Over 50% of global executives and organization managers report that they can’t easily get the right data needed to make critical decisions. Despite the ever-increasing volumes of data that businesses handle every day, most businesses, small or large, still struggle to maintain the quality of data required to ensure smooth functioning and streamlined workflow processes.
Data quality management doesn’t necessarily mean periodically weeding and sniffing out bad data, but is a more focused approach towards data control, monitoring and management to avoid any workflow failures or data anomalies. To realize the real value from the data collected, the business has to achieve efficient data quality management.
Here are five key practices for achieving successful data governance and quality management.
Start by Conducting a Data Quality Assessment
The best way to handle data quality management issues is by performing an analysis of the current state of business-wide data. Any information with errors, missing fields, inconsistencies, duplicates and other inaccuracies can often be hard to identify and correct. Bad data can come from both internal systems or external sources like social media channels, external applications and third-party data providers.
Conducting an independent data quality analysis will provide the organization with a more accurate and in-depth report that includes details about the quality of the data. The report can then be used to streamline data management strategy, workflow and production processes. Data quality impacts critical components in workflow, and is a key part of addressing issues like improving lead time: what is lead time?
Build a Virtual Data Quality Firewall
Data is a strategic asset that should be treated as such. The value of data increases when there are more users who are able to make use of it. Any inaccurate data that enters workflow processes, including warehousing and internal decision making will make it hard f to get critical insights and gather actionable data that can help business. Building a comprehensive virtual data quality firewall will block bad data at the point of entry.
Unify Workflow Data Management
Even with the best data management and governance policies in place, that alone is not enough to protect data. With so much data coming through enterprise-wide systems, maintaining peak data quality at any given time can be a challenge. The secret lies in identifying and prioritizing critical data by unifying workflow data management with business intelligence solutions.
In fact, according to the CIO, master data management in businesses will play a bigger role in allowing organizations to clearly determine which data sets are most likely to be utilized and targeted for quality governance and management. Details like customer preferences and purchasing information can be collected and prioritized for analysis.
Focus on The Business Data Users
Considering that business professionals are the stewards of organizational data, it’s important to have end-to-end data quality management policies that target all data users. Having a data governance manager or director to handle all data-related needs of different areas of the business is highly recommended. By focusing on actual users and having a primary focal-point for data governance, it can resolve any data integrity issues that may come up.
Implement Accountability Measures
In today’s business environment where most decisions are data-driven, it’s important to implement data accountability measures. This can be done by instituting a data governance board that includes IT and business users, who’ll be responsible for setting data quality and management policies and standards, and ensuring there’s a reliable mechanism for resolving any data-related issues and facilitating improvement efforts.
By improving data quality management, business can get the kind of actionable data insights needed to facilitate streamlined workflow processes and enjoy superior business performance. Eliminating inconsistent and inaccurate data also helps business make substantial savings from costs associated with bad data insights.