In any business organization, there is always a constant flow of data and it is important to prioritize and filter the data so that business executives can easily interpret and analyze the stream of data. Businesses obtain data from various sources that may be in structured or unstructured form. In most of the cases it contains inconsistencies, inaccuracies etc. Getting rid of these anomalies is important to derive data insight and make critical business decisions. With the help of a data entry service, all inconsistent and inaccurate data can be converted into a structured, consistent form.
Accurate data improves data quality and data optimization. Data optimization helps to access, organize and cleanse data to maximize the speed and comprehensiveness in deriving valuable insights. However, studies show that most datasets have an error rate of 6.5 percent. Some studies show that proper data entry and data management can minimize these errors. There are several methods that help to improve the quality of data and remove errors from the datasets.
- Set a standard: Maintaining a standard for data when it is entered can bring a huge difference in the quality of data. Use standards about how to deal with abbreviations, word use, and its spelling. To set up a standard you should have good knowledge about the type of data that you receive and how it should be formatted. For example, a company in the US will collect information in a different way compared to that of a Chinese or Japanese company. Both companies will have different sets of standards.
- Set values: Once you have developed your standards, the next step is to fix values for certain blocks. Formatting values related to static input consistently can improve accuracy. Drop-down menus can be made for data such as states, provinces, countries etc. This helps to speed up data entry processing, improve accuracy and minimize typos and other common errors. Make sure that you do not use very small fields for states, countries etc as it may not display the complete entry and create unnecessary confusion among the data entry operators.
- Key data value: Data about every customer, client or item should be gathered and these data should be mandatory and easily accessible. This helps to give a completion and structure to the data entry forms. Make sure to keep the data up to date so that the information entered doesn’t get stale and outdated.
- Duplicate data: Duplication of data is quite common in data entry and it can be a waste of time and also slow down data processing. So, to avoid duplication of data you can use duplication detection scans that highlight whenever there is duplicate entry of data. You can also create key data and fixed value fields to improve accuracy. Do not be skeptical about deleting duplicates due to the fear of loss of data.
- Integration of programs: Businesses using various systems should make data sharing process across programs easier. This helps to save time, improve the efficiency of data entry, and also minimizes the chances of data duplication.
- Run reports: Make sure to run test reports routinely so that errors and other issues can be quickly identified. Running reports also offers the necessary information to revise standards and data optimization efforts.
- Create data administration group: Proper data administration prevents organization systems from inflating. They supervise the standards and set the unalterable fields. This function can also be carried out by data stewards who can test and access data tools and do data design simulations.
- Work environment: For employees to work efficiently and productively, provide them with the right work environment and offer incentives for top performers. Similarly, providing proper ergonomics like comfortable chairs, and encouraging good work culture also help employees to work effortlessly and produce error-free data.
Effective data processing with quality data helps to recognize the patterns around customer preferences, product performance and customer segmentation. With reliable data entry services, business organizations can convert unstructured data into structured form. It also helps to eliminate all incorrect, incomplete, duplicate or outdated data sets and thereby enhances the effectiveness of data analysis.