Picture this: A group of analysts from an external firm is held-up in one of your board rooms. They’ll be there for the next three weeks. They’re running a standard financial audit on your numbers.
Chances are both parties want this financial equivalent of a colonoscopy over quickly, and with as little discomfort as possible. So, no, you shouldn’t be interested in helping them visualize your data. No, you aren’t looking for them to give you faster insights. No, they don’t need to help you come up with cost-reduction strategies or make “better business decisions”.
Ultimately what you’re looking to do is give them exactly what they need, without compromising any internal governance controls, and make their process as smooth as possible so that they don’t keep bugging your people for more information.
So, to that end, here’s a six-point checklist of features your analytics infrastructure should include to help get you through the next financial audit unscathed:
Guest accounts can be “view only”, and are becoming more common with reporting software. Many tools, however, lack the ability to stack row-level and column-level security roles in a way that creates a “custom” data mart in only a few seconds. Guest accounts should be tied to an email address that’s external to the organization (i.e., those of the financial auditors) and should expire after a set period of time (the length of the audit). Expiration automatically deactivates the accounts.
The benefit to custom data marts is that they can be saved and reused again for future financial audits.
Data goes through a number of transformations before a final decision is made. It can be reshaped through pivots, unions, and joins. It can be cleaned by removing spelling mistakes and duplication, or consolidating similar values. Data, lastly, will go through some business rules—e.g., if-then-else rules, or a custom calculation.
In many ways data arriving at the boardroom is a lot like evidence arriving at the courtroom; there needs to be a clearly traceable chain of custody. A financial auditor will need to “trace” the data back through that chain of custody to see exactly what rules touched the data and how it was modified. Sometimes they will go all the way back to the original source system.
Because data can change many hands once it leaves the source system (e.g., an ERP) and arrives in a report such as the one the auditor sees, traceability becomes extremely difficult if you’ve built your analytics infrastructure from separate, off-the-shelf components. This means that an auditor will have to bug your data team to help them trace the data from start to finish.
Having self-directed data traceability is one of the promises of the Minimalist Approach to analytics.
We’ve all heard of the “Time Machine” on MacBooks—it’s when your computer takes a backup of itself at a point in time that you can “rollback” to. Many people think of backing up your data in case of disaster recovery, but disaster recovery isn’t the application we’re talking about.
While your analytics infrastructure should backup data in case of disaster recovery, what we’re referring to here is non-volatility, one of the hallmarks of a data warehouse. It’s the ability to look at your business “as of” a certain date in time, even if there’s no disaster to recover from.
Data is always in flux. Systems get swapped out. Schemas change unexpectedly. Application Programming Interfaces (APIs) can break. New rules will be added to replace old ones. Your data infrastructure is a living, breathing organism, which can make the financial audit process that much more complicated. If your infrastructure is set up to enable this “Time Machine” ability out-of-box, you can set a fixed point in time that the financial auditors should view the business (a sort of “as of” date), while the rest of the company can continue with their operations.
Data is very good at answering five questions: Who? What? When? Where? and How much? But it’s notoriously bad at answering Why. For this, you need human intervention.
This is the human part of the insight, the actual analysis portion. These include conversations, emails, and decisions made in meetings that need to be captured somewhere; the right analytics infrastructure will help you capture these decisions in the form of discussion comments or articles. Depending on what your financial auditors request, this can be enormously helpful in retracing the thought processes behind particular decisions.
Similar to capturing institutional knowledge, the same features can be used to collaborate with your financial auditors. While you can certainly do the same over email, doing so directly in the discussions or commentary features of your analytics infrastructure allows you to keep a record for future reference.
The problem with the above features is that they generally aren’t offered out-of-box, or if they are, they aren’t crafted to work together for this purpose. What this means is your data team is tasked, yet again, with building features internally to fit a business-use case. That’s where a Minimalist Approach to analytics comes in. Under this approach, a single, all-in-one system provides features that are crafted to work together to solve exactly these problems. Achieving the vision of this Minimalist Approach is why we built TypeSift.
The problem with the above features is that they generally aren’t offered out-of-box, or at least when they are, they aren’t crafted to work together for this purpose. What this means is your data team is tasked, yet again, with building features internally to fit a business use case. That’s where a Minimalist Approach to analytics comes in. Under this approach, a single, all-in-one system provides features that are crafted to work together to solve exactly these problems. Achieving the vision of this Minimalist Approach is exactly why we built TypeSift.
If you’re worried about an upcoming financial audit disrupting your day-to-day back-office operations, and would like more information on how a Minimalist system like TypeSift can help mitigate that disruption, please contact us.
TypeSift is a Data Engineering & Design Minimalism Firm. Our expertise is decluttering information and solving problems in your data that are holding back your growth. We build software that corrals data and invokes ingenuity with the fewest moving parts
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