Your SlideShare is downloading. ×
07182006_DAMA_Dallas_Institutionalize_Your_Data.ppt
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Introducing the official SlideShare app

Stunning, full-screen experience for iPhone and Android

Text the download link to your phone

Standard text messaging rates apply

07182006_DAMA_Dallas_Institutionalize_Your_Data.ppt

750
views

Published on

Published in: Technology, Business

0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
750
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
22
Comments
0
Likes
1
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • Today, we first discuss the importance of dynamic blueprint and how Informatica expanded the definition of data governance. Then we will illustrate how to secure endorsement and program initiation using a Financial Services client example. Then Dave Morgan from Informatica Professional Service will discuss how some of our clients successfully completed the initiative engagement. He will provide a resource model including ICC, Integration Competency Center and stewardship activities. I will then conclude our presentation section by illustrating how PowerCenter platform is uniquely architected to automate data governance and management tasks. We will have the next steps and Q&A at the end.
  • Data governance and compliance is the new reality, many attendees said, changing the way they work. The emphasis on governance gives data management more visibility in the corporate world. Data Quality is taken more seriously, data integration is a necessity, and security is an imperative, not a luxury, attendees said. Close to 1,000 data professionals and thought leaders gathered at the DAMA International Symposium and Wilshire Meta-data conference in April. I had a pleasure of meeting with many of the data management industry colleagues at this event. At the booth and at my speaking slot, I have personally sensed the level of expectation and anxiety around data governance and management topics.
  • Business is not "as-usual." Ripple-through effects are that data management cannot be as-usual either. [Click] More organizations are applying six sigma concept for certifying data to bring rigor and measurement and using data as a cornerstone for corporate performance management. [Click] An increased number of customers are starting to use the multiple frameworks and guidelines to standardize IT practice and increase auditability. Accountability and transparency model is being refined to facilitate organization-wide participation in order to derive business benefits beyond just regulatory mandates. [Click] Organizations are also having the rude awaking to the criticality of data. The data is becoming institutionalized as they seek maximum business gains. Premium is now being placed on data to drive top-line, revenue-generating activities and exert governance over the data across a portfolio of off-shore/onshore IT outsourcing projects.
  • Regulatory compliance and security-risk prevention are driving requirements such as access control, reconcilability and audit trails. Corporate performance measures such as revenue optimization, elimination of redundant costs and risk mitigation are constantly pursued from diverse activities such as Customer Campaign, Fraud Detections and Privacy Risk management. Organizations are asking the perennial question of what belongs to [Click] an IT, [Click] business or [Click] both with respect to data service related investments. To ensure proper governance and enterprise-wide visibility, and to fully exploit technology, organizations must adopt a different approach where the planning does not remain just static, just like we cannot remain static about technology architectures, tools and processes.
  • As speed and volume of data movements increase, IT must move from the enablement of business processes, to being a deciding factor in performance management. This is not just because automation would make something cheaper or more efficient, but because a process cannot be performed in allowable timeframes or with sufficient quality, unless data governance and management initiative is established as a foundational discipline. To illustrate this point, consider a case of a customer who has to balance a number of business thrusts. [Click] At the onset, they focused on high priority segments especially on internal control environment design. [Click] As a second focus, they added more cost related measures from supply chain and partner management as well as a risk-based model. [Click] As a third focus area, they identified more compliance-driven measures such as automated vs. manual controls and safeguarding of confidential information, as well as adding sales distribution management, and asset and financial risk management. [Click] Unlike in previous generations when IT blueprints and architecture were relatively fixed just like building construction’s blueprints and architectures are, organizations must have more agility in adapting its blueprint to the changing business climates. This framework will be used in the later part of the section.
  • Now let us turn to the historical context to characterize what we are faced with. Corporate governance is defined as the set of processes, customs, policies, laws and institutions affecting the way a corporation is directed, administered or controlled. And IT governance is defined as the leadership and organizational structures and processes that ensure that the organization’s IT sustains and extends the organization’s strategies and objectives by the IT governance institute. And it has been historically organized around systems and applications. What is becoming apparent is that this approach, while it served the environment in previous generations, it is no longer sufficient in addressing today’s challenges including: Inability to address data-centric issues such as semantic interoperability as well as data quality management Lack of focused consideration to an enterprise data architecture as a foundation Limited response to the complex organizational dynamics across departments, and between businesses and IT, in formulating strategies This is the rationale behind why Informatica has expanded the definition of data governance, in order to help organizations lay the foundation for an integrated approach to data governance and management. This holistic approach enables the reduction of redundant, manual-based controls because equivalent automated controls will be inherent in the data integration architecture with embedded quality and metadata-driven validation in place. We also demonstrate how businesses and IT can optimize the balance of accountabilities and budget control by undergoing a more integrated planning cycle and sharing the information derived from the same trusted data. The net result will be gained by a more value-driven approach to data governance and management. Corporate governance definition source: Wikipedia IT Governance definition source: IT Governance Institute 2003, "Board Briefing on IT Governance, 2nd Edition".
  • The ability to exert governance over the data terrain depends on, first, how well we refine the framework and the key attributes. Through extensive customer and industry interviews, we have selected the following six primary attributes- accessibility, availability, quality, consistency, security and auditability. [FIRST CLICK] then we also captured the attributes within standards, policies and processes, and organization as shown here. Now as we mentioned earlier, there is a need for an automated, unified solution as a means to govern the data movements. So the next task is to view this framework with an objective to select the right blend of architecture, technology and approach in today’s enterprise integration. [SECOND CLICK]. Organizations are mobilizing the internal and external data assets through an SOA-based data service layer. They are choosing the deployment of a data integration platform as a technology of choice. Many of our customers are transforming their IT into an ICC, Integration Competency Center model, an center of excellent approach, to deliver value for the changing business requirements. Let me clarify the scope of today’s discussion. I will be focusing on the technology element, data integration tool, within this framework. Sean will be providing the methodology and client examples across the three components including Architecture, Technology and Approach in the latter half of this session. During the second session of this series next week, we will have a more focused discussion on how to implement the shared services leveraging ICC, as well as to establish policies and design principles, secure program support through value demonstration and provide steps for the initiative engagement.
  • Now let’s us turn our discussion to the actual use of data governance tool readiness for a financial service customer. This customer has multiple business units including: Business and Consumer Banking Wealth Management Institutional Banking Additional retail presence in nearby countries The customer integrated metadata sources including Oracle DB, Teradata, Sybase, SQL servers, DB2, PowerCetner, Cognos, Erwin, etc. The customer deployed PowerCenter to access, discover and integrate the source metadata. The company also implemented the Common Warehouse Meta Model (CWM) as the industry standards and provided the development kit to extend the model from within the toolset. Pre-built reports such as data lineage and where-used as well as custom metadata reports were utilized to automate reporting and analysis. KEY BENEFITS OF USING PowerCenter Metadata Manager included: Met compliance requirements including Sarbanes-Oxley and Basel II Reduced manual reporting, analysis and verification procedures Automated the data lineage proof process in audits Enabled the better cost estimates and contained the costs of changes and additions Let us proceed in taking a closer look at how they scored on the assessment sheet.
  • This is the result of the tool readiness assessment for this financial service client. You can see that they scored highly on consistency and auditability. Now they are embarking on a new data quality management program to improve the data quality score. You can also see the functionalities and options delivered by PowerCenter around each data governance attribute. We are offering this as a means to prioritize your data integration investments using the a bottom-up, tool usage readiness technique. We encourage you to use the spreadsheet that we provide to assess the readiness in your own environment and formulate your investment strategy accordingly.
  • The good news is that the early adopters and thought leaders have already demonstrated successes. Let us begin with the guiding principles that we have learned from those. First, the mission statement sets a mindset. KPI measures will help organizations aligned and focused. Secondly, organizations must make the data management as an integral part of the overall corporate governance and oversight. Thirdly, embedding new standards, practices and processes into the existing environments is vital to avoid major disruptions and gain a solid grip on continuous evolution. Fourthly, throughout the cycles, it is critical for the program team to align with stakeholders and business owners to ensure smoother execution and foster a lasting organizational synergy. Lastly, as we discussed as a key theme during the first session, let us focus on visible wins – as success bands the organization together for a long duration of the program, which will span multiple years, being phased in many subject areas.
