Using data effectively improves the health of a business, helps it move ahead with new ventures easily, avoid unnecessary risks, and improve customer service and satisfaction. Business process outsourcing solutions play a major role in helping businesses maintain quality data. To make the best use of their data, organizations need to manage it properly. If data is not managed properly, it can lead to erroneous records, poor decisions, wasted time and effort, and other problems. Gartner estimated that poor quality data cost businesses $15 million on average in 2017. According to a Forrester survey commissioned by Capital One Software, companies are facing multiple data management challenges.
Key Data Management Challenges Organizations Face
Tech Target defines data management as “the process of ingesting, storing, organizing and maintaining the data created and collected by an organization”. Without proper data management, organizations will end up with data quality problems and inconsistent data sets that affect their ability to leverage business intelligence and analytics applications, and even lead to flawed results.
A Forbes article published in August 2022 summarized the organizational data management challenges identified by Capital One:
- 76% of respondents said they found it was difficult to understand their data
- 82% of data management decision-makers reported that controlling and forecasting data costs was challenging
- Decision-makers noted talent challenges as the biggest obstacle to data management. According to Capital One, organizations lacked staff, in-house expertise, and collaboration between teams and tools to achieve their outcomes.
- About 80% of respondents pointed to lack of data cataloging as a top challenge. This can make it difficult for organizations to understand what data they have, how the data is used, and who owns the data,” noted Capital One.
- Up to 82% of respondents said confusing data governance policies was a major challenge. As companies amass more and more data, proper data governance is needed to make data relevant, easy to find, ready to use, and deliver business value as companies move to the cloud.
Chief data officers, VPs of data platforms, and data engineers are dealing with a significant lack of visibility into the complex data stacks they manage, according to a report from data observability provider Acceldata. The survey, which was conducted by Censuswide, data pipeline failures between 11 and 25 times in the past two years as a result of errors or poor data quality. Lack of data visibility is a major data management pain point.
Costs of Poor Data Management
Gartner estimated that poor quality data cost businesses $15 million on average in 2017. Most organizations don’t realize how poor data management can affect them until the impacts are felt. It can happen that workers may recognize data quality problems, but are unable to convince the management about their views. Not having proper data management strategies in place can have far-reaching consequences. Let’s take a look at the consequences of poor data management:
- Customer experience issues: According to research from Deloitte, an average 71% of consumer data has errors. Data is considered bad if it is obsolete, siloed, unformatted, inconsistent or contains duplicate information. Bad date can cause negative customer experiences. Issues caused by bad data include misspelled names, undeliverable messages duplicate communications, inapt product suggestions, inaccurate transactions and customer service histories. Bad data will affect customer interactions with a company and thwart customer acquisition and retention efforts.
- Waste of time resources: When a company is facing a critical deadline, having accurate data is important to manage things swiftly. If the data has a lot errors, making corrections can be a time consuming and costly process. In fact, Forrester reported that nearly one-third of analysts spend more than 40% of their time vetting and validating their analytics data before it can be used for and strategic decision-making.
- Missed opportunities: Bad data, misanalysis and poor decision-making leads to missed revenue opportunities. For instance, knowing how many new customers the company has acquired in the past quarter is important to set budgets. If the manager cannot get data that can be trusted, the budget would be based more on guesswork than data. A 2019 study from commercial data and analytics Dun and Bradstreet found that nearly 20% of companies lost a customer due to using incomplete or inaccurate information about them, and another 15% reported they failed to sign a new contract with a customer for the same reason.
- Financial impacts: IBM estimated that the financial impact of poor data quality on organizations is $3.1 trillion a year. When companies take decisions based on bad data, revenue declines and expenses increase, affecting their bottom line.
- Compliance issues: Lack of compliance is a major consequence of poor data management. Data compliance laws impact all organizations. They need to identify, classify, and document internal and external personal information to meet data privacy compliance. Poor data management can lead to breaking data privacy laws, compliance issues, and hefty fines.
- Reputational damage: Data management problems can affect a brand’s reputation. Bad data and wrong decisions can lead to products and branding that negatively impact the customer experience and more.
Data Management Best Practices
Here are 6 best practices to ensure that your data management process is on the right track:
- Robust file naming and cataloguing practices
- Metadata for your datasets
- Proper data storage and backups
- Documentation
- Proper training and collaboration across teams
- Security measures to prevent data breaches
Effective data management can boost data accuracy, visibility and access and ensure that people have the assets they need to do their jobs. With reliable, up-to-date data, organizations can respond effectively to market changes, meet customer needs, and reduce compliance risks. Data management also allows organizations to reduce costs, effectively scale data. Outsourced solutions can help. For instance, data processing services provided by a data entry company would cover everything from data capture to scanning of hard copy to document digitization.
Likewise, partnering with an experienced data cleansing service provider can help organizations build clean, consistent, and updated data sets, which is crucial for good data management.
