Victoria Wilson is a Marketing Executive at data quality service provider Data8, where she’s responsible for blogging and social media.
We recently caught up with Victoria to learn more about the lifecycle of data quality. Here’s what she shared:
Can you tell us about the data quality lifecycle? What is it and why should organizations be concerned with it?
The data quality lifecycle covers the various stages involved when dealing with data. From capturing the data, to validating and cleansing it to finally then adding and improving it–the lifecycle covers all areas.
The first point is capturing the data, which is where the data quality strategy should start. As soon as data enters your system it should be correct, meaning your strategy should ensure that errors, salacious names, etc. are spotted and corrected or eliminated. The solution used for real-time capture is data standardization, which can not only verify data but also append correct information.
The next step is data validation. By providing a quick and easy experience, validation tools ensure accuracy and retrieve additional data at the point of capture. There are many validation solutions available such as contact validation, bank and identity validation, and CRM and shopping cart solutions.
One step further and we find ourselves at the data cleansing step. Data quality declines pretty quickly with job roles, addresses and phone numbers constantly changing. Regular maintenance of the data ensures it is up to date and accurate. It works in batch, so you’d use cleansing your current database for contact addresses, suppressions and duplications, goneaways and mover identification, and preference center checks.
Moving on, we reach the enriching stage. You may have clean and accurate data but it could have gaps. Without wholesome data, targeted and multichannel marketing is difficult. Appending solutions for email, telephone and profiles helps to fill in those pesky gaps and reduce bounce backs. Profiling solutions also work to enrich data, for example, for lifestyle and business profiling. They also help with demographic, wealth and financial profiling. This ensures your CRM features extra information from reliable data sources without having to ask the user too many questions. For example, a simple business email address can retrieve full company information such as credit rating, financial information, addresses, etc. using a business profiling tool.
The second to last stage is the analysis stage. With a wholesome and accurate database ready at your fingertips, clear and timely analysis is crucial to understanding customers and also your company’s performance. Without it, what evidence is there to make informed business decisions to drive success and growth? You need a Single Customer View, meaning that all data from every system comes together in one place for complete understanding. Multichannel campaign management is also another benefit from using data management solutions, as are customer insights, which can be used to provide predictive analytics to analyze trends and influence business decisions.
Finally, the last stage is acquire. With growth and development always on the agenda, buying high-quality data that has already gone through the process means it can be put to work right away. It also means you have the information and ability to buy segmented data. For example, if you want to do a mailing of companies with a certain revenue and of a certain size, this will be easily possible.
Companies should definitely be aware of the data quality lifecycle. Many underestimate how quickly data will go out of date, which wastes their time and money and decreases efficiency. Using the DQL, companies can identify where their problem may lie and use data solutions to fix and improve their operations.
What are the most problematic points of the data quality lifecycle for organizations when it comes to data management?
Obviously, each stage will have problems. That’s why data solutions were created. In the first stage and second stage, capturing and validating data, if the solutions are not in place, all kinds of wrong information will be input to the CRM, causing miscommunication and damaging the brand’s reputation. The problem with data cleansing is that data is live, so it can change very quickly. After a cleanse, it can be out of date a few months later. The problem is the same for enriching and acquiring data. The information added in is accurate at that point in time, but regular cleansing would ensure it is always accurate.
How should organizations approach data cleansing? Why is it so important?
Some companies try to manually cleanse their data, which takes a long time. Human errors can occur, meaning some get fixed and some are added in by accident. It’s a lot easier to recruit a specialist data solutions provider to cleanse your data. A full cleanse removes duplicates, verifies information, corrects errors, spelling mistakes, et cetera. Or companies may just want to start with address cleansing, for exampl. It’s easy to talk with a specialist who will identify your problems and advise what route to go down. Always start with a data quality report to see what score your data has and what areas are lacking.
How often should organizations be reviewing and cleaning their data? What are the risks of not developing good data cleansing habits?
Companies should be cleansing and reviewing their data on a campaign basis. This is known as just-in-time cleansing. It ensures that regularly used data is up to date. Also, an annual review of your database means you can be sure to remove any deceased records, identify changed addresses, et cetera. Removing data that is unused (Such as people who have unsubscribed) keeps your company compliant, especially with the new GDPR being enforced in May 2018. Risks of not regularly cleansing your data are compliance and privacy issues, increased costs from time wasted on inaccurate communications and potential damage to a brand’s reputation.
How do you approach data validation? What are the benefits of investing in data validation?
Data validation is great, it works super quickly in real time to verify incoming information. Again, I’d recommend speaking to a data solutions company to identify which solutions your company would benefit from using. For example, if you were an online clothes store, using PredictiveAddress (which is an address validation solution) reduces keystrokes for the user and auto-predicts and fills an address as the user types. Improved user experience equals fewer abandoned shopping carts. Other benefits that go without saying are that only correct information is “accepted” and thus stored in a company’s CRM meaning no Mickey Mouses or Marilyn Monroes will be present. This improves communications, decreases costs and improves efficiency.
What are the most common mistakes or oversights you see brands making with their data management? What advice do you find yourself repeating over and over again?
The main mistake is that many companies keep their data in silos (a variety of databases). Sometimes companies think it is the most organized way of storing data, but it just means it’s difficult to keep track of and create one view of their customer. For example, the sales team might have information based around contact times. However the marketing team could have data based on what website pages the customer was looking at. By amalgamating all this information and bringing it into one 360-degree view of the customer (The Single Customer View), the customer service can be improved, and all the details can be found in one place, which allows for multichannel marketing.
What should organizations be doing today to prepare for the future of data management?
The GDPR is probably the biggest change in the future of data. It is absolutely vital that companies are aware of the changes that will be occurring in May 2018. The ICO provides a plethora of information about the changes, as does the DMA, but I cannot stress the importance of preparation for this new legislation. The changes are quite large and the fines for non-compliance are certainly something to be aware of.
What trends or innovations in data management are you most interested in right now? Why?
I’m always interested in innovations and development in the data industry. I think what really impressed me was how PokemonGo used their data to generate revenue. I’m really interested to see how other companies follow suit. Will they go down the augmented reality route or will they take another direction?
Can you tell us about the mission behind Data8? How are you hoping to impact the world of data management?
Data8 aims to show people that data solutions are not out of reach. High data quality is not difficult to achieve. We want to show people that the journey to good data isn’t complicated or long and that benefits are soon seen. We’ve tried to make our data solutions simple to use and bespoke so that people don’t see it as a large chore which involves hurdle after hurdle.
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