DAMA + Wilshire Meta-Data Conference - Data Strategy Track

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You have a Help Desk to provide assistance with technology issues. Why shouldn't you have a Help Desk for Data? Why shouldn't your business and technical staff have a single point of contact to find out about data definitions, permissions, and data-related rules? Why shouldn't your organization have a way to efficiently and consistently answer questions and address concerns?

In this workshop we describe how to quickly and inexpensively build a help desk designed to field data-related questions from across the organization. We show how having a single point of contact for data issues solves business problems, reduces organizational friction, and manages risk. We describe how a help desk can free your data, metadata, compliance, and process experts from the drain of addressing routine questions, while ensuring that critical issues are escalated appropriately. Participants will step through the process of triaging calls, responding to typical questions, and invoking industry-standard processes for classifying and escalating calls. Participants will leave with workflows, checklists, and a plan for quickly ramping up an inexpensive prototype.

On workshop completion, participants will be able to:
  • Describe the business benefits of having a single point of contact for data-related questions.
  • Articulate business, technical, and compliance risks that can be managed through a Help Desk for Data.
  • Present a plan for a low-cost help desk operation that could be operational within weeks.
  • Describe types of questions a help desk should be able to address.
  • Describe a typical triage and record-keeping process.
  • Describe a typical issue escalation workflow employing existing resources.
  • Describe strategies for aligning efforts with Data Quality, Governance, Metadata, and IT operations.

One of the IT challenges is to provide an acceptable and consistent service to IT customers in supplying timely and accurate information, with predictable cost. In this presentation we will review how performance prediction models can be used to evaluate different architectures, justify hardware, software and DBMS platforms, and optimize database design and applications performance during all phases of the application and information life cycle.

We will discuss how performance prediction models can be used to evaluate alternatives and justify changes in database design, applications and IT infrastructure required supporting growing business needs.

We will review several examples of multi-tier, distributed environment based on Oracle 10g RAC, DB2 UDB ESE and Teradata and several case studies illustrating value of the performance prediction during feasibility study, database design, implementation of the new applications, workload and database size growth, capacity planning and finally server, application and data consolidation.
Treating “data as a corporate asset” has been a data management mantra for years. But what does this really mean? And is there really business value in it? This presentation will explore the topic of “Information Resource Management” (IRM) and its role in maximizing the value a corporation can realize from its data.

The facets of IRM include:
  • data security and privacy
  • data quality
  • data integration (from distinct parts of the company and across the industry)
  • data stewardship and governance functions
  • an understanding of the data, both from detailed and “broad-brush” perspectives. Each comes into play in various business scenarios that we’ll study. We’ll look closely at the business value generated by IRM across these scenarios.

The session will also discuss ways of building a successful IRM program, either as a “top down” corporate directive to build these functions from scratch, or as a natural growth “bottom up” process of moving from designing databases into managing information. In the end, attendees will leave with a good understanding of IRM and its role in maximizing the corporate value from its information asset.
  • Overview of Information Resource Management
  • Business scenarios that spotlight the value of IRM
  • Developing an IRM program
  • Showcasing the value of IRM to the enterprise
Intel recognized the need for an enterprise metadata container and implemented a repository to support data management. This presentation is a case study from Intel Corporation of how the metadata processes and the metadata repository are used to capture and show tangible evidence, in terms of $-value, that the productivity, governance, and alignment objectives of the enterprise architecture program are being realized. It includes a description of the metadata processes that support the creation and packaging of asset building blocks for work products and services. It also includes the philosophy and techniques for valuating and capturing reuse of these assets. It addresses the role of governance and the enterprise architecture framework, as well as the use of taxonomies to aid cataloging, administering, and searching for unstructured information resources, assets, and services. The presentation will discuss how the metadata repository is a key enabling technology for SOA by providing a discovery mechanism that enables consumers to find the reusable services that they need. Finally, it provides Intel’s experience about positive returns for the enterprise architecture program. Intel was recognized for its metadata program in 2004, when it received the Wilshire Award for Outstanding Metadata Implementation.
  • Relationship between metadata, repository and architecture. How we tie each strategy together.
  • Our federated repository approach.
  • Our metadata and asset reuse framework methodology
  • How we determine appropriate standard work products.
  • How we are able to achieve buy-in from the Architecture community.
  • How we align architecture work products to Enterprise Architecture.
  • How metadata about services is captured in the repository, and utilized at design time by architects.
  • How metadata and asset reuse is integrated with architecture governance.
  • How the SOA repository improves programming productivity and increases re-use of software assets
The data management practitioners have recognized the use of metadata for a while now, but serious efforts to develop applications show casing its real value to businesses are still not readily found. There are practical problems in the Data Architect's tool kit to present the cost-benefits to his business partner for active investment decisioning. Often, its reasons are generally deemed elitist and its benefits are justifiably pale in the face of stiffer competition from projects in the CRM, Risk, Fraud or CDI areas.

