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Case Study 2 - Simplification

Program/Project overview:

Conversion of PL-SQL based ETL code to Informatica ETL tool and building a dashboard to help in better monitoring and reduce complexity

Size in FTE / $$:

18 FTEs (5 onsite, 13 offshore)

Scope:

About 472 feeds from various transaction systems including mainframes, Oracle ERP, Siebel, local data marts of sub businesses to be converted from PL-SQL code to Informatica using a Multi Generation Project Plan (MGPP)

Challenges:

· Hybrid approach (converting in phases) created problems in interlinked systems

· Absence of business rules documentation - difficulty in developing correct Informatica mappings

· Scripts to load data were written long back with no documentation

· Testing – users did not want to re test new system

· Small development team – internally funded IT project

· Team expertise in tool

Our Solutions/Value Propositions:

The EIS team member led the project as the COE Leader of the BI COE. Given the challenges and the newness of the technology, a phased wise approach was proposed with a pilot project

Execution Strategies:

. A relatively new BI application was chosen with medium complexity and low business impact was decided as the pilot project. A team was formed with PL-SQL & Informatica developers and Subject Matter Experts (SMEs). The existing code was reverse engineered to develop business specification.

The Informatica team used the business specifications to forward engineer the code. Since functional users would not test it, a creative test strategy was formed. A mirror database was created and loads were executed with the same source data – both with PL-SQL code and Informatica code. The results were correlated to check for data inaccuracies and code was suitably modified. This was done until the entire application was completed and put into production.

The learning and approach were used for the rest of the applications and the organization moved into a single ETL tool at the end of the project. To the users, the effort was seamless and they were quite impressed by the background work. An important positive side effect was the loads were done faster using parallel load techniques as well better job scheduling.

Due diligence was also taken care to ensure that there was a constant review of error logs and this make sure that the improvements did not fall through the cracks.

An alerting mechanism was also set up to ensure that mails were sent to the monitoring team, in case of errors, to ensure faster follow up.

Benefits:

· Single ETL tool for the organization – leading to simplification

· Improved productivity of the monitoring team

· Faster response to rejects because of alerting mechanism

Case Study 1 - Data Quality

Program/Project overview:

Reduce defects in data loading from various transactional systems into the Executive Summary (Digital Cockpit) to provide better and accurate information. The Digital Cockpit was viewed by the CEO of a $14 billion business. The Cockpit showed data about orders, sales, finance, span etc as well as variance across time periods. The Cockpit was the single place for information for the top management to help devise business strategies. However, because of the complexities of the feeds and the various hops, there were usually large differences in actual data vs. the data shown on the cockpit leading to wrong decisions. Slowly, the veracity of the data was being questioned, leading to disillusionment among users and subsequent reduction of DW projects.

Size in FTE / $$:

12 FTEs (6 onsite, 6 offshore)

Scope:

About 150 feeds from various transaction systems including mainframes, Oracle ERP, Siebel, local data marts of sub businesses to be checked for data inaccuracies and reduction of such accuracies.

Challenges:

· Complexity of source systems

· Absence of business rules documentation

· No ETL tool – scripts to load data were written long back with no documentation

· Inadequate or unstructured logging – hence difficult to do root cause analysis

· Small monitoring team, basically meant that rejects were not checked up

· No alerting mechanism

Our Solutions/Value Propositions:

The EIS team member proposed a Six Sigma DMAIC approach to resolve the problems and ensure root cause fix. The DMAIC (Define, Measure, Analyze, Improve, and Control) process offers a structured measurable approach to analyze defects, find causes for it, do subsequent improvements and finally ensure that the improved processes are controlled.

Execution Strategies:

Voice of Customer was taken from various stakeholders to understand business pain. A core SWAT team was formed with experts from business as well as IT professionals. Code was evaluated and logging mechanics were improved. Data from logs were analyzed to find out common causes for errors and rejects during the data load process. A simple example was when a $2 million order was rejected because the date was sent from a mainframe system was inaccurate. Another reason was when a $1.5 million order was rejected because dimensional data was load AFTER fact data, leading to integrity issues. The errors were analyzed and solutions were applied at source rather than the target (DW) system. This was to ensure that data was accurate in BOTH places. Due diligence was also taken care to ensure that there was a constant review of error logs and this make sure that the improvements did not fall through the cracks.

An alerting mechanism was also set up to ensure that mails were sent to the monitoring team, in case of errors, to ensure faster follow up.

Benefits:

· The initial process was measured at 2.51 Sigma with a DPMO (Defects Per Million Opportunities) score of 132,270. The improved process was measured at 4.27 with a DPMO score of 2780.

