High-quality corporate data brings absolute visibility into every aspect of business operations, which allows companies to harmonize data flows and strategically act upon reliable information. However, ambiguous data or incomplete datasets can undermine normal operation mode if overlooked by operation automation specialists.
Outdated and incomplete data pose the most severe risks to an enterprise’s long-term success. According to Gartner, businesses might face profitability loss of up to $12.9 million strictly due to decision-making misled by inaccurate data.
To keep you risk-safe, here are tried-and-true practices helping to deal with the most common data quality issues.
State of Data Quality in 2023
Reliable data ensures trackability and measurability of business processes. Most importantly, it allows stakeholders to decide how to scale operations and advance organizational structure based on robust analytics rather than gut instinct.
Yet, enterprises have it rough regarding increased datasets, the need for ultimate cybersecurity, and tailored data orchestration solutions. That’s not to mention data quality issues caused by human errors when information is manually processed.
The 2022 data research showed that:
- 75% of enterprises outperformed predefined KPIs thanks to improved data quality.
- 72% of respondents admitted experiencing difficulty in prioritizing data management tasks due to excessive data volumes that required purging and replacement of records that informed decision-making.
- Only 44% of stakeholders considered their CRM/ERP data quality and relevance.
In 2023, data governance has come to the forefront. In highly competitive industries, businesses rely on more than 15 data sources. Without standardized and automated data governance systems, Data & Analytics (D&A) personnel would struggle to keep their enterprise data usable, error-free, and consistent.
On the other hand, implementing robust data governance instantly benefits both internal and external performance. Firstly, it helps increase data interoperability a lot by setting default data formats for intended use. Thus, enterprises can achieve company-wide operations alignment. Secondly, resolving data issues saves a lot of debugging time and optimizes cloud data warehouse (CDW) spending. The data pipelines start performing as expected, ensuring seamless data utilization that drives the operational success of an organization.
5 Key Data Quality Metrics
To evaluate the existing records and measure their quality, consider the following 5 dimensions:
- Accuracy – a percentage of data values matching the expected reference value.
- Completeness – a percentage of records with filled-in required fields.
- Consistency – a percentage of records that fall under internal rules and requirements of a data source.
- Timeliness – an average or median time gap between the creation date and availability.
- Relevance – the ratio between case-specific records and those not meeting predefined criteria.
So, how to tell if these KPIs are successfully met and don’t cause any data quality issues? Mainly, it’s possible when two conditions are fulfilled:
- The assessor can compare data samples to some predefined benchmarks.
- The data team leverages an automated data quality monitoring platform that flags troubled assets and detects the root cause of data issues, e.g., incomplete or inaccurate records.
Given that, it would be possible for data teams to identify the weak spot and detect what actually caused low data quality.
8 Enterprise Data Quality Issues You Need to Solve
Before your data team members get on with data audits, they need to map common data issues. The specialists should also prioritize what data issues to work on first by considering which ones jeopardize revenue goals.
Fair enough, different business operation models source and process data in a specific way. However, it is better to start investigating data issues at the very data entry moment. External and internal data sources must be validated and configured to exclude data debris. Then, experts must adjust proper maintenance and an easy-to-digest view of master data. This will allow the D&A team to have clear observability of ETL legacy processes and monitor the overall health of the data cloud warehouse.
Here are 8 data quality issues that organizations should look out for:
1. Irrelevanсe
Internal SQL servers often collect irrelevant data on non-essential user actions. Most of it is irrelevant to business and only adds up a dead weight that slows CDW processing.
That’s a part of data quality issues that need to be handled. It’s worth reassessing data collection algorithms in order not to prevent CDW storage from littering.
2. Incompleteness
Incomplete records can ruin many organizational and observational data operations. These data issues can make essential reports on customer surveys or employee observational assessments unclear and barely useful.
One way to work around these data quality issues is to block form submission if key fields are empty. It’s also possible to complete missing details by cross-matching data records of several sources that contain the same fields.
3. Outdatedness
The fast-flowing data life cycle makes records age pretty fast. In some cases, data decay is a matter of weeks, while in others, information becomes stale and outdated in minutes, which is quite a severe data quality issue. For instance, AI-powered fraud prevention algorithms must get refreshed data every second.
Enterprises experience such data issues mainly due to bad SQL queries, broken data pipelines, and generally outdated source data. Today, most data teams leverage AI-powered data observability products to detect and resolve data incidents caused by late record updates.
4. Inaccuracy
The typical cause of data inaccuracy issues is that some new data source was connected to ETL workflow prematurely and without proper validation. As a result, the inaccurate records that make it to CDW become undiscoverable for SQL requests and useless for analysis.