However, some success can be achieved in carefully orchestrating meta data based application designs embedding its virtues; some examples of which may include-
  1. "Know your data" expeditions that validate the effectiveness of the data and the knowledge used, in say Marketing Campaign planning or Risk underwriting processes


  2. Articulating the purpose and methodology to employ the Meta Data 'on' business rules and transformations in profiling of converted data from legacy warehouses and new acquisitions.


  3. ' Till such time the rigor of meta data based application development becomes significantly easy, competition for business funding must be met by creative means- there is no substitute for hard work! This presentation will share some successful approaches to win partners and investment dollars.
More industries have published standard data models than ever before. These models, often provided for free or only minimal cost, can add a great deal of value to your modeling efforts. In this presentation, Karen discusses some of the benefits and gotchas of working with acquired models - industry standard models. This session includes topics such as:
  • Survey of industry standard data models
  • Costs, benefits, and risks of working with industry standard data models
  • Using the right process
  • Myths in working with pattern models
  • 10 Tips for successfully working with third party models
  • Lessons Learned
Our recent research initiative targets the apparent gap in IS, between the assessment of the economic performance from the business perspective and the design of data management systems and information products from the technical perspective. Arguably, these two perspectives do not inform each other in a significant manner and we see a need to establish a more robust bridge between the design of data management environments from the technical perspective and the economic factors of value and cost. We suggest that addressing these “two sides of the bridge” simultaneously will improve the contribution of data resources and the systems that manage them to maximize economic performance. As a contribution in that direction, we explore a new design approach for data-driven information products and systems, influenced by product/service design and process design methodologies – a quantitative micro-economic modeling of design factors and their effect on value and cost. The framework provides a design tool for optimizing the economic performance of data management systems and their outcome and further developed for the data warehousing environment
  • Our presentation will first demonstrate the optimal design of large and structured data repositories. Design parameters of such repositories
  • such as the time-span coverage, the field structure, and the targeted quality level
  • Introduce significant cost/benefit tradeoffs and the suggested model quantifies these tradeoffs and allows the determination of an optimal configuration of design parameters.
  • We then demonstrate the use of the framework is used for quantitative modeling the multi-stages data processing and flow that is typical to the data warehousing environment. The model embeds representation of random failures and quality hazards that are typical to this environment, and provides a powerful managerial tool. In this presentation we demonstrate the development of economically-driven error correction policy.
  • To conclude, we discuss the implications of our model for data management and information systems design.
Throughout my work a major focus has been aligning the business community and IT/data management. This is a necessity to successfully deliver an Enterprise Data Warehouse. One of the key drivers for the Enterprise Data Warehouses and BI has been to produce consistent consolidated Corporate reporting. On the Business Intelligence side I have found that meta data is one of the most common culprits to inconsistent views of data including definitions, reference values and transformation rules. In data management there needs to be a focus on business meta data and making it accessible to users. I will show just how meta data ties together the overall Data Architecture specifically for an Enterprise Data Warehouse. Throughout the presentation I will reference real-life projects that I have been involved in.