· Improved productivity of the monitoring team

· Faster response to rejects because of alerting mechanism

--------Application Support

EIS offers Production Support that take on the task of maintaining, monitoring, and operating your existing BI applications, wherever in the world it may be; to enable

• Deliver the SLAs promised ensuring minimal disruption to business
• Allows your team to focus on core areas and increase operational efficiency
• Ensures quick response to business needs and user expectations in a timely and cost effectively manner
• Access diverse expertise & tools on a 24x7 basis across heterogeneous environments
• Increase performance and security of mission-critical IT applications
• Leverage Six Sigma, ITIL and Knowledge Management for increased productivity

EIS has a unique 4 Es strategy (Engage, Evaluate, Enable and Enhance) to help clients to improve their existing applications and thus demonstrate our value add.

--------Digital Dashboards

Thanks to continuing efforts at digitization, a growing number of companies are now able to use the Internet to monitor many or all of their key performance indicators daily, or even minute-by-minute. This is done through what have come to be called digital dashboards, or digital cockpits.

For example, a manufacturing company shows three categories:manufacturing, selling and procurement. Within each are a few important statistics, like the manufacturing measure "days without major incidents".

If that particular measurement goes above or below a predetermined threshold, the number will be red. When an executive clicks on that number, she will see another screen with underlying data. If this or any other statistic falls far enough outside the accepted range, executives will be either e-mailed or paged, to alert them to the problem.

EIS has tremendous expertise to help companies react before things get out of hand and feel the pulse of the business. The very existence of these cockpits might even help improve a company's pulse rate . This also helps organizations focus effectively on the core things that drive their business — revenues and costs — leveraging the visibility these cockpits provide.

--------Data Mining

--------Architectural Design

“Everything should be made as simple as possible, but not one bit simpler.” Albert Einstein

--------Master Data Management

At the apex of data integration software is Master Data Management. The MDM system reconciles (matches, standardizes, and consolidates) new input data with its current master reference data, and then stores the master repository in a data model flexible enough to support multiple hierarchies.

For EIS, MDM is much more than technology, as it encompasses policies, practices, and systems that create an infrastructure for collecting, storing, and managing master reference data.

An MDM system offers great advantages. It not only serves as the system of record for customer or product data, collecting and consolidating it from all reaches of the enterprise, including multiple data warehouses, but it also allows the data stewards to design and create data models that roll up to hierarchies that can be adjusted at will for a specific view. These views, or context-sensitive hierarchies, can be saved and used by different corporate functions as their own operations dictate. Marketing, sales, and manufacturing can all view the product hierarchy—from suppliers to chemical composition to distribution channel—as needed. Then, when a hierarchy is placed “in production,” the master data can be published to subscribing applications, where it is either pushed or pulled out to downstream operations.

EIS has helped organizations create master data for Customer, Supplier, Product, Finanicial and Business Hierachies thus helping to create a single version of truth. This also creates opportunities for "Cross Selling" and "Upselling" showing a direct impact on revenues.

--------Data Quality

Research by PricewaterhouseCoopers highlights a profound gap between the clear understanding that data is valuable versus the real-world usage of that data in delivering value. Over 70% of executives polled consider data to be one of their most valuable assets. Yet only 40% say that they use it effectively. This suggests that there is a gap between the perceived value of data and the real value being obtained from data.

Building trusted information is an BI best practice. Organizations build and maintain trusted data at every step in the data supply chain. The concept of the data supply chain has no greater relevance than in the BI context.

Based on our experience , we can see data quality operations exist at every major stage in the chain. Each stage is an opportunity to create, enhance, or just maintain the level of trust in the data. The sooner data quality issues are corrected in the chain, the sooner the firm benefits from greater trust. For example, validating and standardizing data at the initial point of contact with the customer, such as a website where they can enter their information, benefits every downstream operation no matter how far-reaching the enterprise. You can multiply the benefit by the count of all the subsequent operations that use the data.

Conversely, the longer an organization waits to cleanse and improve data integrity, the more upfront operations are sub-optimized because of data defects impacting their effectiveness. Moreover, the earlier the data is cleansed, the less the cleansing costs later on. The reason is the count, type, and most importantly, complexity of data quality problems are less. Rather than letting problems build up to the point where correcting them in the data warehouse becomes a large task, tackling the issues as they arise makes each operation simpler.

EIS believes in following the incremental improvement approach, data quality operations lend themselves to pilot project implementations. The success of each pilot is used to build out the data quality infrastructure as part of the overall BI strategy.

--------Business Analytics

Analytics provide a growing opportunity for businesses due to both market and business drivers. The software that drives data applications today is more sophisticated, easier to use and more readily available. Plus, savvy managers realize the business benefits in turning to data analysis, which combines traditional BI with real-time or predictive modeling to develop new opportunities that can differentiate them from the competition.