  • Given the significant investments required, organizations are expecting more than just meeting narrowly focused goals. [Click] Phase 1 promotes a mindset for institutionalizing data with vision, framework and metrics. To ensure that centralized oversight does not translate into rigid, control-only governance, organizations are encouraged to specify development scope and change management procedures. Formation of a steering committee and linking investments to returns are critical in mitigating this becoming traditionally the restrictive exercise of enforcing standards, processes and policies. [Click] Phase 2 focuses on instituting policies and design principles to maximize and preserve the value of investments. The operating guidelines and rules in the form of policy are to be established. The design principles are also part of the scope. It is essential to shape the enterprise-level thinking around leveraging the best business and technological innovations coming out of specific business units or teams at the point of impact. [Click] Phase 3 establishes a baseline for understanding the areas of priority, risks and open issues in undergoing the transformation. Many of the tangible and intangible issues are meant to be discovered. The information will be used to ensure that the next phase, Phase 4 of program endorsement will be successful. [Click] Phase 4 shows a disciplined approach to program planning and business case development. It is designed to build a case for addressing changing business requirements that are quite dynamic in nature. [Click] Phase 5 is a phase where an initiative engagement gets kicked off. This phase is designed to both show some quick wins as well as to identify risk areas that can expose the underlying data management issues, which will be possibly pronounced at a later phase. [Click] [Click] I will discuss the internal selling example later [Click] and we will describe the initiative engagement approach and the client examples.
  • In the Phase 1, a large financial service firm established the vision of managing information as an integrated enterprise asset. The value of data and the process to effect IT transformation, coupled with corporate standards and governance, are underscored in the vision statement. The guiding principles are established accordingly – we emphasize the importance of re-establishing standards for corporate governance, IT governance and data governance. Transforming data into information, in other words, ascertaining data carrying proper meanings and being interpreted the right way, is vigorously pursued. [Click] Key success factors included the C-level support and robust involvement with lines of business, business leadership oversight and ongoing guidance on architecture and tool selection. This served as a basis for the firm to adapt to changes while being connected to the lessons learned in anticipation of potential changes and tribulations.
  • To treat data as an asset, people, process and technology have to be aligned to ensure that information management be the inherent part of the governance model. To this end, the customer focused on clarifying accountability of people, lifecycle approach from a process perspective, and implementation of an end-to-end architecture. [Click] The next step is to define Scope, Phased Delivery Strategy and Data Governance Metric. The customer focused on establishing an enterprise-wide data warehouse first, followed by master data/data certification and then KPI measurements. Data quality was selected as an initial focus given the visible data governance problems in reconciling general ledgers. They also took master data approach to ensure accessibility, auditability and consistency. [Click] Accurate, reliable forecasting and business agility were the key value proposition. The investment will be measured in terms of the reinforced value of data and its materiality– assigning dollar value to the key areas of revenue, cost and risk. The steering committee was formed including CFO as an executive sponsor, partner and merchant management as business partners, and a dedicated team focused on architecture and tools for this financial transformation, including IT security and infrastructure leadership being unified.
  • You can also establish policies as operating guidelines and rules, including Integrated planning cycle, Stewardship, Data standards and quality, Foundational architecture, Usage validation, and Audit processes as operating Guidelines and Rules. Of particular interest are Stewardship and Audit processes. The definition of data stewardship was very vague initially. Because of the highly heterogeneous nature of the IT environment, the client exercised significant care in establishing the stewardship practice as an underpinning for data standardization and consistency in interpreting data. The customer also underwent a documentation repository review and interviews to ensure that the issues around data including amendments and interpretations are well-represented. As for audit processes, data reconciliation and exception handling were considered serious issues. The client wanted to increase automation to reduce the amount of walk-through and testing and to enable the reliance on work of others with sufficient evidence, as guided by PCAOB’s Auditing Standards 2. This also helped the organization transform from a bottom-up 100% checklist mentality, to a more top-down risk-based approach where materiality of transactions to the financial statement is explicit in its model. A host of risks associated with Sarbanes-Oxley, Basel II, anti-money laundry, privacy and other security is addressed in concert.
  • The Design Principles include Information classification, Record retention and disposal, Functional areas, Metadata management, KPI measurement, Risk management, Training & communications and Shared services. To determine the degree and the nature of structures and methodology as part of the design process, let us take a perspective of enterprise integration solution framework and understand the role of a data service layer. Key areas of evaluation included syntactic and semantic transformation, flow management, adaptors or packaged integrating processes, openness and compatibility with applications and systems, as well as other functionalities such as business activity management or business process management. The upper 4 categories show the design infrastructure. The lower four categories provide the methodologies to ensure that how the structures can be leveraged. Data management solutions can be built to support the specific business rules, for example, to identify and assess the implications of transactions with specificity backed by data.
  • Assessment model can be built around cultural/behavioral analysis, tool usage maturity, control design, preventive versus detective, and automated versus manual. Without automation, it would not be feasible to exert control with consistency for the long run, let alone measuring the existing baseline. Data integration solutions help contain costs and risks driven by cultural/behavioral issues while identifying the baseline and making the activities in multiple contexts auditable. In this case, the company intended on increasing the coverage of preventive, automated control progressively, supplemented by the policy-based manual control, while working on developmental issues in cultures and behaviors. [Click] Assessment results led to the end-state goal setting, gap analysis, role-based mapping, stakeholder analysis and communication and training. Accuracy, consistency and updatability of general ledgers was the top priority. The major gap in data was identified in the areas of documentability, legacy access, risk model rationalization and non-productive activities around data reporting. For instance, auditors demanded sufficient disclosures about why deferred credit went to the income statement instead of the balance sheet, and explanations on the impact to significant accounts. Workflow control and exception handling were mapped to relevant functional areas and individuals. Stakeholder analysis focused on regular reporting cycles and Sarbanes-Oxley walkthrough, which initially required the manual data aggregation and reconciliation from a number of applications and systems across multiple functional areas. They also positioned communication and training to be the change-agent for data-centric, cultural growth.
  • In the Phase 4, let us go back to the notion of the dynamic blueprint where we illustrate progressive expansion of focus when securing the program endorsement. Please note that the framework can be used to scale down the scope in times of restructuring and changes. As shown before that this particular scenario moves from: [Click] Focus 1: High Priority Segments, [Click] to Focus 2: Cost Reduction, and [Click] to Focus 3: Enterprise Risk and Revenue Optimization.
  • We also provide a tabular view of what was shown in the previous slide. When approaching lines of business and articulating the impact of a data governance and management initiative, showing the value in this context ensures that “what gets measured gets results.”
  • The critical aspect of securing the program is how well the steering committee can articulate the value to primary constituencies during the endorsement process. [Click] For executives, they care most about share holder value, earning and compliance accountability. [Click] For legal, finance and operations, financial reporting integrity, especially with Sox, liability and productivity measures are important. [Click] For line of business, revenue, product and customer-related metrics would best resonate, and [Click] IT team – productivity and cost containment are essential. This is the example for the Focus Area 1, to justify high priority segments for compliance, revenue and cost impact. [Click] As for program planning , the customer identified that most prepared area for this was the corporate IT and finance department. They secured the executive sponsorship and received feedback from early adopter and supporter candidates. They also started developing a community of practice. [Click] From the compliance perspective, they focused on workflows and system designs for demonstrating internal control. They ensured that control-related policy and enforcement in place. The role of data is explained in terms of data integrity, auditability and consistency. [Click] From the revenue perspective, having the deferential pricing and the customer master data for cross-sell and upsell activities was deemed imperative. [Click] From the cost perspective, in order to stop non-value added activities for agents related to invoicing, billing and credit management, customers had to consolidate multiple data sources. They also were engaged in data standardization and document automation to lower the maintenance costs. [Click] The risk was out-of-scope. At this point, I would like to have Dave Morgan join to share with us some case studies from his recent client engagements. [Julianna] So Dave, what kind of advice can you offer in terms of addressing the high priority, financial reporting related project business justification, at this stage? [David] One time I was working with publishing company and what the client wanted to do was to build out the profile for distribution of magazines. They wanted to build the distribution by store. Cleaning up data that is associated with financial reporting coming from thousands of retails stories needed to be audited by the internal audit team. In this case, it was really around reconciling bad data for demonstrating internal control in Sarbanes-Oxley.
