The overall reliance on data assets is growing, from gaining actionable insights to providing critical resources for clients and stakeholders. With every year that passes, more and more leverageable assets for an organization are based on data.
Unfortunately, as an organization continues to expand while developing new goals for achievement, the data ecosystem becomes equally mature. This creates more exposure to risk by opening up decisions to poorly sourced information or failing to meet the oversight requirements of a governing body. The last thing any company needs is to base a critical decision about the future of an organization, its clients, team, and stakeholders on invalid data. This does not serve the needs of the business.
To lower this risk, the introduction of data quality measures through monitoring and other procedures is necessary. Otherwise, the organization will have to adjust to lower-quality data assets.
Value of High-Quality Data
Data is only valuable to an organization when it is high quality. Bad data cannot be leveraged for decisions. It does not provide the insights, reporting, or resources needed for an organization to either grow or meet the users’ needs. Extrapolating information from the data collected must always be reliable.
In 2016, IBM reported that businesses relying on bad data suffered a collective $3 trillion USD per year in poor actionable insights. In 2021, Gartner placed that figure at an average of $12.9 million per organization. This is because bad data does more than lower an organization’s revenue. It also creates long-term systemic issues that drive complexity and lead to poor decision-making.
The difference between data that may be risky and data that serves the goals of an organization is all related to its quality. That is why a company should seek out data assets that are:
- Accurate – how well the data describes the real-world, gained parameters of a system to enable interpretation.
- Complete – everything that was intended from the various sources and repositories has been fully received without any missing gaps.
- Relevant – directly useful for the KPIs, goals, and decisions of the organization and its systems.
- Valid – in the correct form, type, and range, or able to be quickly transformed and cleaned into the appropriate form.
- Timely – the more real-time the data, the better for decision-making.
- Consistent – both in format and content, so different groups are not operating under different assumptions.
High-quality data leads to better organizational efficiencies and decisions for the future. A ship cannot sail the oceans effectively without instrumentation and knowledge of the waters ahead.
In the same way, a business needs reliable data that can navigate the challenges and provide new insights for growth opportunities. Otherwise, the boat is floating adrift at sea, hoping for a random gust of wind to point it in the correct direction. Without the proper steps for data quality, the potential risk to the organization increases.
Steps to Improve Data Quality for Risk Management
Organizations have different needs according to daily operations. While there may be variance in how to better manage and monitor the data solutions of a business versus a non-profit, some core steps can drastically improve data quality. These include:
Unification of Data Silos & Sources
The more fragmented an organization’s data assets, the harder it is to trace data lineage and ensure everything has been appropriately cleaned before utilization. These silos create inaccurate information because there are so many barriers to compliant and successful data management. A more holistic view must be adopted to ensure data quality and governance are appropriately achieved.
Create a Single Truth
Organizations need to develop a mindset directed at data quality. This includes a single source of truth in relation to reporting, risk management of data assets, and data monitoring. In addition, inaccurate data should be easily identifiable because of the systems and internal standards in place.
Create Rules
To aid in the data monitoring, create and implement data quality rules. This real-time method ensures all data meets predefined sets, formats, types, and anything else signaled by crucial team members and stakeholders.
Robust Data Monitoring
Ongoing risk management and predictive awareness are not possible without data monitoring. This needs to be proactively in place from end to end to achieve consistency in data quality and control. That includes data lineage and, most likely, an automated system approach.
A somewhat basic approach would be to create detective controls, corrective controls, and preventative controls. The idea being any current errors or poor data outcomes are identifiable, can be quickly repaired/fixed, and future results are more likely because of the proactive nature of the data monitoring solution.
Common Mistakes to Avoid
Unfortunately, modern data collection and organization can uncover some common errors businesses and organizations should seek to avoid. Most often, this is because there are complex systems in place due to multiple platforms and integrations that do not effectively communicate with one another. There are also issues with:
Master Reference Data
Data should be profiled from the source. The more it can be standardized and then used as reference material, the more aligned the rest of the data collection will be to current guidelines and rules.
Having IT Own the Assets
An actionable data monitoring plan should not only be in the hands of a single department. Even if a team is filled with the most advanced, cutting-edge data scientists, the goals or operational blind spots may be missed. A company can benefit more from broader data ownership.
Avoiding Company Wide Adoption
A mindset needs to be in place concerning data quality and monitoring. Everyone on the team needs to understand the value of quality information to make better decisions. Without those assets, the company risks failure or, at the very least, short-term revenue growth.
Stopping at Real-Time Solutions
Risk management is often directed at preventative goals. With a robust data quality plan in place, proactive capabilities and steps should be introduced into the system to avoid potential harm to the organization.
Focus on Software vs. Service
Purchasing an off-the-shelf solution from a vendor that cannot be adjusted and meets an organization’s flexible data quality requirements is a recipe for increased future risk.
Automation & Communication
When a problem does arise, it should be effectively communicated to anyone using the data. Then, the preventative repairs or proactive changes that are integrated need to be automated, so the data workflow is interrupted as little as possible.
Of course, this becomes significantly more challenging if there are no effective methods of accurately monitoring the data from various silos, sources, and repositories.
Ways to Monitor Data Quality
Many solutions and interventions of data monitoring can be implemented for better quality outcomes. The goal should be to observe any problem-dependent issues as well as metrics that ensure high-quality future data outcomes.
This could cover a wide range of items to monitor like:
- Volume – ensuring that the data system collects all the expected data available.
- Timing – keeping data as up-to-date or “real-time” as possible.
- Schema – monitoring data asset structure, format, type, and dimensions.
- Cleaning – cleaning the data to eliminate common errors like duplication, formatting, and missing gaps.
- Governance – ensuring all data meets the oversight and rules of internal controls and external regulations.
- Distribution – ensuring all data is within the acceptable range of parameters.
- Lineage – easily trace all upstream sources and downstream users interacting with the data.
- Accessibility – monitoring who has access, when they were utilized, as well as how they will be integrated.
This could also involve randomized testing or sampling from a data monitoring team. However, many organizations rely on the power of automation for such mundane tasks. As long as there is a robust dashboard in place with alerting policies, data quality indicators, and data uptime checks, the odds are the system, data quality, and pipeline metrics will remain at appropriate levels.
The challenge for any modern organization is finding such a system to integrate that can be adapted to their needs. That is where our team can help.
Conclusion
At NextPhase, we leverage data quality throughout any solution our team creates. We are a service-led data solutions provider, using active data monitoring to ensure high-quality assets are leveraged for decision-making now and in the future.
The risk of poor-quality data is constant, especially given how much data is collected daily. The more cloud-based and mobile operating consumers and business become, the greater the need for quality data will grow.
Schedule a consultation with our team at NextPhase today, and let’s build a solution aligned with your needs that can scale and adapt into the future.