Intelligent Metrix

Data to Metrics to Insight to Intelligent Decisions

Archiving Strategy: Data Relevance

We often think of the relavence of data when we want to include or exclude it from analysis or process.  However, are you thinking about relavence as part of your data quality effort?

Just as you focus data quality efforts to clean existing information, there are invariably records that can’t be cleansed or enhanced.  They have no value in either business analytics or business process.  They are noise, similar to the noise you have when there is bad data.  To save and maintain them in your database can affect your ability to accurately analyze information, continue to deflate confidence in data, and if a significant percentage of your database, will cause problems in performance and added maintenance.  Developing an archival strategy as part of your data quality practice is a significant component that should not be overlooked.

Benefits of Data Relevance

  • Trust in data
  • Enables process
  • Accuracy of analysis
  • Supports decisions
  • Database optimization

It can be tempting to simply delete records from your databases.  Though, this can have a detrimental affect due to data dependencies within your databases as well as causing non-compliance in regulated environments.  Instead, it is best to formulate a strategy that flags non-relevant data removing or suppressing it from user interfaces and analytics.

Components of Archiving Strategy

  • Data decay rates – Attributes of records that loose relevance over time.  This component is a good guide on the frequency at which you will focus cleansing efforts.  It also provides an indicator on when data is approaching a horizon when a record will lose its relevance.  Age of the data and activity related to a record, even if a record is complete, can signify whether the data is relavant and open to archiving.
  • Minimum requirements of record viability – Records should continually be assessed to determine if they meet the minimum standards of use.  Failure to meet minimum requirements is a leading indicator that the record is a candidate for archiving.
  • Relevance of record to analysis, process, decisions – If a record is not going to be used in analysis, process, or decision making, there is not need to keep it in use.  This may be the case if processes have been optimized and certain information is no longer needed.  Or, it could be that it was a candidate for archiving due to decay rates and minimum data requirements.  Additionally, relavance may be determined when integrating systems where old records with old transaction history is not relevant to the existing or new business.
  • Regulatory compliance – In highly regulated environments like health care, there are standards on what you can and cannot remove.  Records may not be useful in existing process, analysis, and decision making, but might be required in certification or other compliance related activities.  Archiving ensures that information is not deleted from primary systems.  Although, you may have to provide a mechanism that provides adequate access to data for compliance.

An archiving strategy is a critical component of data quality best practices.  It will continually help you focus on improving and refining your data quality projects as well as thinking strategically about how you use and manage your data on a daily basis.  Establish an archiving strategy at the forefront of your data quality initiatives and you start your efforts off on the right foot.

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Business Intelligence: Decisions, Decisions

Business Intelligence is all about supporting business decision.”

How many times have you heard that?  It’s become the standard mantra.  It is so ubiquitous that I don’t think anyone questions anymore the validity of the statement.  It just is.  However, this is probably the hardest part to facilitate when building out you business intelligence practice.  Facilitating decisions is what makes BI stragetic.

Just what is the business decision? What does a business decision look like?

Elements of a Business Decision:

  • Purpose:  drive a business outcome – ex: revenue, shareholder value, profitability, market share
  • Position:  leads a company, division, department
  • Point in Time:  transition along a process or environment

A typical approach during the business analysis phase for BI is to at business decisions across a business process and where questions are asked to change behavior in that process.  Although, the difficulty with this level of granularity is that it is too deep.  These transition points are tactical.  Intelligence across this process and at these decision points is important, but you don’t get the strategic value of BI at this level.  You need to look at the outcome of the process and provide a platform that supports the decision of what to do next.  This is the unstated question.

Let’s take an example.  Sales management will always want a perspective on the pipeline and forecast.  This shows them how they are meeting their numbers quarter to quarter.  However, outside of conversion and volume, there are business decisions that sales managers need to make.  Should they adjust their territories to capture new opportunity or shore up existing business?  Are there changes needed in commissions to incent sales people along certain products and services to improve profitability or revenue?   BI can lead sales management with insights that will guide them to optimize their processes and management rather than just data.

Purpose:  market share, revenue, profit
Position:  sales
Point in Time:  aligned to quarterly pipeline and forecast

To align BI to the business decision it is important to include executives in the discussion.  Get beyond the reports they want to see and ask the question about how they manage their business.  Walk through scenarios of what they ask as changes in the market or the business arise and how information can help them make a decision.  The better able you are to see how they manage their business, the more valuable the BI practice will be to supporting the business.

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