5 Differences Between Static Data Warehouse Design & Agile Data Governance

Healthcare organizations have enterprise-wide data sets of which data governance can be time-consuming and labor intensive. Yet, healthcare needs to move towards the 21st century in terms of data capabilities so data can be utilized to inform research, trace population health and increase patient outcomes.

According to a 2011 HIMSS survey, only 58 percent of health care organizations utilize business intelligence tools to help with data governance, and 75 percent of organizations still need more IT resources. Hospitals and health systems have options for data governance, they just need to identify data needs and determine the best option.

One significant strategy for governing hospital data is a data warehouse. A data warehouse is beneficial if a hospital wants to merge individual databases to a central location for robust reporting and analysis. Data sets from a hospital or health system's electronic medical records, special disease registries and radiology reports reside in a data warehouse with a pre-defined warehouse dictionary, and data sets can be pulled from here and analyzed to inform decision making.

Marshall Maglothin, principal at Blue Oak Consulting and senior vice president of the healthcare sector for Phasic Systems, estimates that 75 percent of large health systems have tried some form of data warehousing over the past 10 years, with many of those efforts being for large, comprehensive warehouses. "To be successful, a data warehouse has to be specific, highly governed and closely managed," says Mr. Maglothin.

Data warehouses that have a narrow purpose, such as specific research protocol, are easier to limit and govern. When a hospital needs a data warehouse for its cost accounting system, the purpose is less defined, the data comes from every aspect of the hospital, and the data warehouse maintenance can become costly, time consuming and quickly dated.

Data governance should make data more accessible and controllable but that is not always the case. According to Mr. Maglothin, data warehouses are no longer the best solution. "A static data warehouse would work if health care data was static, but it is not," Mr. Maglothin says. "Data is only going to continue to be overwhelming; [hospitals] need to face the issue now with state-of-the-art technology."

What is state-of-the-art technology for data governance? According to Mr. Maglothin, it is agile data governance software. "Agile data governance has robust capabilities to manage data and make an accurate data warehouse much more feasible and useful" says Mr. Maglothin.

Agile data governance software is different from traditional resource-intensive data warehouse design in a several key ways.

1.    Navigation. Agile data governance software allows data to remain where it originated. Navigation may be easier because data codes do not need translation to fit into a warehouse. "An agile solution would be more like TurboTax that would access data bases which remain separate, and then produce the tax return needed at that time. To do next year's tax return, one would not need to maintain consistency of the data warehouse for the entire year. With agile governance, one could run TurboTax next year and let it again retrieve the data from the separate databases it needs. Agile data governance would perform similarly for clinical research protocols and other requests," says Mr. Maglothin. In comparison, a static data warehouse for personal financials would take all your income, bank, and tax info and put it in a much larger database where it would be stored until needed, with data only being updated using static definitions for each element.

2.    Capabilities. Agile data governance is learning-capable software. It uses an algorithm to understand data meanings based on their various origins. Agile governance software could recognize different meanings, learn the codes and apply that information later. A static data warehouse requires the construction of a very specific data dictionary. Rules are needed to instruct how to handle the merging of data sets, says Mr. Maglothin. For example, merging an EMR with a patient’s name consisting of five separate name data fields and an EMR from another department or specialty with one big name field would present difficulties in static data warehouse construction.

3.    Privacy. Since an agile data governance system allows data to remain in its original location, privacy may not be a huge issue because hackers may not be able to access all the data locations. A static data warehouse may be more vulnerable to hackers because one password and username could potentially break into the whole system, says Mr. Maglothin.  

4.    Time commitment. As mentioned above, an agile data governance system uses an algorithm to learn data meanings. In comparison, for a static data warehouse IT teams need to build a data dictionary and extensive merger tables/spreadsheets. "Building a data dictionary and the data warehouse architecture could take 18-48 months and still not deliver accurate and timely reports," says Mr. Maglothin. "In contrast, an agile data governance solution could be up and running in 3-6 months."

5.   Cost. Many hospitals want to transfer data and increase its accessibility, yet budgets cannot support these needs. Agile data governance may be an option that fits the budget. "On estimate, static data warehouses could run a hospital from $1 million–5 million of capital expense, while construction using agile data governance may cost $200k-$500k," says Mr. Maglothin. "Most organizations already have data management tools in place and possibly some warehouse architecture; agile governance intelligently uses the hospital’s existing tools to produce productive data warehousing." Depending on the hospitals needs and abilities, the benefits of the data governance software with its speed in managing data and its usability, could create a high return for hospitals looking to utilize their rapidly expanding data resources.

In order to face the onslaught of healthcare data, hospitals and health systems need to integrate sound strategies for data governance. Looking outside of health care for examples of data management success can be informative. For example, Phasic Systems handled a data governance project for the U.S. Navy using agile data governance software. "The U.S. Navy worked three years to build a data warehouse for one million service members' human resource records and could not complete it. Their original objectives were accomplished by the fresh approach of agile data governance in three months," says Mr. Maglothin.

The emergence of these new data governance tools have now made possible overall agile data warehousing. Agile warehousing uses additional new technologies adapted for corporate environments, such as NoSQL, a broad class of database management systems that differ from classic models, to further reduce the overall time and cost associated with data warehouse projects.  
“Healthcare now has both the challenge and potential of improving clinical outcomes and reducing costs,” said Mr. Maglothin; “whether we succeed may well depend on efficiently mining our data rather than becoming buried by it.”

Read more about Phasic Systems.

Related Articles on Data Governance:

9 Leading Hospital, Health System CIOs Share Top Goals
The Future of Healthcare: 9 Capabilities for Post-Reform Success
AHIMA Names 7 Initiatives to Focus on in 2012

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