DATA QUALITY

BEST DATA QUALITY MANAGEMENT SYSTEMS

WHAT DOES DATA QUALITY MEAN TO YOUR BUSINESS?

Many businesses talk about it and understand that it’s important, however, the majority are not really sure how to tackle data quality issues when they arise. The good news is, Marketsoft can help with professional data quality management services for businesses.

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Make DQ a priority

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Acknowledge the Importance of Data Quality (DQ). Effective business decisions depend on the quality of the underlying data used in analysis. Ignoring this valuable aspect of your business can cost you in the long-term as more and more companies are using to great effect.

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DQ assessment

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How data is collected can vary significantly depending on how it was collected, stored, cleansed, and processed. So it’s important to assess the state your data is in. Also, it is important that your data is sourced accurately in an easy to understand language.

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Align DQ to business needs

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To ensure data quality initiatives are successful, align them to business initiatives. Our expert data analyst team will ensure that your business needs are met by improving the quality of your data and eliminating weak links from it.

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Identify DQ imperatives

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Improving data quality is not about achieving 100% accuracy. It’s about improving and maintaining the data so that it is fit for purpose. We will see to it that your data scores 10/10 in terms of reliability, accuracy and quality.

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Implement continuous improvement

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Ensuring Data Quality processes are implemented at capture and storage stages is essential to ensuring a successful DQ program. The real cost of poor quality data is much higher then many realise.

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Have the right DQ controls

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Implementing the right data quality metrics will help drive the right behaviour across your organisation. Only a few companies acknowledge the real costs and their impact to revenue. Our database maintenance services are aimed to make your business more productive and eliminate unnecessary costs.

CUSTOMER DATA

DATA QUALITY MANAGEMENT

Customer data is central to many initiatives. A successful data quality management program has both proactive and reactive components. The proactive approach consists of establishing governance, such as defining the roles and responsibilities, establishing the quality expectations as well as the supporting business practices, and deploying a technical environment that supports these business practices. Specialised tools are often needed in this technical environment. 

Unfortunately, many companies learn about the importance of data quality management & maintenance the hard way. Only after a number of major setbacks with data usage do they recognise the need to improve data quality.

Research shows that 25% of data within an organisation is typically erroneous across at least one of these five data quality metrics:

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CONSISTENCY

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COMPLETENESS

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ACCURACY

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PRECISION

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MISSING

We know Data hygiene and standardisation is a key part of ensuring data quality. 

Unfortunately, data standardisation is often left out of planning and discussions, especially when you’re implementing CRMs and martech systems.

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