How to address poor data quality in your organization
Between responding to supply chain disruptions, adapting to the economic slowdown, responding to inflation, retaining and attracting new customers, and better managing inventory and production, data quality has never been more critical to your business.
In the digital age, data is a company’s most valuable resource. Data collection, data analysis, and data governance strategies set leaders apart from the rest of the pack. And data quality runs through the entire data architecture.
What is data quality?
A Forrester survey found that leading customer intelligence professionals rank the ability to integrate data and manage data quality as the top two factors hindering customer intelligence. But data quality is about more than just customers. Executives and top-level management use internal data to drive day-to-day operations and achieve business goals.
Quality data should be accurate, complete, consistent, reliable, secure, up-to-date and not isolated. High-quality data is often defined as data that is “suitable for use in operations, decision-making, and planning.” High quality data also represent real designs.
The difference between internal and external data and what makes it “useful” is important. External data is generated from a company’s customer base and may be of high quality for marketing campaigns, but not of high quality nor appropriate for use in specific business decisions that require internal data. Whether external or internal, data quality should always be verified and meet or exceed expectations.
As businesses and organizations embrace digital transformation and migration to cloud and hybrid cloud environments, the need to break down data silos becomes imperative to data quality. For companies on this digital journey, it is crucial to understand the consequences of not correcting data quality.
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What are the business costs or risks of poor data quality?
Data quality directly affects your bottom line. Poor external data quality can result in missed opportunities, lost revenue, reduced efficiency, and neglected customer experiences.
Poor internal data quality is also responsible for ineffective supply chains – a problem that has made headlines over the past year. The same factor is one of the main reasons behind the Great Resignation, as HR departments working with bad data are challenged to understand their employees in order to retain talent.
Additionally, there are serious immediate risks that organizations need to address, and the only way they can do that is by addressing data quality. The cybersecurity and threat landscape continues to grow in size and complexity, and thrives when poor data quality management policies prevail.
Organizations that work with data and fail to comply with data, financial and privacy regulations risk reputational damage, lawsuits, fines and other consequences related to non-compliance.
Gartner estimates that the average financial impact of poor data quality on organizations is $9.7 million annually. At the same time, IBM says that companies in the US alone lose $3.1 trillion annually due to poor data quality.
As the new economic slowdown and recession threaten every business, data quality becomes key to navigating new economies. make difficult decisions; and preparation of short-, medium- and long-term plans.
Common data quality issues
The most common data quality problems are duplicate, ambiguous, inaccurate, and hidden and inconsistent data. New problems include siled data, stale data, and data that is not secure.
But another growing problem with data is that it’s often tightly managed by IT departments, even though an organization should have a multi-level approach to data quality. According to McKinsey, organizations should think of data as a product and manage their data to create “data products” across the enterprise.
How to deal with data quality issues
When data is managed like a product, quality is guaranteed as the data is ready to use, consumable, and sellable. The quality of this data is unique. It is verified, reliable, consistent and secure. Like a finished product that your company sells, it is double checked for quality.
Gartner states that to address data quality issues, organizations must align data policies and quality processes with business goals and missions. Executives need to understand the connection between their business priorities and the challenges they face, and adopt a data quality approach that solves real-world problems.
For example, if a company has high churn rates and its primary business goal is to grow its customer base, a data quality program will help strengthen performance in those areas.
According to Gartner, once the business objective and challenges are understood and the data teams have selected appropriate performance metrics, the organization should profile its current data quality.
Data profiling should be early and frequent, and high data quality standards should be established to measure progress toward a goal. Data quality is not a one-time task; It is a constant, active management approach that needs to evolve, adapt and perfect.
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Improvement of data quality issues
McKinsey explains that teams using data shouldn’t waste their time looking for it, processing it, cleaning it, or making sure it’s ready for action. Proposing an integral data architecture to deal with data quality, it ensures its model can accelerate business use cases by 90%, cut data-related costs by 30%, and keep organizations free from data governance risks.
To improve data quality, companies need the right model. McKinsey warns that neither the basic approach, where individual teams stitch data together, nor the big bang data strategy, where a centralized team reacts to all processes, will yield good results.
In an effective data quality model, different teams are responsible for different types of data, classified by use. Each team works independently. For example, data consumers use in digital apps should be managed by a team responsible for cleaning, storing, and preparing the data as a product.
Internal data used for reporting systems or decision making should also be managed by a separate team responsible for closely monitoring quality, security, and data changes. This focused approach enables data to be used for operational decisions and regulatory compliance. The same goes for data used for external sharing, or information used for advanced analytics, where a team needs to cleanse and develop the data so it can be used by machine learning and AI systems.
Organizations that excel at creating data products must establish standards and best practices, and track performance and value across internal and external business processes. This attention to a productive version of data is one of the most effective ways to guard against data quality erosion.