The countdown to the holidays has begun, and with it, so has the annual countdown to developing your plan for next year’s marketing strategy. According to Melody Chien, a Senior Director Analyst for Gartner, “Good quality data provides better leads, better understanding of customers and better customer relationships. Data quality is a competitive advantage that leaders need to improve upon continuously.” We couldn’t agree more. If adding data quality checks and balances is on your pre-planning to-do list, (and it should be if you’re a fan of revenue generation) you’re in luck. In today’s blog we’ll outline all the steps you need to take to seamlessly integrate a host of proven ROI-boosting data improvement initiatives enterprise-wide.
Step One: Win Support for Data Quality Initiatives
Although you likely already understand the importance, potential, and benefits of data quality, others in your organization may not. Before you can reap the benefits of high-quality data, you will need to help leaders and followers understand the impact data quality has on their tasks, goals, responsibilities and outcomes. Give everyone on your team permission to buy-in to data quality initiatives by sharing stories of how improved data brought about a better outcome that relates to them. For example, you could share with your CEO how last month after cleansing your data, open rates on email campaigns doubled and there was a spike in customer satisfaction and deals closed.
Step Two: Perform a Data Quality Audit
It’s impossible to plan where you want to go, if you don’t know where you currently stand. Make it your mission to know the true state of your data by conducting a thorough investigation and evaluation of it. Trace its entry-points and movements throughout all systems and note any issues with data-sharing technology integrations across marketing, sales, customer service, etc. As you inspect the current state of your data, be sure to evaluate and score its levels of completeness, accuracy, consistency, and relevancy. Make a note of all areas that need improvement, and then develop a prioritized list of data improvements you’d like to see happen.
Step Three: Define Your Data Quality Objectives
Experts say that if you want to achieve a goal you don’t need to go into all the details and write a manifesto about your ‘Why,’ but you do need to be very specific about what it is you want. This means that instead of saying, “My goal is to reduce campaign waste,” you should say, “My goal is to reduce duplicate data by 95 percent in the next six months.” Because your goal follows the S.M.A.R.T. approach to goal-setting—Specific, Measurable, Achievable, Relevant, and Time-bound—you and your team will be better able to determine what actions need to be taken in order to accomplish each goal.
Step Four: Implement Data Collection Best Practices
Being able to control the quality of data that is allowed to come into your systems at the point of entry is critical to your ability to consistently maintain quality data. After all, what good is it to cleanse and enrich the data you have today, if tomorrow you allow in a slew of dirty data that subsequently pollutes your systems.
Your objective in implementing data collection practices is to ensure that the data your organization is collecting is consistent across all touchpoints (channels) and departments. Standardized forms and fields are essential in helping you avoid data discrepancies. In addition, using data validation and verification tools to automate the process of validating and verifying data is an indispensable move when it comes to preventing incorrect or incomplete information from entering your databases.
Step Five: Establish Data Governance Policies
Data governance is how you ensure that the data that your team and company use to populate campaigns, interact with customers, close deals, make decisions, and perform projections with is clean, complete, consistent, current, and trustworthy.
In establishing how data will be governed as it moves throughout your organization, you will need to create guidelines for its entry, storage, and ongoing maintenance. Ideally, most of the aforementioned processes should be automated; however, to ensure your data has the best chance of living in a utopian state, you will also want to assign various responsibilities for data quality management to trusted human members of your team. On that note, be sure to provide training on data quality best practices and the tools you’re using. You want everyone on the team to understand how vitally important their role is in maintaining high-quality data.
One of the easiest and most powerful things you can do to ensure the data your organization is using is clean and enriched, is to simply integrate all data-housing tools in your tech ecosystem with software that perpetually cleans and enriches your data. These automated data-savers perform a litany of functions, including identifying inconsistencies, removing duplicates, correcting errors, and appending missing information. Once integrated, we recommend setting systems up to continually clean and update your database, so that your data is always campaign-ready.
Of course, with regard to appending information (data enrichment) there are a few things to consider, such as what type of data you are missing, what data fields you would like to have, and which third-party provider has what you want. At Sureshot, we partner with several top-tier third-party data providers to equip our customers with quality choices when it comes to accessing data that helps complete customer profiles, open new market opportunities, enhance segmentation strategies and more.
Step Seven: Monitor Data Quality Metrics
It’s vital to your success in improving data quality that you are able to define, measure and track key performance indicators (KPIs) for data quality, such as accuracy rates, completeness rates, and duplicate rates. Regularly track and report on these metrics, and allow the insights they provide to help you review and refine data quality processes. Finally, make sure you track how improved data quality is contributing not only to marketing’s efforts, but to your organization’s ability to generate revenue in other areas, such as sales and customer service. Be sure to share metrics that are impacted by data quality — like conversion rates, customer satisfaction, and ROI — with leaders whose ongoing support you value and need.
Bonus Step: Get By With a Little Help From a Friend Data quality is an ongoing effort, but it doesn’t have to be a struggle. At Sureshot, we have a team of experts and a portfolio of data solutions that are fully committed to helping your data deliver on its promise. If you are ready to harness the power better data can bring to your marketing strategy and initiatives, contact us, or start by taking our FREE Martech Assessment.