Business Performance Management & OSSM - Part # 1

Recently I was having a discussion with one of my colleague on the Oracle BI & EPM Technology Stack and apparently found that if anyone has not followed the Oracle BI & EPM journey with the acquisitions of Oracle for last 2-3 years, it might be confusing to them on the components and some of the tools might replicate the same functionality in multiple toolsets.

Though my intention is not to clarify the entire Oracle BI & EPM stack but am trying to break a tip of iceberg conceptually as well as technology stack perspective - Oracle Scorecard & Strategy Management (OSSM) - an Enterprise Performance Management tool.

Before getting into the OSSM as such, it is important that we need to understand the basics of the theory associated with this tool.

What is Enterprise Performance Management?

Enterprise Performance Management is a broader term covering the business methodologies, metrics, processes (such as Planning, Budgeting, Consolidation etc.,) and systems used to drive the overall performance of an enterprise.

To be in simple terms, for an enterprise to be successful, there are 3 main activities,

1. Identify the Right Goals & Objectives to achieve
2. Consolidation of relevant information to organization's progress against these goals
3. Improve Performance and initiate appropriate process improvements for achieving these goals

Synonyms of Enterprise Performance Management includes Business Performance Management & Corporate Performance Management.

EPM enables an enterprise to model and change its business processes to meet the specific needs of the business quickly and more cost-effectively by linking the respective strategies into execution.

EPM has been strongly influenced by the Balanced Scorecard Framework.

Balanced Scorecard Framework

In early 1990s, Dr. Robert S. Kaplan & David Norton developed a management approach called "Balanced Scorecard Framework" that defines a set of measures linked to the vision and strategy against the following four Perspectives,

• Financial
• Customers
• Internal Business Processes
• Learning & Growth

Balanced Scorecard Framework - Transalation Vision & Strategy - Four Perspectives

In the above diagram, the Customer perspective is also being referred as "Stakeholder" within the framework.

 

An Enterprise Strategy is summarized in a Strategy Map, which is a visual representation of what the execution team determines to drive their strategy which is usually a series of objectives that shall lead to accomplish the goals of the above 4 perspectives that are defined within the Strategy.

Scorecard Components

The above scorecard components are tightly linked together.  Strategic goals link down to objectives, objectives link down to measurements, and measurements link to targets. 

 

In the next section(s) of the blog shall cover the following aspects,

• Sample of Strategy Map
• Constructing a Strategy Map
• Role of OSSM within Oracle BI - EPM Stack
• OSSM Concepts with a sample

Till then.. please wait.. I appreciate your patience though I shall try my level best not to take another 1 month for me to complete those sections J

 

 

Operational Analytics & Hybrid Data Integration

Traditional Data Warehousing & Business Intelligence solutions has been designed and built to address the Strategic decision making in an enterprise among the C-grade Executives and key decision makers however in addition to the Strategic BI initiatives, the trend is among developing solutions with the Operational BI capability.

What is this new Buzz word - Operational Analytics?
Operational Analytics is primarily to provide decision making ability for the mid-level management staff and operational managers to manage and optimize the day-to-day business operations with appropriate information on the business events.  For example, a store manager making a decision for on-the-spot promos/offers on Car related products such as Car Perfumes, Car cleaning materials based on the event of Car parking getting filled/occupied in the store parking lots.

It is important that the historical data and the on-going operational data needs to be combined to make the operational analytics more effective; this includes various product lookups, inventory status, past promo effectiveness, capturing of events and alerting of them, etc.,   Hence for any such realtime data warehousing; the data acquisition and data integration is a critical factor for the success of the initiative.

There are different types of Data acquisition for Real time data warehousing

• Batch oriented ETL/ELT (with near real-time)
• EAI
• Log-based Change Data capture approach

Each type of Data acquisition has its own Pros & Cons however EAI being able to handle either Low or Medium amount of data volume, it is always been treated as low-priority or not an ideal approach for data warehousing. 

Log based Change data capture approach is more towards a "Push" approach to deliver data from source to targets. In this approach, the changed data from the database transaction logs are captured which does not impact the performance on the source systems, which is not the case that of change data capture that uses database trigger or table scanning.  Oracle Golden Gate uses log based, CDC capabilities to enable real-time data integration and management by capturing and delivering updates of critical information as the changes occur and providing continuous synchronized data across heterogeneous environments.

ELT (Extract, Load & Transform) has been a key factor for the real-time or operational data warehousing as the transformations tends to take place in the datawarehouse. 

For Operational datawarehousing, a hybrid approach of Log-based CDC & ELT is being leveraged to consolidate data from the heterogeneous source systems into the appropriate data warehouse/datamarts. 

The solution shall be designed in such a way it offers transactional, real-time data capture using the appropriate "Push" approach, i.e., as soon as a new database transaction is committed in the source system, the data is immediately captured via the database transaction logs and loaded to the datawarehouse/staging area of the datawarehouse.  However, in this approach, the data transformation is expected to be minimal as much as possible, in fact, only basic row-level transformations are performed.  For heavy transformation needs, the solution can be integrated with appropriate ETL/ELT components to enable end-to-end solution for data integration in the data warehouse/data mart.

For example - Oracle Golden Gate can be integrated with Oracle Data Integrator (ODI) for integrating the data from source systems in a real-time data warehouse to handle data load using log based CDC with minimal transformations and leveraging the ELT capabilities of ODI for heavy transformations.

Operational data warehousing & analytics allows the users to leverage the underlying historical data and real-time transactional data to access and respond to information in real time to improve business decisions and actions. Continuous low-latency data capture and delivery infrastructure is a critical success factor for the establishment and maintenance of such real-time data warehouse.  It is becoming evident and getting proved that Organizations that leverage the most up-to-date BI in their day-to-day operations significantly improve their operational efficiency, reducing operational costs and thereby enhancing their productivity and the overall Gross Margins.

Is Project Management for DW-BI Projects different from traditional application development / maintenance project?

There has been many of my colleagues question me  that "Is Project Management for DW-BI Projects different from traditional application development / maintenance project?"

My personal opinion towards it is "YES" but the principles of Project Management remains the same.

The reason towards that is pretty simple,

• Along with the Project Management responsibilities, the DW-BI projects requires a deep dive on the technical aspects of Data warehousing & Business Intelligence implementation in order to understand the scope & manage it better
• Non-technical competent Project Manager requires a strong dependency from the Technical Lead / SME to understand the day-to-day project scope related changes
• Impact to one column of a table can impact the ETL & Reporting scope to a larger extent
• A complete End-to-End DW-BI implementation involves multiple tools and technologies and more importantly multiple skillsets which makes the Project Manager to be careful on resource loading and leveling while executing the project
• Technical risks shall occur throughout the life cycle of the project (as compared to traditional application development/maintenance); continuous monitoring and proper risk management of these risks are very critical.
• Very important to identify the dependency of tasks well in advance (for example - resolution to data quality to be determined and closed as per schedule) and managed well in the Schedule management
• Quality Management has been challenging particularly in areas such as Configuration Management and Release Management as there are many tools getting involved (though currently many BI tools provide versioning features) and Traceability of requirements; maintennace of traceability matrix has been challenging in DW-BI projects

The above reasons clearly signifies that managing DW-BI projects requires some technical background and/or ability to appreciate technial aspects however the core 9 knowledge areas as stated in PMBOK remains the same however focus on certain knowledge areas (such as Scope Management, Risk Management, Schedule Management and Quality Management) with technical appreciation is very much needed.

Any specific thoughts??