The Million Dollar Bad Data Risk: How Inaccurate Data Can Bankrupt Your Business

By: Matthew Klepac, Vertify CEO and Founder

The value of accurate and reliable information for each and every business cannot be overstated. But, some level of inaccurate data is also inevitable for every business. After all, data never takes the happy path – there are simply too many variables at play for data to always be perfect and clean. For businesses, data is not just a tool for decision making; it’s a strategic asset that can make or break an organization. When data is good, it can pave the way for growth and success. However, when it’s bad, the consequences can be catastrophic, potentially leading to millions of dollars in losses. 

Speaking from experience, I have witnessed organizations that I have either worked in or with have both data successes and data failures. Companies that have struggled with contradictory, redundant, and fragmented data across application silos know the power of data failure all too well. I am here to tell you that ignoring this challenge is not the answer. When you choose to embrace a happier data path solution, you will see results that propel your business – instead of tearing it apart. 

In this post, we are breaking down areas that companies can lose big on if they ignore data quality. Don’t be another data quality statistic – learn how data automation and integration can (and should) help you get a grip on your data quality woes, once and for all. 

Inaccurate Data’s Many Faces

Inaccurate data can manifest in several different forms, including:

• Inaccurate data: Information that is incorrect or outdated
• Incomplete data: Records that are missing critical information essential for decision making
• Inconsistent data: Data that varies in format or structure, making it difficult to integrate and analyze
• Duplicated data: Repetition of data entries and records, causing confusion and skewed analyses
• Irrelevant data: Information that is not pertinent to the business’s goals and objectives, causing noise and confusion in your systems of record

Do any of these resonate with you? If yes, continue reading and let’s get a grip on this, together. 

Related: The Cost of Inaction for Data Silos

Financial Consequences of Inaccurate Data

I am likely preaching to the choir here, but any post that talks about data quality challenges must include the actual financial consequences that are ahead if you choose not to tackle the data quality monster. 

Misinformed Decisions

Inaccurate and incomplete data will lead to misguided decisions, whether in product development, marketing, sales, or resource allocation. This can result in wasted resources and lost opportunities. Forecasting against bad data is a recipe for disaster. Allocating marketing budget while benchmarking against it is futile. Making decisions based on it leads to loss of jobs, not hitting goals, angry customers and prospects, and disappointed investors.

Skyrocketing Customer Churn

Inaccurate customer information can lead to poor customer experiences, causing churn and lost revenue. If you are communicating to your customers with bad or inaccurate customer data as your backbone, you will not be able to speak into the listening of your customer base. And, they will simply start to ignore you, or worse they will leave like a ship in the night. It’s estimated that inaccurate and incomplete data is costing businesses up to 20% of their revenue annually. Once again, you do not want to be that statistic. 

Legal Issues

In certain industries, compliance is critical. Data quality issues that lead to non-compliance can result in hefty fines and legal troubles. In my previous life before Vertify, I worked in the media industry. More specifically, I worked in the content rights industry for large broadcasters and MSOs. If these companies distributed content in the wrong market at the wrong time, fines and legal trouble were imminent. Likewise, here at Vertify, we work with companies across financial services, healthcare, manufacturing, and higher education, all of which can’t afford to allow inaccurate data to hinder their operations and compliance. 

Productivity Hits Across Industry

Using manual or brute force to attempt to clean and maintain data across all of your customer databases is not sustainable. Human hours spent on data cleaning and preparation can add up quickly. In addition, if you are asking your staff to conduct these manual data reconciliation efforts, you are pulling them away from revenue generating or more high value activities. When staff is focused on data quality correction day in and day out, culture and satisfaction plummets. This leads to a revolving door of great people that simply do not want to live in the data dungeon.

Brand Damage

Poor data quality can tarnish your business’s reputation. Customers who experience errors or inaccuracies are likely to lose trust in your brand, which can be incredibly difficult to regain. Negative word-of-mouth and social media backlash can further exacerbate this problem.

Did I Mention the Operational Nightmares?

Let’s take some real world examples from our customers and illustrate the impact that inaccurate data can have on your business operations.

1. Supply Chain Manufacturing Company: Inefficient processes was bleeding the company dry. Inaccurate inventory data was leading to overstocking and under-stocking, impacting supply chain efficiency and profitability. This had to be resolved in order to stop the bleeding. That is when Vertify was brought in to automate workflows across revenue channels and improve data quality and governance in transit. 
2. Software Technology Company: Customer service failures were costing them thousands. Data quality issues were leading to customer service nightmares, with customers receiving incorrect information, products, and billing. You guessed it – workflow automation and data hygiene components from Vertify helped transform these challenges into opportunities for the org. And, they did this using automation, not human intervention. 
3. Mid Market Automation Software Company: Marketing misfires were costly. Inaccurate customer data resulted in poorly targeted marketing campaigns, which not only waste money but also damage a brand’s reputation. Every time untrustworthy data was allowed to flow from marketing to sales, the cycle simply continued. With automated data movement and quality powered by Vertify, they transformed from untrustworthy to trustworthy. 

The Cost of Fixing Inaccurate Data

So, how much does this really cost? Cleaning up bad data is not only time-consuming but also expensive. Companies spend significant resources on data cleansing and validation processes. According to Gartner, the average company spends about $15 million per year on data quality.

And, if companies do not make that investment, in most cases, they will lose twice that in return. The cost to fix this data, however, does not have to cost $15 million per year. If you choose to throw your own internal staff at the problem, you will see spend balloon and you will not be able to keep up. There is a better way.

Preventing the Million Dollar Risk

So, can we agree that you can’t afford to constantly run in circles with the bad data within your organization? And, do we also agree that you can’t logically spend $15 million a year to try to keep up with your data hygiene challenges? To mitigate the million dollar risk associated with inaccurate data, consider the following strategies:

• Invest in Eliminating Data Silos: Believe it or not, siloed data is a direct contributor to low quality data. So, consolidate data from multiple systems into a central hub, to start. 
• Invest in Automated Data Quality: Make data quality a priority, investing in elastic data management tools and processes. Data management tools that put data at the center of their products and processes are critical to your success. 
• In Transit and Regular Auditing: Regularly audit and validate your data to ensure its accuracy and reliability is critical. Your data management and automation platform must be able to monitor, alert on, and fix issues on the fly. 
• Data Governance: Implement strong data governance policies and practices to maintain data consistency and integrity. Ensure your data automation platform follows these rules as well.
• Employee Training: Train your employees on the importance of data quality and accuracy, and encourage them to report any discrepancies both internally and to your data automation partner.
• Data Reporting, Error Handling, and Analytics: Leverage data analytics to identify and rectify bad data sources proactively.

Related: How to Resolve Data Quality Challenges

Let’s Wrap This Up

In the digital age, data is the lifeblood of business operations. Inaccurate data isn’t just a minor inconvenience; it’s a million dollar risk that can threaten the very existence of your business. To safeguard your organization, prioritize data quality, implement strong cloud data management practices, and invest in the tools and training necessary to keep your data clean and reliable. In the end, protecting your data means protecting your bottom line and your brand’s reputation.

Vertify offers a broad suite of automated data integration and hygiene solutions, complete with data cleaning tools and automated multidirectional integration between platforms, including multiple CRM integrations at scale. Vertify is the system of record and has the best service for revenue operations professionals across the globe. Spend $15K – not $15M – to get a grip on your data hygiene by getting started today!