Few would argue that, when it comes to data driven marketing, the quality of the data is an obvious essential. But what do we mean by ‘data quality’?

Simply put, quality data is data that you can use for its intended purpose with confidence. As such, data quality means taking the necessary steps to ensure that data is accurate, valid and consistent, but also that it is complete (you have all that is necessary to meet the need) and that it is readily accessible in a timely fashion.

For marketing, the primary intended purpose of data is to help the business develop customer understanding, using this to power better customer experience and therefore ultimately achieve acquisition, retention, cross sell and satisfaction targets. Similarly, marketers turn to data to help them identify which marketing activities are driving the best results, when compared to the costs and investment required to undertake them. As such, data quality for marketing should be primarily focused on those areas that directly impact understanding customers and the performance of marketing activities.

Why bother?

Poor quality data gets in the way of these core marketing objectives; how can you deliver a consistent, personalised customer experience if you don’t trust the data and associated customer insight that shapes it? If poor quality data leads to the wrong conclusion, how will that experience impact the client’s perception of your brand? In a recent Experian report, 75 per cent of organisations stated that inaccurate data was undermining their ability to provide excellent customer experience - a depressing statistic.

Similarly, if poor quality data prevents you from identifying the best performing activities from the poorest, you will continue to spend money in the wrong places, squandering budget and leading to difficult questions from the business.

How do we tackle it?

There are a number of practical steps you can take to tackle the data quality challenge head on:

1. Identify your starting point - Take stock of the current state of your data and the impact it is having on your business, start by identifying the core data you need to support the business objectives you have by following our guide to creating a definition of your data landscape. Once you have this, then come the activities to audit the data those systems hold and identify how fit for purpose they may be.

Data profiling technology is a great help here, assisting you in identifying the key attributes of your data, such as per cent populated, accuracy, duplication and patterns in data error. Alternatively, you could partner with a specialist who undertakes this activity for you and who may also be able to help you contextualise the extent of the issues and opportunities in your business by providing a comparison with others they have worked with.

You also need to identify the benefits that could be realised if the data issues were addressed. This could be in terms of saving costs (for example eliminating constant ad-hoc data correction), increasing revenue (for example through better customer understanding delivering better targeting) or indeed could be the softer benefits (for example removal of manual, repetitive tasks that impact employee satisfaction). Key to this activity is working with the business stakeholders that will benefit from better quality data.

2. Plan, prioritse and mange - Once you understand the current state and the potential benefits, it is possible to start to define and prioritise activities by comparing them to the overarching objectives you have.

Categorise the data quality activities in terms of their alignment to objectives, the costs to undertake and the benefit they yield, aiming to focus initially on those activities that are low cost but likely to deliver a high degree of benefits aligned to your goals.

When you have this view you can then start to work through the activities, but not before you determine how you will manage the initiatives going forward. Key to this will be in getting the appropriate technology, people and processes in place to support data quality:

  • Technology - key capabilities essential to an ongoing programme of data quality including data integration and transformation, mastering data management and understanding data profiling. In essence, find technology that helps to make data readily accessible and can manipulate it in line with business needs, technology that allows for master definitions of data to be agreed and then enforced throughout the business and that helps with the statistical evaluation of data.
  • People - creation of a cross-functional working group comprising of the representatives from the areas of the business that contributed to the benefits review and those that are likely to be involved in the execution of data quality initiatives. This group will be responsible for controlling the execution of the data quality plan. An executive level sponsor should also be assigned to give it senior level support.
  • Process - creation of a governance framework and measurement strategy that embeds the data quality initiatives within the organisation and operationalises the activity.

Do. Review. Learn. Repeat

Once you have a plan and a view of how to manage its execution, the next step is to start progressing your initiatives. The essential element of this stage is to review results using the measurement strategy within your governance framework, comparing actual impacts with those that were expected, and refining activities to focus on the elements most important to the business.

Committing to quality

Where marketing is trailblazing data quality within the organisation, it’s likely that you will face resistance and may need to rapidly prove some value before the business commits to fully going down the path of data quality management. In this situation, the key is to start small. Pick the easiest issue to solve that offers the greatest benefit and do the minimum needed to ensure you can control the initiative. Then use the resulting benefits to support your business case for a broader, more comprehensive approach to data quality management. After all, data quality needs commitment. Without the organisational structure to support quality, and without investment in people, process and technology, your data quality objectives can’t be met. But with commitment and investment, every organisation can turn the data it has into the data it needs.

 

By Gary Arnold, solution strategy director at Occam

 

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