Leveraging Information across the Enterprise

By Daren Hubbard, Interim CIO & Associate VP-Computing and IT, Wayne State University
140
224
49

By Daren Hubbard, Interim CIO & Associate VP-Computing and IT, Wayne State University

The data revolution has been fully underway and enterprises are facing multiple challenges on the road to full data maturity. Successful data programs have multiple components in common, including: governance, strategy, common data dictionary and most important for this discussion, extensive data integration.

In order to realize measurable benefits from an enterprise-wide data initiative, extensive data integration is essential. The promise of the ERP SYSTEM was and is that of ease of integration and enhanced relationships between disparate data sets. For example, in higher education we take for granted the ability to relate students’ grades to their financial aid award, their ability to access library resources and current accounts receivables. These relationships are both powerful and foundational and required for holistic constituent support, and yet they would not exist without active and pervasive data integration.

It could be argued that the burgeoning field of predictive analytics is made possible through mature data integration practices and capabilities. It is also important to note that while providing the information and integration is central to a well-functioning data program it is not the end of the story. The ability to leverage integrated data to build a compelling and effective analytics platform is closely coupled with an organizations adherence to sound data management practices and adoption of real time web services API’s to facilitate frequent data transfer and refresh. Maintaining persistent transactional integrity is integral to efficient ETL processing and a core component of cross-functional reporting and analysis.

A core integration strategy should focus on three key ingredients: 1. Leveraging persistent interface technologies; 2. A well-defined and documented map of data elements exchanged in the interface; 3. The ability to capture and collect all relevant data elements for use in longitudinal study, data warehouse or adhoc real time reporting.

Persistent Interface Technologies

The information that flows for your business needs to use a streamlined, high bandwidth infrastructure to power the teams of data analysts working to maximize business value. Design of that infrastructure should be optimized to allow for real time updates and real time integration with other essential data elements. The infrastructure also needs to be easily aggregated and normalized to allow for advanced analytics to be performed. Leveraging API’s that are designed to be efficient methods for maximizing data transfer will result in the creation of efficient systems that natively bridge end user interfaces and back end

Organizations that exercise a focused and disciplined approach to data management and data integration have an opportunity to harness business insights data and meta data structures. This reduces the complexity of the table relationships and reconciliation processes needed to produce meaningful actionable reporting and analytics.

Defining Data Elements

Organizations that are able to communicate across the enterprise with common data definitions and business process definitions are often in a better position to make advances in data-based decision making. While the process of defining and publishing a data dictionary can be arduous, it is a necessary step to ensuring a common framework for consistent data representation and data manipulation. The data dictionary is especially important to data integration services because it defines the superset of data elements that must be exchanged or integrated with any new service whether on premise or SaaS based. Where appropriate, the data dictionary can serve as the basis for a layered training and support mechanism, further extending an organizations decision making capabilities by providing consistent information to all consumers of integrated data including the systems themselves.

Data Collection and Transformation

Successful reporting, analytics or warehousing initiatives begin by defining all the appropriate data sets to the correct environments ensuring frictionless transformation. Once the data has been fully integrated, related and normalized, reporting across multiple dimensions becomes a matter of identifying objectives and executing. The expanded power of multi-dimensional relational data sets are unlocked once that data is systematically collected and transformed for analysis. As a side benefit, articulating the data integration points serves as a foundation for master data management and a valuable tenant of strategic data governance. “In data stewardship and archiving, segregated data and systems are giving way to integrated warehouses and tools.” states Gregory Jackson, IT Policy and Practice Consultant.

Keys to Success

Our modern information-driven economy thrives on the free unrestricted flow of persistent, well defined, transformational data. Organizations that exercise a focused and disciplined approach to data management and data integration have an opportunity to harness business insights. Data integration is the unsung hero of the data driven economy. When enabled, it creates synergies between institutional data elements and well-designed business process that can propel an organization’s strategic goals forward.

Read Also

The Unexpected Virtues of Open Data

Jon Walton, CIO, San Mateo County

A $50B Flash Tsunami is Coming!

Lee Caswell, VP Storage Products, VMware

3 Lessons Learned from the Death of My Company

Byron Sommardahl, CTO and Chief Architect, Acklen Avenue

Enterprise Web Applications: Game- Changer for IT Management

Anthony W. Perrone, VP, Enterprise Technology Solutions, ProNexus, LLC