Last time we spoke about crossing organizational siloes to access data without increasing the burden of manual labor, expending scarce resources, or introducing significant costs. This time we take our customer profitability model and describe how to recognize value immediately.  This is a critical success factor.  Being able to demonstrate progress, to realize benefits by operationalizing existing data, and to build incrementally is vital in any dynamic environment.

Traditional efforts, like data warehousing or messaging, often need technical resources to design and build programs that populate data structures which are then exposed for reporting.  Even when data processing efforts are limited, manual efforts are still required for modelling and translation to business needs. These steps are not required when using NexJ CDM, as any model that is defined is also exposed by the tool using a pre-defined service rendering capability.  This service can be quickly interrogated by available query and reporting tools, allowing for the examination of content as it is being defined.

With NexJ CDM, analytic initiatives can start much earlier with content arriving as it is defined.  A model is defined through mapping to organizational systems and begins with the attribute definition itself.  The attribute can be mapped to one or more systems whether they are database, real time streams, web services or flat file-centric.  More systems can be added and existing ones removed – the CDM engine will take care of the cutover.  Attributes can also be defined as computations, kind of like a formula in Excel, performed by the NexJ CDM engine.

Customer Loyalty Analytics

  • Relationship insights through hierarchies. Attributes can be associated with hierarchies to define rollup and aggregation operations or to facilitate associative actions like complex inter/intra party relationships. Unlike traditional hierarchy operations, a relationship can be established between any attributes without the additional complex navigational level to node entries typically required. Hierarchy changes and impacts on specified rollup and aggregate operations are performed by the CDM engine.  Agility in creating or modifying hierarchical structures is the key ingredient for enabling relationship insights, be they between persons, companies, securities, instruments or whatever results are of interest.
  • Tailor content with business rules. Each attribute can also participate in a business rule defined to enforce data quality, to assign survivorship, or to ensure correct enrichment processing.  But the rule can also act as an escalation vehicle so bad elements are tolerated to a certain point but, when bad starts to become terrible, an escalation process is invoked.  This can be integrated into alerting mechanisms too.  Maybe a system crashed or a cable was unplugged or some other fault occurred?  Processing can be suspended or halted until remediation efforts occur; or just audited for downstream processing.  When the fault is corrected or the data remediated, CDM will resume processing.
  • Monitor attributes progress. It’s the view that contains the actual content being interacted with. As each attribute is created, it can be added, removed or migrated between views – the CDM engine will take care of the processing.  Attribute modification, like changing a computation or changing the source system, is also done by the CDM engine.  Results can be monitored using a variety of query, reporting, analysis, or visualization tools by accessing views – the CDM engine will ensure view content is provisioned.

For example, a dashboard can be built from semantic views that not only monitors the progress of the project but gauges effectiveness with the end-user community. These activities can occur in parallel without lengthy waits. At last you can examine the structures and measure the content as it is being defined!   Join us next time for Part IV: Computing Lifetime Value Example when we apply these techniques to computing customer lifetime value.