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Optimizing Your Enterprise Information Environment, Part 2: Steps to Success

BI Strategy

In my March column, I discussed some problems that companies face in using their existing analytical capabilities to meet rigorous compliance requirements and solve increasingly complex data management issues. I also talked about how many companies are addressing data quality problems and eliminating redundant reporting, as well as finding ways to cut costs by undertaking projects to optimize their current foundational enterprise information environments.

As I pointed out in that column, the needs that most companies' information systems were designed to meet have changed significantly over the past five years. Unfortunately, the information systems simply haven't kept up with the changes, which has created a plethora of enterprise information problems. There are as many different problems with information systems as there are companies who have them. However, typical problems I've seen at many companies include:

  • The existing enterprise information environment is difficult to maintain and does not provide the enabling infrastructure to support critical analytical and information processing needs.
  • Current information management solutions are too cumbersome and complex.
  • There is often too much system maintenance required due to overloading and overuse.
  • This expanded business support for functional and line-of-business operations - along with growing data volumes - limits flexibility, manageability and performance.
  • There are enormous expenses related to the IT function, and their cost is "hidden" and difficult to pin down correctly.

In this month's column, I'm going to propose a way for companies to solve these problems - and to minimize risks to existing operations as well as keep costs down. The solution I'm suggesting relies on making near-term strategic investments in optimizing the flexibility, manageability, performance and sustainability of the enterprise information environment - especially the information architecture.

An optimized enterprise information environment has several crucial, foundational components, which have specific characteristics to support sophisticated and robust analytical functionality. These elements include:

  • A top-notch extraction, transformation and loading (ETL) engine that functions as a staging hub where data is extracted from source systems, cleansed, made uniform and loaded into the next components - the enterprise data warehouse (EDW), various data marts and/or an operational data store.
  • An EDW, online analytical processing (OLAP) and relational data marts, and an operational data store (ODS): The EDW contains centralized, very granular data that is used for long-term analysis. The data marts contain distributed, summarized, subject-oriented data that is used by lines of business and business functions for more detailed analysis. The ODS contains near real-time data, with a limited history, as well as operational data feeds. It's used for real-time decision-making.
  • A sophisticated business intelligence (BI) layer contains operational reporting and decision support applications that are matched well with enterprise information needs. It also includes metadata and identity management tools.
  • A portal and dashboard layer that contains standard reports, proactive alerting capabilities, intranet access, spreadsheet applications (if needed), ad hoc query support and OLAP analysis capability.

These elements are probably nothing new to most IT people. But how do you achieve an architecture that contains an optimized combination of them? Glad you asked.

There are several concrete steps companies can take to achieve the optimized enterprise information environment I've just described. The first step is to select a methodology for data warehouse/mart and BI solution development that includes a comprehensive, customized plan for optimizing your information environment and a vendor partner that is willing - and able - to work with your company closely from start to finish and to transfer knowledge completely at project's completion.

With the methodology in place, the next step is to conduct an assessment of the existing information environment and identify needs that are unfulfilled, consolidation and cost-savings opportunities. This step also includes formulating a plan to achieve the improvements necessary to optimize the environment.

Next, build a business case that describes, in detail, over a specified period of time, the optimization plan you've developed - along with its corresponding benefits, costs and risks. The business case should contain a prioritized roadmap for moving forward with your plan. The roadmap would ideally describe the phases of the optimization plan and include timelines for their completion. Essential elements of the plan would include:

  • The estimated business value of the plan, including current dependencies and future benefits.
  • Technology costs, including hardware and software, internal resources and consulting costs - if applicable.
  • Investment scenarios, including business needs, technology alternatives, vender analyses and training requirements.
  • A recommended path that reconciles expenditures with presumed benefits, a future state vision, the implementation roadmap I described above, and a description of how this project would synchronize with other ongoing projects.

The fourth step is to leverage existing centers of excellence within the company - or create them if you don't have them - to put your plan in motion by engaging in benchmarking projects, project pilots for improvement areas identified in the first step, and by investigating and implementing any outsourcing opportunities that might be of benefit to the company. In conjunction with these activities, it will be necessary to embark on a project to revamp the enterprise data model to support the optimized information environment I previously described.

Change to any environment is never simple or quick. This rule applies in spades to an enterprise information environment. There will be naysayers who wish to keep the status quo. There will be cost-based objections from all corners - especially the C-suite until you can satisfactorily justify the expenditure of precious dollars. There will also be obstacles - surely unforeseen - along the way that must be overcome to keep the project going. However, in the long run, the gain will almost surely be worth the pain.


Rich Cohen is a principal in Deloitte Consulting LLP's Information Dynamics practice where he is responsible for the strategy, development and implementation of data governance, data warehousing, decision support and data mining engagements to support the emergence of world-class business intelligence applications. Cohen has more than 27 years of experience in the design, development, implementation and support of information technology in a variety of industries. Over the last 18 years, he has had extensive experience in the creation of technology strategies, implementations and deployment of CRM and business intelligence solutions to drive improved business performance.

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