At its simplest definition, data quality refers to the accuracy, currency, organization, and usefulness of any types of data. Data quality is a way of measuring these desirable qualities of different types of data, and of identifying areas that need improvements. The principles of data quality management can be applied to various kinds of data, such as transactional data, customer contact data, and supply chain data.
Data quality is a definite concern for businesses with concerns for customer relationship management and continuing improvement of targeted marketing efforts. The main tools of data quality management include content management systems and customizable database software. One of the best ways to improve the quality of your business’s data is to enhance and adjust your database program to your company’s unique needs.
Why Data Quality is so Important
Complete, accurate, current data is essential for building meaningful relationships with target customers. High quality data will in turn translate into information that’s more useful for promoting products and services these customers are likely to purchase. Some areas of focus for this kind of data quality include currency of customers contact information, accuracy of past purchase histories, and timeliness of data migration into meaningful reports that can be used to help with promotional efforts. Data quality management is also essential for generating new sales leads and maintaining their impressions that a company’s products or services are relevant and necessary for their repeat purchases.
Data quality measures don’t only apply to customer data. They’re also necessary for assessing the quality of internal operations, the accuracy of financial data, and the success of certain employee performance improvement goals. Accurate, complete, and consistent data provides a road map for each of these facets of business management. It shows where the company is going, and whether that course may need to be adjusted.
Assessing Data Quality
The process of identifying areas of improvement for your data quality can seem like a complicated process, and low data quality doesn’t always have an easy solution. In this kind of scenario, the best approach is a well-planned one with solutions that can be adjusted as necessary. The most common problem areas in data quality include completeness, relevance, and accuracy. Two steps of simplifying this problem-solving process include understanding data quality’s core definition and knowing the key differences between clean data and good data. Good data provides complete and useful information to specific questions related to the company’s overall objectives. Clean data is simply consistent and accurate, greatly increasing its usefulness when gathering information.
To assess the quality of any type of data, it’s essential to devote some time and technology resources to the analysis of the data, uncovering common patterns and trends. This process will allow you to make solid, evidence-based decisions concerning steps towards data quality improvement. Using quality data is vital for planning any initiatives concerning performance improvement, whether the goals entail the improvement of sales numbers, product performance, or employee performance.
CRM Software and Data Scrubbing
A good enterprise-level CRM platform will allow you to enter current sets of data with different filters and restraints designed to assess its current quality. The same software has additional features that provide areas of weakness or inconsistency in the data. With a good CRM program designed for data quality assessment and evaluation, you as a business leader will be able to implement new and improved procedures and policies about the gathering, maintenance, and application of various types of company-related data.
To accomplish these goals with a CRM software platform, the management side of your business will need to collaborate closely with an information technology department, whether in-house or outsourced. Management has the responsibility for creating the rules for governing the use of data, and also for the generation of informational reports. The IT group or department is responsible for creating, updating, maintaining, and customizing the CRM platform, along with any periphery databases and hardware needed. Consistent and frequent communication between these two divisions is essential for keeping a high level of data quality.
Once these technology tools are in place and the rules are in place, your management and IT departments can start with the process of data profiling, which will uncover common weaknesses and gaps in different types of entered data. The top-rated CRM software uses algorithms that discover inconsistencies in the data before it’s stored or used for various objectives. This kind of software allows technicians to set specific data rules that will automatically correct redundancies, inconsistencies, or inaccuracies. The built-in algorithms therefore allow for data scrubbing, which will lead to improved data quality with minimal need for manual changes to the data rules.
Data Quality and Pattern Analysis
Enhancing the quality of a CRM database also involves pattern identification through the use of CRM data rules. These tools within the CRM platform can analyze several different types of string data at once, discovering common types of data records and the percentages of data strings that conform to the established data rules. When this pattern analysis returns a higher percentage of inconsistencies and inaccuracies, the software can return additional rules and constraints that will scrub these mistakes and help prevent future iterations of the same gaps in data quality.
Using the steps of data profiling and pattern analysis, you can uncover more detailed information about your customers, your products’ performance, and many other facets of your business you may not have known about without the use of this kind of enterprise-level CRM software. The process of data quality analysis, improvement, and maintenance is a time- and resource-intense one, and this kind of technology tool can make it much easier as well as produce better results.
Good data quality translates into useful information that will help with your making the best operational decisions. Ringlead’s data quality management tools can automatically remove duplicate entries, standardize and streamline all types of common data, and pull contact information from several online sources. Our product line includes a comprehensive set of software tools that will enhance, scrub, and secure your company’s customer contacts, B2B contacts, sales lead data, and any other essential types of data.
Our complete service suite will make day-to-day operations much easier for your sales personnel, providing them with more comprehensive and accurate information about your company’s target customers. When it comes to effective data quality management, this platform will keep all of your business data from various sources organized, consistent, and accurate.