Why accurate data is important

It only comes into the spotlight when something goes drastically wrong like a flawed report or an ineffective marketing campaign. These statistics prove that inaccurate, poor data is a persistent problem in most organizations and one that has a tremendous impact on ROI, company reputation, and customer confidence.

The three primary goals companies want to achieve with data. This is not achieved by more data. Depending on the industry, data accuracy can make or break businesses. The real-world implications of inaccurate data cannot be ignored. Millions are being invested in data management solutions.

Companies are struggling with maintaining data accuracy because their focus is only on gathering more data, instead of making current data usable. Poor Data Culture: Companies have not yet truly embraced a data-driven culture. There is significant investment in technologies but little to no investment in data awareness training.

Employees are mostly oblivious to concepts like data quality or data accuracy. For a long now, these practices have been restricted to the IT department.

When it comes to customer data, business employees make changes at will with no adherence to any defined standards or data quality protocols. These gaps make it impossible for companies to achieve data accuracy, therefore compromising on data integrity. Companies are spending millions in big data technologies gathering more data every passing day.

But there is no system in place to make sense of that data. There are no resources available to clean, sort, manage the data in time. There is no automation and definitely no processes put in place to resolve data quality issues which leads to the third obstacle.

All of which are incapable of handling the complexities of modern data — especially customer data obtained via social media, third-party vendors or web forms. Rife with errors, inaccuracies, and oddities, this data cannot be manually treated or prepared as it would take months for a company to clean and match thousands of rows of data.

Not acknowledging the urgent need to ensure data quality hampers your progress and affects your ROI — which leads to our next important point below. ROI determines all if not most corporate decisions. Like every other process, we will tie our best to justify spending on data quality by measuring its ROI and most executives will use a traditional approach to this:.

Fact is, the ROI of any data quality initiative is elusive. But the cost of poor data is pretty much evident. Businesses lose millions of dollars annually because of duplicates, outdated data, incomplete data, mismatched data, inaccessible and disparate data.

A subscription-based online learning company needs to match a million records obtained from three sources: lead forms, CRM, customer service. Using traditional ETL tools, the company performs the match but ends up with 3. This figure alone has costed the company hundreds of dollars in sales, manpower hours in manually reviewing each false negative and positive. Eleven million in wasted revenue.

You may be tempted to hire a data analyst. Or perhaps change your CRM. Or maybe task your IT teams again to come up with a solution. With these immediate steps, you can prepare your teams to handle essential business operations such as an upcoming migration initiative, a major promotional campaign or a business intelligence report. The fundamental goal to possessing accurate data is to ensure data integrity. Your data can make or break your business. Do you care enough to fix it?

Cleveland Brothers Equipment Company Inc. This means the company deals with multiple customer data sets coming from multiple sources, with multiple interests and needs. The company needed a data quality management solution that could allow them to dedupe data , cross-reference contact information such as names, phone numbers, billing addresses, and company names.

They also needed the solution to help them with data cleansing and data standardization. Download case study and see how the leading equipment reseller saves time managing multiple customer lists. Data quality is the goal. Data accuracy is the outcome. With the right human and technological resources, your company will be in a better position to step into the future confidently. How best in class fuzzy matching solutions work: Combining established and proprietary algorithms.

Cutting right to. At the heart of AI technologies are algorithms that use data to make predictions and iteratively refine these models as more data is collected. AI models need little human intervention after being deployed, which puts a premium on accurate data. While AI continues learning on its own, it cannot tell if it is using inaccurate data. This means that the predictions made by AI models could be flawed or incomplete, which could impact customer relationships, competitiveness, and revenue growth.

Data accuracy is the hidden pillar of the digital enterprise. The industry trade press is replete with dazzling success stories of up-and-coming technologies like AI, Customer Relationship Management, Supply Chain Management, Digital Marketing, and more.

But what is often left unsaid — and is poorly understood — is the criticality of feeding accurate data into these technologies, and the hard work of data governance to ensure that accuracy. Why Does Data Accuracy Matter? Bad data doomed the mission.

What is Data Accuracy? Businesses can: Increase revenue. Reliable and cleansed data supports effective decisions that help drive sales. Save money.

Up-to-date and accurate data can help prevent wasting money on ineffective tactics, such as sending mailers to non-existent addresses.

Improve customer satisfaction. Accurate and current data about your customers will help your marketers deliver the right messages at the right time and in the right place to move potential buyers to the next step in their customer journey. Save time. Properly governed data should require less time and money to remediate.

In insurance, bad property information could cause revenue to be lost on premiums if they are set too low because of the data. One example is where property locations are estimated, instead of precisely specified. In most cases, that might not matter, but where the difference is a property — or a whole neighborhood — located inside or outside of a flood zone, revenue losses could be significant.

Reputational damage: Reputational costs range from the small, everyday damage that organizations may never be aware of to large public relations disasters. This is a BETA experience. You may opt-out by clicking here. More From Forbes. Nov 10, , pm EST. Nov 10, , am EST. Edit Story.