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?


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 II: Data Provenance

The process of turning data into information to present it in a simple manner can be incredibly complex.  I believe this irony is primarily because most available data is not formatted for analysis.  Building a large, custom data set with the exact list of features you desire to analyze (Design of Experiments) can be very expensive.  If you have pockets as deep as big Pharma or are ready to dedicate years to a PhD, it’s definitely a great way to go.

Our last blog on trusting data analytics explored how the industry practice of “data cleaning” can spoil the reliability of an entire analysis.  But problems can also occur with perfect, clean, complete, and reliable data.  In this post we will explore the topic of data provenance and how the complexities of data storage can sabotage your data analytics.

Data Provenance 2

The truth is… business data is structured and formatted for business operations and efficient storage.  Observations are usually:

  • Recorded when it is convenient to do so, resulting in time increments that may not represent the events we actually want to measure;
  • Structured efficiently for databases to store and recall, resulting in information on real world events being shattered across multiple tables and systems; and
  • Described according to the IT departments’ naming conventions, resulting in the need to translate each coded observation;

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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

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Should You Trust Analytics?

Just one-third of 400 surveyed CEOs responded that they trust their data analytics according to KPMG’s 2016 CEO Outlook article.  This is an astonishingly low rate that the decision science industry should take as a wake-up call to shine light on their processes.  Auditors should also take note because most of these CEOs are from companies that have invested heavily in data analytics.

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