Enabling the Data-Driven Organization

Obtain Actionable Business Insights with Enterprise Information Management

The term “data rich, information poor” can be used to describe organizations that have not established the people, processes, and technologies to transform their vast amount of data into actionable business insights. While these companies want to become “data-driven”—defined by CTG as using dashboard-enabled performance management techniques to improve business insights and information transparency—this vision is often not clearly defined, or there is an inherent assumption that the requisite people, process, and technology components have already been established.

Becoming data-driven requires providing decision-makers with timely access to clean, consistent, reliable, and most importantly, actionable information. Simply put, these companies are fully committed to the management of information as an invaluable strategic corporate asset. By arming leadership with this insight, organizations can make timely, proactive decisions regarding access to care, costs of care, and quality of care—thus fostering greater stability and growth while better serving their communities.

In our experience, eight people, process, and technology components must be established in order for organizations to become truly data-driven. These eight components are collectively known as Enterprise Information Management, or EIM.

Survey Results Indicate Healthcare Organizations Struggle with EIM

In May 2017, CTG sponsored a Data Governance survey conducted by HealthSystemCIO.com. Seventy-six percent of the survey’s respondents identified themselves as C-Suite executives. This white paper outlines key findings from the survey, and provides an introduction to the eight EIM disciplines: Governance, Data Governance, Master Data Management, Data Architecture, Data Acquisition, Technical Architecture, Metadata Management, and Business Intelligence.

Governance

Governance includes C-suite sponsorship of the organization’s EIM initiatives with support from a blended team of business and IT stakeholders, who establish priorities, obtain funding, eliminate roadblocks, and monitor implementation progress.

• 62% of survey respondents feel they have strong C-Suite sponsorship.
• 90% of these organizations have established a blended team of clinical, IT and business stakeholders.

Our survey results indicate a majority of organizations recognize the importance of executive sponsorship and effective oversight of information management initiatives by information stakeholders from across the enterprise. This is an encouraging finding as I have never seen a successful EIM implementation that did not have effective governance in my 20 years in the industry.

Data Governance

Data Governance includes the roles, responsibilities, and business processes required to ensure greater accountability for information quality and more consistent definitions and business rules for effective information management. In my opinion, no other EIM discipline is as vitally important. Without clean, consistent, and reliable information, how can leadership make sound business decisions? However, our survey results indicate that this remains a challenge for many
organizations.

• 52% of respondents have no formal Data Governance program.
• Of the 48% of organizations who have implemented Data Governance programs:
  • 33% believed their program was highly mature.
  • 45% displayed a high level of confidence in the program’s reporting capabilities.

Not only do these responses demonstrate that over half of the participating organizations have not yet implemented Data Governance, but of those who have, only one third feel their program has reached maturity. More importantly, almost half of those who have implemented Data Governance are still not highly confident in their reporting capabilities. The survey results shed some insights on the root causes of this lack of confidence.

Nearly 70 percent of the established Data Governance programs are led by IT. While IT clearly plays an important role, best practice Data Governance initiatives are led by business leaders or “Data Owners.” These individuals have ultimate accountability for one or more types of enterprise wide reference data, or Master Data. Additionally, Data Owners collaboratively manage the definitions and business rules for Operational Data (e.g., Key Performance Indicators
(KPIs) and other business metrics). It is important to note that, as Data Owners are often Director-level individuals or even Vice Presidents, they require significant support from Business Data Stewards—subject matter experts with a solid background in a particular business area.

Mature Data Governance programs not only have established roles and responsibilities, but they have implemented viable workflows to:

1. Proactively Identify Issues: Data quality issues are often identified “upstream” when an executive or stakeholder questions the information contained in a report or dashboard. Extensive effort is then required to identify the root cause of the issue, only to find that data was entered incorrectly or that it was transformed in some way using invalid business rules. An effective Data Governance program includes data profiling tools and associated workflows to spot data quality issues before they become visible and costly. However, 55 percent of survey respondents indicated
   that they do not utilize data profiling tools to proactively identify data quality issues in source systems.
2. Reactively Resolve Issues: Many organizations have weak business processes in place to remediate data quality issues. Far too often, the IT department is held accountable, when the actual causes of the problem are poorly defined business rules, inconsistent data definitions, or undocumented and unapproved workflows. Data Governance ensures that well-documented workflows are established, and business stakeholders are held responsible for data quality with support from IT.
3. Enforce Standards: Many data quality issues are caused by lack of uniform data definitions, algorithms, and business rules. Data Governance establishes roles and responsibilities to ensure consistent standards, which helps improve this invaluable “metadata.” This also gives end users much greater confidence in the information they use to make business decisions.