Therefore, data sources must be validated to verify whether they contain correct values. Data teams ensure records validity by looking into their metadata and checking whether data samples fall under data profiling criteria. On top of that, a sure way to prevent data inaccuracy is to automate as many data input cases as possible and implement automated corrections for data entries.
5. Duplicate Data
Duplicates don’t pose a severe threat like other data quality issues. That said, they are still a deadweight strapped to the datasets. We advise purging them with automated deduplication. Alternatively, there’s a worthwhile practice of merging duplicate records into a single with a richer set of parameters.
6. Orphaned Data
An orphaned data issue occurs when some data bits are incompatible with the existing system or don’t convert automatically into a usable format. The goal of the D&A team is to detect such unusable data by using monitoring software that detects inconsistent formatting of files and allows data managers to correct it at once.
7. Hidden data
Hidden or dark data is a massive share of data collected by an enterprise, though not utilized. A 2023 survey by Splunk shows that hidden data amount to nearly 55% of information collected by organizations. Yet, only 15% of 1,3000 surveyed businesses utilize AI-driven solutions to fetch this data. It lets many enterprises succeed in the strategic development of customer experience improvement.
One way or another, revealing dark data is worthwhile. Regarding its considerable volume, it would be wise to compress or delete it to minimize storage costs. Conversely, some of it can be insightful and prompt strategic decisions.
8. Cross-System Inconsistency
Businesses often face data quality issues due to inconsistent formatting. Those may occur when sending out datasets between different platforms.
That’s why D&A professionals insist on having organization-wide (or even inter-organizational) standards for formatting. The easiest way to achieve that is to connect databases to an AI-powered data governance platform that automatically converts records into default format.
6 Steps to Solving Enterprise Data Quality Issues
Now, let’s review 6 practical steps that ensure consistent and productive data issues management:
1. Practice Data Profiling
Implement data quality (DQ) profiling at the very onset of business operations and make it a part of the data stewardship routine. The key purpose of it is to examine the structure of datasets that are sourced both externally and internally. Thus, you will conclude what data is safe, complete, and reliable enough to act upon.
2. Adopt Organization-Wide DQ Standard
D&A leaders must develop enterprise-wide data quality standards and communicate their necessity across all departments and stakeholders. Given that D&A will get across the actual impact of poor data quality on key business objectives, it will encourage heads of departments to double-check inputs from data sources and reports.
3. Swipe “Truth-Based” Semantic Model for a “Trust-Based” Model
Use the “trust” model instead of the “Truth” model for external data. It’s reasonable since D&A leaders can’t do anything about data quality issues that might be hidden in third-party assets.
Hence, evaluating how much you trust a particular data provider is better. Assess how much of your previous operational and commercial success can be credited to this specific data source. If there were minor data issues that didn’t harm your business decision, maybe it is possible to correct them by cross-matching with other data sources.
4. Determine DQ Responsibilities and Procedures for Data Stewards
Data stewards take on data quality issues detection so your assets remain actionable and fit for business purposes. The D&A leader’s objective is to harness and guide data stewardship to fulfill the organization’s data quality demands.
That’s why the D&A team really needs governance scope mapped by stakeholders. It will make regular checks and reporting much more streamlined.
5. From an Interest Group for DQ Inspection, Led by CDO
A dedicated business unit headed by the Chief Data Officer will combat data quality issues and enhance organizational risk management. It’s primarily recommended for enterprises that wish to reduce operational costs and share the best DQ monitoring practices between departments.
6. Automate DQ Management with DQ Monitoring Software
DQ monitoring automation is the ace up your sleeve, giving you a competitive edge. Go for it If you strive for truly exhaustive and timely data quality issues governance.
How Revefi Ensures Data Observability and Prevents Data Quality Issues
Revefi is the real deal when it comes to running data quality management to ensure cloud data warehouse health 24/7. It is a fast, convenient, and secure Data Operations Cloud – your trustworthy copilot for data teams to get actionable insights on data issues before they reach downstream users:
- Full data visibility. It takes minutes to set up the dashboard and get the first alerts on data quality issues.
- Zero-touch effort. Your data assets are automatically discovered and added to the exploration panel. All that is needed is your metadata.
- Get to the root cause of data issues. Revefi utilizes AI algorithms that analyze the entire lifecycle of data issues and get to the root causes.
- Full data security compliance. Revefi is SOC2 Type 2 certified.
Unlock cost-efficient and flexible data operations management that supercharges operational success by reducing data-related debugging. Try Revefi for free!
