Business Intelligence Challenge – Product Upgrades & Migrations, Moving the Code – 4

Last time we discussed about Impact Assessment , the next logical step after this is to perform the actual upgrade or migration of the code.

Moving the Code: Performing Upgrade or Migration of the Objects

When we talk about product upgrades, always the product vendor provides tools by which the objects in the earlier version can be upgraded to the latest version. Yes we would see some objects failing through while using such tools; these are the ones that would need rework after the upgrade process.

When we talk about product migration like moving from Cognos to Business Objects or Business Objects to Cognos, there is good scope for us to look for some ways to automate the code migration. Earlier discussions have been on how to leverage the metadata for understanding the environment, now we are looking at an option on how to manipulate or transform the metadata so that an object in platform ‘A’ becomes compliant to platform ‘B’.

Steps involved in building an automated product migration process

Perform metadata level object mapping between the two platforms, determine the gaps. This would actually be a ‘by product’ of ‘Step 2’ in Impact Assessment

Build individual components that would

• Read the metadata from the source platform and prepare a repository
• Have the knowledge of the match & gap between the platforms, could be reference tables
• Transform the ‘source’ metadata and write out as understood by the ‘target’ platform by using the reference tables

Benefits of Automated Migration

• Helps avoid creation of objects from scratch
• Ensures availability of time for testing (core task) than code development
• Enables team to have a flexible skillset
• A faster way of delivering things when a ‘one to one’ migration from the source platform is seen as a must

Automated Migration Challenges

Transforming the source metadata to the target platform would be a challenge, especially with data manipulation functions. Having a good understanding of the gaps will help; a reference table mapping the functions between the platforms would be useful. In scenarios where a function cannot be converted to the target platform, a comment can be written into a log file enabling quicker attention.

Have seen good success in writing such automated migration components though not 100%. With almost every products providing good SDK kits for reading and as well writing metadata and as well with the support for XML structures, writing such bridges for object migration are getting easier.

Whether the objects in a product are migrated/upgraded in an automated way or not, the following activity of ‘Validation’ plays a key role in ensuring the final quality, next time let us discuss on some of the means for effective validation ….

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Zachman Framework for BI Assessments

The Zachman Framework for Enterprise Architecture has become the model around which major organizations view and communicate their enterprise information infrastructure. Enterprise Architecture provides the blueprint, or architecture, for the organization’s information infrastructure. More information on the Zachman Framework can be obtained at www.zifa.com.

For BI practitioners, the Zachman Framework provides a way of articulating the current state of the BI infrastructure in the organization. Ralph Kimball in his eminently readable book “The Data Warehouse Lifecycle Toolkit” illustrates how the Zachman Framework can be adapted to the Business Intelligence context.

Given below is a version of the Zachman Framework that I have used in some of my consulting engagements. This is just one way of using this framework but does illustrate the power of this model in some measure.

Some Salient Points with respect to the above diagram are:

• The framework answers the basic questions of “What”, “How”, “Who” and “Where” across 4 important dimensions – Business Requirements, Conceptual Model, Logical/Physical Model and Actual Implementation.
• Zachman Framework reinforces the fact that a successful enterprise system combines the ingredients of business, process, people and technology in proper measure.
• It is typically used to assess the current state of the BI infrastructure in any organization
• Each of the cells that lies at the intersection of the rows and columns (Ex: Information Requirements of Business) has to be documented in detail as part of the assessment document
• Information on each cell is gathered through subjective and objective questionnaires.
• Scoring Models can be developed to provide an assessment score for each of the cells. Based on the scores, a set of recommendations can be provided to achieve the intended goals.
• Another interesting thought is to create a As-Is Zachman framework and overlay that with To-Be one in situations where re-engineering of a BI environment is undertaken. This will help us provide a transition path from the current state to the future.

Thanks for reading. If you have used the Zachman framework differently in your environment, please do share your thoughts.

Valuing your Business Intelligence System – Part 1

Sample these statements:

• Dow Jones Industrial Average jumped 200 points today, a 2% increase from the previous close
• The carbon footprint of an average individual in the world is about 4 tonnes per year which is a 3% increase over last year
• The number of unique URL’s as on July 2008 in the World Wide web is 1 trillion. The previous landmark of 1 billion was reached in 2000
• One day 5% VaR (Value at Risk) for the portfolio is $ 1 Million as compared to the VaR of $ 1.3 Million a couple of weeks back

Most of us buy into the idea of having a single number that encapsulates complex phenomena. Though the details of the underlying processes are important, the single number (and the trend) does act like a bellwether of sorts helping us quickly get a feel of the current situation.

As a BI practitioner, I feel that it is about time that we formulated a way for valuing the BI infrastructure in organizations. Imagine a scenario where the Director of BI in company X can announce thus: “The value of the BI system in this organization has grown 15% over the past 1 year to touch $50 Million” (substitute your appropriate currencies here!).

The core idea of this post is to find a way to “scientifically put a number to your data warehouse”. Here are a few level setting points:

1. Valuation of BI systems is different from computing the Return on Investment (ROI) for BI initiatives. ROI calculations are typically done using Discounted Cash Flow techniques and are used in organizations to some extent
2. More than the absolute number, the trends are important which means that the BI system has to be valued using the same norms at different points in time. Scientific / Mathematical rigor helps in bringing the consistency aspect.
3.  

