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? 
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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.
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Contact us today 

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?
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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