to lost revenue. Employees spend hours troubleshooting the issue instead of completing their work, causing a decrease in productivity. The inaccurate data also increases risk, as decisions are being made based on faulty information.
Experts in the field agree that data downtime can be just as damaging as system downtime, and the consequences can last even longer. Organizations need to focus on improving their data literacy and training programs to prevent human error from causing data downtime.
Many veteran data specialists emphasized the very facts of data downtime and how it can affect an organization. “Data downtime can be just as damaging as system downtime, and the consequences can last even longer” said Chris O’Connor, CEO of Persistent Systems who understands how important the data is. Daniel Newman, Principal Analyst at Futurum Research also stressed “Data downtime is often caused by human error, which means that organizations need to focus on improving their data literacy and training programs” highlighting the need for the quality data.
Real Case Scenario of Data Downtime That The Team Faced
We were developing a Fiscal year report (for the year 2022–2023) for a leading MNC. We have developed the dashboards and it has been used by the Business for many more months. As the fiscal year comes to an end, they wanted to allocate budgets for the next fiscal year. They were doing reconciliation and sharing the reports with business stakeholders and suddenly they realized that numbers did not add up.
They reached out to our team and were saying that the data coming up in the dashboard is not as expected and were insisting us to check. We cross verified the data from the source and did not find any major issues in the data. But the client was insisting to check further. The time was running as they had to make some critical decisions based on the data but unsure of the quality of the data.
How The Team resolved
We connected the data to our quality product to check if there are any issues with the data. We came to know that in the price column there were couple of anomalies detected. One of the the value is “145673.00 USD”. By looking at the value, seems to be okay. But there were no other values close to this. We went back to business saying one of the records is having a value of “145673.00 USD” and asked if the record is accurate. They checked the source application and data was same there but found out that the person who has entered the value made a typo. The value should have been “1456.73 USD” instead of “145673.00 USD”. Imagine the error that has caused by misplacing the decimal points. Fortunately the issue was identified and fixed. This stresses the fact that data downtime is indeed a problem to be addressed but most of us are not spending time to clean the data.
How to Address Data Downtime
To address data downtime, organizations need to take a proactive approach to data management. This can involve several strategies, including:
- Data Quality Monitoring: Organizations should monitor their data quality on an ongoing basis to ensure that data is accurate, complete, and consistent. Many such tools are available and to name a few are Qualdo, Bigeye, Accel data
- Automated Data Validation: Automated data validation tools can help to identify data issues quickly and efficiently, reducing the risk of data downtime.
- Data Governance: Implementing a data governance program can help to ensure that data is managed consistently and effectively across the organization.
- Data Backups: Organizations should also implement regular data backups to ensure that data can be quickly restored in the event of a data outage or failure.
- Data Security: Finally, organizations should ensure that their data is secure, both in transit and at rest, to prevent data breaches or other security issues that can lead to data downtime.
So, yes, Data Downtime is a hidden cost that affects almost every organization. By taking a proactive approach to data management, organizations can reduce the risk of data downtime and ensure that their data is accurate, complete, and reliable.
Data downtime is like a game of Jenga — one missing block and everything falls apart! Don’t let your data be the weak link in your business decisions. Keep it with precision to avoid a Jenga disaster!
Happy Data !!
