A-E - Data Analytics ( 1 )
One of the most striking findings in PwC’s 22nd Annual Global CEO Survey is that the ‘information gap’ — the gap between the data CEOs need and what...
Closing the skills gap
in data analytics
Using data in the interests of the business,
its customers and its stakeholders
Setting the context:
the big issues
with Big Data
Danger, skills gap ahead!
One of the most striking findings in PwC’s 22nd Annual Global CEO Survey is that the ‘information gap’ — the gap between the data CEOs need and what they actually get — is just as wide in 2019 as it was 10 years ago. This is despite massive advances in the range of data available and the significant levels of investment being made in data analytics solutions.
Adaptability and lifelong learning
This gap has three key causes: a shortage of analytical talent, difficulties in accessing the data that’s held in silos across the business and
the reliability (or otherwise) of how that data
As digital technologies continue to demand new skills and make old ones redundant, there’s a very real danger that employers, educators, public policy-makers and employees might all expect one another to close the widening skills gap. The biggest danger relates to those skills needed to implement and exploit Big Data. And somebody needs to take responsibility.
Employers are rightly concerned about this shortage of analytics talent. But they seem more inclined to recruit experienced hires than to develop their own people. To future-proof their careers, employees will increasingly need to develop an ethos of self-reliance, adaptability and lifelong learning.
Leaders and business-unit managers are used to making decisions and managing performance based on their own experience and personal judgment. These decisions are not only subject to biasi ― they’re also increasingly out of date. Acumen acquired through historical experience provides little guidance to a digital future.
For example, numerous factors might influence an outcome. And while a human is unlikely to refer to more than three or four factors when articulating his or her view, an algorithm (or any advanced statistical analysis) will take account of many, many more.
Information, analysis and culture
So, the need is growing for a rational, measured discipline to ensure that decision-making and performance management are based on proper analysis and all relevant information. Its focus must be on delivering value to the business and its stakeholders.
If the insights gained are to be properly applied to improving performance, not only must business leaders demand information and analysis but evidence to support views must be expected when making decisions as part of
Management accountants may be well-placed to play this key role. They already come into
contact with all aspects of a business,
providing and using information in the management control cycle and providing professional objectivity to ask questions and address bias in decision-making.
This gives them a firm foundation to expand their remit well beyond just assembling and analysing financial information. And it gives them the potential to play important roles in ensuring Big Data is accessed and analysed in the interests of the business, its customers and all its stakeholders. It’s a very exciting and
“The biggest danger relates to those skills needed to implement and exploit Big Data. And somebody needs to take responsibility.”
Does your business expect management accountants to
be able to conduct advanced analytics (for example to
be able to write code and
Big Data — big usage
The spread across sectors
The definition of Big Dataii in Figure 1 illustrates how enterprise data and financial data can be seen as subsets of the vast range of data — ‘Big Data’ — that’s now available to a business. And it’s growing rapidly — data from outside the enterprise is proliferating on websites, in social media and other forms of communication across the internet.
The sectors that initially made the most use of data were those that had the most of it — the likes of retailers, insurance companies, banks and airlines.
Now, however, businesses in almost every sector are using data analytics to improve efficiency and the services they offer.
Figure 1: Big Data defined
- Financial data:
Standard financial metrics, well-tracked
- Enterprise data:
The above, plus broader operational and transactional data that may be used to bolster analysis and forecasting
- Big Data:
The above, plus new types of internal and external data, much of which is unstructured, but some of which could yield new insights into business performance, risks and opportunities.
