The Data Management Playbook
Your ultimate guide to data quality
Data. It’s long been acknowledged as an organisation’s most valuable asset.
But even those with data experience can find it difficult to sustain an efficient data management strategy.
That’s why we’ve compiled the Data Management Playbook – your ultimate guide to all things data.
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Why is a clean database so important?
We’re used to keeping our business assets in a condition that’s fit for purpose. We readily invest in upskilling employees and maintaining equipment.
And data shouldn’t be treated any differently. Why? Because good quality data underpins your organisation’s strategic outcomes and is an essential tool in helping you achieve your wider business goals.
Use these tools to anticipate your customers' every wish:
1. Profitable customer relationships
We all want to build long and profitable relationships with our customers. But customer communication is reliant on having accurate information on hand.
With high quality data, you can reach your customers at the right time, via the right channel, with the right message.
2. Better decision-making
With cleaner data comes more possibilities for faster decision-making, stronger go-to-market strategies, and a deeper understanding of your customer base.
Your business can gather valuable insights into customer preferences, interests, and buying habits, and use this to fuel communications strategies and loyalty schemes.
3. Support organisational speed and agility
High quality data puts your organisation in a position to leverage insights, make informed decisions, and act in either real-time or as quickly as it suits both you and your customers.
The greater your agility, the faster you can respond to the market environment and needs of your customers, giving you a competitive advantage.
4. Meet stringent regulatory requirements
Bad data can lead to violations of regulatory compliance. With clean, high-quality data, you can rest assured that your business is compliant with GDPR and other increasingly stringent regulations.
5. Reduce wasted marketing costs
A poorly maintained database can result in marketing budget being wasted and inefficiently allocated. From channel selection and campaign performance to sender reputation, the quality of your data has an impact.
6. Increase productivity of internal teams
When incorrect data is removed or updated, organisations are left with the highest quality information so you don’t have to waste time and resources wading through irrelevant and incorrect data.
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Measuring the quality of your data
Data audits
To truly understand the quality of your database, it’s a good idea to run regular audits.
The frequency of your audits really depends on the nature of your business and the volume of data you’re acquiring. But generally speaking, the longer you leave your data between audits, the higher the associated costs of cleaning it will be due to the extent of data degradation.
You can be smarter with your data audits by narrowing the focus across either different products or customer types. This establishes a basis for comparison, helping you internally benchmark performance across business areas and identify any issues and the potential root cause.
Top tip
Schedule your data audits to run at fixed intervals and top-up your data quality when it falls below a pre-determined level. Taking this approach will regulate your data management investments and audit frequency, enabling a high level of data quality to be self-maintained.
Use data quality dashboards to visualize the data that is important to you, measure it, identify key patterns, and set objectives to improve.
Red, amber, green (RAG) status is a traffic light system and a method that can be used to clearly identify areas that need immediate attention, helping you prioritize the implementation of actionable insights from your data audits.
Agree a process in your team to make these improvements, implement them, then start the process over and measure again. Remember, this should be a cyclical process to enable continuous and sustained improvement.
Data benchmarking
Our data health check tool generates a data quality benchmark score from an algorithm that securely tests your data against the following measurement criteria:
- Volume of duplicate records
- Volume of decease records
- Address quality
- Number of individuals that have ‘moved away’ from the address in your database
- Number of forwarding addresses
- Number of addresses that meet Royal Mail PAF standards
- Number of invalid mobile numbers and landlines identified
- Number of invalid emails identified
We compare your customer and prospect records against our own data sources to reveal how accurate, complete and compliant your data is.
We then provide you with a visualized data quality audit, and a unique Data Quality Index Score, giving you insight into how you measure up against other companies who hold similar data.
The six dimensions of data quality
To help assess the quality of your data, the Data Management Association UK outlines six dimensions that your data should meet: accuracy, completeness, consistency, timeliness, validity and uniqueness.
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Implementing a data quality improvement plan
Understand your data’s value and prioritize where attention is needed
Once you’ve audited your data, it’s time to prioritize the issues identified and implement an actionable data quality improvement and management plan.
The challenge with data is that there are often large volumes of it and your audit may highlight several issues across different areas, so how do you prioritize what should be addressed first?
You need to understand the value of the data to your organization and the impact that it has.
Data, like a business’ equipment, is an asset that holds a value. To provide perspective, imagine if all the data in your organization was lost tomorrow, what impact would this have? How much would it cost to replace it?
