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«The Challenge IT Executives are challenged with issues around data, compliancy, regulation and making confident decisions on their business ...»

-- [ Page 1 ] --

Whitepaper

Data Governance Roadmap for IT Executives

Valeh Nazemoff

Acolyst, Delivering Information Management Solutions

The Challenge

IT Executives are challenged with issues around data, compliancy, regulation and making confident decisions on

their business performance based on the information they receive. Data is the key asset of an organization and

the lifeblood. How does an executive guarantee that the data they need will be reliable, accessible, accurate,

complete, and secure? Executives receive vast amounts of data on reports, but do not know how to instantly translate them to their relevant needs.

Data is important because executives need to address strategic objectives to portray what needs to be accomplished, what performance measures will be used to measure progress against targets, what strategic initiatives have been identified to make actionable and operational, and what method will be used to articulate their strategy and communicate strategic objectives to both employees and internal and external stakeholders.

Do executives then have instant financial and non-financial information about the organization at their fingertips? Do they know how they are currently measuring against targeted goals? Of all the measures received, do they know why the organization has chosen those specific measures? Do they have a dynamic illustration that can simultaneously represent the historical performance, future outlook, and negotiated priorities across departments consistent with the business?

What are the metrics that are important to the business, setting the right targets, and demonstrating and reporting IT’s contribution to the business? IT Executives often ponder on how they can demonstrate the value to the business. Many IT organizations have a wide range of metrics that they measure with executives receiving various reports, but these reports are not presented in a summary fashion that is most relevant to the executive’s need to be able to communicate to their customers the service performance provided and how well they are meeting their Service Level Agreements (SLAs). Another driving need is being able to communicate back to the customers the spending of planned versus actual, value-added services provided, reporting achievements, and methods to drive improvement.

The key is to capture the right data and to present it in business-relevant terms. Businesses usually do not need reports detailing server-uptime or how long it took for help desk to respond to their call. They want reports that show how IT is contributing towards their objectives. The problem is how to communicate and translate something technical into a meaningful and useful language for all parties to understand and relate to.

Data presented in a dynamic data visualization format (Figure 1 shown below) that can be drilled up and down in order to discover relationship and conduct root-cause analysis would resolve the many concerns in addition to addressing the governance of data.

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Figure 1 – Hospital Facility Revenue Scorecard Image - Courtesy of Microstrategy In this paper we will cover the Importance of Data Governance, the Key Drivers to Data Governance, and Acolyst’s Methodology and Roadmap for Implementing Data Governance.

The Data Governance Solution Following recent trends, we see more and more business users defining the applications they need to conduct day to day activities, the demand for speed and instant access to data, movement to the cloud, and planning for the next big event, causes IT Executives to set Data Governance framework in place.

Data Governance is the overall management of the availability, usability, integrity, quality, consistency, and security of the data employed in an enterprise. It helps organizations meet compliancy with legislative laws, regulations, and mandates such as Sarbanes-Oxley (SOX) Act, HIPAA, and the HITECH Act.

Data Governance is a component of Enterprise Data Management, providing and enforcing enterprise-wide data standards, common vocabulary, reports, and the development and use of standardized data and processes which also helps organizations to improve regulatory compliance. Several approaches are used to organize Enterprise Data Management efforts such as Business Intelligence (BI), Data Security & Privacy, Master Data Management (MDM), and Data Quality Management (DQM).

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Figure 2 – Enterprise Data Management Importance of Data Governance There are real world issues that arise due to the fact that a Data Governance framework was not set in place.

These are issues that can deal with life and death scenario. Looking at healthcare organizations, can a provider be able to view trend in test results for a new drug that has been launched in the marketplace? What about the ability to have instant access to information relating to the patient diagnosis and medical history?

An effective Data Governance framework can help organizations manage data more efficiently. It provides consistent definitions, and measures and tracks the quality of transactional and analytical data used across the organization. It enables organizations to more easily integrate, synchronize and consolidate data from different departments and to exchange data in a common format allowing for faster decision to occur.





Furthermore, it coordinates communication between businesses and IT and provides insight into the data across the business applications through shared terms and report format. Businesses are able to coordinate activities due to standardized processes and access to enterprise-wide data, causing improved business intelligence reporting.

Costs are often reduced as well with the implementation of an effective and efficient Data Governance framework. Savings are achieved by reducing the number of IT applications and systems and standardizing the ones that remain. Replicated data stores throughout the organization are eliminated, and data cleansing costs

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are reduced as a result of data quality and data integration. Data quality also causes capacity to improve due to proficient reporting and analytical capabilities.

