Radius: Bad Data Is a “Rotten Ingredient”

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Stephanie Kong, Product Marketing Manager at Radius, recently compared dirty data to rotten food.  Working with either consumes more expertise and results in sub-par results:

Handing dirty data over to data scientists is tantamount to passing rotten ingredients to a chef and expecting that he/she transform the inputs into a gastronomical masterpiece. In both instances, the quality of the inputs impacts not only the quality of the outcome, it also impacts the experience and efficiency of the professional– how much time can be spent experimenting and applying the artistry for which the professional was hired versus overcoming hurdles to get to a sufficient baseline.

Bottom line: the quality and state of your internal data can impact– and even worse, impede– the ability of even the most talented data scientist to generate breakthrough ideas. Many turnkey data solutions can help you maintain data, even enhancing accuracy and comprehensiveness, in addition to extracting insights. It’s not simply a means of “killing two birds with one stone”; accurate and complete data is a critical first step. In other words– and without being too macabre– good data is the essential and necessary “first kill.”

Marketers are becoming more strategic in their approach to data as they realize the limitations and costs of poor data.  Predictive Analytics systems are only as good as your underlying data.  Bad data is simply noise (or as Kong would call it, “rotten ingredients”) that obscures the underlying signal.  Without accurate data, how can you expect your predictive systems to give you anything more than random nonsense?

Likewise, the shift to Account Based Marketing requires strong firmographics for identifying the companies you wish to target.  Furthermore, strong linkage is necessary for targeting subsidiaries and branches.  Whether you are extending an MSA or looking to establish a beachhead, you need a holistic view of the organization across industries, regions, and job functions.  You also need an accurate set of contacts spanning all functions, levels, and locations.

When evaluating B2B content vendors offering predictive or DaaS solutions, ask about their

  • Data Processes: Data sourcing, update cycles, verification and validation, feedback processes
  • Hygiene Services: Do they offer email, phone, and address verification, field standardization, deduplication
  • Matching Capabilities: Is it a direct match or probabilistic match based upon multiple fields? Are fields standardized prior to matching? Is the focus on company or contact matching?
  • Connectors / Integrations: CRM, MAP, DaaS cloud, API, etc.
  • Ongoing Data Refreshes: Frequency, Cost, Level of Automation
  • Contact Coverage: Emails, direct dials, functions, levels, bios,
  • Company Data: Scope, depth, firmographic fill rates, identifiers, linkage, etc.
  • Other Data: Intent data, technology platforms, business signals, etc.

Data quality is a strategic asset so your content and technology partners need to be thoroughly vetted.  It is important to understand the strengths and weaknesses of each offering during both the vendor selection and implementation stages.  Otherwise, you may only partially address your “rotten ingredients” problem.

Photo: Wikimedia Commons

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