Tips to Improve Data Quality

resources - invensis Apr-23-2021222 361
resources - invensis
  • In a survey covering B2B records, it is noted that businesses face many challenges with data entry services. Focus is more towards a data-driven culture in both public and private sector organizations across the globe. As per the 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.


    Apr-23-2021

361

Below mentioned are a 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 regarding timeline, quality, and accuracy, check on the data source at the start. This is an important step in improving data quality, and if you fail here, then the whole data processed after this stage is a waste of time and effort.

Create a cross-functional data governance team

Before working on a data entry outsourcing project, set up a data governance team where authority and accountability are 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 the right decisions are being taken.

Identify 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 the manual entry of data, vague understanding of data processes, etc. The more you delve, the possible sources of mistakes keep 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 that will be difficult to solve in the future. While identifying broad-level root causes, always remember the 80-20 rule. You select only a few of the data segments, which is causing the maximum damage, and trying to work it from there. This helps you to isolate the causes better and resolve them in quick succession.

Create a sustainable solution

With the constant addition of new data / changing existing data, achieving data quality cannot be a one-time exercise. The solution to the problem should be sustainable for a longer period. 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 automation and the more sustainable the initiative would be in the longer run. Proper training and communication channels will help build awareness during data processing, which will help reduce errors.

Be mindful- 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, it is good enough to analyze and make decisions based on that. Improving data quality is an ongoing process, and it takes time to get it right.

Don’t underestimate 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 regarding data improvement initiatives. The metrics chosen should be useful and relevant to the end-user and direct links to the business performance.

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

Invensis is an experienced offshore data entry company with well-trained, data entry experts, guaranteeing 100% accuracy in data entry. With the help of our expert data entry India team, our offshore services will help you achieve significant cost-savings of over 40%. Visit Data Entry Services for more information on our data entry services.

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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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Solution IT Service - invensis
What We Offer at Invensis

Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s,