Fueling the data-driven enterprise: Eight critical elements

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The term “data rich, information poor” refers to organizations that have yet to transform their data into actionable insights. Their attempts to become data-driven are often stymied by ambiguity surrounding the project, along with a lack of awareness of the people, process and technology considerations that are vital to the success of these initiatives.

To become data-driven, organizations must provide decision-makers with timely access to clean, consistent, reliable, and actionable information, while avoiding the myriad challenges that lie in the way. With that in mind, let’s look at eight critical elements that underpin a successful, long-term information management strategy—and how organizations can implement them to become truly data-driven.

1. Governance
It’s essential that any enterprise information management initiative have strong C-Suite sponsorship. Effective governance programs, however, must also include representatives from all areas of the business. A blended team of clinical, IT and business stakeholders is critical to establishing priorities, obtaining funding, eliminating roadblocks and monitoring the initiative’s progress.

2. Data governance
To make accurate business decisions, management must have access to clean, consistent and reliable information. Data governance addresses these requirements by defining the roles, responsibilities and business processes needed to ensure data quality. It also establishes definitions and formulas for the organization’s Key Performance Indicators (KPIs).

Data governance programs have historically been led by IT, but to be successful, they need to be spearheaded by the business leaders who are ultimately accountable for one or more categories of enterprise-wide reference data—a.k.a master data. Fr the data governance initiative to run smoothly, these director-level data owners need to be supported by subject matter experts—a.k.a data stewards—from their respective business units.

3. Master data management (MDM)
Closely intertwined with data governance, MDM is a set of tools and associated methodologies that foster the integration and maintenance of master data, and it’s a best practice for companies to develop an MDM strategy in conjunction with their data governance initiative. Failing to do so, significantly increase the chances that the information shared throughout the organization with be inaccurate or incomplete.

4. Metadata management
This discipline helps companies achieve a more holistic understanding of their data by providing easy access to data definitions, formulas and other essential details. Metadata management solutions identify all the tables, reports, dashboards and other elements that may be affected by a change to the database from which their data is drawn. These tools allow organizations to conduct an impact analysis prior to making any changes. They also facilitate data lineage—a visual depiction of the data’s origin and any changes that have been made to it. These and other capabilities create a high degree of data transparency and can greatly improve the user’s confidence in the final information.

5. Business intelligence (BI)
BI is the most widely known information management discipline—and arguably offers the greatest ROI. BI tools let users drill down into subsets of information, conduct ad hoc queries, run predictive modeling and provide a variety of other functions essential to becoming a truly data-driven enterprise. While BI’s benefits are evident, many businesses struggle with this technology. A key challenge hindering more widespread BI adoption is that many organizations have a poor information management platform—a.k.a. data architecture.

6. Data architecture
The journey from raw data to actionable analytics is full of twists and turns. To help them navigate, companies should adopt a three-tiered data architecture, with each layer designed to meet specific objectives.

The first layer serves as a “landing area,” onto which the raw data from disparate systems is extracted. The second is a “conformance layer,” into which this raw data is integrated. The third and final tier is the “analytic layer,” where the data is transformed into a usable format suitable for self-service analytics and other BI initiatives.

7. Data acquisition
To populate the three layers of their data architecture, companies need data acquisition tools—a.k.a Extraction/Transformation/Load tools, or ETL for short. Also needed is a comprehensive strategy to detect changes in the source system data. Organizations that fail to develop a “change data capture” strategy will find it difficult to maintain their historical data.

8. Technical architecture
What should all these tools, programs and architectures run on? A robust technical architecture composed of dedicated, properly configured, high-performance servers.

Taken together, these eight elements allow companies to make the most of their information. Unfortunately, it’s quite common for organizations to struggle with some, or even all of these, due to challenges with the people, processes, and technologies involved. Obtaining a better understanding of each element and how it fits into a comprehensive approach to information management provides a strong foundation for a truly data-driven enterprise.

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