Menu & Search
Data Hygiene: The Real Cost of Data Entry and 5 Tips to Improve Data Quality

Data Hygiene: The Real Cost of Data Entry and 5 Tips to Improve Data Quality

When we talk about Data Hygiene, we are talking about clear concise data that is impervious to errors, therefore deferential to chaos. With stream of new data coming in everyday in greater volumes, data management can become overwhelming.

Such overwhelming odds are what give rise to mistakes by businesses while managing data. Duplicate data, spelling errors, lost data become common practices for businesses undertaking their own data entry and word processing services.

This unkempt egregious data is the real bane of any enterprise trying to sail in the dangerous waters of modern business. Businesses end-up losing tons of money, time and market standing dealing with data.

Here are some numbers to back it up.

In a recent report published by Data Warehousing Institute, it is estimated that businesses in the U.S.A lose around 600 billion dollars a year due to data quality issues. An IBM study suggests that bad data can cost a company around $3.1 trillion every year. The same study also goes on to claim that almost 30% of business leaders are not confident about the data they are using.

Why Dirty Data Costs So Much?

The above numbers are testaments to how bad a case of dirty data can be. Dirty data is data riddled with inaccuracies and redundancies – a perfect recipe for disaster. Now, you might be wondering how dirty data can be so expensive.

Well, everyone dealing with data like data scientists, business leaders, sales executives, managers etc. have to incorporate this data into their work. Imagine the confusion an erroneous data can cause when you have to handle it in overwhelming volumes. Thus, there has been a dire clamoring for data management by business leaders who do not want their goals of success to be hindered by the incompetent handling of such data.

But, enough with the problems. You are here for solutions. So below are 5 tips that are going to help improve your data quality.

5 Tips to Improve Your Data Quality

1.Partner Up with a Company Specializing in Data Management

When undertaking data management, you must first determine your internal capacity of getting the job done. Most commonly companies do not have the expertise or technology needed to carry out efficient data management. This is where third party data entry service providers come into play. Partnering with them will not only get experts working on your data, but also cut your operation costs by 30% to 60%.

2.Machine Learning

Machine learning is one of the most potent solutions to the issue of dirty data. Machine learning has been known to enrich data more efficiently than any other method. It uses reactive data maintenance protocols to improve data quality. It also eases the process of data discoverability so relevant third parties can find it.

3.Hire In-house Data Experts

If you don’t want to outsource your data management tasks, then the alternative is to hire an in-house staff that specializes in data handling and nothing else. Their job would be to process, capture, and cleanse data that is valuable to the company. Hire personnel that have an experience that is needed to get the job done quickly and efficiently. With a team dedicated to handling a crucial part of your business, you can focus on other core areas of your business.

4.Eliminate Data Preparation Silos

Never keep your important data in a state of silos. It should never ever be an inter departmental competition. Data in silos can lead to multiple problems if the data is to be referred by multiple departments and stakeholders who rely on the same information.

Develop groups that are solely in charge of data collection, provisioning, and preparation. This will not only promote collaboration amongst your team, but also help in eliminating data silos.

5.Normalize Your Data

Data standardization is crucial in establishing a common approach to entering data points. It is imperative as data is collected from a variety of sources and come in a variety of spelling options. For e.g. the United States can also be recorded as U.S or U.S.A. This can directly impact smart lists, data segmentation, etc. You can use Data management software like ‘Marketo’ to normalize smart campaigns or hire a third-party service provider to do the job for you.

Maintaining Data Hygiene

The issues with data management are aplenty and overwhelming to take care of. It is often difficult to find and store; it is often duplicated, and there are frequent issues with data security. Thus, it is crucial to adopt a proficient data management plan. 

The above tips if followed diligently can do wonders for you when it comes to getting rid of dirty data. Data today is the beating heart of any business, as such it should be taken care of just as you would take care of your own heart.

About the Author: Patricia Dolan

 Patricia is a Senior Content Marketing Strategist working for Perfect Data Entry, an offshore data entry company that is lauded in the industry for their top-quality outsourcing services and unparalleled price. The company is known for its clean data entry and word processing around the globe. Throughout her long reputable experience working as a content strategist, she has helped her company create content that both engages and converts prospects into loyal clients. Over the years Patricia has been the recipient of many accomplishments and rewards. She is a master of her field and continues to add great value to the digital industry.

Leave a Comment

Copyright © 2018 All rights reserved. All other trademarks are the property of their respective owners. Protection Status
The terms "GoJek", "Uber", "Zomato" and many more are the popular brands located all around the world. AppCloneScript has no connection with these brands, clone uber used in our blogs just to explain their workflow with clarity. Our purpose is just to spread awareness and we wish not to cause any harm or disrepute any company.

Trademark Legal Notice : All product names, trademarks and registered trademarks are property of their respective owners. All company, product, images and service names used in this website are for identification purposes only. Use of these names,trademarks and brands does not imply endorsement.