Studies show that maturing analytics capabilities help organizations achieve high performance by maximizing the value from data through smart analysis and resulting insights. Sound integration between analytics and technical concepts like SOA can reinvent how business and public service agencies operate.

EIS can help high performance IT organizations use analytics to begin to predict and influence customer behavior, product uptake, supply chains and market behavior in ways that impact the bottom line. More specifically, they can:

• Generate customer insights. This involves modeling customer behavior, identifying the most profitable customer segments, tracking loyalty and honing cross- and up sell strategies.
• Accelerate product innovation. This allows an organization to correlate market opportunity, customer requirements, R&D and service data to develop more effective products, understand market gaps and maximize the cross-sell potential of new offerings.
• Optimize the supply chain. This entails identifying key metrics for efficient planning in order to create accurate demand forecasts, optimize inventory and warehouse procurement, and create the best pricing.
• Understand financial performance. As a result, the organization can pinpoint the true drivers of positive financial performance to translate competitive knowledge into actionable strategies for maximizing revenues.

Service Offerings

EIS offers services in all three core areas of BI applications - Architecture, Development and Support.

Our service offerings cover the entire spectrum of data warehousing and business intelligence solution lifecycle. Our architecural designservices will help you define your business intelligence roadmap and performance management framework.

The application development services will enable you to realize your current and future information needs by helping you build a future ready business intelligence platform.

Our application maintenance and support services enables in maintenance and evolution of existing Business Intelligence solutions. Business Intelligence services include maintenance and support as well as enhancements ranging from day to day fixes to large scale activities like Tool / Platform Migration, system re-architecture and Consolidation.

Value Propostion

EIS’s offers a unique value proposition with 4 Key levers

People Expertise in different technologies - confluence of experience gives us a unique knowledge of the technologies required to develop a comprehensive solution Sense of Ownership - you have peace of mind that the best people in the business are looking after your applications Pride – in the company and the work that they do Streamlined HR process –we pick only the best with the necessary aptitude and work ethics and continually assess performance to ensure customer satisfaction.

Process ITIL – this remains our backbone in offering services. ITIL is the leader in IT guidelines and best practice publications; it has been tested in real world environments for over a decade and is proven to work. EIS offers the entire gamut of ITIL process ranging from Service Desk, Incident Management, Problem Management, Change Management and Release Management. Lean Six Sigma is a model of eliminating waste in processes as well as data driven data analysis ensuring better service to the end user We have combined the best of Lean Six Sigma and ITIL to ensure that our processes are streamlined to reduce defects, eliminate wait time and ensure better service delivery Escalation driven matrix to help you get timely delivery of services

Technology We use best of breed tools for – Service Desk – Knowledge Management – Dashboard – Detailed reporting

Organization Experienced team with prior expertise in supporting critical business applications in BI CustomerFirst focus - Commitment to end users Organization focus on data security and privacy

Our Expertise

EIS has previous experience in implementing and supporting large BI applications for top Fortune 100 companries on a 24x7x365 basis both on a hybrid as well as offshore model. EIS technology stack includes Informatica, Business Objects, Oracle Sales Analyzer, Oracle Financial Analyzers, Digital Dashboards (Digital Cockpits) and native ETL tools using PL-SQL and Oracle SQL Loader.

EIS has also vast experience in supporting business critical application like Oracle ERP, Siebel and SAP. EIS has prior experience in architecting, developing, implementing and supporting these applications using a Global Delivery Model. The GDM offers a unique model which helps in cutting costs but at the same time ensuring closer interaction with the customers as well as ensuring data security.

Background

Business intelligence (BI) capabilities that provide better analysis of and insight into operations will be needed for organizations to achieve high performance amid their biggest future impediment: an uncertain economic and political environment.

A recent study that found that nine in 10 senior executives at Fortune 1000 companies place strong analytical and business intelligence capabilities at the top of their list in preparing them for their biggest challenge ahead. Those companies best prepared to thrive in this climate will be those that take advantage of their capabilities to collect and analyze internal and external data.

EIS can help high performing companies to focus on how to extract and analyze data that can generate insight and value for the organization. This includes support of business processing and decision-making at the strategic, tactical and operational levels. Today, this data is acknowledged as a corporate asset, with C-level executives now viewing it from a business rather than technology perspective. Through the use of data warehouses as the fundamental enabler, and the business analytics that leverage it, business intelligence offers a strategic, competitive advantage that examines trends and histories in order to predict the future as well as models scenarios and creates benchmarks to make the future.

Specifically, EIS's business intelligence team helps organizations improve performance by:

• Providing insights that can help pinpoint new revenue-generating opportunities
• Improving operational efficiencies and visibility across the organization
• Optimizing the return on such existing business and IT investments as customer relationship management and enterprise resource planning.