The big changes driven by Big Data:iii
Tractors with sensors can collect data on seeding rates, crop yield and ground conditions, allowing farmers to predict production rates more accurately. Farmers can also analyse weather patterns to better prepare for poor conditions.iv
Manufacturers are using advanced analytics to manage the risk of potential faults in equipment and product anomalies during production. They are also using it for forecasting, inventory management and production planning.v
Retailers are using data to forecast customer demand and understand their preferences, allowing them to become more proactive and better able to predict behaviour. They are also using Big Data to create new digital product offerings.vi
Players in the transportation industries (such as airlines, rail and trucking companies) are using predictive analytics to manage their costs through preventative maintenance scheduling, inventory parts management and warranty claim management.vii The airline industry is using analytics to understand customers, predict demand and optimise pricing.viii
Telecoms providers are using analytics and Big Data to reduce customer acquisition costs, segment target subscriber audiences and rank prospective subscribers according to their propensity to buy.ix
Banks and insurance companies are using predictive analytics software for fraud analysis,x while credit card companies use analytics to manage credit lines and collections.xi
City administrators are increasingly using Big Data to understand the needs of their citizens and proactively plan for future needs in transport, policing, health care and more.xii
Educators are using data from student tests and assessments to determine patterns and performance levels. This is helping them to adapt courses and tailor the way they teach.xiii
To improve health care delivery and reduce costs, health care organisations are using Big Data analytics to analyse large amounts of structured and unstructured data to improve the efficiency and quality of care.xiv
Getting to the heart of the business
Big Data isn’t just about improving services and efficiency levels. It can also have applications in every aspect of the business model, as illustrated here using the retail sector:
“ … businesses in almost every sector are using data analytics to improve efficiency and the services they offer.”
Do you believe that your business handles sensitive customer data well enough to fully deserve trust in its brand?
- Yes it does
- It does not
- There is room for improvement
- Don’t know
Culture and talent
The key to extracting value from data
As well as the right data, a business
must also have the ability to analyse it. And, to achieve impact, it’s vital to have a culture where:
The use of data is expected, encouraged
Skills are applied to ensure that insights gained are used to generate value.
Skills for success
Achieving this requires a commitment to talent development that equips the business with a set of competencies and skills (Figure 2), including:
Figure 2 Potential roles for management accountants
This table outlines the competencies a business needs to get value from Big Data — and the key roles finance professionals could play. xv
Competencies a business needs
Data integrity is essential. Business managers’ lack of confidence in the underlying data will undermine any data-based initiative. So, businesses need to ensure that:
Their systems and processes capture relevant data correctly, first time and every time.
The data is then stored accessibly, ready to be used consistently in subsequent interactions with internal users, customers and other stakeholders. This makes processes more efficient and improves the users’ experience.
Customers are concerned about “how” their data is used and if the company is only using the data for their stated intended purpose. Data privacy reviews and transparency is key to customer confidence.
Customers must also be confident in the business’ brand and its systems. They need to know that their payment records, contact details and any other sensitive data are always handled properly and maintained securely. Business managers, too, will be concerned that all forms of commercially sensitive data should not be vulnerable to cyberattack. So, data management is fundamental to ensuring the quality of the data that businesses rely on to inform their key decisions. It’s also vital to ensure conformance with data management policies and procedures. As data is captured and managed on systems, this usually falls into the remit of the IT professional.
Roles finance professionals can play
This sort of work can be seen as the domain of the IT professional. However, many management accountants maintain a good understanding of their business’ information systems. As trusted providers of accounting information, they certainly have the professional disciplines necessary to help ensure data quality.
They may also be engaged in contributing discipline and credibility to data information projects or data planning. This is because they are often best positioned both to articulate the needs of users and to determine what data is necessary to inform decisions and manage performance. They should certainly lead on data planning and agreeing on the measures to be tracked. This helps to ensure that the data to be used in performance analysis or management reporting is captured correctly. This includes data about the performance indicators to be tracked, the intended outcomes and the drivers of these outcomes.
Competencies a business needs (continued)
The scale and complexity of the data sets now available to businesses often require advanced
‘data-scientist’ levels of analytical skills for data mining, deriving algorithms and predictive analytics.
A SAS reportxvi has highlighted the shortage of these skills. Some industry experts see this as a looming crisis and have called for widespread training of data-science skills.