  • For Focus area 2 , the customer had a goal to drive additional cost reduction while laying a groundwork for the next Focus Area with expanded risk management. [Click] From the program planning perspective, the customer identified supply chain and partner management as the area of focus. They added the VP and partner executives as sponsors. They also integrated domain subject matter experts from supply chain and partner relationships into the early adopter and supporter feedback loop. The community of practice focused on making the existing best practice reusable. [Click] From the cost perspective, they consolidated the data that are used for partners and merchants so that agents can tap into the historical data for negotiation. [Click] From the risk perspective, they established a model for business partner risk management. They also ensured that dependencies are kept clear by modelling data flows and lineage associated with business relationships. They also categorized and assigned risk impact and likelihood. [Julianna] Hey Dave, do you have an example of a customer who had added the cost reduction as an added measure to an existing project which was originally driven by compliance and revenue optimization? [David] Yes, with the same customer for reducing the cost of processing claims. For processing the information, they were trying to move it in-house for analyzing distribution by retail store. There were a number of disparate systems required to process that data - there were many manual activities that resulted in inefficiencies and inconsistencies in processing that data.
  • Let us look at Focus Area 3 where the customers expanded the scope to address enterprise-wide risk and revenues. [Click] From the program planning perspective, the customer now mobilized enterprise IT as well as the additional lines of business. They secured additional sponsorships from other BUs. They also put a formal survey and training in place. The community of practice is focused on re-establishing best practice. [Click] From the compliance perspective, some of the previously manual areas were automated to detect or prevent material weakness. They also aligned the treatment of confidential data with security and privacy practice. [Click] From the revenue perspective, they increased oversight for partner management with metadata mgt and added reference data from sales distribution to leverage customer and product data for planning. [Click] From the cost perspective, it sustained the focus area 2 effort. [Click] From the risk perspective, they launched the integrated risk management while initiating automated correlation and verification to provide sufficient evidence for estimation and interpretation. [Julianna] Dave, do you have an example of customer now who is in the process of expanding their program to enterprise-wide including the fair value of data asset? [David] Sure. Let me share with you about the initiative that one of my customers has worked on regarding the dollar value of errors at a financial service institution. They have looked at lines of credit and realized that there were data errors and resulted in the potential credit that can be extended or should not be extended that was costing a company. They have used a data profiling solution to further quantify. This approach was applicable to Basel II, Sarbanes-Oxley, other relevant credit, and market risk management initiatives. [Julianna] Thanks Dave. Now I will turn this over to Dave for a discussion on the initiative engagement.
  • Before kicking-off the five-step approach to the initiative engagement, let me share with you how we view a resource model that provides the roles of the governance steering committee, as well as integrated competency center (ICC) with its relationship to the rest of IT to address key subject areas or line of business initiatives.
  • Let me give you a brief on five step processes for the initiative engagement. It is important to distinguish here that this is a project level process as opposed to the overall program phases.
  • One area that is often overlooked is the area of practice development. Many organizations start with the data governance program without paying attention to the fact that the organization’s mindset also needs to evolve to make the process successful. This is the reason why we recommend that you take a look at existing practice, areas for improvement and development goals in your first initiative engagement.
  • Fully understand development needs Identification of key subject and functional areas Individual or group-level educational requirements Design a stewardship development plan Objectives, scope and tasks for strategic, operational and domain steward (or similar roles) Identify educational vehicle Create a progressive plan to adapt to changing infrastructure Practice development tasks aligned with infrastructure changes
  • Dsm> In reviewing this thought process on the differences between Strategic, Operational and Domain stewards. Some customers I have spoken see the importance of each, but sometimes doesn’t invest in the Program or Operational stewardship component. Major energy company did not have operational stewards although they had strategic and domain stewards – they were able to address the tracking of metrics once they introduce the operational stewards.
  • Describe how you can start moving from the immediate areas categorized by investment required Focus on the areas that have the accelerated time to returns.
  • Pragmatically select “Gap” areas can be used as an “Exemplary” case Areas of visible governance issues that only require point or add-on solutions Combined use of policy and guidelines Characterization of before / after in hours/work impact…. Be careful with change management! Test / prototype solutions/suggested changes Small areas that can be tested short term Validate stewardship model through initial projects Identify areas for elimination or retirement Removal of non-value added activities to free up time for focus areas
  • The same energy company decided to have the better definition on three roles – strategic, operational and domain to make sure that they can balance strategic agenda and tactical activities.
  • A bit more on what kills or drives the project to a fruition is your disciplined approach to balancing Strategic Agenda and Tactical Activities
  • Demonstrate the value through early projects Hours saved, dollars collected, people taking on more strategic assignments, etc. Presentation of benefits through shutting down non-value added components Get proof points on validity, applicability and recommended areas for future implementation Anecdotal stories about paybacks through a pilot from businesses and extended team Perception-building through active dialogs Position to extend value through an extended pool of resources No major full-headcounts yet. Early adopters and champions to grow the extended team
  • We have interviewed over 120 of our customers who have at least started down the road towards some type of Integration Competency Center and as a result of those interviews, we have recognized that there are about four different types of Integration Competency Center models. To start with, there is the best practices model, which may be as simple as documenting your standard operating procedures into a shared repository of some kind and passing that around to the various project teams responsible for tackling data integration. That way you are at least not re-inventing every spoke on the wheel of the data integration project every time you have an initiative. Another model that we have identified is the technology standards model, which is where you standardized on a common technology, but you don’t necessarily have a shared infrastructure or shared platform for doing all the data integration within the enterprise. You are sharing the best practices or standard operating procedures as from the former model. The third model is the shared services. This is the model where you have a centralized team responsible for the administration and the care and feeding, if you will, of the data integration environment, the ICC environment, and then each project team is responsible for doing the manual labor or the factory work of the data integration initiatives, based on the best practices established and maintained by that shared ICC and done on the shared platform maintained and implemented by that shared ICC. The next model is the central services model. This is the model where you bring all the work into that centralized unit, so that you have control over not just the care and feeding, but also the actual development work, the manual labor of building each and every mapping or connection or interface or whatever it is that you may want to call it. This model provides the most control over all integration within the enterprise, but it often requires investment in infrastructure for infrastructure’s sake. It also requires that the individual project teams who may be accustomed to having their own budget, to having autonomy over their own projects have to relinquish some of that control to this centralized unit. So there often is some political pushback. Now this is another way that I have seen organizations actually phase the implementation of their Integration Competency Center. Often companies build their ICC along this continuum as a maturity model -- slowly taking more and more control as they prove value to the enterprise and to the project teams. There are really no good or bad models and it all depends on the enterprise itself. The right model for each company depends on its organization, its funding structure, its cultural and political environment as to which of these models will deliver the most value within the organization.
  • Organization and communication diagram – importance of tying multiple constituencies in stewardship activities.
