In the second in a series of five articles from digital transformation guru Chris Argent, we explore the roles available in a modern finance team working with sophisticated analytics.
Most people see analytics as for the few, not the many. Accountants and finance professionals, therefore, believe analytics roles are for highly trained experts in mathematics, statistics and programming.
But that’s only one role in analytics. It’s a team sport and there is a role for you.
To understand how we can fit in to this new world of analytics, it is important to understand the end to end process of delivering Analytics, where we can add the most value, and where we are likely to need support from those scientists.
The Analytics lifecycle
What’s the problem? Discovery, context and observation
The first thing to consider is suggesting questions that analytics can answer based on the business problem you or your managers have encountered.
It is important to take a holistic view of the problems. Think about the impact on the stakeholders to ensure the outcomes have the highest value to the business.
During this phase, the team will consider the existing resources, skills and technology to ensure they can progress the project once the big questions have been agreed on.
When the team have agreed on the what, why and how, they build a hypothesis (to be proved or disproved) that will help to answer the business question. This also helps frame the analytics for the data scientists and to agree what a successful outcome would be.
Why you should learn about analytics
“Plenty of people out there can read spreadsheets. But not many can interpret data. Most don’t know even the most basic techniques for doing so. Interpreting will be a huge part of accounting”.
Heather Darnell, founder, Ask the BOSS (accountancy practice)
What data do we have? Data understanding and preparation
This phase is often a pregnant pause while the technical aspects of the business models, required results and data availability is worked on.
Huge amounts of data, often from multiple sources and in various formats, need to be aggregated, transformed and loaded into your analytics platform.
What model do we need? Model planning and execution
A model is a set of constructs and rules that are used to break down the big question or hypothesis. It is then used to create the analytics output.
This is led by data scientists, but it is important to include all stakeholders when building the framework for the model, to agree the methods and techniques that will be used to answer the business problem.
At this stage, business context and knowledge is very important to ensure the model performs well, and creates a strong result.
It is likely at this stage that any data availability issues are resolved, as the importance of creating the analytics becomes clearer and the impact on the business becomes more tangible.
What did we find out? Share and test (pilot)
After the model collates the data and it is visualised for better understanding, the data scientists will share their findings for review.
This is a critical phase for accountants. You will need to understand how well the model has performed, assess the findings and decide if the new data answers your question.
You are also likely to be the person communicating the results to the business. Not only is this critical to the project success and to you personally, but it is a critical part of developing interest in becoming a data-driven company, helping leaders and teams get excited by an analytics-led approach.
Once approved, good practice would be to summarise lessons learned from the project, measure the original success criteria and add any suggestions to create a continuous improvement plan.
Use data to look ahead
“Traditionally, accountants are reactive; they wait a year to tell clients how they did. That’s shifting. If people don’t adapt quickly, they’ll go out of business. The boring, traditional accounting firms are already suffering”
Paul Pritchard, founder, Abacus Accountancy
What next? Apply findings to the business
So far the data has been operating in a private, prototype environment. Now the model can be used – deployed into the live, main production system.
Sometimes projects are a one off, and success is simply by delivering the prototype. But if there is ongoing value from using the model in the business with live data, it will be deployed and used.
The project team will need to document the code, the technical and functional specifications, data flow diagrams, and data architecture models from the prototype environment and hand them over to the ongoing support team.
Once in the live system, you can use live data to create more information and insight, and support better decision making on an ongoing basis.
For more information on Data Analytics, we recommend these articles.
Your role in data analytics
There are several roles that someone from a finance background might perform in a project team. They can be split into four key areas, listed below. There are also technical disciplines (namely data science, and data visualisation), but they are specialised and not covered here.
1. Business Partner
The Business Partner is the one who talks to the business and draws out an understanding of what issues need to be addressed. It requires knowledge and the ability to communicate.
The Business Partner role is ideal for someone with a finance background. As an accounting professional, you will already be aware of the challenges in the business. By talking to stakeholders within the business, you will get a firm sense of what questions need answering, and which questions are hardest to answer. Your mission is then to select the right question to answer with analytics.
Think big and try not to use standard measurements or Key Performance Indicators. Try to find new questions that need answering. Take lots of people for coffee!
2. Domain Expert
The Domain Expert role also requires in-depth knowledge. But instead of facing the business, this role is about supporting technical specialists in the analytics team.
The Domain Expert uses their knowledge to provide answers and context.
Data scientists (those in the technical project roles) are unlikely to be close to the inner workings of the business. So the Domain Expert will work alongside them answering queries and identifying issues with the analytics framework and model being built.
The Domain Expert will have knowledge about what affects the business. For example, sales trends, supply chain realities, even factors such as the weather or holiday patterns.
3. Business Analyst
The Business Analyst is another role with detailed business knowledge, this time from a process point of view. The Business Analyst needs to know how data gets collected and stored within each process of the organisation.
This knowledge will be vital to the technical team members. Obtaining the right data, creating relationships and correlations, and joining and linking data are critical to data modelling. The Business Analyst will be a practical ally to project technicians, helping them to achieve these things.
Building a great model is a fundamental step for a successful project. It should be a team activity and will bring huge value to the findings. This is critical to the communication phase and the overall success of the project.
4. Analytics Champion
The Analytics Champion is the person who influences the business to participate in the analytics project. It requires seniority and credibility to mobilise the project and key players. They are the face of the analytics project within the business, a true advocate of data, analytics and cultural change.
Analytics projects are only as good as the findings and its ultimate impact on the business. The champion’s role is critical in building and maintaining support. They need to communicate the achievements and needs of the project team clearly so the rest of the business understands.
The Analytics Champion leads change. They need the ability to see the big picture and take an overview to make sure that projects do not slow or stop.
Different combinations of these roles
The four functions above may be performed various ways on different projects.
On a small project, one individual might handle several of these. Alternatively, on a large project there may be many individuals in one discipline such as Business Analyst.
Some of the common considerations include understanding the required business changes that need to happen during and after the project; identifying key stakeholders; helping to mitigate resistance to and change; communicate your vision to create buy-in, and learning more about design thinking and agile ways of working, so outcomes are user-focused and built in a workable way.
With as much as 50% of the analytics being about discovery, modelling and communication, not to mention the change management required, accountants and finance professionals have a big role to play in driving it forward.
Success with data is a fine balance between technology, data, people, and processes. It’s very much about being curious, collaborative, empathetic and excited about the future, as well as having the courage to change.
Analytics Model is a mathematical equation that describes relationships among variables in a historical data set. The equation either estimates or classifies data values, which can then be used to forecast new future data.
Design Thinking is a methodology used to solve complex problems and find desirable solutions. A design mindset is not problem-focused but solution focused and action oriented towards creating a preferred future for a user or customer.
Agile methodology is an approach to software development under which requirements and solutions evolve through the collaborative effort of self-organizing and cross-functional teams and their customer/end user.
This article was written by Chris Argent and was first published in AAT Comment. It forms the second article in a series of five, access them all here:
Data Analytics Series
- Data analytics – 1 – here’s what you need to know to get started
- Data analytics – 3 – visualisation techniques to bring your data to life
- Data analytics – 4 – how data science and machine learning fit in
- Data analytics – 5 – how to communicate your insights
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