Business leadership is increasingly demanding that management accounting professionals traverse the mass of internal and external data to help drive improvements to enterprise value. As a result, management accountants must adapt and evolve in reporting, business intelligence, and data science (see Figure 1). To that end, best-of-breed management accountants of the future will need a blend of domain, technical, and analytical capabilities.


It’s common for organizations to invest in technology without achieving desired outcomes. Minimizing this risk requires certain prerequisites to be in place before embarking on a complex technology initiative. One of these prerequisites is centered on human capital—having the right domain or subject matter experts who are capable of designing a solution that achieves key project goals. Make no mistake, business intelligence and data science tools are critical to the success of an organization, but they’re vehicles that need to be driven by people with the right skill sets. People and strategy grow an organization from descriptive to diagnostic and finally to the competitive advantage of predictive analytics.

In practice, the intelligent integration of domain knowledge, technical ability, and analytical skills creates an environment that supports successful analytics projects and moves a company forward in its analytics maturity. These three core skill sets or pillars are what set the stage for success.


Domain knowledge, initially the most important skill set, is made up of knowledge of the industry, the typical metrics used to measure success, and the overall objectives of the analytics efforts.

A domain knowledge expert will ask broad questions, such as:

  • What value is being created for the business (profitable revenue growth, operational efficiency, etc.)?
  • How is value being created (product or geographical expansion, organic sales acceleration, strategic pricing changes, etc.)?
  • What is the value proposition for customers (internal or external)? How does it change their experience with a business and/or technology?
  • Are we providing information about the benefits of our goods so that more people can be aware of them (marketing effectiveness)?

A broader perspective is imperative for successful analytics initiatives and must be part of any effective requirements or solution design process. Unfortunately, it’s in this area that companies often fall short. Domain expertise is a prerequisite for successful project engagement.


The next pillar is technical ability. This is where tools come into play. Technical ability isn’t just about knowing one specific tool or another. It’s about knowing which tool, relative to business goals, provides the best fit. Goals may vary from business to business based on their needs from a cost, functionality, and/or security perspective, just to name a few.

If a desired outcome can be achieved using standard programming languages and skills, then it may be more economical to leverage open source code when considering options for building out an analytics platform. Thus, it’s recommended that both internally developed open source solutions and commercial third-party options should be considered.

So, again, technical ability is about knowing what the best tool is for the job and how to use it. First, there are data platforms. Data platforms may be as simple as a shared hard drive or a database to organize and relate data, such as Microsoft SQL server, Postgres, or MongoDB. Data platforms may also be complex, such as the different Hadoop frameworks that create a Hadoop distributed file system (HDFS) to store flat files and enable you to access them with any other tool, such as Hive or H base.

Next are your data interface tools, which allow you to access the data platform and do anything from data cleansing to neural network modeling. Data interface tools are typically data-focused programming or scripting languages for custom solutions and extract, transform, and load (ETL) processes and integration applications for more industry-standard use cases. Languages include Spark, Scala, Python, Pig, R, or any of the SQL languages. Examples of integration tools are Boomi, Business Works, SSIS, and Informatica.

Finally, the data needs to be represented to the end user with the application layer, the tool by which your solution is distributed to the larger organization. These can be prebuilt platforms like Spotfire, Tableau, SiSense, or Click View, or they can be the one-stop-shop type of tools like the SSRS, SQL deployment layer, or the whole .net stack. They may even be build-your-own tools. An example of this might be using Python to not only interface with the data but also to create a data application. The same can be said for Scala and Java.

Since data science and analytics can be quick and agile, there’s generally no need to be limited to one specific platform or tool.


Finally, we get into mathematics and statistics with the analytic skill sets. This is where the analytic solution is “architected,” meaning not necessarily why we’re doing it (that’s up to the domain experts) but how we’re going to do it. In short, the analytic skill set encompasses the framework for acquiring the data and how that data’s shaped, from data ingestion to data modeling and finally on to the representation of that data.

Understanding how that data needs to be represented—the shape or appearance of the data—is different from what we use to represent that data. The what is a technical question. It’s often the case that an analyst will be able to represent some information in a certain way, using a certain tool, but that might not be the best tool for the job.

What’s important is that an analyst isn’t stuck trying to figure out how to use that tool. That analyst can say, “I want this represented in this manner. Here I’ve done it in R, but I understand that R may not be the most enterprise-deployment-friendly application. But I’m not going to spend the next six weeks learning a business intelligence tool. I’m going to talk to my technical liaison, who will determine the best platform, based on his or her knowledge and skill set, and have that analysis deployed in a fraction of the time, with the amount of rigor and double checks on the data required.”

A project that’s focused on only one specific tool simply because that tool is available or because someone’s used it before limits the options and the resource pool. Having three separate specializations—domain, technical, and analytical—in your analytics road map allows for all the necessary skill sets to be covered and increases the probability that a project will succeed.

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