  • Design a team model and resource plan Emphasis on initiative engagement Previous experience and problem-solving mindset plus Alternative approaches to be presented Provide scenario assessment Pros and cons of specific resource model and requirements Risks and open issues clarified Get endorsement for a small team Secure baseline to demonstrate focus area value Communication and training plan in place
  • Ensure ongoing communication on the value of the program IT investment defined – tangible/intangible Value – revenue, cost, compliance and risk Particular components –worked/worked less Make small incremental changes tuned to business needs Delivery of results and incremental changes reflective of ongoing business changes Positive organizational impact highlighted Get support for developmental areas Reinforcement for people, process and technology Communication and training plan in place
  • Investment is heavy for the first year. Perform rigorous assessment on the initiative phase Reassessment on architecture, tools, skill sets, processes, training, and communication Leadership effectiveness- organization dynamics typically a major problem! Get departmental/functional buy-ins to expand scope Current major objectives defined Find “small” ways to make a difference Progressively automate with an expanded scope Incremental value add defined – with less risk Preventative, automated measure, key to success
  • Switched with previous slide to end with picture.
  • This is an architectural view of how the Informatica platform has been constructed to automate data integration tasks, a key technology component of data governance and management. The platform enables you to access data in any backend data source and in any form, including semi-structured industry formats like HL7 and ACORD, and unstructured format such as .xls and .pdf. The platform supports the entire data integration lifecycle from discovery and profiling -> cleansing and matching -> transformation -> delivery (including federation) -> monitoring. This date integration functionality can be accessed by consuming applications (on the top) such as portals and business process management over a broad array of protocols including JMS, Web Services, SQL, and JDBC/ODBC These data integration capabilities are rooted in the metadata service layer on an industry-unique infrastructure, the only viable choice for organizations demanding a truly enterprise-scale solution. Our tools enable business analysts, developers and administrators to manage data drawn from the trusted sources to ensure organizational alignment and consistency. We are also advancing our capabilities to meet your future needs for data accessibility, auditability, availability, consistency, quality, and security. We are singularly focused on assisting your organization in maximizing the value of data asset, On behalf of Informatica, I would like to extend our appreciation for your participation in our Data Governance and Management series. We look forward to receiving your feedback and meeting again in the near future. I would like to now open up for taking questions. Now let me turn this over to Janet to conclude the series.
  • Question 1: “How does this compare and contrast to Velocity, Informatica Professional Services methodology?” Could you elaborate Dave? Answer 1: Sure it is complementary to our Velocity. Velocity is xyz. This methodology that we provided today is an overall framework. Question 2: “What kind of advice can you give about a situation when things suddenly change due to an acquisition and I am part of the company being acquired?” Answer 2: Well, that’s also when you can really apply this principle to get all parties aligned from executives, businesses and IT. IT tends to make or break acquisitions sometimes and you can take a leadership role in defining the scope, identifying the areas for wins and start consolidating critical data. Question 3: “How is PowerCenter different in supporting data governance and management tasks?” Answer 3: The major difference between PowerCenter and other platforms is its ability to automate all aspects of data integration tasks on a metadata driven foundation. Metadata governs architecture. Other alternatives do not have the unified architectures and their data integration capabilities do not meet the data governance metrics including accessibility, auditability, availability, consistency, quality, and security as we discussed throughout this series.
  • Transcript

    • 1. Institutionalize Your Data: Designing and Implementing a Dynamic Blueprint for Data Governance and Management Julianna Sakamoto, Senior Manager, Informatica [email_address] Tel. 650-385-5010 Provided for DFW DAMA Meeting on July 18 th – 11:30 am to 1:30pm
    • 2. Welcome
      • Critical Time to Examine Your Data Governance and Management Practice
        • Sarbox 3 rd year; Foreign companies on the US exchange mandated to comply
        • Business is NOT as usual – Our Webinar attracted 881 registrants!
        • Even playing field in a flattening world – or is it?
      • Scope
        • Intentionally kept broad to meet varying degrees of interest and experience levels
          • Perhaps follow-on break-up sessions or workgroups in the future?
        • Some sections will be for reference or further reading only
        • Electronic copies available
      • Follow-ups
        • Julianna Sakamoto, [email_address] , cell: 415-407-4817
        • Informatica Team
    • 3. Agenda
      • Importance of Dynamic Blueprint to Data Governance and Management
        • Heightened Need of Data-Driven Approach
        • Challenges of Linking Data to Corporate Measures
        • Agile Data Governance and Management
      • Expanding the Definition of Data Governance
      • Best Practices for Securing Endorsement and Program Initiation
        • Case Study – Financial Services
      • Initiative Engagement – Start to Finish
        • Establish Practice Development Strategy
        • Design an End State and Conduct Gap Analysis
        • Identify Quick Wins and Design Project Plan
        • Establish Resource Plan and Team Model
        • Measure and Control Goals
        • Transition to Expanded Scope
      • PowerCenter for Automating Data Governance and Management Tasks
      • Q&A and Open Discussions
    • 4. Importance of Dynamic Blueprint to Data Governance and Management
    • 5. Elevated Expectation and Anxiety Around Data Governance Source: DAMA International Symposium and Wilshire Meta-data conference, April 2006 Data governance is the new reality “ Data governance and compliance is the new reality, many attendees said, changing the way they work. The emphasis on governance gives data management more visibility in the corporate world. Data quality is taken more seriously, data integration is a necessity, and security is an imperative, not a luxury, attendees said. A competitive global marketplace and laws such as Sarbanes-Oxley bring the promise of increased resources -- but the pitfalls of higher stakes.”
    • 6. Sarbanes-Oxley Adverse Reports over Internal Control Decreased in Year 2
      • Adverse reports on the decline
        • 16% to 7%
      • Marked (>10%) improvements
        • Entertainment & Media
        • Industrial products
        • Retail & Consumer
        • Technology
      • Lowest % of adverse report ‘05
        • Banking & Capital Markets
        • Pharmaceutical
        • Real Estate
      Source: PricewaterhouseCoopers Webcast, May 06 7% 16% 2917 3633 199 566 Grand Total 8 19 433 703 37 136 Technology 10 16 226 275 23 43 Services 8 20 235 337 18 66 Retail & Consumer 3 11 205 197 7 21 Real Estate 3 10 191 223 6 22 Pharmaceutical 0 7 13 14 0 1 Investment Management 6 7 141 136 8 10 Insurance 16 22 97 130 16 28 InfoComm 5 16 349 480 18 78 Industrial Products 9 9 79 93 7 8 HealthCare & Government 9 22 161 204 14 44 Entertainment & Media 8 15 265 285 20 42 Energy & Utilities 4 12 469 484 17 57 Banking & Capital Markets 15 14 53 72 8 10 Automotive 2005 2004 2005 2004 2005 2004   % Adverse Industry Total Adverse Reports Industry Sector
    • 7. Heightened Need for Data-Driven Approach
      • Applying Six Sigma Concept for Certifying Data
        • Bring rigor and measurements in data management
        • Cornerstone for corporate performance management
      • Increased Layering of Frameworks for Auditability
        • Increasing use of ITIL, CobiT, COSO, and ISO 9000/17799
        • Refining accountability and transparency to drive organization-wide participation
      • Attempt to Link IT Investments to Compounding benefits – Institutionalize Data as Strategic Asset
        • Participation in revenue-driving activities beyond traditional IT cost reductions and risk management
        • Off-shore/onshore IT outsourcing prevalent with large companies
    • 8. Continued Challenges in Linking Data to Business Value Data Governance Metric
      • Audit Trails
      • Legacy Data
      • Access Control
      • Reconcilability
      Business Value-Driven Revenues Cost Risk
      • On-Demand Availability
      • Accuracy
      Reports
      • Supply Chain Costs
      • Distribution Management
      • Customer Campaign
      • Fraud Detection
      • Regulatory Compliance
      • Privacy Risk
      IT Issue? Business Issue? Or Both? Stewardship Definition Regulatory Compliance Certification Roles and Processes Business Rules Business Performance Goals
