In this special guest feature, University Notre Dame Professor Scott Nestler, contends that the quality of the data is often dependent on who exactly is gathering and analyzing the information. In the end, if you have someone not only best trained, but more to the point specifically trained in consistent methodologies, you will likely find the results to be more reliable, and therefore trustworthy. Scott Nestler, PhD, CAP, PStat, is an associate teaching professor in the Department of Information Technology, Analytics and Operations (ITAO), Mendoza College of Business, at the University of Notre Dame. He previously served as the chairman and vice chairman of the Analytics Certification Board and was a lead author of the Code of Ethics for the Certified Analytics Professional program. Nestler has a Ph.D. in management science from the University of Maryland – College Park, a master’s in strategic studies from the Army War College, a M.S. in applied mathematics from the Naval Postgraduate School, and a B.S. in civil engineering from Lehigh University.
Now, more than ever, CEOs are relying on an abundance of data and analytics to make critical management decisions, but according to a recent survey from KPMG, a significant number of chief executives may not have confidence in the data they receive.
KPMG’s recent survey of 400 U.S. chief executive officers discovered that only 33 percent (one-third ) of participants have a high level of trust in the accuracy of their data and analytics. In addition, 77 percent of those surveyed are concerned about the quality of the data that is the basis of their decision making process.
While there could be many reasons for this crisis in confidence, it is likely that inconsistency is a major contributor. No matter how much an organization invests in the collection and analysis of data and analytics, if the systems and processes along with the individuals charged with management of those processes, are not working together towards consistent results the entire effort can suffer credibility issues in the corner office.
One of the simplest and most effective solutions to this problem is making sure that the people responsible for the management of data and analytics in the organization are following the highest standards of their profession, meeting quality and consistency expectations, as well as ethical standards.
Further, when a CEO receives data from an individual who has the “seal of approval,” if you will, of an objective third party it is more likely that the CEO will trust and act upon that data. That is part of the thinking behind the Certified Analytics Professional (CAP®) program, www.certifiedanalytics.org.
Why Make Sure Analytics Professionals are Certified?
Standards by definition emphasize quality and consistency. But standards must be maintained and enforced, and the role of a respected neutral third-party certifying body is invaluable.
At the employee level, having a CAP demonstrates a commitment to the field and an interest in skills maintenance and enhancement. Further having a CAP demonstrates that an individual has reached the level of knowledge of the analytics process that subject matter experts have determined is essential in order to hold this credential.
At the employer level, having employees with CAP credentials shows that they, too, recognize the standard of practice for analytics professionals. Further, it shows an interest in the employee when employers support the CAP and its requirement for continued professional development and skills maintenance. When employees are encouraged to stay abreast of the latest advancements and currently accepted approaches in the data and analytics field, it leads to implementation of current and state-of-the-art approaches to data cultivation and curation that, in the end, produce analytics that are consistent and reliable.
The CAP combines education, experience and effectiveness with a rigorous exam and unique analytics code of ethics to guarantee that everyone with a CAP certification is held to the highest standards in the analytics field.
The CAP program – which is managed by the Institute for Operations Research and the Management Sciences (INFORMS) – is the premier global professional certification for analytics practitioners. Following a job task analysis conducted by a group of internationally selected subject matter experts, seven unique analytics areas of practice were identified upon which the program is built. CAP ensures that even after individuals have earned certification, they are required to continue to build on their experience and education to maintain their certification. The program is beneficial for analytics practitioners at every stage of their career, even for new professionals with entry level experience who can take advantage of the Associate CAP program. The proven advantage of a CAP certification is already recognized by leading Fortune 100 companies, with 20 percent employing at least one analytics professional with a CAP certification.
Recognition of the CAP program is not confined to the United States. Analytics professionals with a CAP certification are now employed by more than 200 different companies in more than 20 countries worldwide.
When an analytics professional is certified by the CAP program, employers know and trust the quality of the data that this individual will be able to produce. They know they are investing in individuals who are committed to continuing to develop their abilities, improve their accuracy and stand apart in the analytics field.
As the data and analytics field continues to play an increasingly significant and visible role in how organizations function, it is essential that those organizations make sure that their analytics teams are held to a higher and consistent standard.
When those same organizations are provided with data from a professional that adheres to those standards, and that data can be consistently translated into positive, identifiable results – be it increased efficiency or reduced expenses – this will enable those in leadership roles to develop a greater trust and reliance on the data.
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