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3 tips on how to stop misusing or under-utilizing corporate data

The importance of data management in virtually every organization today cannot be overstated. Managers depend on data for all aspects of their business – from executive level strategic vision and strategy to tactical risk assessments.

But few organizations have impactfully assessed how their data can be put to work in the most productive way. This leaves organizations vulnerable to inefficiencies and can prevent important information from making its way “to the top.”

A 2019 survey by NewVantage Partners shows that companies continue to invest heavily in big data and artificial intelligence (AI), but “they are not seeing commensurate results. Though 62 percent report measurable results from their big data and AI investments, less than half say they are competing on data and analytics (48 percent), have created a data-driven organization (31 percent), or have forged a data culture (28 percent).”

One reason for this ongoing struggle could be rather simple – failure to properly “visualize” the data. An organization may have credible data in its systems, but its managers may not have the ability to utilize that data and enable impactful visualization techniques. Data on a screen or in a spreadsheet is insufficient unless it is put into context that can be used to yield optimum results.

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Here are some tips on how an organization can avoid misusing or under-utilizing its data:

  • Use data in an established framework.
  • Build intuitive and thoughtful data models in your organization’s databases that consider the necessary relationships that need to be established. This model can enable a manager to establish a clear vision for how integrated data can be impactfully visualized. For example, establish thoughtful risk categorization and hierarchies which can help organize tactical risk statements and data into more enterprise-level views. This type of data model allows an organization to begin clearly aggregating tactical risk results into meaningful categories which can help normalize risk results.
  • Establish a transparent governance and organization structure. An effective risk management program needs to establish clear and intentional relationships that organize an enterprise by top-level business units, functional areas, products or services and, processes.

Too often, organizations assess risk only at the business unit or departmental level without a goal of normalizing or aggregating risk results. Conducting risk assessments in organizational silos should be considered a practice of the past.

The “best practice” in enterprise risk management is a top-down, holistic approach. Build the organization structure and technology on the front end, then make the process scalable and repeatable across the various divisions of the enterprise. Enable the alignment of standard classifications across the enterprise to deliver consistent and repeatable risk management processes, meaningful data aggregation, dashboarding, and reporting downstream.

Risk categorization enables an enterprise to, among many things, aggregate its risk information and data for dashboarding and reporting at various levels of the risk framework. Controls and control activities should exist in direct purview of an organization’s existing risks. Categorizing controls in an organized taxonomy can greatly enhance an enterprise’s view of its control environment.

By organizing and creating a detailed control inventory, an organization can align its data risks to its overall business risks. This functionality will allow an enterprise to document control activities and facilitate meaningful context for various operational risk management solutions (e.g., risk assessment, control testing, etc.).

Data is not cheap. The proper management of data should be a key concern for every risk manager. With the correct data visualization posture, managers can only reduce the risks or data misuse but help increase the productivity of the organization a whole.

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