Master Data Management (MDM)

Data Governance is tightly coupled with the EIM discipline known as Master Data Management (MDM). Data Stewards must be able to easily maintain the Master Data domains for which they are responsible. However, only 41 percent of organizations we surveyed have developed an MDM strategy. MDM is a class of tools and associated methodologies that foster the integration and maintenance of Master Data.

While a comprehensive discussion of MDM tools is beyond the scope of this whitepaper, I must stress the importance of developing an MDM strategy in conjunction with your Data Governance initiative. Many organizations fail to do this, and thus, put their Data Governance program at a higher risk of failure.

Metadata Management

Metadata is often defined as “data about data,” although I personally don’t find this definition very helpful. When I discuss the concept of metadata with clients, I often ask questions such as:

• Do you have concerns about the definition of a KPI or other business-defined measures when you’re looking at an executive dashboard or a report?
• Do you ever question the formula that was used to calculate the KPI or measure, or wonder about the data’s origin?
• Do you trust the quality of your KPIs? Would it be helpful to have each flagged as “high quality,” “somewhat suspect,” or “use at your own risk?”

Metadata Management solutions help address the underlying issues within these questions by providing easy access to data definitions, formulas, and other pertinent details about the information.

More importantly, Metadata Management solutions provide impact analysis capabilities that identify all components, such as database tables, reports, or dashboards, that can be affected by a potential database change. An even more valuable feature offered by these solutions is data lineage, which provides a graphical depiction of where the data came from and what changes were made to it before it appeared on the executive dashboard or report. This transparency can dramatically improve user confidence in the final presented information.

Based on our survey results, it is apparent that a significant majority of organizations are struggling with Metadata Management. Only 24 percent feel they have easy access to approved KPI definitions and business rules and are able to perform impact analysis and data lineage reporting.

Business Intelligence

Business Intelligence (BI) is the most visible EIM discipline—and arguably the most important—to information stakeholders and consumers. The wide variety of products used for executive dashboards, drill-down analytics, ad hoc queries, predictive modeling, and data mining are collectively known as BI tools. Although it is not possible to become a truly data-driven organization without utilizing BI solutions, our survey revealed that a high percentage of organizations face challenges in this important arena:

• 55% have not yet implemented executive dashboards with drill-down capability.
• 59% have not yet established self-service analytics.

In my experience, a poor information management platform—or Data Architecture—is a common challenge preventing organizations from implementing executive dashboards and providing users with self-service analytics capabilities.

Data Architecture

CTG recommends a three-tiered EIM Data Architecture with each layer designed and modeled to meet specific objectives. Data from disparate source systems is first extracted as is into a “Landing Area,” then integrated into a “Conformance Layer” such as an Enterprise Data Warehouse, and finally transformed into a more usable format in the “Analytic Layer.” This is the primary “end-user-facing” layer of the EIM Data Architecture. As the data in the Conformance Layer is essentially still in its “raw” form, it must be significantly altered to make it easily consumable for use in executive dashboards or drill-down analytics.

Data Acquisition and Technical Architecture

Data Acquisition tools (also known as Extraction/Transformation/Load or ETL tools) are required to populate the Data Architecture outlined above. A sound Technical Architecture composed of dedicated, properly configured servers is also required to meet the ever-increasing demand for high performance and reliability.

Summary

Timely, reliable and actionable information is the lifeblood of a successful organization. However, many companies face challenges implementing the people, processes and technologies required to become truly data-driven. EIM’s overarching goals are to establish executive sponsorship, engage information stakeholders, ensure data quality and consistency, transform raw data from across the organization into useful information, and foster better decision making.

The eight EIM disciplines outlined above collectively provide a sound platform to manage information as a strategic corporate asset and achieve these goals.