    • 9. Dynamic Blueprint – Agility as Part of DNA Dynamic blueprint - value-driven approach to data governance validated through incremental project progression tuned to business demand
      • Risk-Driven
      • Enterprise Business Risk
      • Asset /Financial Performance Management Risk
      • Business Continuity/ Disaster Recovery Risk
      • Personnel/Organizational Risk
      • Geopolitical Risk
      • Cost-Driven
      • Supply Chain / Inventory Management Efficiency
      • Partner/Supplier Negotiation (merchant/sell-side)
      • Invoice, Billing and Credit Management
      • IT management - tool and human resource use
      • R&D and Product Development/Delivery
      • Revenue-Driven
      • Pricing Optimization
      • Cross-sell / Upsell
      • Sales And Distribution Management
      • New Customer Acquisition
      • Collection And Fraud Prevention
      • Compliance-Driven
      • Internal Control Design
      • Detective Vs. Preventive Measures
      • Risk Level Assignment
      • Automated Vs. Manual Controls
      • Safeguarding Of Confidential Data
      Focus 1 Focus 2 Focus 3
    • 10. Expanding the Definition of Data Governance
    • 11. Governance: Historical Context Corporate Governance The set of processes, customs, policies, laws and institutions affecting the way a corporation is directed, administered or controlled. IT Governance The leadership and organizational structures and processes that ensure that the organization’s IT sustains and extends the organization’s strategies and objectives. Data Governance The processes, policies, standards, organization and technologies required to manage and ensure the availability, accessibility, quality, consistency, auditability and security of data in a company or institution. Business Processes CRM System ERP System Order Mgmt System Finance System HR System Customer Data Product Data Supplier Data Finance Data Employee Data
    • 12. Expanded Data Governance Framework to Underscore Importance of Technology Corporate Governance The set of processes, customs, policies, laws and institutions affecting the way a corporation is directed, administered or controlled. IT Governance The leadership and organizational structures and processes that ensure that the organization’s IT sustains and extends the organization’s strategies and objectives. Data Governance Data Integration Infrastructure Standards Organization Data Accessibility Data Availability Data Auditability Data Consistency Data Quality Data Security Policies & Processes Enterprise Data Model Data Definitions & Taxonomies Master/Reference Data Technology Standards Data Access & Delivery Data Definition Monitoring & Measurement Data Change Management Planning & Prioritization Roles & Responsibilities Organizational Structure Org. Change Management Integration Competency Center (ICC) Approach Service-Oriented Architecture– Data Services Architecture Data Integration Platform Technology
    • 13. Financial Services Customer Case Study Enabling Enterprise Integration via Metadata Management
      • Inability to automate metadata source handling
      • Inability to retain knowledge even with IT staff departures and project completions
      • Lack of clear KPI definitions
      • Uncertainty with project costing
      • Informatica PowerCenter
        • Oracle, SQL Server, Teradata, Sybase, SQL servers, DB2, Cognos, Erwin
      • PowerCenter Metadata Manager 2.1
        • Metadata directory, search, lineage and where-used reports
      • Simplified reporting & reconciliation processes
      • Improved management decision processes and outcomes
      • Mitigated cost/impact from potential non-compliance
      • Improved estimates for change costs
      • Key Business Requirements:
      • Meet statutory requirements – BASEL II, Sarbox, etc.
      • Improve reporting and management decision
      • Facilitate future development of analytical applications
      • Approach:
      • Provide a consistent and integrated data integration mechanism for management and reporting
      • Allow impact analysis before project initiation
      Go to the Data Governance Tool Readiness Assessment Challenge Solution Results
    • 14. Financial Service Customer Case Study Data Governance Self-Assessment Map Data Accessibility Data Quality Data Security Data Consistency Data Auditability Metadata Management Dashboard, Data Lineage, Impact Assessment and Data Dictionary/Business Glossary Unstructured Data Mainframe Legacy Data Quality Lifecycle Management (Scorecard, Monitoring, and Remediation) Data Profiling Data Cleanse and Match Team-based Deployment Encryption Support Privilege Management Data Classification Data Availability Server Grid Push-Down Optimization Data Federation Real-Time Partitioning
    • 15. Best Practices for Securing Endorsement and Program Initiation
    • 16. Guiding Principles for Program Initiation
      • Begin with a clear top-down mission statement and key performance indicators that will be boosted by the program
      • Make data management as an integral part of the corporate governance and oversight process – not a separate new initiative
      • Embed the new standards, practices and processes into existing functioning framework where applicable
      • Seek to align with stakeholders and business owners to dissolve resistance and accelerate approval cycles
      • Drive “ visible” wins through “selected” subject areas or data governance metrics according to value and risk levels
    • 17. 5 Phases of the Data Governance and Management Program
      • Dynamic Blueprint approach
      Phase 1. Establish Vision, Framework and Metrics Phase 3. Conduct Readiness Assessment Phase 4. Secure Program Endorsement Phase 5. Conduct Initiative Engagement Phase 2. Institute Policies and Design Principles
      • Vision
      • Mission statement
      • People, process and technology
      • Deployment scope
      • Phased delivery strategy
      • Governance metric
        • Availability
        • Accessibility
        • Auditability
        • Consistency
        • Quality
        • Security
      • Value proposition
      • Linking investments to returns
      • Steering committee formation
      • Policy
        • Integrated planning cycles
        • Foundational architecture
        • Stewardship
        • Usage validation
        • Data standards and quality
        • Audit processes
      • Design Principles
        • Information classification
        • Record retention and disposal
        • Functional areas
        • Metadata management
        • KPI measurement
        • Risk management
        • Training & communications
        • Shared services
      • Assessment model
        • Cultural and behavioral
        • Tool usage maturity
        • Control design
        • Preventive vs. detective
        • Automated vs. manual
      • Assessment results
        • End-state goal setting
        • Gap analysis
        • Role-based mapping
        • Stakeholder analysis
        • Communication and training
      • Program Planning
        • Identification of areas most prepared
        • Exec sponsorship
        • Early adopters and supporters feedback
        • Community of practice
      • Business Case
        • LOB initiatives/pain points
        • Dynamic blueprint
          • Regulatory compliance
          • Revenue boost
          • Cost reduction
          • Risk mgt
        • Financial and op. analysis and buy-ins
        • Value/risks defined
        • Proposal/Approval
      Step 1: Establish Practice Development Strategy Step 2: Design End State and Conduct Gap Analysis Step 3: Identify Quick Wins and Design Project Plan Step 4: Establish Resource and Team Model Step 5: Measure and Control Goals Step 6: Transition to Expanded Scope
      • Internal selling example
      • Focus for the second half
    • 18. Financial Services Customer Case Study For Data Governance and Management
    • 19. Financial Services Firm Best Practices: Phase 1: Establish Vision, Framework and Metrics - 1
      • Vision
        • The firm manages information as an integrated enterprise asset
        • Organizations must plan their future needs, and effectively utilize and manage information to support decision making processes
        • Corporate standards and governance must be established in conjunction with the IT transformation
      • Guiding Principles
        • Data must be managed as an integrated business asset
        • Data standards, policies and processes must be institutionalized
        • Standards for corporate governance, IT governance and data governance are to be re-established
      • Key Success Factors
        • Launched by CFO and supported by finance and LOB
        • Business leadership provides oversight and day-to-day support for key subject areas
        • IT governance committee and other leaders guide architecture and tool selection process in concert with directives from business
    • 20. Financial Services Firm Best Practices: Phase 1: Establish Vision, Framework and Metrics - 2
      • People
        • Identification of existing programs
        • Accountability mapped to functional areas and processes
        • Key stakeholders apprised of project deliverables, milestones and gating factors
      • Process
        • Integrated , planned and coordinated – lifecycle approach
        • Regular and ad-hoc work activities structured to manage in support of business objectives