Assigning a monetary value will not only help you prioritize where your attention is most needed but will also help you defend and justify investments in data management.
The focus when determining the value needs to be on the concept, securing the understanding of data’s worth, and the need for regular maintenance, rather than the specific details of whether your company’s data is worth $5m or $5.2m.
Here are three possible approaches that can be taken to ascertain the value of your data:
- Consider how much your company would be worth if it was sold without customer data. Take the company’s market value and assign a percentage of this to your data – this calculation is rudimentary but can make people think and start discussion.
- You could look at the incremental sales that are driven by data added to the cost savings from the use of quality data. This is a more precise, but harder to achieve calculation. You are looking at response and conversion rates, but other factors such as collateral, special offers, and salesperson effectiveness can also impact results.
- Lastly, you can calculate its value based on the volume and completeness of your data. This is a middle-ground calculation between options one and two, and it’s not overly time-consuming or complicated to calculate.
Let’s run through a worked example calculation of Company A:
Once you have applied this calculation, you will have three figures to present to senior management:
- The value of your data
- The potential increase in value if the inaccuracies were fixed
- The investment required to maintain data accuracy and quality
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Outsource the dirty work
So, you’ve completed your audit and prioritized areas that need your focus, but you now need to cleanse your data. Where do you start this process?
Data cleansing can be complex. Considering your data’s value, you want to make sure this is done correctly, so you may need to seek support from an external partner who is a specialist in this area.
When selecting a partner, it is important to consider the following:
- Make sure this partner satisfies your InfoSec and compliance requirements. As a valuable asset, your data should be protected. You do not want it to be exposed to security risks or shared beyond what is necessary.
- Ensure the data sets they use to validate your data are credible and varied. Some suppliers rely on their own internally generated datasets and many don’t cover all sources.
- Check they have access to multiple comprehensive datasets.
- Ask if they have the autonomy to validate your data against a broader range of data sources. An organization that is not independent may only validate against their own data. As a result, the validation may be less comprehensive.
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Delay the decay
You’ve cleansed your database, but the work doesn’t stop here.
Data decays over time and not all data will decay at the same rate. By the end of the year, your customer data on average will have decayed by around 15-20% and your business data by 30-40%.
For this reason, ongoing maintenance is essential to ensuring your database is kept clean and the quality of your data is optimal.
Top tip
If analysis of an audit reveals patterns in the types of errors occurring, use this information to understand where the changes need to be made. The issue could be resolved by changing how data is captured at the source.
Data management and maintenance should be viewed as a cyclical process – it’s all about continuous improvement.
Data Cycle for Continuous improvement
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Data capture
Validate, verify, and enhance at the point of capture
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Audit, analyze, and prioritize
3
Cleanse your data
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Monitor data quality
By shifting from a reactive to a pre-emptive data management approach, you’re avoiding future costs by putting in the groundwork upfront.
The reputational, financial and legal costs of failing to maintain your data put your organization at risk and are excessive compared to the investment required to sustain its quality.
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Customer story
Challenges
- Boost the effectiveness of customer communication across Rank Group’s multiple brands
- Embed a safer gambling ethos throughout the business, and identify and interact with vulnerable customers
- Meet stringent industry data regulations
Solution
- We undertook a large-scale data cleanse on customers who had interacted with Rank Group over the past 24 months. Checked against Royal Mail PAF – the most up-to-date Postcode Address File – addresses in the database were parsed, standardized, verified, cleansed and formatted. A suppression cleanse was then carried out to flag any house movers or deceased customers
- We carried out an email and mobile validation cleanse. Both of these cleanses were key in enhancing Rank Group’s communication strategy, improving customer service, and making sure the intervention team could get in touch with customers quickly to provide safeguards if problems arose.
Results
- Improved customer communication and customer experience
- Reduced marketing costs (mainly mailing and SMS) by up to £500,000 per annum
- Worked towards improving fidelity of a single customer view to detect fraudulent accounts faster and reduce gambling harm
- Formulated a data quality dashboard allowing data quality metrics to be shared across the business
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Get started with Loqate's Data Maintenance service
We all know how vital data is to our organization - but it can also seem overwhelming when we consider the task in front of us. We can help!
You select and send the data you would like us to analyze, we ensure this exchange is safe, secure and compliant.
We analyze the data, create a visual report, and talk you through the findings. Alternatively, we can send you the report directly.
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