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Data in Audit - 3 sets of software and 3 considerations when selecting software

February 6, 2019

If you work in risk or assurance i.e., internal audit or performance audit, there are 3 types of data/analytics software that your team should have access to, at a minimum.

Of course, there's a lot more to analytics than just software e.g., objectives, hypotheses, process understanding, but software enables the outcome.

The minimum set of analytics software for your team

1. Data Cleansing:

  • Data are rarely clean or in the format you need for your analysis.
  • Newer, automated software makes the cleansing process easier e.g., leveraging AI to help with the cleansing.
  • There are open source options; for basic needs, there are Excel add-ins like ASAP Utilities.

2. Data Analysis (can be the same tool as the one used for cleansing):

  • You can still write code, if you really need to, but do you want to invest in that?
  • Point and click options (workflows rather than code) are becoming quite popular.
  • There are open source options for both workflow style software and for coding.

3. Data Visualisation (some also handle cleansing and analysis):

  • To better understand, explore, interact with and present results.
  • There are quite a few mature tools available.
  • There are open source options, but most have high learning curves (for now).

If you are considering external advice

You may look for advice regarding software selection. If you do this, make sure to ask:

1. Does this align with my corporate technology stack/architecture and existing licensing?

  • Don't upset the tech team. You want to do the right thing, and not lose your tech support.
  • There is no point in buying if you already have licenses for a good alternative.
  • It can be difficult to implement software if it does not fit into the existing architecture.

2. Have you been given all the information you need to evaluate the recommendation?

  • We recently saw a report that recommended two sets of software.
    • For the visualisation software, an analyst's magic quadrant was included. The high ranking software options were considered in the report and the recommended option was rated highly.
    • For the analysis software, the magic quadrant was not included. The high ranking software options were not considered in the report and the recommended software was not rated highly. It turns out that the recommended software vendor provides good commission rates to their partners.

3. Will your team use the software?

  • Does the capability exist or can it be acquired reasonably easily?
  • Does the team want to use that software. If they're objecting, is the objection reasonable?
  • Is the software too simple or too advanced for the intended use cases?

As a leader, how are you equipping your team to use your data to better serve your customers?

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