My perspective to valuation is based on the “Outside-in” logic where the fundamental premise is that the value of the BI infrastructure is completely determined by its consumption. Or in other words, if there are no consumers for your data warehouse, the value of such a system is zero. One simple, yet powerful technique in the “Outside-in” category is RFM Analysis. RFM stands for Recency, Frequency and Monetary and is very popular in the direct marketing world. My 2-step hypothesis for BI system valuation using the RFM technique is:

• Step 1: Value of BI system = Sum of the values of individual BI consumers
• Step 2: Value of each individual consumer = Function (Recency, Frequency, Monetary parameters)

Qualitatively speaking, from the business user standpoint, one who has accessed information from the BI system more recently, has been using data more frequently and uses that information to make decisions that are critical to the organization will be given a higher value. A calibration chart will provide the specific value associated with RFM parameters based on the categories within them. For example: For the Recency parameter, usage of information within the last 1 day can be fixed at 10 points while access 10 days back will fetch 1 point. I will explain my version of the calibration chart in detail in subsequent posts. (Please note that the conversion of points to dollar values is also an interesting, non-trivial exercise)

Am sure that people acknowledge the fact that valuing data assets are difficult, tricky at best. But then, lot more difficult questions on nature and behavior have been reduced to mathematical equations – probably, the day on which BI practitioners can apply standardized techniques to value their BI infrastructure is not too far off.

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Business Intelligence Challenge – Product Upgrades & Migrations, Impact Assessment – 3

The next step after ‘Object Consolidation’ is Impact Assessment.

What is Impact Assessment? The process in which, we try to determine the variations or gaps in the existing objects/reports by comparing against the target platform.

The gaps or variations in an existing environment could be due to an existing function being replaced by a new function or an existing function not being supported with an equivalent function in the new platform.

Steps Involved in Impact Assessment

We try to do this comparison against the target platform ‘Impact Assessment’ in an automated way by leveraging the underlying metadata of the existing environment.

1. Take the ‘object metadata’ collected as part of the Object Consolidation

2. Collect details of the possible issues faced during the upgrade or migration process. The source of details would be from

• prior experience in executing similar projects
• the manuals and release notes provided by the product vendor
• the pilot project executed with the subset of objects from the existing setup

3. Convert the ‘issues’ identified into a relational structure table.

• An issue could be an observation like the function ‘sum’ has been now changed in the newer version of the product to the word ‘sumif’
• One way of converting this issue to a relational structure is to have two column names ‘issue_case’ and ‘issue_type’ where in issue_case will carry the value ‘sum’ and issue_type will carry the value ‘aggregate’.
• Converting to a relational structure enables using SQL queries for automated search of the impacted objects by joining the ‘object metadata’ table with the ‘issue’ table

4.Run SQL queries joining the ‘issue’ table with the ‘object metadata’ table to determine the impacted existing objects

5.Classify each object by the degree of impact(number of impact points) and decide on the strategy of upgrade/migration for each these groups

Benefits of Impact Assessment

• Foresee the challenges and being well prepared for system upgrade or migration
• Ability to estimate and plan the execution & testing phases very effectively
• Enable building comprehensive test cases
• Minimize surprises and provide confidence to the execution team
• Helps in making decisions of whether we should consider a object to be built from scratch or upgrade/migrate

Impact Assessment Challenges

First gathering the knowledge of issues; the options are talk to a person who has done an upgrade project to collect the issue details or perform a quick pilot with an appropriate sample from the set of objects to determine the issues.

Second conversion of issues logs into relational structure and running of queries to determine the impacts; both of these would require a good understanding of the underlying metadata structure, so explore the metadata structure and understand them to the fullest from the point of analysis.

Next time let us discuss on one other key task in an upgrade project…

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Business Intelligence Value Curve

Every business software system has an economic life. This essentially means that a software application exists for a period of time to accomplish its intended business functionality after which it has to be replaced or re-engineered. This is a fundamental truth that has to be taken into account when a product is bought or for a system that is developed from scratch.

During its useful life, the software system goes through a maturity life cycle – I would like to call it the “Value Curve” to establish the fact that the real intention of creating the system is to provide business value. As a BI practitioner, my focus is on the “Business Intelligence Value Curve” and in my humble opinion it typically goes thro’ the following phases as shown in the diagram.

Stage 1 – Deployment and Proliferation

The BI infrastructure is created at this stage catering to one or two subject areas. Both the process and technology infrastructure are established and there will be tangible benefits to the business users (usually the finance team!). Seeing the initial success, more subject areas are brought into the BI landscape that leads to the first list of problems – lack of data quality, completeness and duplication of data across data marts / repositories.

Stage 2 – Leveraging for Enterprise Decision Making

This stage takes off by addressing the problems seen in Stage-1 and overall enterprise data warehouse architecture starts taking shape. There is increased business value as compared to Stage-1 as the Enterprise Data Warehouse becomes a single source of truth for the enterprise. But as the data volume grows, the value is diminished due to scalability issues. For example, the data loads that used to take ‘x’ hours to complete now needs at-least ‘2x’ hours.

Stage 3 – Integrating and Sustaining

The scalability issues seen at the end of Stage-2 are alleviated and the BI landscape sees much higher levels of integration. Knowledge is built into the set up by leveraging the metadata and the user adoption of the BI system is almost complete. But the emergence of a disruptive technology (for example – BI Appliances) or a completely different service model for BI (Ex: Cloud Analytics) or a regulatory mandate (Ex: IFRS) may force the organization to start evaluating completely different ways of analyzing information.

Stage 4 – Reinvent

The organization, after appropriate feasibility tests and ROI calculations, reinvents its business intelligence landscape and starts constructing one that is relevant for its future.

I do acknowledge the fact that not all organizations will go through this particular lifecycle but based on my experience in architecting BI solutions, most of them do have stages of evolution similar to the one described in this blog. A good understanding of the value curve would help BI practitioners provide the right solutions to the problems encountered at different stages.

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