Topics
  1. Overview of an Enterprise Data Warehouse Data Architecture
  2. Overview of meta data in an Enterprise Data Warehouse environment
  3. Discussion of alignment between Business and IT on data management and meta data
  4. The value of meta data to the success of an Enterprise Data Warehouse
Money managers and hedge funds not only make millions, they also lose millions - but not just from bad bets. As shown by forward look's recent experiences with these firms, 'metadata non-management' results in significant opportunity costs and portfolio implementation shortfalls. This session details these outcomes of sub-optimal data management, and the actions that were, and could be taken to address these information quality gaps.

Opportunity cost relates mainly to lost mandates. The majority of searches by institutional investors for money management services rely heavily on investment consultant's databases. Consequently, the quality of the information provided to those databases is vital to competing for and securing investment mandates.

Implementation shortfalls refer to the 'drag' on the investment performance of the model portfolio (eg, the inability to buy or sell securities at the expected prices and times). Order and trade management systems, sophisticated as they may seem, are only as effective as the underlying metadata their algorithmic engines rely on. In many instances, the lack of actionable metadata is a clear contributor to these implementation shortfalls that typically range from 1-8% of a portfolio's expected returns.

In addition to serving up novel perspectives on metadata use from a unique corner of the financial services industry, this session also offers novel insights into:
  • the stresses stemming from shifting business conditions on metadata design and infrastructure
  • opportunities for information and infrastructure providers to significantly upgrade metadata usability.
If information is an asset, is there any relevance in extending that metaphor to an actual valuation of the asset? What if business cases could actually show the real value of IT, meta data , and information management to an enterprise?

This session will examine the various aspects of determining the risk and value of information assets and efforts, and provides some guiding principles to help you develop a better case for proactive information management. Specifically, this talk will examine:
  • The components of information value
  • Specific techniques for valuation and risk assessment of information components
  • Definition of effective business cases for meta data and governance
Is your company losing money because your business resources are spending too much of their time finding and gathering data rather than analyzing the data and making decisions? In the future, we’ll use Corporate Performance Management (CPM) to measure the performance of the enterprise. But until CPM is pervasive, business users will depend on data shadow systems - groups of spreadsheets (Microsoft Excel) and local databases (Microsoft Access or Microsoft SQL Server) that gather and transform data into relevant business information. Most people already understand the risks of having multiple spreadsheets, but the real problem is the lack of data integration and consistency that increases business risks and costs.

How do you make a smooth transition from the use of data shadow systems today to CPM in the future? Despite what the IT group may want to do, taking data shadow systems out behind the woodshed and shooting them is not an option. It’s imperative to leverage these data shadow systems for effective performance management now, and not lose the valuable business information they contain. We will examine the root cause and potential solutions to address these systems.

Attendees will learn:
  • How to engage the business to determine what data and functions their data shadow system provide
  • How to re-architect the data shadow system to increase business satisfaction and improve system integrity, transparency & maintainability
  • What are the limitations of current ETL implementations that dive business user to create data shadow systems
  • What vendor offerings enable the transition to performance management systems
The Information Technology Infrastructure Library is a library of "best practices" for IT Service Management. While often associated with the operational side of IT management, it is also relevant to data architects and managers. ITIL practices such as Configuration, Change, and Capacity Management have direct implications for the practice of data management, while an ITIL initiative itself requires careful data architecture applied to the problem domain of information technology challenge faced also by metadata management. Come and learn all about ITIL from Charles Betz, the author of the newly published book "IT Service Management, Resource Planning, and Governance: Making Shoes for the Cobbler's Children (Morgan Kaufman/Elsevier, 2006). (please note: this is not an ITIL certification class) a Topics of the briefing will include:
  • ITIL and ITSM overview
  • How ITIL can assist the goals of Data and Metadata Management
  • How Data and Metadata Management may support an ITIL initiative
  • Configuration Management and Metadata Management: two sides of the same coin?
  • ITIL's limitations, challenges, and future prospects

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