Tips to Improve Data Quality

Tips to Improve Data Quality

In a survey covering B2B records, businesses are facing multitude of challenges with regards to data entry services. Focus is more towards a data driven culture in both public and private sector organizations across the globe. As per 2013 QAS survey, it is said that 94% of businesses believe there is some level of data inaccuracy. When data is accurate and reliable, it can transform businesses. When you outsource your data entry services to an offshore location, it becomes even more imperative to check on data quality. Below mentioned are few of the tips to improve data quality.

Check for Data Quality at the Source

Before you start processing the data, check whether the data at the source is good. If the source of data at the beginning itself is bad, then it is a case of Rubbish in - Rubbish out. Instead of analyzing the data after it is being processed with regards to timeline, quality and accuracy, check on the source of the data at the start. This is an important step in the process of improving data quality, and if you fail here, then the whole data processed after this stage is a waste of time and effort.

By creating a cross functional data governance team

Before working on a data entry outsourcing project, set up a data governance team where authority and accountability is well defined. Here you can break the data and bring in the right people at the right time to make informed decisions. The team can also serve as a forum to assign priorities and to escalate any data issues. The data governance team ensures that the data is being cared for across the organization and right decisions are being taken.

By identifying broad level root causes

The source data could be wrong due to various reasons such as extracting data from different sources with contrasting information, mistakes happening while manual entry of data, vague understanding data processes etc. In fact, the more you delve the possible sources of mistakes keeps on increasing at the enterprise level. Hence, identifying broad level root causes at the beginning of the data processing makes sense, if not, it may come back and haunt you with a larger problem which will be difficult to solve in the future. While identifying broad level root causes, always remember the 80-20 rule where you are selecting only few of the segments of the data which is causing the maximum damage and try working it from there. This helps you to isolate the causes better and resolve them in quick succession.

By creating a sustainable solution

With constant addition of new data / changing of existing data, achieving data quality cannot be a one-time exercise. The solution to the problem should be sustainable for a longer period of time. Temporary tweaks can fix the issue for the time being, but errors sneak up in due course with that approach. Automation certainly helps in reducing the number of errors and improving the data quality. The more of automation and the more sustainable would the initiative be in the longer run. Proper training and communication channels will help to build awareness during data processing which in turn will help to reduce the errors.

Achieving perfect data is a never ending chase

Enterprises need to remember that getting 100% data accuracy is not the final objective. But the insight that you will be able to derive is the main goal. Do not wait till you get everything right, move ahead if you believe that the data is accurate within a certain range. Enterprises need to balance their efforts and time with a reasonable outcome. Even if your data is 5-10% off, is good enough for analyzing and to take decisions based on that. Improving data quality is an ongoing process and it takes time to get it right.

The importance of measurement and metrics

There are various elements that combine in determining the data quality such as relevance, data accuracy, legitimacy, completeness, accessibility and timeliness. Measuring data quality is very important; if not how else would an enterprise show progress with regards to data improvement initiative. The metrics chosen should be useful and of relevance to the end-user and which directly links to the business performance.

Obtaining high quality data is a never-ending quest and it requires a collective effort from both the client and the outsourcing partner to work together to achieve robust data quality and accuracy rates. Unless there is awareness about the data quality within the organizational culture, we will not be able to utilize the potential of the available data to its maximum extent.


About Invensis:

Invensis is a leading outsourcing data entry service provider with 15+ years of experience. The company offers you best in class data entry services which is both accurate and of highest quality. Invensis provides data entry services which include: order entry and processing; purchase order management; transaction data processing; data mining; CRM database management; records indexing, survey forms processing and more. We are in an elite group of top ten outsourcing data entry service providers in the world and we provide services and solutions to our clients which helps them to accelerate growth, cut on cost and improve their day-to-day business operations.


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