        • Operating model and rollout defined
      • Technology
        • Implementation of end-to-end financial reporting system
        • Enterprise-wide data warehouse
        • Common infrastructures, standards and interfaces
      • Scope
        • Areas for financial planning, budgeting, allocations, forecasting, and regulatory reporting
      • Phased Delivery Strategy
        • First Year – Enterprise-data warehouse
        • Mid- Master data/Data governance certification
        • Latter stage – Linking to business KPI
      • Data Governance Metrics
        • Initial focus on Quality
        • Accessibility improved through master data approach
        • Auditability and Consistency considered crucial
        • Access control and classification key to Security
        • Availability tuned to reporting cycles
      Value Proposition Gain more accurate and reliable forecasting , and the reporting architecture to ensure timely response to business changes Linking Investments to End-State Goal World-class organization through business and IT innovation; Reinforced value of data Steering Committee Formation Executive Sponsor Business Partners and Domain SME Technology / Project Leadership
    • 21. Financial Services Firm Best Practices: Phase 2: Institute Policies and Design Principles -1
      • Integrated planning cycle
        • Data management as formalized discipline
        • Planning for acquisition, creation, transformation, usage and retention lifecycle
      Data Governance and Management Policies (Operating Guidelines and Rules)
      • Foundational architecture
        • Organizational, solution and IT architectures designed to maximize value
        • Enabler to formalized data management and governance practice
      • Stewardship
        • Accountability for data management to treat data as an asset
        • Business definitions and standard guidelines
        • Consistent interpretation of information
      • Usage validation
        • Data usage patterns defined and validated
        • Tasks performed by authorized individuals
        • Data in custody managed in compliance with privacy security, compliance and other legal requirements
      • Data standards and quality
        • Standard descriptions and common libraries
        • Monitoring, reporting and anomaly prevention
        • Accuracy, conformity, completeness, consistency, duplicates and integrity as ‘data quality’ solution considerations
      • Audit processes
        • Walkthrough and testing guidelines according to control and risk levels
        • Classification of preventive versus detective, and manual versus automatic measures
        • Certification workflow
    • 22. Financial Services Firm Best Practices: Phase 2: Institute Policies and Design Principles - 2
      • Information classification
        • Information inventory
        • Supporting resources
        • Functional and subject area
        • Domain use/reuse
      Data Governance and Management Design Principles (Structures and Methodology)
      • KPI measurement
        • Target metric and definition
        • Prioritization and categorization framework
        • Review model
        • Alignment to organizational goals
      • Record retention and disposal
        • Retention period by class
        • Secure disposal according to biz, legal and regulatory mandates
        • Record keeping
      • Risk management
        • In/out of scope
        • Indicators and impact
        • Likelihood analysis
        • Control designs
        • Preventive / detective -testing
        • Automation
      • Functional areas
        • Subject area model
        • Boundaries and accountabilities
        • Process integration
        • Common and reusable structure
      • Training & communications
        • Data treatment cultural assessment
        • Gap analysis
        • Foundational messages
        • Logistic and frequency
      • Metadata management
        • Integrated repository
        • Data flow validation
        • Reconciliation across formats, categories, and types
      • Shared services
        • Service definition
        • Resource design
        • Model design – mix of distributed and centralized
        • Business partners
        • Practice development
    • 23. Financial Services Firm Best Practices: Phase 3: Conduct Readiness Assessment
      • Assessment model
      • Cultural and behavioral
        • Interviews of selected employees and management
      • Tool usage maturity
        • Quantitative and qualitative
        • Deployed and planned
      • Control design
        • Control selection
        • Evaluation metric for controls
      • Preventive vs. detective
        • Data asset inventory
        • Assign risk class and resulting control type
      • Automated vs. manual
        • Kept open initially
        • Policy-based mitigation for control that cannot be automated
      • Assessment results
      • End-state goal setting
        • Unified process, infrastructure and format for GL
        • Timeliness and precision for monthly, quarterly and annual reporting
        • Full change management capture and traceability
      • Gap analysis
        • Completeness and consistency in documentability – key risk areas
        • AP handling/legacy retirement
        • Enterprise risk model/reporting integrity
        • Excessive low/no value-added activities
      • Role-based mapping
        • Workflow control and exception handling
      • Stakeholder analysis
        • Impact and risk areas for regular reporting cycles and Sarbanes-Oxley walkthrough
      • Communication and training
        • Part of the career development program
    • 24. Financial Services Firm Best Practices: Phase 4: Secure Program Endorsement - 1 Progressive Expansion of Focus - Focus 1: High Priority Segments -> Focus 2: Cost Reduction -> Focus 3: Enterprise Risk and Revenue Optimization
      • Risk-Driven
      • Enterprise Business Risk
      • Asset /Financial Performance Management Risk
      • Business Continuity/ Disaster Recovery Risk
      • Personnel/Organizational Risk
      • Geopolitical Risk
      • Cost-Driven
      • Supply Chain / Inventory Management Efficiency
      • Partner/Supplier Negotiation (Merchant/Sell-side)
      • Invoice, Billing And Credit Management
      • IT Management - Tool And Human Resource Use
      • R&D And Product Development/Delivery
      • Revenue-Driven
      • Pricing Optimization
      • Cross-sell / Upsell
      • Sales And Distribution Management
      • New Customer Acquisition
      • Collection And Fraud Prevention
      • Compliance-Driven
      • Internal Control Design
      • Detective Vs. Preventive Measures
      • Risk Level Assignment
      • Automated Vs. Manual Controls
      • Safeguarding Of Confidential Data
      Focus 1 Focus 2 Focus 3
    • 25. Financial Services Firm Best Practices: Phase 4: Secure Program Endorsement - 2
        • Invoice, Billing And Credit Management
      • Cost
        • Supply Chain / Inventory Management Efficiency
        • Sales Distribution Management
        • Cross-sell / Upsell
        • Asset Management / Financial Performance Risk
      • Risk
        • Enterprise Business Risk
        • Partner/Supplier Negotiation (Merchant/Sell-side)
      • Revenue
        • Pricing
        • Safeguarding Of Confidential Data
        • Automated Vs. Manual Control
      Risk Level Assignment Detective And Preventive Measure
      • Compliance
        • Internal Control Design
      Focus Area 3 Focus Area 2 Focus Area 1
    • 26. Financial Services Firm Best Practices: Phase 4: Secure Program Endorsement - 3
      • Revenue
        • Better, more targeted pricing model, differential to segments and customer behaviors
        • Developing customer master data to ensure completeness for cross-sell and upsell
      • Compliance
        • Demonstrate adherence to internal control through clear workflows and system design
        • Risk-driven approach to manage audits
        • Control related policy and enforcement practice in place
      • Cost
        • Stop non-value added activities for agents related to invoicing, billing and credit management
        • Remove unnecessary documentation and codes that require maintenance cost
      Focus Area 1 Goal: Justify High Priority Segments
      • Risk
        • OUT OF SCOPE
      Steering Committee Executive Sponsor Business Partners and Domain SME Technology / Project Leadership
      • Program Planning
        • Identification of areas most prepared Selected corporate IT and Finance Dept
        • Exec sponsorship CFO/CIO
        • Early adopters and supporters feedback Reflected in the vision, policies and design principles
        • Community of practice Practice development phase
      Relevant benefits articulated to each segment
      • Executives – Share holder value, earning and compliance accountability
      • - CEO/CFO
      • - CIO/CTO
      • - BU General Managers and VP
      • Finance, Legal and Operations- Financial integrity, liability and productivity measure
      • - Auditors / Analysts
      • - Controllers
      • - Compliance officers
      • - General counsel
      • Line of Business –Revenue, product and customer
      • - Sales operations
      • - Marketing
      • - Customer analytics
      • IT Team – Productivity and cost containment
      • - Enterprise architect.
