New skills for the digital era
In the era of big data, digital technologies, such as robotic process automation, artificial intelligence and predictive analytics, mean the finance function can increasingly add business value through greater accuracy, efficiency and strategic insight. To grasp these opportunities, teams need the right mix of digital skill sets and business intelligence.
Many businesses are struggling to assemble the kind of team that can embrace and adapt to the pace of change, largely because of a “digital knowledge gap” in their workforce. Without a core understanding of how to apply and exploit these disruptive technologies for maximum benefit, the finance function will be poorly placed to take advantage of them.
|New digital skill sets will help maximise the potential business benefits of tech innovation in the following areas:
• Automation: RPA and AI will automate core processes creating more effective and efficient (touchless) routines and processes, releasing human potential to focus on more added value services.
• Anticipating risks and opportunities: programming tools will provide forward-looking insight on emerging risks and opportunities for value creation.
• Advice: digital outputs will assist finance teams in building trust-based relationships with stakeholders to solve problems and improve data-based decision making.
Future proofing for success
In an increasingly digitalised world, the success of the finance function depends on combining the right technology with the right talent and skill sets. An organisation’s ability to implement and reap the benefits of corporate digital strategies will be highly dependent on members of the team developing both hard technical and softer data communication and presentation skills.
Many businesses are grappling with the challenge of finding, nurturing, deploying and retaining the right blend of talent to allow finance to thrive in the digital future. This makes it critical for CFOs to act now to identify the skills needed in the short, medium and longer terms, and plug any existing or predicted knowledge gaps by up-skilling existing team members or through wider recruitment policies within and outside the organisation.
Key questions to ask:
- Do your recruitment and training programmes ensure that your workforce is sufficiently agile and skilled to extract the greatest value from digital technologies in the next three, five or 10 years?
- Have you performed a gap analysis to determine existing and potential shortfalls in talent?
- Have you identified the specific variety of roles and associated skill sets required?
- Does your organisation provide the necessary culture, incentives and rewards to attract and develop talent that can successfully exploit digital technologies?
Creating team vision
At a strategic level, finance leaders need to set an overall “vision” for the digital era and put together teams that are able to:
- build strong awareness and understanding of the impact of digital tech on finance;
- articulate the value of digital processes and systems throughout the organisation;
- develop a strong foundation of digital fluency to support transformational change; and
- inspire and reward curiosity about digital applications and related opportunities.
To achieve these goals, CFOs will increasingly have to think outside the box and look for a multitude of skills. With greater automation, they also need to consider how individuals and machines interact and complement each other.
Finding the purple people
Alongside adding robust digital skills to their existing expertise, finance teams will have to become more diverse, creative, flexible and collaborative.
Finance specialists will work hand in hand with business analysts and data scientists to interpret data, address challenges, solve problems, and identify business risks and opportunities.
|Download: Data skills framework
ICAEW has written this framework to help organisations and individuals map out the data science skills that they need to take advantage of the opportunities offered by big data.
While most finance and accounting specialists will not need to become fully-fledged data scientists, it is imperative they understand the key concepts of data science, can talk to data scientists within their teams in their own language, and ultimately bridge what otherwise risks becoming a “digital divide”.
“Purple people” is a term pioneered by business intelligence and analytics expert Wayne Eckerson. He argues that these are the team members who are able to combine “red” tech-speak with “blue” business-speak, translating between data scientists and decision makers.
The so-called purple people will become increasingly valuable. Through blending relevant business and technology skills, they can help to articulate business requirements, identify data needs and work with technical specialists to produce workable systems. Although tech specialists may write the code, develop the algorithms and shape the models, the systems need input from the purple people to achieve their business goals.
Core skills for the digital future
When building teams fit for the digital future, CFOs must take a long-term approach that adapts easily to the quirks of tech innovation. In the next three to five years, some of the key data science skills likely to be most important for finance professionals range from data intuition and communication to programming and statistics.
Data intuition skills are about identifying and understanding what is important and significant from the numerous outputs. They cover:
- how to apply and interpret data to solve priority business challenges;
- asking the right questions of the data at the right time; and
- developing subject expertise in relevant areas to bring in critical thinking and scepticism.
Programming tools and languages allow users to access, retrieve, query and present data. Spreadsheet tools such as Excel and SQL are at the most basic end of the skill set, while knowledge of programming languages, such as Python and SAS, are becoming increasingly important.
Data communication and visualisation are critical skills for supporting and making robust data-driven decisions. Those in the finance function must be able to describe their conclusions to technical and non-technical colleagues. Useful tools to be familiar with include matplotlib and ggplot, as well as Tableau, Excel and PowerBI.
A basic understanding of statistical methods and tests (and when to use them) is a pre-requisite for accurate data interpretation. Key areas include: A/B test analysis (testing of two sample hypotheses), sampling and the difference between descriptive and inferential statistics.
Data wrangling – or cleaning, blending and transforming data – is another core skill, particularly where organisations are at a relatively early stage in their digital journey and are faced with unstructured data from many diverse sources. Python, Power Query and relational databases (SQL) are key tools to aid wrangling.
Business partnering – If there is one word to describe "business partnering", it’s relationships. To continue to be a valued business partner, at the heart of shaping and delivering the analytics agenda, finance professionals will need to intermediate and build trusted relationships between the wider business and data scientists. This will include communicating with clarity, challenging by instigating breakthrough conversations and bringing together cross-functional teams to support data-driven decision making.
Beyond these core competencies, emerging skills for finance data analysts could include a deeper understanding of mathematics, such as linear algebra and calculus. These will help with appreciating statistical methods and understanding the basic concepts behind machine learning.
In the longer term, analysts may want to add a greater knowledge of machine learning to their repertoire, as well as other simple software skills, such as code testing and debugging, version control (Git) and Hadoop or Spark (languages for working with data at scale). The finance team will not be required to develop new machine learning algorithms, for example, but should be familiar with the most common algorithms and how and when best to apply them.
This content is an extract from: Tim Leung's webinar: Visualising finance transformation in a digital world and from ICAEW and Deloitte's eLearning module: Work.