Summary: If you rely on a CRM, ERP, DW or Direct Marketing automation solution, you know that you survive on good, clean data. How can you restore the health of your data?
Three Dimensions: The key database dimensions that are the lifeblood of your business model are:
1. Marketplace Participants: customers, prospects, addresses, geographies, brands, buying categories and competitors.
If you are a B-B model, the marketplace may include direct business account attributes as well as channel/resale business attributes and their channel territory relationships.
If you are residential/consumer oriented, your model may require the retention of demographic data on both individuals and households.
2. Commerce: Products & services and the commercial relationships defined by past transactions.
3. Interactions: communications, messages, interactive events related to efforts to buy and sell.
Data Perishability: Maintaining this data is a constant battle against the forces of entropy.
Data is very volatile. It significantly changes at a compound rate of 5-10% per year. If your data is not continuously refreshed, it will very likely be 30% incorrect, invalid, and misleading in 3 years.
Data decomposes right before your eyes. Reality changes like the clouds…slowly if you watch it, but before you realize it a sunny day becomes overcast. The damage to your business is significant. How can you be sure your fact-based analysis and decision management is giving you the inputs to make good decisions if your data is wrong.
Causes of Data Depreciation: Here’s how data decays inside a database.
1. The real world is highly dynamic
Capturing dynamic data requires constant attention. Reflecting data representing real world subjects is not a mirror. It can’t be managed with a “camera”; it requires a “video cam”. Snapshots in time always represent the past. On the other hand, data representing historical transactions is static (by definition). You can grab it and it’s one and done. But even this data in the full context of the real-world participants will be misunderstood if it’s associated to obsolete dynamic data or invalid attributes at the time of capture. This can cause data aggregates to be misleading.
2. Common changes and sources of data quality degeneration
Names change all the time
Decision-maker roles change
People move between jobs, companies and kids leave the home
People re-associate with competitive alternatives and change their buying footprint
Phones, emails change, and people move in and out of social channels
Physical relocation occurs at a rapid pace
Data can be corrupted from everyday use by business users of your systems
Some data is just missing. It was never captured
Some data attributes are incoherent when compared to the same attributes across like records
Often a single participant or entity is tracked multiple times in a single system or when multiple database applications are comingled and integrated, the records are replicated.
Invalid database schemas often miss opportunities to track data or proliferate data in different ways that frustrate coherence.
Cost of Bad Data: Here’s the downside of bad data and what you can do about it.
1. Trust & Utilization: When users of a business application see enough bad data in an enterprise solution, they will look for ways to bypass that solution and track “good” data in proprietary databases and applications. We call this a problem of user adoption, but often it’s a problem with users not being able to trust the data in their systems. Do not forget the opportunity cost of this: bad data begets disuse, which begets more bad data. Also, when users opt out and store data elsewhere, they compound the data proliferation and fragmentation problem.
2. Fragmentation & Redundancy: When invalid data is encountered, users who are trying to leverage data to engage in business-building and decision-making will use ad hoc tools and procedures to attempt to restore some integrity. This can be very expensive in licensing and personnel time and might result in sub-optimal job performance. It is also an invitation to make mistakes.
3. Errors: Errors are the worst kind of mistake 😊. If you send the wrong messages to the wrong audience or if you track efforts and results to the wrong segments, you not only offend your marketplace, but you may be steering things in the wrong direction. Fear of making mistakes? Go to #1 above – Trust and Utilization.
Vitamin D: Here are some of the business benefits of clean data:
Postal and shipping delivery cost reduction from valid, clean addresses
Value-added information at the point of sales engagement
Refined and accurate segmentation and message relevance
Improved likelihood of communicating solutions and product value to the right person at the right time
Reduced email and SMS bounces and opt outs
Improved reputation with ISP’s and ESP’s
Improved brand value with buying and market audiences
Consumer and regulatory compliance
Competitive advantages of fast, accurate messaging
Ability to evaluate and refine marketing and selling approaches by accurately measuring what is working for which segments
Ability to better correlate and predict results from expensive campaigns and sales initiatives
Ability to structure optimal offers
Buyer retention with less churn due to a sense of familiarity and relationship
Recognition of pattern changes and exception intervention processes. Good data encourages automated approaches, which enable speed to market
Personalization accuracy and affinity from rich, up to date customer and prospect data
Many business applications have weak data integrity features, and many consultants overlook this vital requirement. We use an approach called “Customer Science™” to "repair and return" your data. We improve and protect your investment in business data.
Contact us today to find out how to add value to your data and restore trust in your business applications. email@example.com; 920-428-5669.