From the tax preparer who provides guidance that will help ensure their clients are in the most advantageous position next tax season, to the bookkeeper who advises on expected cash flows, and everywhere in between, everywhere you look today, data drives insights and decisions.
But the concept isn’t a new one for accountants. Descriptive and diagnostic analytics ranging from inventory availability to variance analysis have been performed since Luca Pacioli created double-entry accounting. Long before user-friendly dashboards existed, we used Excel spreadsheets and charts. And before Excel? We did it by hand.
Thankfully modern accountants can leverage advanced technologies to perform analysis at scale and speed, dramatically increasing the amount of accounting and non-accounting data available to shape our analysis.
Our biggest challenge today with accounting data analytics isn’t a lack of data; it’s how quickly the sheer amount of data available can become overwhelming. As the utilization of big data in accounting continues to grow and more and more data becomes available for analysis, it’s becoming a challenge to determine which data is relevant, let alone leverage the data to make more informed decisions.
So how do you find and separate the relevant data? You need to know your audience, what you’re trying to accomplish and utilize technology to prevent information overload.
Identify Your Audience
Why does the audience matter? Because while the data doesn’t change, the story that the data tells may change from person to person. That’s not to say that the data tells one stakeholder the sky is blue and another stakeholder that the sky is yellow. But different stakeholders may have very different questions. Knowing who will be asking questions is just as important as the question itself.
Let’s consider time and billing data. The most common application of this data is to determine utilization. Staff and seniors would be interested in the details of how they compare to their peers, such as details about where they are exceeding expectations (or falling behind). Managers probably don’t want quite that level of detail, preferring a summary view that highlights only those who fall outside the first or second standard deviation.
However, managers and partners may be interested in learning more about staff efficiency and would benefit from an analysis of which staff are most effective at various types of engagements
Even if the audience is just you, it’s important to identify that. All too often when we are the only audience the question becomes secondary, and analysis becomes the purpose – which is not the best use of your time.
Know What you’re Trying to Accomplish
To perform an effective analysis, you need to have a question, purpose, or objective. Data analysis for the sake of analysis, while fun, isn’t productive. A poorly constructed question can lead to costly and time-intensive data reviews that don’t accomplish anything.
Before diving into the data, determine what you’re trying to discover. What is the goal of this analysis? What decision-making will it facilitate? What outcome would be considered a success or a failure? Remember, data analytics results will only be as good as the questions you ask – when preparing your questions, also consider factors such as your audience, strategic goals and budget.
If you’re struggling with understanding what questions to ask, start broad – even if it’s just “
For example, let’s say that your goal is to increase profits. Driving data analysis with the question, “how do we increase profits” may not result in useful results. A better place to start would be to ask, “where are there opportunities to increase capacity with my existing staff?” or, “what engagements were the most profitable last quarter, and how can we replicate that success?”
With a clear understanding of what you are trying to accomplish, the analysis is more focused, and it’s easier to determine which data is relevant.
Implement Automation to Prevent Information Overload
Consider the data that accounting firms and tax preparation businesses track, often without thinking about it.
There’s internal data, from time tracking and how clients are served to practice management data such as billing, collections, and business development. There’s also client data, information about the client that is collected during the engagement process. And there’s data that’s a mix of the two – client and prospect interactions with internal content such as emails, webinars, websites, and social media.
Technology has allowed us to collect the data listed above and so much more. Technology has also allowed us to perform our data analysis faster and at a much larger scale. But there are downsides to all advances – and for accounting data analytics, information overload is one of them.
Technology created the problem of too much data. It can also help us find the relevant data. Advanced technologies such as machine learning and AI can automate the base data analysis, giving structure to unstructured data and providing accountants with the most relevant information. With automation sifting through the available data to identify information relevant to the question and the audience, we gain back capacity to focus on other things.
And with that additional capacity, we can perform higher-level data analysis, find the answer to the question, and understand how to shape the answer for the intended audience.
Complex data manipulation and analysis is a critical part of any business strategy, regardless of firm or practice size. Knowing which data is relevant – and having the tools to assist in that determination – is even more critical. With the right questions, an understanding of who the audience is, and automation to help perform base analysis at scale and speed, accountants can more easily guide their clients – and their business – towards success.
In Wolters Kluwer’s annual industry report white paper, over 800 tax, auditing, and accounting professionals told us how they are leveraging people, processes, and technologies to go beyond limits and drive growth.
