Content Marketing Strategy

Data Quality

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!

Marketsoft is a data specialist, and over the years, a common observation we make is that data quality is the most under-appreciated element in most data-related projects (a little ironic don't you think?). A lot of businesses don't understand just how much effort goes into getting the data right, so that the real value of data can be maximised downstream - and over the long-term - through analytics, data science, and actionable insights.

Many organisations are starting to utilise data and analytics in their day-to-day operations and customer interactions. This means the need to improve and maintain data quality is now, more than ever, an imperative rather than a nice-to-have. We are seeing new roles emerge within businesses trying to deal with organisational concerns around data quality and compliance legislation. Marketsoft can help you tackle your data quality issues.

Our team helps businesses get their data into shape so that it doesn't have to be a big bang approach.

1. Make DQ A Priority

Acknowledge the Importance of Data Quality (DQ). Effective business decisions depend on the quality of the underlying data used in analysis.

2. DQ Assessment

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.

3. Align DQ To Business Needs 

To ensure data quality initiatives are successful, align them to business initiatives.

4. Identify DQ Imperatives

Improving data quality is not about achieving 100% accuracy. It's about improving and maintaining the data so that it is fit for purpose. 

5. Implement Continuous Improvement

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. 

6. Have The Right DQ Controls

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. 

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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.

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Unfortunately, many companies learn about the importance of data quality management 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:

  • Consistency;
  • Completeness;
  • Accuracy;
  • Precision, and/or;
  • Missing

Tell us about your Challenges

Let us help you get your business data quality issues sort it with passion

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. Our team of data quality experts will run your data through our data audit and validation processes, saving you valuable time in hunting down the issues.

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