Technology

Financial Executives Are Going All in on Data


As there is reliance on data every day, it is imperative that the foundations of quality, security, management and overall governance for these key data assets are in place. Here are the key considerations of these three priorities.

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A recent survey of senior-level financial executives reveals that data is top of mind for 2020. The top three priorities were: security and privacy of data in finance applications, enhanced data analytics, and data analytics process improvement. Protiviti’s 2019 Finance Survey Report was sent to top finance executives at both public and private organizations with revenue from $100 million to $20 billion or greater. The results were shared at the December 2019 FEI Committee on Finance and Information Technology meeting.

As there is reliance on data every day, it is imperative that the foundations of quality, security, management and overall governance for these key data assets are in place. Here are the key considerations of these three priorities:

Security and Privacy of Data in Finance Applications

  • Which data is most sensitive?
  • Who has to have access to what for their job?
  • What regulatory requirements do I have for both privacy & security?
  • Who manages the risks?

Enhanced Data Analytics

  • Am I asking the right questions from my data assets?
  • What are the skills required?
  • Who should maintain the tools and infrastructure for our analytics?
  • How do I assure quality on analytics?

Process Improvement:   Process and Data Analytics

  • How can I benchmark the effectiveness of current processes?
  • Where do I have training needs?
  • Where is there revenue leakage?
  • How do I measure effectiveness of process changes?

Security and privacy of data requires a focus on multiple areas of controls, including many of those listed below. The majority of these are classic data governance.

  • Risk Management and Regulatory Reporting: Addresses regulators’ increased focus of data quality and control procedures and on the availability of accurate, timely and reliable information for reporting.
  • Data Privacy and Protection: Enables the identification of all instances of employee and customer data and who has access to sensitive data.
  • Master Data Management: Ensures proper ownership and controls for management of the full data lifecycle.
  • Data Lineage and Traceability: Understand systems that are leveraging data, maintaining it, and transferring it throughout the lifecycle of the data.
  • Regulatory Compliance: Establishes the rigorous data standards, policies, and processes that are required by regulators and ensure accountability for and auditability of data.
  • Data Dictionary/Metadata Management: Understanding of your data assets, where they reside and what the business and technical definitions may be in order to help classify them for security and privacy.
  • Data Ownership and Stewardship: Affirmative data ownership and stewardship managing the proper and appropriate us of data assets.
  • Data Classification: Understanding how sensitive information may be, who should have access and why.

Enhanced data analytics priority can transform a flood of data into meaningful information.   It facilitates process efficiency, measurement and profiling and answers important business questions.  It can also increase insights into performance and data needs by:

  • Providing true error rates rather than error estimates for processes
  • Highlighting trends and factors that may not be noticed through conventional reviews
  • Identifying interesting subsets for identifying new unseen relationships and trends
  • Providing management with new insights for decision making

Enhanced data analytics can also increase productivity and efficiency and deliver value-added suggestions and/or provide ongoing analytics tools to management.

Process and analytics process improvement can be realized though process mining tools which can fundamentally change the way that we can analyze processes.  These tools automate the walkthrough process and the data tells us what is actually happening in a process versus what we think is happening.   Analytics support risk assessment activities – identifying “hot spot” areas, for focus.  Data mining tools also provide quantification for observations (# transaction, $ of transaction, time to process, etc.)