Ten top tips for effective data management

When you look at data quality, it's vital to start at the end. Think what your objectives are, what you want to do with the data, what it will be used for. Your objectives impact on the amount and type of data you need.

Typical applications might include order taking, fulfilment and delivery, billing, reporting, cross-selling and up-selling to existing customers, and prospecting for new customers who share a similar profile to your existing customers. Knowing what you are aiming to do at the beginning will help you ensure you capture all the fields you need to do it, and that you have the ability to capture the data across all customer touch points.

Considering your objectives and starting at the end ensures your data is fit for its intended purpose.

There's much more to a customer or prospect record than a name and address. This is just the starting point for a whole host of additional information that can enrich your relationship and help you communicate with them more effectively. A great starting point is to gather the data you already have and to decide upon its relevance. Analysing your current data should indicate where you have gaps and what additional data is needed.

The key point is to think about everything you might want to know about a customer or prospect.

No matter how good the systems and processes you put in place to capture data across all customer touch points, it is essential that you have a way of measuring the quality of the data over time.

Every year in New Zealand:

• 500,000 people move
• 50,000 new addresses are added to New Zealand Post's Postal Address File (PAF)
• 20% of Auckland mail is addressed incorrectly

And just as alarmingly, business to business data decays by 38% each year. With statistics like these, it's clear that your data quality processes must include provision for updating your customer and prospect data on an ongoing basis.

Benchmarking is a key part of any data quality programme. If your current processes are in a poor state, there may be too much to tackle all at once, so identify the areas that are having the biggest negative impact on the business, and prioritise those. QAS can provide you with a data audit document to assist with this and advise you on your strategy going forward.

With improved data quality procedures in place, you will be able to assess how much your data quality has improved over time against the targets you set for yourself.

Many data quality projects fail because they do not gain the necessary support from all relevant stakeholders in the business. It is not always evident to people why data quality matters, and how it can affect the bottom line. The fact is, customer and prospect data affects everyone in the organisation, from the CEO down. Education is vital to get everyone on board by explaining what's in it for them.

Think about the key drivers and the pain points for all the key stakeholders of the business, and then show how the data strategy will help to improve things. If a manager is charged with reducing costs, explain how a co-ordinated data strategy will reduce the amount of returned mail, and so help achieve his ends. For the hands-on users, appeal to their competitive nature by rewarding those individuals and teams that show the greatest improvement in data collection or data entry. Buy-in will only come when you make data quality relevant to your audience.

A good data quality programme is an investment in the long-term success and profitability of your business.

Like any investment it needs to be justified to those holding the purse strings. Connecting the proposed investment to the company objectives and to the five areas below should act as a good business case for investment.

• Reputation
• Revenue
• Cost
• Profit
• Compliance

A data strategy alone is not sufficient in ensuring your organisation's data quality is upheld. Putting processes in place to capture, clean and maintain data will provide clear guidance on how you aim to achieve your goals and clear instructions to those working with you. Ensure that everyone in the organisation is trained in the importance of maintaining good-quality data, and hold regular review meetings to ensure that your data quality processes are up to date, efficient and effective, and remain so.

The challenge of maintaining accurate data can sometimes seem like a daunting one, but the technology is out there to help you do it. Most companies now have enough computing power sitting on the average desktop to make the most of the sophisticated software that can help ensure the accuracy and quality of your data. The technology you implement should be tailored to your specific organisation; concentrating on your business needs will give you a more sophisticated and effective solution.

Tools are available to help you:

• avoid potential data pollution
• manage data decay over time
• profile and segment contacts

The tools are out there; there is no excuse for not using them.

With a good data quality system in place, some of the improvements may be evident for everyone to see. But to ensure you are getting a return on your investment, you should measure and report on the performance of your data quality programme on an ongoing basis.

There are some key metrics that will help you see how well, or otherwise you're doing, including:

- Customer satisfaction - if you're data quality really has improved, this should be reflected in improved customer satisfaction scores.

- Speed - are you saving time capturing and cleansing data, and executing campaigns? Do you have systems in place that can measure how long these activities take now, and how long they took last year, or the year before?

- Accuracy - if you have benchmarked where you started out from, and where you are now, you should be able to see improvements in the accuracy of your data reflected in a reduction in the amount of returned mail, and in the volume of people you need to mail in order to achieve a given response. If your mailings are better targeted, you should also see an improvement in your response-to-conversion ratio.

When everything seems to be running smoothly, and your data quality programme seems to be doing its job, it's easy to let up. Don't. Revisit your initial objectives to see how your results are improving, review the process regularly, and make sure the key stakeholders are kept fully informed of your performance.