For any data-driven business, bad data can lead to bad decisions. For any data driven organization, data professionals must protect the quality of the data they handle to fit their business requirements. Maintaining the quality of increasing volumes of data is growing to be a challenge for the smooth functioning of many businesses. For those in retail business, wrong customer contact can create delivery issues and customer complaints and thus result in missing marketing opportunities. Data entry companies can assist such organizations with online/offline data processing support.
Harvard Business Review had earlier reported that bad data leads to an annual cost of $3 trillion for U.S. businesses. These costs include wasted human effort and resources, undetected fraud or compliance penalties, and indirect costs ranging from lost customers to decreased brand value.
Useful data is data that is complete, valid, unique, consistent as well as accurate. Having appropriate data quality processes in place directly correlates with an organization’s ability to make the right decisions and ensure its economic success.Protecting contact data is crucial to stay in contact with your customers. Every firm should implement a well-executed quality program that involves key steps such as performing an audit, establishing standardization rules, updating data as frequently as possible and establishing data quality responsibilities across teams.
Key best practices to improve data quality include:
Data quality cycle
By adopting best practices like data quality cycle, you can ensure quality and accuracy of data. This quality assurance cycle is made up of the following phases:
- Based on your business requirements, first define data quality goals that form part of the overall data quality strategy
- Understand what data needs to be analyzed
- During data analysis, check data value, validity and accuracy
- Data cleansing and enrichment helps with systems and business processes
- Continuously monitor and check data to ensure quality
Most of the technologies available in the market to assist in data profiling, quality functions and issue tracking today are now aligned with the QA cycle and it provides in-depth functionality to assist various user roles in their processes.
Application programming interface (API)
Data quality tool with an API is of great help in data management. Application programming interface (API)integration allows one application to interact with another application. With this technology, your staff does not have to switch between different applications, which saves time and reduces the chance for errors. API integration also allows real-time update of customer information, minimizing bad data storage in your CRM system. DataMatch Enterprise is one such system that comes with an API to allow you to achieve all the cleaning, matching and address validation you need without leaving your main system. With API assistance, this system can make calls to DataMatch Enterprise and always present you with clean and accurate data.
Machine learning in data management helps enterprises to ensure that their staff is working with accurate and reliable data. Implementing machine learning algorithms in data management system scan help you deal with any data type.Machine learning can be integrated into key areas such as understanding and quantifying data quality, better data matching and de-duplication as well as data enrichment. It can also be leveraged to monitor, score, and improve the data quality to stay ahead of data challenges.
Data matching, a key part of the standardization processes could also be automated by making machine learning tools to learn and predict the matches routinely.
Follow standardization rules
Data standardization refers to transforming or directing data into a consistent format. Standardization rules play a key role in preventing bad data. With these rules,
- all numbers and nominal values should be converted to a consistent representation
- case sensitivity should be eliminated unless it is necessary
- salutations and spellings should be normalized according to a standard dictionary
- abbreviations should be converted to their long forms or vice versa
Make sure that these rules also apply to internal systems as well as customer-facing ones.
Data wrangling is the process of cleaning and merging any complex data sets for easy access and analysis. In this process, data will be manually converted or mapped from one raw form into another format, which allows convenient consumption and data organization. These outputs are ideal to be used in analysis and for a variety of other business purposes.
Implement a data governance strategy
As reliable data sets are crucial to evaluate enterprise performance and make management decisions, such a strategy helps. Implementing data governance in your organization to ensure the data you are using is clean, accurate, usable, and secure. This also helps to avoid duplication of effort within your business. It is also important for organizations to make sure that people and processes are in place to maintain consistent data quality procedures as well as regulatory compliance.
Data entry services provided by an experienced BPO company can help businesses in any industry to manage their data with better quality.