      • - Dir. Of IT
      • - IT Analyst
      • - Data modeling
      • - Data warehouse manager
    • 27. Financial Services Firm Best Practices: Phase 4: Secure Program Endorsement - 4
      • Revenue
        • SUSTAIN FOCUS AREA 1 EFFORT
      • Compliance
        • SUSTAIN FOCUS AREA 1 EFFORT
      • Cost
        • Provide metadata-driven supply master to handle complex network of supply chain relationships
        • Unify the partner merchant negotiation data systems so that agents can us
      Focus Area 2 Goal: Drive Cost Reduction
      • Risk
        • Lay foundation for business partner risk management
        • Model data flows and dependencies associated with business relationships
        • Assess risk impact and likelihood
      Steering Committee Executive Sponsor Business Partners and Domain SME Technology / Project Leadership
      • Program Planning
        • Identification of areas most prepared Added supply chain and partner management
        • Exec sponsorship Added VP and partner execs
        • Early adopters and supporters feedback Domain SME integrated
        • Community of practice Reuse existing best practice within subject areas
      Relevant benefits articulated to each segment
      • Executives – Share holder value, earning and compliance accountability
      • - CEO/CFO
      • - CIO/CTO
      • - BU General Managers and VP
      • Finance, Legal and Operations- Financial integrity and liability and productivity measure
      • - Auditors / Analysts
      • - Controllers
      • - Compliance officers
      • - General counsel
      • Line of Business –Revenue, product and customer
      • - Sales operations
      • - Marketing
      • - Customer analytics
      • IT Team – Productivity and cost containment
      • - Enterprise architect.
      • - Dir. Of IT
      • - IT Analyst
      • - Data modeling
      • - Data warehouse manager
    • 28. Financial Services Firm Best Practices: Phase 4: Secure Program Endorsement - 5
      • Revenue
        • Increased oversight for partner management with the use of metadata management
        • Add reference data from sales distribution to leverage customer and product data optimally used for planning
      • Compliance
        • Increased automation versus manual control for cost containment and liability mitigation
        • Align treatment of confidential data with security and privacy practice
      • Cost
        • SUSTAIN FOCUS AREA 2 EFFORT
      Focus Area 3 Goal: Secure Enterprise Risk and Revenue Optimization
      • Risk
        • Launch an integrated risk management tied to financial and asset management
        • Initiate automated correlation and verification for risk assessment data for future expansion
      Steering Committee Executive Sponsor Business Partners and Domain SME Technology / Project Leadership
      • Program Planning
        • Identification of areas most prepared Mobilized corporate IT and selected lines of business
        • Exec sponsorship Expanded to include major BU
        • Early adopters and supporters feedback Formal survey and training in place
        • Community of practice Reestablishing best practice
      Relevant benefits articulated to each segment
      • Executives – Share holder value, earning and compliance accountability
      • - CEO/CFO
      • - CIO/CTO
      • - BU General Managers and VP
      • Finance, Legal and Operations- Financial integrity and liability and productivity measure
      • - Auditors / Analysts
      • - Controllers
      • - Compliance officers
      • - General counsel
      • Line of Business –Revenue, product and customer
      • - Sales operations
      • - Marketing
      • - Customer analytics
      • IT Team – Productivity and cost containment
      • - Enterprise architect.
      • - Dir. Of IT
      • - IT Analyst
      • - Data modeling
      • - Data warehouse manager
    • 29. Initiative Engagement – Start to Finish and Expand Scope
    • 30. Resource Model Integrated with Data Governance and Management Initiative Departmental BU Extended Partners Integration Competency Center (ICC) Audit Legal Compliance Privacy Risk Management Financial Reporting Corporate IT Key Subject Areas / Lines of Business Governance Steering Committee
      • Practice
        • Policy, Standards and Guidelines
        • Corporate Standards
        • Tools
        • Training
        • Implementation Support
        • Operations
        • KPI Measures
        • Reporting
      • Enterprise Integration Strategy and Development Services
        • Enterprise Architecture
        • Data Integration Services
        • Business Process Improvement
        • Data Warehouse Development
        • Reporting Services
        • IT Security
      Integral to all aspects of practice development, sensible strategy design and execution
    • 31. Phase 5: Conduct an Initiative Engagement Overview of Six Steps
      • Step 1: Establish Practice Development Strategy
      • Step 2: Design End State and Conduct Gap Analysis
      • Step 3: Identify Quick Wins and Design Project Plan
      • Step 4: Establish Resource and Team Model
      • Step 5: Measure and Control Goals
      • Step 6: Transition to Expanded Scope
    • 32. Phase 5: Conduct an Initiative Engagement Step 1: Establish Practice Development Strategy -1
      • To succeed, data governance and management program must include practice development strategy and plan in place
      Existing Practice Areas for Improvement Developmental Goals Management Infrastructure Project silos dominate without organization-wide standards Integrated, reusable architecture; Formalized stewardship People, technology, process misalignment Data Valuation Information classification and controls designed Unified data asset valuation with common vocabulary and classes Valuation incomplete; Stakeholders with different lists and metrics Data Governance Metric Departmental readiness evaluated – quality considered major Institutionalized data governance and management monitoring and tracking No enterprise-wide program formalized Accessibility Auditability Availability Consistency Quality Security
    • 33. Phase 5: Conduct an Initiative Engagement Step 1: Establish Practice Development Strategy - 2
      • Fully understand development needs
        • Identification of key subject and functional areas
        • Individual or group-level educational requirements
      • Design a stewardship development plan
        • Objectives, scope and tasks
        • Identify educational vehicle
      • Create a progressive plan to adapt to changing infrastructure
        • Practice development tasks
      • STEP 1: Checklist
      • Review existing templates and documents to pinpoint deficiencies
      • Identify and interview key affinity groups and business users
      • Identify key business initiatives that will gain benefits when practice is developed
      • Determine what areas of data governance metric improvement provide accelerated value to those initiatives
    • 34. Phase 5: Conduct an Initiative Engagement Step 1: Establish Practice Development Strategy - 3
      • <Example Stewardship Plan> - can take different forms but important to assess existing roles and activities
      Enter here based on interviews Objective: Implementation of guidelines Scope: Business/Functional Level Task: Work performed to the specified requirements Objective: Supervision and operational oversight of policies, standards and guideline enforcement Scope: Program-Level Task: Sustain operational activities and meet guidelines Objective: Top-down, risk-driven value creation Scope: Executive-Level Task: Ensure strategic alignment with corporate goals, focus on enterprise-level. Domain area intervention as needed Data Accountability Standards Developmental Areas Domain Stewards Operational Stewards Strategic Stewards
    • 35. Phase 5: Conduct an Initiative Engagement Step 2: Design End State and Conduct Gap Analysis -1 Example: Focus Area 1 – High Priority Segment Low Integrated handling of structured and unstructured data. Data profiling and quality management Complete, accurate invoice management End-to-end order mgt Days sales outstanding impact Low performing cash flow management Missing invoice and inaccurate description of products and services rendered High Master data Integrated Metadata and Data Quality Management Dynamic packaging of prod/services with differential pricing Inefficiencies in sales promotions Limited understanding of customer profiles Medium to High Enterprise risk framework integrated with data entry and reporting cycles Continuous regular and material event reporting with sufficient evidence Bottom-up examination o f ALL types of financial transactions Risk level assigned without being integrated with financial reporting system High End-to-end integration with access security and full dashboard control Automating preventive measures Used for regular walk-through with auditors. Extensive testing Internal control classification and design in place Investment Req’d Solution to Address Gap End State Business Impact Current State
        • Invoice, billing and credit management
        • Partner/supplier negotiation (merchant/sell-side)
      • Cost
        • Supply chain / inventory management efficiency
        • Sales distribution management
        • Cross-sell / upsell
      • Revenue
        • Pricing
        • Safeguarding of confidential data
        • Automated vs. Manual control
        • Risk level assignment
        • Detective and preventive measure
      • Compliance
        • Internal control design
      Focus Area 1
    • 36. Phase 5: Conduct an Initiative Engagement Step 2: Design End State and Conduct Gap Analysis -2
      • Pragmatically select “Gap” areas can be used as an “Exemplary” case
        • Areas of visible governance issues
        • Combined use of policy and guidelines
        • Characterization of before / after in hours/work impact
      • Test / prototype solutions/suggested changes
        • Small areas that can be tested short term
        • Validate stewardship model
      • Identify areas for elimination or retirement
        • Removal of non-value added activities
      • STEP 2: Checklist
      • Enumerate pain areas for the focus area
      • Complete gap assessment sheet through walkthrough and interviews
      • Examine both tangible and intangible factors impacting the results
      • Identify key affinity groups, supporters and champions who will support the cause
      • Conclude this step with a proposed master plan
    • 37. Phase 5: Conduct an Initiative Engagement Step 3: Identify Quick Wins and Design Project Plan - 1 Operational Initiative Engagement Program Involvement IMPERATIVE - Disciplined Approach to Balancing Strategic Agenda and Tactical Activities. Choose Nature and Degrees of Involvement According to Value Delivery Strategic Domain Operational Domain Strategic Degree Nature Value
    • 38. Phase 5: Conduct an Initiative Engagement Step 3: Identify Quick Wins and Design Project Plan - 2
      • Internal selling of the data governance and management program for ‘Business Value’ delivered
      • Overview of automated, reusable solutions vs. hand-coded alternatives
      • Proof of usability and validity
      • Continued supporting during project lifecycle
      Initiative Lifecycle Process for evaluating new initiatives as well as qualify and stage them in the overall master plan. 2. Overview 1. Early Adopters 4. Proof 3. Demo 5. Project 6. Integrate 7. Control and Monitor Initiative Engagement
    • 39. Phase 5: Conduct an Initiative Engagement Step 3: Identify Quick Wins and Design Project Plan - 3
      • Demonstrate the value through early projects
        • Hours saved, dollars collected, more strategic assignments, etc.
