Modernizing the Global Audit Machine for the Future

Audit is broken, obviously in need of modernization. Accountants are being overwhelmed with the volume, pace, and complexity of information.  Global multinational organizations using hundreds of spreadsheets to fulfil and manage their external reporting process. The status quo of financial accounting, reporting, auditing, and analysis is doomed.  But the question of what will be the replacement is still on the table.  While the status quo is certainly going away; it is still unclear what the replacement will be. But the flywheel is moving.

A first step in the process of modernizing the global audit machine for the future is to provide the fundamental machinery that will be used by that global audit machine. Is the Excel spreadsheet the right machinery? What are the workable alternatives? Maybe logical spreadsheets. Here is some additional information to help you contemplate the modernized capabilities: (think compilation, review, audit working papers)

Figuring out the right machinery can be challenging because you have different vendors in a marketplace that that are very certain that their specific offering, the tools that they sell, are always the best for you and you will never have any CONs that go with the PROs of their offering.

You want your system to actually work.  The starting point in understanding your needs is to clearly understand the definition of "work".  A good definition seems to be that the system fulfills the goals and objectives that you need fulfilled by the system that you need to build.  Another thing you will what is the new system to be better, faster, and/or cheaper than any existing system that you have.

Considering all that, what is the best way to modernize the machinery of the global audit machine?

My analysis of the different alternatives available for building this new machinery for the global audit machine can be grouped into three problem solving system categories: the Semantic Web Stack, Graph Databases, and Logic Programming.

Which is the best approach?  Well, it may not really matter because enterprises tend to like to use different technology stacks so the reality is that multiple technology stacks are a fact of life and different enterprises will use different approaches.  Each of the approaches has a basket of PROs and CONs and the probability that you could get every enterprise to agree on only one approach is ZERO.

But you still have to pick your technology stack. Which will you and your organization pick?

This series of articles related to Graph Fundamentals is a great history lesson.  The author describes the article, "this is neither marketing nor is it a tutorial — it’s a warts and all description of their strengths and weaknesses — stuff I have learned over 20 years of pain and suffering, in the hope that I can help somebody else out to avoid having to learn these lessons the hard way."  The articles help one navigate the current contest between those promoting "The Semantic Web Stack", "Graph Databases", and "Logic Programming" approaches to satisfying the goals and objectives of the stakeholders of some system:

  1. Part 1: RDF Database
  2. Part 2: Labeled Property Graphs
  3. Part 3: Graph Schema Languages
  4. Part 4: Linked Data
  5. Why Graph Databases will Win
  6. The Semantic Web is Dead – Long Live the Semantic Web!
If you read the articles, you might have the question, "Whatever happened to relational databases?" Relational databases are excellent tools for the right task.  Excel spreadsheets are also an excellent tool.  But you want to use the right tool for a job.





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