Data Quality Management
Introduction to Data Quality
In 2018, Experian's Global Data Management Benchmark Report indicated that 95% of C-level executives believe that data is an integral part of their business strategy. There's no doubt that data is a significant asset of every company. But is all data just as valuable? The short answer is no.
While it might seem like collecting data is half the battle, the real challenge is to maintain high standards of data quality throughout its entire lifecycle.
To make it more difficult, around 50% of companies don't seem to agree on who is responsible for managing data. The task is usually spread out across operations teams, decision-makers, and professionals of different departments managing data on a daily basis.
If you were chosen to manage data within your team, you need to know how to measure and ensure the quality of your data, as well as the tools available to help you out in this task.
To make it more difficult, around 50% of companies don't seem to agree on who is responsible for managing data. The task is usually spread out across operations teams, decision-makers, and professionals of different departments managing data on a daily basis.
If you were chosen to manage data within your team, you need to know how to measure and ensure the quality of your data, as well as the tools available to help you out in this task.
1. What is Data Quality?
Data Quality is basically the shape that all your information is in. Is your company's data fit for purpose? Is it complete, accurate and reliable?
Our clients rely on data driven insights, whether it is to develop key strategic initiatives or to help improve relationships with clients via marketing and servicing. The quality of data will determine your ability or inability to solve business problems and will greatly influence your ability to make sound and accurate decisions. In short, the impacts data quality cannot be underestimated.
2. Why is Data Quality important?
Data Quality has a big impact on your bottom line and is a key factor that can differentiate you from your competitors. However, as the issue of data quality is complex and spans multiple business units, the impacts of data quality often go unchecked and unmeasured.
Data Quality issues are systemic across many businesses: For example:
The impacts of data quality
Data Quality issues can have direct and indirect impacts on your organisation. The direct impacts are easily quantifiable, including how much money is being spent on 3rd party data, data cleansing, and manual data remediation efforts. However it is the indirect impacts that create the higher cost for many organisations.
Three common in-direct impacts of data quality include:
So what is data quality costing your business? What is the opportunity cost of leaving this unchecked?
No doubt, with some measurement and investigation, there is a strong business case for value creation by addressing this often overlooked and under managed issue. It takes an analysis of the direct and indirect impacts of data quality to get a true sense of why data quality is important and to understand the true cost to your business.
Data Quality issues are systemic across many businesses: For example:
- Dun and Bradstreet estimate that 41% of companies cite inconsistent data across technologies, such a CRM’s, as their biggest challenge.
- Chief Marketing say that only 16% of companies characterise the data they are using as ‘very good’
- Reach Force predicts that in the next hour, 59 business addresses will change, 11 companies will change their name, and 41 new businesses will open, and some will close.
The impacts of data quality
Data Quality issues can have direct and indirect impacts on your organisation. The direct impacts are easily quantifiable, including how much money is being spent on 3rd party data, data cleansing, and manual data remediation efforts. However it is the indirect impacts that create the higher cost for many organisations.
Three common in-direct impacts of data quality include:
- Reduced effectiveness in decision-making – Low data quality can lead to reduced confidence in analysis and scenario modelling, and can lead to wasted time debating about data inconsistencies and reporting measures. This then leads to an inability to make timely and effective decisions and actions, which could lead to a slow market response and/or loss of first mover advantage.
- Reduced Productivity – Good data quality enables a focus on actual revenue producing activities as opposed to continuously validating and fixing data; especially for Sales and Marketing teams.
- Reduced Marketing effectiveness – Better data enables more accurate targeting and communications, and as a result more effective marketing return on investment, with a higher response rate and reduced media wastage.
So what is data quality costing your business? What is the opportunity cost of leaving this unchecked?
No doubt, with some measurement and investigation, there is a strong business case for value creation by addressing this often overlooked and under managed issue. It takes an analysis of the direct and indirect impacts of data quality to get a true sense of why data quality is important and to understand the true cost to your business.
3. Assessing your Data Quality
Assessing Your Data Quality
Assessing the maturity of data quality in your organisation can be difficult. Data Quality ownership may be distributed across individuals and business units, with those responsible holding varying points of view of issues and priorities. A data quality framework can help ensure that an assessment is complete and systematic, providing coverage across multiple inter-dependent data quality dimensions.
Benefits of using a Data Quality Framework
A Data Quality framework can help you assess data quality confidently and objectively. For example, they can be useful in exploring:
The risk of attempting a data quality review without a framework in place include:
Dimensions of a Data Quality Framework*
It is important to be mindful of the full breadth of data quality dimensions when assessing your data quality and formulating a framework. The dimensions of your data quality framework should include aspects like:
Tracking maturity over time
After defining your framework, you're ready to conduct a maturity assessment. This will help to provide a baseline of where your organisation is performing well versus areas for improvement. The maturity assessment and framework dimensions can now be used in tandem to track and prioritise projects that will progress your organisation towards data quality maturity.
Outcomes of your data quality strategy can be measured through identifying a small number of data quality metrics. It is helpful to narrow down to the "metrics that matter", so as not to overwhelm with analysis paralysis, choosing the key leading and lagging indicators of data quality performance. Easy visualisation and frequent inspection can be enabled through the use of out-of-the-box CRM dashboards and simple governance practices to support ongoing improvement.
Assessing the maturity of data quality in your organisation can be difficult. Data Quality ownership may be distributed across individuals and business units, with those responsible holding varying points of view of issues and priorities. A data quality framework can help ensure that an assessment is complete and systematic, providing coverage across multiple inter-dependent data quality dimensions.
Benefits of using a Data Quality Framework
A Data Quality framework can help you assess data quality confidently and objectively. For example, they can be useful in exploring:
- What does the business need and expect in regards to data quality?
- Does data quality mean the same thing to everyone in the organisation?
- Do we have the right tools and controls in place?
- Who makes decisions about what information we need and how it should be used?
- Do we know what our obligations are in terms of legislation and compliance wherever we operate?
The risk of attempting a data quality review without a framework in place include:
- Multiple projects addressing the same data quality dimension, leading to duplication and wastage.
- Key aspects of data quality being missed, particularly root cause issues, e.g. data ingestion processes
- Resourcing projects based on subjective measures, rather than prioritising based on business value.
Dimensions of a Data Quality Framework*
It is important to be mindful of the full breadth of data quality dimensions when assessing your data quality and formulating a framework. The dimensions of your data quality framework should include aspects like:
- Governance – Management and oversight of data at the expected level of quality
- Technology – The tools and platforms that support data quality protocols and processes
- Policies – The data management policies that governs data collection and usage
- Procedures – The operational aspects of a system that implement, inspect, and validate your data management activities
- Standards – Ensures that there is conformity with both the internal and external exchange of information.
- Measurement – A performance management program is used to continuously manage data enterprise-wide.
Tracking maturity over time
After defining your framework, you're ready to conduct a maturity assessment. This will help to provide a baseline of where your organisation is performing well versus areas for improvement. The maturity assessment and framework dimensions can now be used in tandem to track and prioritise projects that will progress your organisation towards data quality maturity.
Outcomes of your data quality strategy can be measured through identifying a small number of data quality metrics. It is helpful to narrow down to the "metrics that matter", so as not to overwhelm with analysis paralysis, choosing the key leading and lagging indicators of data quality performance. Easy visualisation and frequent inspection can be enabled through the use of out-of-the-box CRM dashboards and simple governance practices to support ongoing improvement.
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