Key Drivers to Data Governance As identified by David Waddington in an article published in Information Management Magazine (Sep/Oct 2010), titled Data Governance, MDM and Data Quality: Information Difference Research Searches How Organizations Tackle Data1, we see that there are 9 (nine) core business drivers for IT Executives to implement Data

Governance:

1. To Support BI/Data Warehousing Initiatives

2. To Support an MDM Initiative

3. To Facilitate the Migration of Legacy Data

4. To Meet Compliance and Legislative Requirements

5. To Reduce Corporate Risk

6. To Improve Corporate Flexibility and Business Agility

7. To Support Operational Software Upgrades (e.g. ERP, CRM, etc.)

8. To Reduce Costs

9. To Support Handling of Mergers and Acquisitions When looking further into the key drivers, we can see that when implementing Data Governance to accomplish a specific goal, it automatically overlaps with other initiatives that can be accomplished within the organization. For example, if a healthcare organization needed to meet compliance and legislative requirements (4th Driver Listed), this would mean that to establish Data Governance, they would need to migrate data from legacy systems into new systems and formats (3rd Driver Listed).

Acolyst’s Methodology and Roadmap for Implementing Data Governance There are 6 (six) components of Data Governance that would need to be considered and addressed for implementation.

The six components of a Data Governance framework are:

1. Organization

2. Policies, Principles & Standards

3. Processes, Practices & Architecture

4. Investigation & Monitoring

5. Gap Analysis

6. Tools & Technology Waddington, David (Sep/Oct 2010) Data Governance, MDM and Data Quality: Information Difference Research Searches How Organizations Tackle Data: Information Management Magazine, http://www.informationmanagement.com/issues/20_5/data_governance_mdm_and_data_quality-10018763-1.html Acolyst, Delivering Information Management Solutions

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Figure 3 - Six Components of a Data Governance Framework Organization To implement a Data Governance program, the organization needs to make sure that representative participation and commitment from both IT and lines of business exist, there is senior level executive sponsorship from both, and active consulting practices to drive and champion Data Governance implementation are conducted. Within IT, a dedicated active owner of data assets across the enterprise needs to be established.

We call this person, Lead Data Officer (LDO).

The LDO is tasked with:

 Setting and authoring the direction of the Data Governance initiative, aligning business and IT goals, and managing organization data as a strategic asset.

 Driving business priorities and compliance with regulatory mandates.

 Defining roles and responsibilities for data owners.

 Creating data policies, procedures and standards for the organization as a whole.

 Directing how the data should be used, managed, and monitored across the organization.

Policies, Principles, and Standards A policy must be developed for enforcing data standards and governance procedures that specifies who is responsible and accountable for various segments and aspects of the data, including its accuracy, accessibility, consistency, completeness, and updating.

Acolyst, Delivering Information Management Solutions

For example, a healthcare organization with several applications might capture the date and time of a procedure in a specified field, where another system allows for the system user to enter the date and time within the notes section of the system. This results in inconsistency, risk of missing data, and data integration issues that would need to be assessed.

Setting policy demonstrates the importance and value of the data within the organization. Data is the most important asset in an organization and without standards and quality, the organization does not function effectively and productively.

Figure 4 – Five Core Data Decision Domains There are 5 (five) core data decision domains that set the policies, principles and standards of data, as shown in Figure 4. Data Principles establishes the direction for all decisions and sets the organization’s standards for Data Quality, which is how data is interpreted (Metadata) and accessed (Data Access) by users. Data Lifecycle are the decisions that define the production, retention and retirement of data.

Processes, Practices, and Architecture

Processes must be established and formalized to guide principles for how policies, processes, and standards are created, collected, modified, implemented, and distributed across the organization. Without formalizing the process, IT constantly finds itself demonstrating their value add to the business lines. This is due to the issue that IT departments do not set formal Service Level Agreements (SLAs) with business units since they are within the same organization and other internal concerns that might be on the horizon. Setting formal processes and practices continues to identify and document how the organization manages its data. They define how the data is “to be” stored, archived, backed up, and protected. Practice and procedures are also instituted to ensure compliance and government regulations and audits are met.

Data Governance processes and practices helps organizations face challenges of enterprise level data integration concerns and includes enterprise standardization for data and systems. Figure 5 shows the importance of the Data Governance program structure to meet these challenges.

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Figure 5: Data Governance Program Structure The Enterprise Data Model Standard shown in Figure 5 is best known as the Data Architecture. Data Architecture addresses how the data is to be organized, integrated and includes enterprise data standards, data models, data flow diagrams, mapping spreadsheets, data definitions, and a metadata dictionary, in addition to security and privacy measures.



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