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Assessing Your Data Quality

30/3/2021

 
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 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:
  • 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.
 
Collagis is committed to helping businesses like yours to optimise workforce and organisational effectiveness.
We'd love to share with you how we can help you address data quality in your organisation.

​
Contact us today 
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​*Adapted from The Practitioner’s Guide to Data Quality Improvement. By David Loshin

Why is Data Quality Important?

22/3/2021

 
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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: 
  • 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.


Collagis is committed to helping businesses like yours to optimise workforce and organisational effectiveness. We'd love to share with you how we can help address data quality in your organisation.
​
Contact us today 
REQUEST A CALL
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What is Data Quality Management?

10/3/2021

 
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In the age of analytics, data quality plays a critical role in helping organisations achieve better and more sustainable results. However, as it is a complex issue often with distributed ownership, many organisations fail to tackle the issue of data quality holistically. Siloed business units and individuals can have different points of view of what data quality is and where data quality issues may exist. Unconnected projects can pop up that may not tackle root causes and may not work together towards a common goal. Because data is such an intrinsic part of the way we do business today, we see data quality as a foundational element of organisational effectiveness.

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.

Defining Data Quality Dimensions

Data quality management  is multifaceted, so when defining data quality in your organisation, it is important to create a common language and understanding of​ the dimensions of data quality.

Here's an example of  6 dimensions of data quality that are useful in defining data quality.
 
1. Consistency - Is there only one version of the truth? Can you compare data across data sets reliably?
2.  Completeness – Do you have all the information you need? Are your key data attributes populated for your data set?
3. Accuracy – Is your information correct? Have you a process to manage errors?
4. Uniqueness – Does the information you have uniquely describe each individual? Can you identify a unique individual across data sets?
5. Timeliness – Is the information fresh? Do you have real time access to the data?
6. Validity - Is your information in the correct format?  Make sure that the data you have is user friendly and aligned to business rules.

Data quality can be a very complex and challenging business problem to solve, but breaking down the problem and reaching a common understanding of what the problem is, can be a helpful first step to commencing a well designed Data Quality Management Strategy.
 
Collagis is committed to helping businesses like yours to optimise workforce and organisational effectiveness.
We'd love to  share with you how we can help address data quality in your organisation.

Contact us today
Request a Call
CASE STUDIES

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  • Home
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