The Knowledge Exchange / Business Analytics / More examples of how industry challenges drive information management

More examples of how industry challenges drive information management

A look at financial services, healthcare and government

David Loshin, President of Knowledge Integrity, Inc.

In the previous post, we reviewed how the current state of telecommunications and energy/utilities is shaping information management adoption. These are just two industries that are evolving to meet an expanding portfolio of information. Let’s examine a few more.

Financial services
In the banking and financial services industry, the aftermath of the recent credit crisis has led to increased concern about accuracy in assessing and managing risk. A number of governmental agencies as well as international oversight authorities have drafted regulations intended to ensure that financial institutions are properly capitalized to guard against excessive risks. Most of these regulations and guidelines are tightly coupled with data delivery and information management. For example:

  • Dodd-Frank – In the United States, the Dodd-Frank Wall Street Reform and Consumer Protection Act empower new government research agencies to request data from banking institutions to ensure financial stability.
  • Solvency II – These insurance industry regulations empower a supervisory authority to request information which can comprise “qualitative or quantitative elements,” “historic, current or prospective elements,” as well as “data from internal or external sources.” This information “must reflect the nature, scale and complexity of the business,” “must be accessible, complete in all material respects, comparable and consistent over time,” and “must be relevant, reliable and comprehensible.” (see the Solvency II site for more details)
  • Basel III – These international banking accords establish a standard for bank capital adequacy, stress testing and market liquidity risk. Basel III accords rely on the availability of accurate information for analytical calculations of capitalization requirements intended to improve risk management and provide enhanced predictability of financial stability.

Healthcare and Life Sciences
After the US Supreme Court upheld the Affordable Care Act in 2012, issues arose for Medicare and Medicaid. This act impacts the information management expectations for government, health care providers, payers, as well as pharmaceutical companies and medical device manufacturers. Some examples include:

  • Health Information Exchange (HIE) – The desire to migrate towards electronic health records requires the creation of HIEs that enable the electronic exchange of healthcare information within a region or among a community of participants.
  • Dual-Eligibles – Both state and federal government agencies are charged with aligning the care provided to “dual-eligibles,” or individuals who are covered by both Medicare and Medicaid programs. Reducing duplicated provision of service of payments requires an effective way of managing and merging data from multiple systems.
  • Physician Sunshine Reporting – Increased scrutiny of pharmaceutical incentives to healthcare providers has led to increased requirements for reporting and analysis, both by the reporting companies and the agencies collecting the data. Accurate and complete reports require broad data visibility across multiple business functions and systems. At the same time, government agencies aggregating reported exchanges of value to providers must be able to uniquely identify providers as well as link provider records from across the multitude of reports filed by the pharmaceutical and medical device companies.

Aside from the examples we have already seen related to government information management, there are additional drivers within government to devote increased attention to improved information management. Some examples include:

  • Management and retention – Agencies may collect significant amounts of information over time, requiring the implementation of data archiving and retention policies.
  • Cross-agency integration – Cooperation among agencies for providing social services, assessing government debt obligations, and analyzing security threats all suggests the benefits of establishing methods for efficient and high quality data exchanges.
  • Transparency – Directives for transparency of government operations has driven the creation of data sets for public consumption. The processes for creating and publishing these data sets require data accessibility, data integration, and organization.

In my next post, we will drill down into the requirements for information management and consider the implications for establishing the tools and techniques that can help organizations satisfy their enterprise data demands.

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