8 years, 4 months ago

Solving the Polyglot Persistence Puzzle – Defining the Information Value Lifecycle





I believe an Information Architect’s primary purpose is
to increase the value of the information assets belonging to an
organization. Securing and making
information available is no longer sufficient to grow the competitive
capabilities of an organization. Information architects must get:

  • the right information
  • to the right person

  • at the right time
  • in the right format
  • so the best decisions can be made at all levels of the

To assist Information Architects in understanding and
defining a process to increase the value of information assets, I have created
an Information Value Lifecycle map. This
is the first step in understanding the characteristics of information on the
way to building Polyglot Persistent Architectures.

Building an Information Value Lifecycle map is done in 7

Step 1 – Build the Information Value Lifecycle map
layout.Each organization has multiple levels of decision
making. For each level of decision
making, there are Information Usage patterns and the Information Structures
needed to support the usages. The
example below starts with the Transaction Owners, the staff that create,
maintain and own the transactions required to run the business. At the highest level are the CEO and Board of
Directors (BoD). Maps will differ to
reflect each organization’s hierarchical process of decision making.

Step 2 – Define the Information Usage patterns and the
Information Structures needed to support the Information Usage pattern.

Typically different levels of decision making require
different levels of aggregation and summarization of information – from simple
transaction reporting to cross line of business and industry aggregations, analytics and predictive analysis. Information architectures over the years have
evolved well know sets of information structures (most commonly 3rd
Normal Form, Star, Snowflake and Cube schemas) needed to support these Usage

Step 3 – Define the processes needed to transform and
aggregate information from transactions to the highest level of the decision
making process.

Extract, transform and load (ETL) processes move
information from one level of decision making to the next based on the
information usage patterns. Mapping
these ETL process at a high level ensures data linage is understood and
information accuracy is guaranteed. Some
applications provide capabilities ‘jump’ the information past some levels to
the highest levels of the decision making process. Oracle Hyperion is an example.

Step 4 – Record Master Data Management usage patterns

Understanding Master Data usage patterns gives insights
into which types of information are most important to an organization. They also indicate the level of information
management maturity – more usage of master data reflects an understanding of
the value of master data and a willingness to invest to realize that value.

Step 5 – Identify Big Data usage patterns

Most organizations have begun the process of deploying
and realizing the value of Big Data. Recording
Big Data usage patterns shows the maturity of an organization in relationship
to their ability to adopt and deploy new technologies.

Step 6 – Identify the ‘Gold Nuggets’ in Big Data

Identify where Big Data data mining and analytics has
increased the quality and/or quantity of information inputted into the Information
Value Lifecycle. These processes are
commonly referred to as finding the ‘Gold Nuggets’ of information that were previously
not known. It’s important to understand
the value of the ‘Gold Nuggets’ in the decision making process of an
organization to justify the level of effort and expense of deploying Big Data

Step 7 – Identify new Big Data information value

The low cost of some Big Data architectures has allowed
organization to capture new sources of data that have lead to new ways of doing
business. Many of these use cases
include social media as a way of judging the success of marketing campaigns and
new product lunches. Capturing these Big
Data opportunities shows the agility and innovativeness of an organization.

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