        • Shut down non-value added components
      • Get proof points on validity, applicability and recommended areas for future implementation
        • Anecdotal stories about paybacks
        • Perception-building through active dialogs
      • Position to extend value through an extended pool of resources
        • No major full-headcounts yet! Early adopters and champions to grow the extended team
      • STEP 3: Checklist
      • Conduct initial projects either with policy / guidelines or ideally with add-on solutions
      • Assess the results within the core team
      • Design a pragmatic project plan for 3-6 month cycle with the vision for 2-3 years
      • Conduct small team meetings to refine a plan
      • Seek an approval of a proposed project plan with initial results
    • 40. Phase 5: Conduct an Initiative Engagement Step 4: Establish Resource and Team Model - 1 Integration Competency Center Models Central Services Shared Services Technology Standards Best Practices Benefits Project Silos Project Optimization Leverage knowledge Consistency Resource optimization Control For Initiative Engagement, while investment returns vary by environment, gradual move toward Shared Services may often yield better results Independent Independent Independent Distributed Defined Recommend Distributed Defined Standardized Hybrid Defined Shared Centralized Defined Shared Organization Processes Technology
    • 41.
      • Steering Committee nominate resources to work with team lead and assign stewards
      • Data Stewards perform tasks with team leads
      • As needed, stewards work with team members directly
      • Analysts, SMEs and Metric Experts (HA, security, quality, etc.) work as a team
      • Data Integration provides resources and work with IT strategy and architect team
      Phase 5: Conduct an Initiative Engagement Step 4: Establish Resource and Team Model - 2 Inner working of the data stewardship activities Data Integration Expert (s)/ Resource (s)/ ICC Data Stewards Steering Committee IT Strategy and Architect Team Lead for the Subject Area Team Lead Business Analyst IT/Data Governance Metric Experts Business Subject Matter Expert
    • 42. Phase 5: Conduct an Initiative Engagement Step 4: Establish Resource and Team Model - 3
      • Design a team model and resource plan
        • Emphasis on initiative engagement
        • Previous experience and problem-solving mindset plus
        • Alternative approaches to be presented
      • Provide scenario assessment
        • Pros and cons of specific resource model and requirements
        • Risks and open issues clarified
      • Get endorsement for a small team
        • Secure baseline to demonstrate focus area value
        • Communication and training plan in place
      • STEP 4: Checklist
      • Develop task descriptions and qualification guidelines
      • Informally interview or ask for referrals to identify advocates
      • Look for champions who are both business and technology savvy (all areas of IT)
      • Identify skill gaps
      • Seek approval of a proposed resource plan including skill development
    • 43. Phase 5: Conduct an Initiative Engagement Step 5: Measure and Control Goals
      • Ensure ongoing communication
        • IT investment defined – tangible/intangible
        • Value – revenue, cost, compliance and risk
        • Particular components –worked/worked less
      • Make small incremental changes tuned to business needs
        • Delivery of results and incremental changes reflective of ongoing business changes
        • Positive organizational impact highlighted
      • Get support for developmental areas
        • Reinforcement for people, process and technology
        • Communication and training plan in place
      • STEP 5: Checklist
      • Get updated on businesses about their current directions
      • Verify whether the current data governance initiatives are generating intended results
      • Clearly document root cause analysis results if the results are less than what you expected
      • Make a call whether you proceed with the current scope or alter – don’t make a huge change – incremental ones only
    • 44. Phase 5: Conduct an Initiative Engagement Step 6: Transition to Expand Scope - 1
      • Perform rigorous assessment on the initiative phase
        • Reassessment on architecture, tools, skill sets, processes, training, and communication
        • Organization dynamics
      • Get departmental/functional buy-ins to expand scope
        • Current major objectives defined
        • Find “small” ways to make a difference
      • Progressively automate with an expanded scope
        • Incremental value add defined – with less risk
        • Preventative, automated measure in place
      • STEP 6: Checklist
      • Use the initiative engagement results as a guide to approach target BU or functional areas
      • Project prospective results ‘what if’ you expanded scope to the next areas
      • Examine all metrics that are to be affected by the expanded scope
      • Revise a project plan with an expanded scope
      • Step up to evaluate and use tools to automate and move preventive
    • 45. Phase 5: Conduct an Initiative Engagement Step 6: Transition to Expand Scope - 2 Governance Steering Committee Audit Legal Compliance Privacy Risk Management Financial Reporting Key Subject Areas / Lines of Business Departmental BU Extended Partners Integration Competency Center (ICC) Corporate IT Program Direction Technology Enablement Operational Areas
      • Select specific areas of implementation
      Realign Implement Assess Go Live Measure
      • Re-alignment
      • Buy-in
      • Resourcing
      • Role augmentation
      • Deployment
      • Training
      • Hand-off
      Implement Assess Go Live Measure Realign
    • 46. Lessons Learned
      • Achieve sponsorship and organizational alignment with a compelling business case quickly
        • Linking the data governance to a major business initiative such as SOX or Basel compliance, or merger consolidation becomes a thrust for executive buy-in and funding approval
      • Utilize supporting tools and methodologies to accelerate approval and implementation cycles
        • Maturity assessment tool and economic value of data framework raise the profile of data governance and management
      • Progressively increase automation to reduce personnel or culturally driven issues, as well as to normalize changes
        • Preventive measures help mitigate cost impact and risks
      • Ensure communications and training to promote a new mindset and vigorous approach toward data
        • Making data “asset” management as part of the DNA – keep it simple and robust
    • 47. Concluding Remarks
    • 48. PowerCenter 8 - Platform for Automating Data Governance and Management Tasks INTERNAL EXTERNAL DATA CONSUMERS DATA SOURCES Infrastructure Services Security, High Availability, Scalability Metadata Services Metadata Repository (Semantic Catalog) Exchange, Data Lineage, Impact Analysis, Data Stewardship Delivery Services Web Services, Messaging, JDBC, ODBC Integration Services Data Profiling, Data Cleansing, Data Transformation, Data Movement, Data Federation Access Services Packaged apps, Mainframe, RDBMS, Msg. Systems, Flat Files (Structured, Unstructured & Semi-structured Data) Tools Admin Tools, Developer Tools, Metadata Tools, Analyst Tools Web Services BI Tools Portals Applications Applications Processes Applications Databases Messages Flat Files XML Unstructured Data Mainframe JMS Web Svc SQL JDBC WebSvc
    • 49. Harnessing the Power of Data through an Automated Approach
      • Exploiting Data Management Technology for Business Performance
        • Take a unified approach to data integration
        • Ensure data standards as the cornerstone of an effective data governance and management program
        • Institutionalize your data
          • Applications come and go, but the data largely stays the same
          • Data governance and management decisions you make today will have profound impact on your business
    • 50. Q&A Open Discussions