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Seven Steps to Data Confidence

October 31, 2019 | By Julian Archer

  • Account and contact data confidence is only achievable through governance and individual discipline, which involve irksome, tedious and thankless tasks
  • SiriusDecisions’ seven-step approach gives organizations confidence in an effective marketing data strategy
  • The steps provide a tried and tested approach to data management, clarifying how data should be treated to be the efficient fuel that drives business activity

Data stewards often speak of data as the fuel that drives marketing strategy, plans and actions — and thereby its contribution to the business. In our optimistic moments, we talk of data-driven decision-making as the only way forward. Objective, rational and considered decisions must be made on the basis of solid, fact-based insights. With our operational database(s) clean and effective, we are sure that, as data stewards, we are providing the means to drive campaigns and customer outreach.

employee using tablet in data center

And yet, in our pessimistic moments (which, let’s face it, often outnumber the former), we imagine that every email or phone call we receive will be someone complaining about our “lousy” data. The trouble is that “good” data — and with it, the confidence we seek — is only achievable through governance and individual discipline, which involve irksome, tedious and thankless tasks. To improve your confidence in your data and your teams’ confidence in you, we recommend implementing our effective seven-step approach to sales and marketing data management:

  • Establish governance. Data management demands structure and order. An important step is establishing a managed process that will set rules and guidelines. Thus, define which individuals are responsible for data policy and the extent of their authority. Agree on the cross-functional participants in a governance council, determine the scope of data to be managed and ensure the council has the authority to set data policy.
  • Identify processes. Many teams frequently skip this step or believe they have covered it already. It seems so obvious to ask which revenue engine processes are in critical need of data and what data elements are required. Sadly, if you ask multiple stakeholders, it isn’t so obvious. All too often, teams using the data assume that all their data wishes will be fulfilled by the magic data wizard. Similarly, data stewards must gain a helicopter view of all vital processes across the organization to bring structure and efficiency to their downstream efforts. Highlight processes especially reliant on data, such as outbound outreach, lead or demand unit routing, and personalization, and rank these in order of dependence on “good” data.
  • Capture the voice of the (internal) customer. In this step, dig deeper to make sure your internal customers clearly understand the data elements they need. Moreover, agree on what constitutes an acceptable record quality and what is simply a nice-to-have, considering compliance requirements, data format and volume. To drive efficiency in data collection and maintenance, structure these “internal customer” conversations across the organization to allow for the collation of data requirements.
  • Audit the current state. Conduct account data setup and contact acquisition in relation to the organization’s target audience. To determine the gap between the data you have and the data you need, be specific about the volumes and demographic and firmographic coverage necessary to satisfy internal customer needs, and audit your current state. Adopt a formal measurement of record completeness using a defined set of populated and validated fields as a completeness metric. Additionally, complete a port-of-entry audit (measuring entry volume and quality of data) of the locations through which data enters the organization’s ecosystem to identify the biggest problems.
  • Acquire data. Having obtained a view of the issue’s scale, review the actions needed to acquire the missing data. This is a whole topic in and of itself. The organization must implement a solid contact acquisition process that spans sales and marketing (including tele and channel teams), allows the acquisition of compliant data, fills the gaps in the target market segments, and provides for data at a rate and cost that will drive efficient business.
  • Curate data. Without solid de-duplication, quality checks and various other maintenance routines, the data in your systems will age and steadily decrease in value. The curation step requires the data steward to implement processes and policies that will maintain the data over time. Part of this task is to communicate the steps and actions others must take to ensure continued data effectiveness.
  • Measure results. Good performance measurement helps tells a story by showing what happened and why, comparing results to expectations, and revealing what remedial actions must be taken. Database measurement must consider the requirements of performance and operational data. Select relevant metrics to gauge data improvements. Use the SiriusDecisions Metrics Spectrum to differentiate between readiness, activity, output and impact metrics. Measure improvements in the data quality and the impact better data is having on the processes identified in step two.

The steps above compose a structured, tried and tested approach to data management. They clarify what data is necessary and why, and how data should be treated to be the efficient fuel that drives business activity. If you would like to discuss any point in this blog post, please reach out to me at [email protected]

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Julian Archer

Julian Archer is a Senior Research Director of Marketing Operations Strategies at SiriusDecisions. He has more than 25 years of international b-to-b demand creation experience within corporate and pan-European field functions. Follow Julian on Twitter @julianarcher

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