Data quality is a key component of a business’s long-term success. The factors that determine the quality of the data include completeness, consistency, accuracy, validity and timeliness. High quality data provides accurate and timely information. It helps in better decision making, increasing productivity and customer retention, increasing revenues as well as ensuring better customer experiences and compliance with the industry standards. However, obtaining high-quality data is not an easy task. The biggest challenges faced by enterprises today include improving the data quality and sustaining good quality data output. Data quality and completeness are a major challenge for many organizations. Partnering with an experienced data entry company could help businesses obtain high quality data in specified turnaround time.
A study by the Experian QAS has revealed that nearly 45% of business enterprises do not even possess a data quality strategy. These companies also feel that implementing a data quality strategy can turn out to be a daunting task. Poor quality data can lead to poor decision making, customer dissatisfaction, increased operational cost and a reduced ability to make and execute strategy. In addition to that, it hurts employee morale, breeds organizational mistrust and makes it more difficult to align the enterprise. Therefore, it is essential to maintain data quality. Here are some effective steps to take to improve data quality.
- Data Profiling – This technique analyzes the correctness and uniqueness of data. It also examines whether the data is reusable by collecting appropriate statistics. In the same way, data mining tools can be used to assess data quality. Experienced data entry service providers can make data mining easier with the help of advanced software.
- Data Normalization – As data is collected from various sources, it may include a variety of spelling options; this may confuse CRM(customer relationship management) systems as they see them as different data points. Therefore, data standardization is required to establish a singular approach and to remove redundancy.
- Semantic Metadata Management – When the number and variety of data sources grow, end users in different parts of an organization may misinterpret some of the data concepts and terms. Therefore, centralizing the management of business-relevant metadata is required. This will help set up corporate standards and reduce inconsistent interpretations.
- Data Quality Firewall – As data is a strategic information asset to an organization and has a huge financial value, it must be protected. A data quality firewall uses software to keep data error free and non redundant.
- Understand the data – A large number of resources are used for collecting and maintaining the data. If business enterprises collect data that is not required, it will incur huge costs to them, as it involves various processing and storage fees. This can even make it difficult for enterprises to accurately track the data that is needed. So, it is important to spend some time to understand each piece of data that is collected and ensure that they are of some use. If unused data is accumulated, it would reduce the overall data quality. Businesses must be as forward-looking as possible and must focus only on data that is necessary for accomplishing the goals. They must also ensure that the data is coming from knowledgeable and trusted sources.
- Validate data – Businesses should follow the basic step of validating data systematically or manually according to the business rules in order to get clean data. Errors in email or mailing lists can be easily corrected by using scrubbing programs. Typically, data verification tools helps fill in any missing details before formatting the address to comply with the email standards. To prevent bad data from entering the business database, incomplete elements are highlighted by the software as the information is being keyed in by the staff. When all this is done, the business database must be checked to ensure that the information is complete and standardized.
- Correct all the data issues at the source – If there are any issues including incorrect financials, errant codes, missing data elements, and so on with incoming data, then it is crucial to go all the way back to the original source to make corrections. The simplest way to do this is to establish a specific contact at each vendor or other data provider that can resolve issues as they occur. Businesses must also schedule regular meetings to keep things running smoothly.
- Be vigilant when bringing in new data sources – On-boarding data from new sources needs a higher level of vigilance to safeguard against improper data conversion or data loss.
- Limit free-form text wherever possible – It is desirable to limit free-form text to names, addresses, short descriptions as well as notes. Instead, use codes or lookups in all places. In fact, coded fields help improve accuracy, facilitate reporting and ensure consistency across the board.
- Consolidate data into a single system – Businesses should avoid using multiple systems to track data. Maintaining separate systems is expensive and may leads to workflow, data consistency and reporting problems. Consolidating data into a single system provides companies more control over the integrity of data coming in and going out.
- Single administrator for the system – Businesses should assign the responsibility of adding or altering fields, codes and locations to a single admin user or user group. Any potential changes to the database should be reviewed and approved through a clearly defined change-control process.
- Run reports regularly – Organizations should regularly run a core set of baseline reports to review the data and identify anything unexpected.
Outsourcing data entry tasks to a reliable data entry company is a practical and affordable way to improve business data quality.