Inaccurate data has real-world implications across industries. In law enforcement, inaccurate data could mean booking the wrong person for a crime. In healthcare, it could mean making a fatal mistake in patient care. In retail, it could mean making costly mistakes in business expansions. In finance, it could mean violating sanctions rules and lists. Show
Data accuracy is no longer “just” a component of data quality. It is quite literally the most important characteristic that makes data usable and purposeful. In this detailed guide, I’ll be covering everything you need to know about:
Let’s dig in. What is Data Accuracy?Data accuracy refers to error-free records that can be used as a reliable source of information. In data management, data accuracy is the first and critical component/standard of the data quality framework. A popularly cited work, Data Quality: The Accuracy Dimension by Jack Olson explains form and content as two of the most important characteristics of data accuracy. Here’s an example: In the US database, dates follow the MM/DD/YYYY format, whereas, in the EU database and other countries of the world, it’s DD/MM/YYYY. If 09/10/2020 was converted to content, which of the two do you think would be accurate? Is it September 10th or is it 9th October? This is the classic meaning vs form problem that threatens data accuracy. And this generally happens due to the lack of data standardization and rules. It’s imperative that any data stored in a data warehouse is accurate and appropriate for use. It impacts an organization’s business intelligence, forecasting, budgeting, and other critical activities. If the data is irrelevant, incorrect, incomplete, and inaccurate it can disrupt processes & hamper operational efficiency. What are the Causes of Data Inaccuracy?The causes of data inaccuracy are many. Some of the most common causes are: Poor Data Entry Practices: Data inaccuracy is the outcome of poor data entry practices. An organization that does not have data governance in place will see data entered in multiple formats, styles and varieties. For instance, one customer’s name may be written in three different ways by three different reps. Worse, data acquired from social media is highly prone to mistakes, typos and copy/paste errors. Not Regulating Data Accessibility: The CRM is a good example of this point. Accessed simultaneously by sales, marketing, customer service, and account managers, CRMs can become a hotbed of duplicated, inconsistent, inaccurate data. For instance, a marketing rep may want to verify the client’s company name before publishing a case study, only to see an incorrect spelling or a short form of the name in the Company Name field, that was perhaps typed or modified by a sales rep. The marketing rep will have to go through multiple verification rounds to fix this error. Worse, the client may end up pointing the error if the case study was published without verification. Data Quality is Not Usually Addressed: Teams are too busy with selling, marketing and promotion to think about incorrect information in the data set. Leadership is too busy thinking of investments in cloud, big data systems, fancy software and technologies to worry about data. IT teams are too busy helping leadership in ‘transforming’ to fret about disparate, duplicate, inaccurate data. Data quality or accuracy is not a boardroom discussion matter. It only comes into the spotlight when something goes drastically wrong like a flawed report or an ineffective marketing campaign. All these causes lead to losses in millions. The High Cost of Inaccurate DataSome statistics to drive home the point.
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. Why It’s Essential to Maintain Data AccuracyInsights. Analytics. Intelligence. The three primary goals companies want to achieve with data. This is not achieved by more data. It’s achieved by accurate, complete, timely, reliable data. It is through these goals that a business makes key decisions such as:
… and so much more. Depending on the industry, data accuracy can make or break businesses. The real-world implications of inaccurate data cannot be ignored.
Download this whitepaper and learn the struggle of hosting data in Salesforce, Fusion, Excel, and other asset management systems, and how data quality is the key factor to business success. DownloadWhy are companies struggling with data quality and data accuracy?Millions are being invested in data management solutions. Yet, an HBR study involving 75 executives reveals only 3% found that they had accurate data within the acceptable range of 97 or more correct records out of 100. Companies are struggling with maintaining data accuracy because their focus is only on gathering more data, instead of making current data usable. In our experience with 4,500+ clients, there are three essential roadblocks preventing companies from maintaining accurate records. 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. Data Hoarding Instead of Being Data-Driven: There’s really no end to data hoarding. 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. A Stubborn Reliance on Outdated Methods and Technologies: In most organizations today, data is still manually prepared using Excel, SQL or ETL tools. 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, or chatbots. 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. How to Calculate the ROI on Data AccuracyROI 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: How much will we make in $ if we spend $ in fixing our data? Well, here’s a non-traditional answer. You increase your ROI when:
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. Here’s an example: 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 3.5% of false negatives and 6.8% of false positives. This figure alone has costed the company hundreds of dollars in sales, manpower hours (in manually reviewing each false negative and positive). If the company had undertaken a data quality initiative serious, they would have prevented 11% of their total records from being wasted. 11% of a million = 11,0000 Assuming each customer pays $100 That’s 11,0000 x 100 = 11000000 Eleven million in wasted revenue. That’s a huge blow to any company’s profitability. Ready to do what’s necessary to improve data accuracy? Here’s what you can start with. What Immediate Steps Can You Take to Improve Data Accuracy?
Stop. None of this is going to work. So, what will? Some immediate steps I recommend would be:
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?
Data profiling is a crucial part of data conversion, migration and data quality projects. Download this whitepaper and find out the ten things to check when profiling your data. DownloadCleveland Brothers Case StudyCleveland Brothers Equipment Company Inc. operates as an equipment dealer and supports a number of industries that include construction, agriculture, power generation, landscaping and more. 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. DataMatch Enterprise was the company’s tool of choice that helped them perform several key operations at once and because the tool is easy to use and requires no additional programming expertise, even the company’s business resources were able to prepare data easily.
Cleveland Brothers Engineering efficiently matches multiple lists to eliminates duplicate records. Read case studyTo Conclude: Don’t Compromise on Data Accuracy. Your Company Needs Data It Can TrustData 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.
Download this guide to find out the vast library of features that DME offers and how you can achieve optimal results and get the most out of your data with DataMatch Enterprise. Download |