Four Steps to Achieving High-Quality Data Across the Enterprise

How to Increase Revenue and Opportunity with Better Data

Mike OrtmannJuly 10, 2019

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Today, there is an ever-increasing desire by organizations to leverage their data as an asset to derive meaningful insights and enable better decision making. To achieve the desired results, companies are investing more in data management processes and technology. Unreliable and poor quality data undermine analytical initiatives and result in flawed insights and faulty business decisions. In data management, there is nothing more challenging, or more important, than having high-quality data. The effects of bad data reverberate through an organization, breeding distrust among data consumers and negatively impacting customer experience. This leads to lost opportunities and lowered revenue.

The good news is, there are four steps organizations can take to ensure enterprise-wide data integrity:

  1. Establish standardized controls and audit checks to ensure data accuracy
  2. Incorporate data quality into a data governance framework
  3. Layer in analytics capabilities for increased automation
  4. Implement an integrated data intelligence platform

Starting a data quality initiative can seem like boiling the ocean, but by following these four steps in limited capacities and growing the scope of effort over time, businesses can achieve quick success with high-value data assets.

Standardized Controls and Audit Checks to Ensure High-Quality Data

Every business wants their data to be a strategic business asset, but it is less likely unless it’s accurate, consistent, complete, timely and valid. The challenge is that data is always in motion, and often transformed, which leads to risk of data corruption. To maintain data integrity, then, it must be monitored across an organization’s data supply chain. The first step to high-quality data is assessing potential quality gaps and establishing business rules for reconciliation and checks to identify errors, duplicates, missing data and more.

Once configured, these automated controls can be scheduled to run on a schedule or a triggered-on-data-arrival basis, provided the data environments are relatively static. In constantly changing environments, however, even advanced controls may need to be augmented. In the current-day enterprise environment, systems, sources and volumes of data, are constantly changing. When these changes occur, competitive drivers require quick resolution of alterations, not leaving enough time to follow a controlled process.

Because of this, the best strategy to automate and ensure data integrity across a data supply chain is through a comprehensive data governance framework.

Incorporating Data Quality into a Data Governance Framework

Data governance programs and initiatives enable organizations to coordinate people, processes and technology through data governance, creating a culture of understanding, collaboration and accountability. Fundamentally, it’s about ensuring users understand their data. Data quality is critical to preserving data as an enterprise asset, no matter what new processes, uses and transformations it is exposed to. By including data quality capabilities for parsing and standardization, cleansing, profiling, and monitoring within a data governance framework, businesses create a standardized approach to continuous data quality improvement.

Establishing enterprise-wide data integrity along with data understanding builds trust and means data is appropriately used to maximize value, mitigate risk, and encourage users to leverage data assets for business analytics. In addition, using analytics within a data governance program can automate data quality monitoring and improvement to further reinforce trust.

Layering in Analytics for Increased Automation

The more efficient and inclusive data management processes become, the easier it is for users to leverage data for analytics. By incorporating machine learning processes into a data governance framework, businesses can automate the data quality tasks to improve overall data integrity. Analytics capabilities such as machine learning algorithms alongside traditional controls lead to improvement in quality processes as new business rules can be identified and suggested. Self-learning capabilities not only monitor quality levels, but improve them over time.

All of these strategies require the right tools to execute a data governance program that establishes enterprise-wide data quality and trust. Businesses need to invest in new technologies and capabilities that are essential for managing today’s complex data environments.

Implementing a Data Intelligence Platform

To maximize success in establishing enterprise data quality, companies must begin with an integrated data intelligence platform that delivers advanced capabilities for data governance, data quality and analytics. Data quality capabilities within the platform should enable high-volume data quality checks. As the amount of data to be validated grows, it’s critical to verify the quality of data at scale and ensure continued trust among users. In addition, the platform should incorporate analytics capabilities and apply machine learning algorithms for self-learning to continuously improve data integrity.

Data governance capabilities within the data intelligence platform should ensure that data is easily understood, appropriately accessed, and entirely trusted by all users. That way, it can be leveraged to generate insights and drive business decisions. Ultimately, data owners, stewards and consumers are empowered to effectively manage, share and utilize data to drive growth and increase revenue.

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