Beyond Desktop Databases

We recently spoke with a experienced auditor  about how his organization has made analytics its top audit-related priority.  This is a very reasonable decision given that work can be automatically executed, documented, and continuously recycled as a “continuous audit” procedure.  The efficiencies are quite appealing to auditors who perform much of their re-work on a quarterly/annual cycle.  Ironically, my colleague noted their primary tool is Microsoft Access (R), which is capable of none of the most valuable benefits.  Let’s examine… Access (R) is limited in how it cannot natively:

  • Produce sharable analytic procedures to import, clean, and transform data;
  • Automatically execute analytic procedures to perform work without human action;
  • Produce logs of multiple processing steps that serve as audit documentation; and
  • Restrict access to private information.

So where is an inspiring analytic auditor to start?

sql

What tools can an experienced decision scientist recommend developing analysts?  We feel SQL is a valuable first step in any analytics career.  SQL (Structured Query Language) is so pervasive that the International Organization for Standardization (ISO) has codified it.   In today’s digitalized world with massive amounts of data being gathered every day and stored into a database, knowing how to query and program with SQL is the most useful tool we can imagine for an analytic auditor.  Lots of people use it, so it’s a transferrable skill.  Furthermore, SQL solutions are strong in many performance areas that are key to analytic auditing, including:

  • Connect to multiple SQL data sources, which is a popular platform for operational data;
  • Produce scripts that perform multiple processing actions and can be shared among different individuals and retained as audit documentation;
  • Provide for access controls to databases, tables, and individual records.

There are multiple “flavors” of SQL, it is used by Microsoft SQL, Oracle, MySql, Amazon’s Redshift, and many many other popular platforms.  Each of these solutions uses a slightly different version of the SQL language because each product has custom functions they have developed to differentiate their products.   But the good news is, these functions are not necessary to perform all of the basic steps in the analytic process.  If you’re organization uses a type of SQL, then we suggest you begin using it and almost all of the skills you learn will be transferrable to the other solutions!  The most important decision is the decision to begin using SQL if you are pursuing a career in analytics.  Learning is not supposed to be comfortable, so just get started! To help you on this journey, we’ve compiled some useful resources:

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Should You Trust Analytics III: Analytics Process

Lack of trust in source data is a common concern with data analytic solutions. A friend of mine is a product manager for a large software company that uses analytics for insights into product sales. He told me the first thing executives and managers do when new analytic products are released in his NYSE-traded, multi-billion dollar  company is…  manually recalculate key metrics.   Why would a busy manager or executive spend valuable time opening up a spreadsheet to recalculate a metric? Because he or she has been burned before by unreliable calculations.

I’ve been exploring the subject of unreliable data since a recent survey  of CEOs revealed that only 1/3 trust their data analytics.   I have also been studying for an exam next week to earn a Certified Analytics Professional designation  to formalize my knowledge on the subject.  While studying each step in the analytics process on INFORMS’ analytic process, the sponsoring organization for the Certified Analytics Professional exam, I’ve considered how things could go wrong and result in an unreliable outcome.  In the flavor of Lean process improvement (an area I specialized earlier in my career), I pulled those potential pitfalls together in a fishbone diagram:

Analytic Errors Fishbone

Continue reading “Should You Trust Analytics III: Analytics Process”

Quit Sampling!

This post may disturb some “old school” auditors.  In fact, used to be a self described old school auditor.  If I couldn’t find a work paper in the permanent file, someone was going to get an earful about their ability to keep reliable documentation.  But the business sector has evolved at a frightening rate since those days.  Surprisingly, many audit professionals t-teststill consider sampling and testing to be their go-to procedure.

I’m not knocking the old student’s t-test.  It’s still the right tool for some situations.  But it’s been harder and harder for me to find those situations in recent years.  Most of the subjects I audit now can easily be scrutinized using data mining, or even scripted into an automated monitoring report.  Continue reading “Quit Sampling!”

Journal of Accountancy on Analytics

While trying to get this blog to appear higher in internet searches, I ran into an interesting article on the Journal of Accountancy discussing Analytic Auditing.  The article is written from an external audit perspective and focuses on two benefits.  First analytics provides auditors with greater insights into their clients’ business that help to quickly get up to speed on the external audit customer’s business model.  Also the article mentions how analytics provide better service to Continuous Auditing Processclients.

The article also states a position that I’ve had since learning analytics as an external auditor… that external auditors’ use of analytics lags far behind that of internal auditors.  I think a key reason for this is access to and familiarity with data.  As an external auditor it took several weeks for me to gain access to a new client system. Once the client granted my access, I didn’t have much time to pull something useful together.  Rarely did my projects have more than one or two models.  As an internal auditor, I’ve had the same difficulties getting initial access to the system.  But once granted access I can continue to develop models as long as my results are useful to the organization.  On certain projects/systems this period lasted for several years and allowed for deep exploration and understanding.

To me one of the most impactful sentences from this article is “The profession [external auditing] needs to achieve a “quantum leap” to redesign audit processes using today’s technology, rather than using information technology to computerize legacy audit plans and procedures. ”

See more at: http://www.journalofaccountancy.com/issues/2015/apr/data-analytics-for-auditors.html#sthash.QvfD7Lqt.dpuf”