You know that junk drawer you have? It holds an unruly collection of random items that sometimes prevents you from getting the drawer opened or closed.

Does your business have a metaphorical junk drawer as well—jammed full of disorganized data that prevents you from working effectively?

Poor data quality is not a trivial problem. It costs the U.S. economy an estimated $3.1 trillion per year.

As businesses, we rely on data to…

  • Understand information about who our customers and partners are and how they interact with us
  • Classify and segment our customers and partners so we can understand their needs and capabilities, and how best to support and grow our relationship with them
  • Determine customer and partner purchase and sales trends
  • Measure performance to determine which customers and/or partners are most and least valuable, and where there are opportunities for cross and up selling, greater market penetration and expansion

If we rely on data for all these important purposes, don’t we need to make sure it’s complete, accurate and up to date?

How dirty is your data?

According to research by Experian, 83% of businesses view data as an integral part of their business strategy, yet they suspect 30% of their contact and customer data may be inaccurate.

Antiquated data management strategies cost you much more than you may realize. Not only do you waste time, but because you can’t trust your data, you are guessing to make decisions where you can and should be relying on data to give you insights to make effective business decisions.

A proper data management strategy and processes keep data organized, accurate, usable, and protected. In addition to eliminating duplicates and standardizing formats, good data management lays the groundwork for data analytics, which transforms information into actionable insights.

Do you have these essentials?

How do you know if your data management model is complete? DAMA International, the global data management community, spells out eleven must-haves:

  1. Data governance – ensuring availability, usability, consistency, integrity, and security of your data
  2. Data architecture – understanding the structure of your data and how it fits into a broader enterprise architecture
  3. Data modeling and design – data analytics and the design, building, testing and maintenance of analytics systems
  4. Data storage and operations – physical hardware used to store and manage data
  5. Data security – protecting data and ensuring only authorized users have access
  6. Data integrations and interoperability – transformation and maintenance of data in a structured form
  7. Documents and content – unstructured data meant to be accessible to/integrated with structured databases
  8. Reference and master data – standardizing data values to reduce redundancy and other mistakes
  9. Data warehousing and business intelligence – management and application of data for analytics and business decision making
  10. Metadata – creating, collecting, organizing, and managing metadata
  11. Data quality – monitoring data and data sources to ensure data integrity

Think of these as important ingredients in a recipe. If you’re missing any of them, the final product will be a flop. For example, without metadata management, you can’t easily categorize data. Without quality data, your entire data structure becomes untrustworthy and analytics will yield useless information rather than actionable insights.

So, what’s the status of your channel data management capabilities? Do you have the 11 essentials…or an overflowing drawer of junk?

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