Let’s confront the truth: Despite its paramount importance, data quality often remains unloved and undervalued within many organizations. It’s often perceived as excessively technical, requiring significant amounts of tedious effort and time to manage. Consequently, enthusiasm for shouldering this crucial responsibility dwindles. 

Within the rapid pace of the business environment, data might not always garner the attention it truly warrants. As data continues to be generated and processed at an astonishing rate, upholding its accuracy, completeness, and consistency can feel overwhelmingly challenging. Additionally, the intricacies tied to data integration and governance further amplify the hesitance to fully embrace the dimension of data. 

So, how can teams reignite their enthusiasm and passion for data quality? How can they transition from a general disinterest in data to nurturing a lasting commitment to refining it? 

What is Data Quality? 

Data quality is the extent to which a collection of inherent attributes meets system requirements, determines its suitability for use, and ensures compliance with those requirements. It’s crucial to bear in mind that data standardisation is an ongoing endeavor. What constitutes high-quality data today may not hold true tomorrow, as the demands of the present will inevitably evolve in the future. 

An analogy I employ to reinforce this concept is the notion of ‘potato quality in a fast-food chain.’ Sizeable, spherical potatoes serve as excellent quality inputs when the aim is crafting french fries. Nevertheless, this quality might be inconsequential when the objective shifts to preparing mashed potatoes. 

In essence, it becomes imperative to establish data quality metrics tailored to a specific purpose or use case. 

Challenges Causing Data Quality Problems 

If we delve deeper, inadequate data quality frequently stems from various systemic issues. Presented below are instances of such challenges. To better understand these problems, let’s contextualize them within a specific domain – for instance, product pricing within a retail setting. 

Neglecting data concerns at the initial stages and attempting to rectify them downstream results in an inefficient allocation of resources. This approach forces teams to expend a significant amount of effort adjusting data within subsequent systems. Consequently, valuable time is squandered, and the overarching data quality inevitably suffers. For instance, if the data inputted into a pricing algorithm, such as prices or sales information, is erroneous from the outset, any attempts to rectify the resulting price recommendations won’t yield a substantial enhancement in the overall data assessment. 

Neglecting data quality initially leads to wasted efforts downstream. Trying to fix issues there disrupts the overall process and loses crucial context. For instance, flawed pricing data not only affects algorithms but also reporting, creating misunderstandings due to missing background like short-term discounts. It’s vital to address data quality at the source to maintain accuracy and consistency throughout the pipeline. 

Absence of strategy: Data teams frequently tackle data quality issues in a piecemeal manner. Without a comprehensive framework spanning an organization’s entire product and platform ecosystem, data quality remains erratic. However, the solution isn’t a solitary hub for all quality checks; it’s about offering a self-service integrated framework for data quality that’s accessible across the data platform. Take the example of product prices utilized in ten diverse instances, each possibly governed by distinct rules. Could the integrated framework streamline these data quality checks? 

Establishing inconsistent definitions: Organizations often neglect the effort to define standards and resolution procedures for data quality concerns. This results in a deficiency of confidence in the fundamental data. 

Creating isolated solutions: Addressing data quality based solely on user perspective leads to tactical, inadequate solutions that often miss the root cause. For instance, correcting faulty price recommendations through rule-based adjustments doesn’t tackle the underlying issue – whether it’s flawed algorithm input or algorithmic problems themselves. 

Neglecting the Extent of Influence on Business and Morale: Businesses overlook the fact that poor data quality doesn’t solely affect downstream systems like business intelligence or predictive dashboards, but also has an impact on team morale. As trust in the data platform erodes within teams, it significantly hampers effective change management efforts. 

To mitigate these challenges, it’s essential to treat your data as code and uphold the same level of rigor for data quality as you would for code quality. This entails implementing a structured approach, akin to a test pyramid, encompassing automation tests and fitness functions. 

6 Fool-Proof Tips to Improve Data Quality Issues 

Step 1: Identify and Record Data Quality Concerns 

To effectively identify data quality concerns, establish a clear set of metrics that gauge business outcomes. Utilize tools like HPQC and ServiceNow for monitoring, managing, and documenting data-related issues that lead to subpar business results. Remarkably, numerous organizations possess robust incident or outage management systems but neglect to extend the same treatment to data issues, even though the ramifications of poor data can be equally detrimental. After addressing critical issues, the attention should shift to resolving problems categorized by their impact and frequency, spanning high/medium, medium/high, and medium/medium. 

Step 2: Own and Resolve Data Issues 

Secure clear ownership of data issue resolution. This involves capturing, analyzing, developing fixes, testing, implementing, and verifying. 

In large organizations, pinpointing the root cause and the responsible fixer can be challenging due to intricate data flows. A collaborative, learning-driven culture helps experts dissect issues promptly and find origins. This culture also anticipates and prevents similar issues. 

Strong data ownership and governance prioritize solutions, allocating resources smartly. Enterprise-wide governance fosters a data-centric approach. For example, the Basel Committee on Banking Supervision (2013) mandates unified standards, fostering data management awareness. 

Step 3: Fix Data and Root Causes 

Address both current data flaws and their underlying causes. Correcting data involves complex decisions, like adjusting reference data to amend transaction errors. After data correction, rerunning reports may uncover additional complexities. 

Root issues can stem from the source but impact other parts of data lineage. Large systems face challenges in tracing and resolving such problems. An open, collaborative culture aids rapid issue identification and resolution. 

Step 4: Establish Data Quality Program 

Organizations often set up focused programs to address data standards comprehensively. These involve senior leaders and staff across levels. A two-pronged approach, involving both senior sponsorship and bottom-up efforts, is crucial. CEO endorsement, like discussing data quality in town halls, fosters a culture of data value. Incorporating data quality goals in employee performance and regular internal assessments further enhance data legiblty. 

Step 5: Define Data Quality Metrics 

Set clear KPIs aligned with business objectives, ideally with quantifiable financial impact. For example, track data quality incidents like missing settlement instructions and their average cost to fix. 

Metrics fall into real-time monitoring and scorecards. Monitors alert breaches instantly, while scorecards provide time-aggregated views. Trends show progress. Individual scores against thresholds, rated and trend-indicated, aid business understanding. 

A holistic approach covering commercial, financial, operational, and personnel aspects is advised. This comprehensive view empowers data quality improvements effectively.  

Uncover the True Potential of Your Data  

A recent study conducted in the Netherlands, in partnership with ICT Media, Tilburg University, and TCS, uncovers a tangible link between highly successful digital enterprises and their level of data maturity. Organizations that prioritize data as a vital asset, implement effective data governance and ownership, measure data quality, and employ strong processes to rectify subpar showcase positive outcomes. As businesses progressively embrace the digital landscape, adopting a proactive stance on data quality becomes crucial to harness the full potential of data in boosting business performance.