Data Quality and Transitions in the Customer Life Cycle

November 30, 2010 by · Comments Off on Data Quality and Transitions in the Customer Life Cycle
Filed under: Business Impacts, Data Analysis, Data Quality, Performance Measures 

I have been doing some further research into the interdependence of business value drivers, related data sets, and considering financial impacts. One area of focus is customer retention, and I have been looking at a number of aspects related to some related performance measures. The one I would like to look at today involves maintainin the relationship with the customer at various points across the customer life cycle.

Thre are two aspects to the concept of customer life cycle: events associated with the life time of a product once it has been purchased by the customer, and specific events associated with the customer’s own life time. An example of the first involves a product’s manufacturer’s warranty. The warranty is associated with some qualifying criteria, such as a time period or measurable wear (such as a “3 year/36,000 mile” automobile warranty). A product life cycle event could be associated with a customer touch point. For example, three months prior to the end of a product’s warranty period might be a good opportunity to contact the customer and propose an extension to the warranty. An example of a customer life cycle event is the purchase of a home, often registered as public data with a state registry. Customer life cycle events can also trigger touch point opportunities, such as contacting a new home buyer with a proposal for a new water filtering system.

There is value in knowledge of customer life cycle events and transitions, especially in maintaining long-term relationships. That same new mother who, in 2001, registered for diaper coupons is probably going to be dealing with a toddler in 2003, a kindergartener in 2006, and a teenager learning to drive in 2017. An effective long term marketing strategy may take these life cycle events into account as part of customer analytics modeling.

That being said, the question of the impact of data quality on customer acquisition or retention has to do with the degree to which data errors increas or decrease probability of long-term customer retention and/or continued conversion at critical life cycle events. And this implies a strong command of many different data sets, their potential integration, and corresponding analysis.

Let’s continue the example: the sale and installation of a water filtering system in a recently purchased home is but the first transaction in what should be an ongoing sequence of subsequent maintenance transactions. The filters will need to be replaced on a periodic basis (every 12 months or so?), and the entire system may need to be flushed and cleaned every few years. Therefore, it is to the water filter company’s benefit to maintain high quality information about customers and their transaction dates. But since the filter itself is associated with the property, that information needs to be managed separately as well.

So here, if the customer moves to a new location, that could trigger two life cycle events: one to contact the existing customer at the new location and begin the sales cycle from the start, and one to contact the new customer at the existing site to establish a new maintenance relationship. The quality of the data is critical, since attempting to continue to provide maintenance to the existing customer at the new site would not really make sense until a new filter is installed.

But it is important to note that it is not just the quality of the data that is important: it is the business process scenarios in which the data is used. Without having specific tasks associated with the life cycle event trigger, the entire effort is wasted.

Value-Driven Data Quality Projects

November 17, 2010 by · Leave a Comment
Filed under: Business Impacts, Data Governance, Data Quality 

There are roughly two types of justifications for data quality programs. The first leverages a business case or return on investment analysis to identify wyas that improved data quality increase value. The second is that maintaining high quality information is a best practice that mature organizations do.

Today I’ll focus on the first type of justification, especially in the context of scoping the program. In recent customer interactions, we have found that there are a number of questions that need answers to validate the business justification. Actually, in most of our discussions, it turns out that the questions are not even asked, let alone answered. So I thought it might be worth throwing some of these questions out there:

One can focus on specific areas of the business or target the organization as a whole. In either case, one question to ask is whether the actual data quality tasks to be performed are “value-driven”? In my book I detail a number of dimensions of value that can be used to link data quality problems to business impacts, including financial, trust, productivity, and trust. I am starting to work on a project that will provide another level of detail about assessing business impacts, and will keep you updated as the project nears completion.

The second question is determining the degree to which the expected value depends on the data and how much depends on alternate factors. Another way of asking this is what additional organizational changes are necessary to derive the benefit of high quality data?

The third question is intended to assess your success – are there business performance measures linked to the quality of the data?

And the last one for today is more of a conundrum: Identifying a data quality issue with business impacts and eliminating the root cause of the data quality issue can be seen in two different lights. In the first, by eliminating the data issue you increase value by reducing or eliminating any negative business impact. In the second, by eliminating the data issue you are also eliminating the possibility for negative business impact. And in that second light, if there is no possibility for a negative impact, what is the value of the continued “operational data quality” investment?

The first view is critical at the beginning of the project, when funding is needed, but the second view becomes more of an issue later in the program lifecycle when seeking continuing funding. At that later stage, it appears as if there is a large investment for reduced value, forcing the data quality team to consider best methods to communicate the ongoing value. Mor eon this soon…

Refining a Definition of a Master Data Set

November 10, 2010 by · Leave a Comment
Filed under: Data Quality, Master Data, Metadata 

While in many of my Master Data Management (MDM) presentations and papers I must provide some overview description of what master data is (as well as what master data management is), I have been very careful to qualify the description by saying that it is specifically *not* a definition. Because my description was cobbled together from a bunch of other proposed definitions, it occurs to me that we are still lacking a standards definition for master data.

I guess my real issue is that the existing definitions are not precise enough to use as an “acid test” to decide if a data set is or is not a master data set. However, the past few Knowledge Integrity consulting engagements have shed a little light on practical aspects of master data and, correspondingly, MDM tools and technology, especially in the context of (and need for) data governance. The main issues we keep seeing with our clients’ approaches to MDM is the variance in structures, formats, definitions, and meanings of source data systems and the challenges of merging those data sets together in a coherent manner.

That being said, I am moving closer to having a definition of a “master data set” that exemplifies what the resulting “data artifact” should be:

“A master data set is a structurally and semantically consistent unified view of a single data entity concept shared by more than one business process or consumer entity.”

Here is how this breaks down:

1) The resulting data set’s data element structures must be consistent with the corresponding source data elements.

2) The understood meaning of the master data set’s semantics must be consistent with the corresponding source data as well as the downstream consumers.

3) The master data set must contain data that is used by more than one consumer (either a process or an entity).

4) The data mus tbe suitable for sharing.

Suggestions or comments? email me

Business Impacts of Poor Data Quality

November 4, 2010 by · Leave a Comment
Filed under: Business Impacts, Data Analysis, Data Quality 

One of Knowledge Integrity’s approaches to developing a business case for data quality improvement involves analyzing the degree to which poor data quality impedes the achievement of specific business objectives. We are developing a classification scheme for business impacts whose highest level lists primary categories for either the negative impacts of bad data or the potential added value derivable from improved data quality:

  • Financial impacts, such as increased operating costs, decreased revenues, missed opportunities, reduction or delays in cash flow, or increased penalties, fines, or other charges.
  • Confidence and Satisfaction-based impacts, such as customer, employee, or supplier satisfaction, as well as decreased organizational trust, low confidence in forecasting, inconsistent operational and management reporting, and delayed or improper decisions.
  • Productivity impacts such as increased workloads, decreased throughput, increased processing time, or decreased end-product quality.
  • Risk and Compliance impacts associated with credit assessment, investment risks, competitive risk, capital investment and/or development, fraud, and leakage, and compliance with government regulations, and industry expectations.

Categorizing business impacts this way allows the analyst to link data issues to business issues, and can help in identifying risks that are directly related to data quality issues as well as determining which data errors are largely benign. And while these areas of risk (and their originating sources) differ, they are similar in the need for mandating high quality information and the means to demonstrate adequacy of internal controls governing data quality. Organizations must be able to assess, measure, and control the quality of data as well as have the means for external auditors to verify those observations. Ultimately, the objective is to maximize the value of the information based on reducing the negative impacts associated with each set of potential problems while increasing the positive impacts with better data accuracy and consistency.

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