However, others believe that most analytics procedures will, with time, become automated and commoditised. Data visualisation tools are already enabling users to recognise trends and correlations. So, as new software enables business users to analyse data on a self-service basis, advanced data skills may not always be necessary for complex analysis.
That’s in the future. In the meantime, forming centres of excellence, ensuring that repeated analysis is automated and providing managers with self-service tools for less advanced analysis will be necessary to ensure the efficient use of data scientists’ scarce expertise.
Roles finance professionals can play (continued)
Data scientist (data analyst)
The shortage of data scientists will make the field attractive to some more ‘quantitative’ management accountants.
It’s likely that there will be opportunities for those prepared to invest in further education to complement their accounting skill set with greater expertise in advanced analytical techniques.
Over time, the current shortage of data scientists may become less of a concern. The combination of natural language processing (NLP) and machine learning will enable management accountants and others to commission some forms of advanced analytics and gain insights without the skills of a data scientist.
Competencies a business needs (continued)
For a business to benefit from developments in data and analytics, it must develop a culture of stewardship where:
Decisions are based on evidence and taken in the interests of stakeholders.
Data is valued as an important strategic asset used to improve the competitive position and enable new strategies.
Assumptions are challenged, and decisions are taken on the basis of the best available evidence.
A business also needs the ambition to be innovative. It must be prepared to develop new strategies and make acquisitions to acquire new capabilities or revise its business model.
It also must exercise integrity in how data is managed, going beyond compliance alone to ensure that customers and suppliers have the confidence to allow their data to be gathered and used.
Roles finance professionals can play (continued)
Data champion (steward)
Management accountants already provide professional objectivity. This gives them the potential to be champions of evidence-based decision-making — ensuring that decisions are considered on the basis of their long-term value to stakeholders.
They’re also well-placed to help businesses develop strategies for exploiting the value in data.
For example, the CFO can influence the business culture at a high level. Finance business partners can then cascade that influence throughout business functions and to the operational level in business units where managers will increasingly take ownership of the potential in their data.
Competencies a business needs (continued)
Information technology professionals, data scientists, finance professionals and business managers must all collaborate to commercialise analytical insights and achieve impact. The business acumen to identify an opportunity to create value is an important enabler. Intangibles such as customer relationships, human capital and intellectual property are the basis of a business’s potential to generate value.xvii Measuring and managing these intangibles takes data.
True, strategic insights into customer behaviours that provide new opportunities for profitable revenue may get more attention. But managers must also be alert to other opportunities to create value, through:
• Increasing efficiency
• Reducing risk
• Generating profitable sales
• Improving cash flow
• Focusing on cost management to drive incremental innovation and continuous improvement in business-process efficiency.
This last point can often be the most significant of all.
Roles finance professionals can play (continued)
As their roles bring them into contact with all aspects of the business, management accountants are well-positioned to become business partners for business managers, IT professionals and data experts.
Do you expect to increasingly work as the finance business partner to business-unit managers, IT professionals and data scientists to help your organisation maximise value creation?
- Very strongly
- Fairly strongly
- Not at all strongly
- Don’t know
“As well as the right data, a business must also have the ability to
Moving beyond financial data
Putting the ‘science’ into data analysis
Traditionally, financial planning and analysis have been about financial data. Assessing performance against budget has been a cornerstone of financial management. And it’s relatively straightforward: the mathematics involved is no more complicated than the calculation of percentages.
On the other hand, understanding the causes of performance or divergence from budget requires a better understanding of the business. It means asking pertinent questions and carrying out root cause analysis. And higher-value analysis, such as predictive or prescriptive analysis, demands more advanced analytics that usually requires the expertise of a data scientist (Figure 3).
Figure 3 The Gartner Analytic Ascendency Model and Finance Function Reporting Focus xviii
The data scientist’s role and responsibilities
Framing the problem:
Clarify the actual issue: translate an unclear request into an analytical problem.
Determine what data is available and might be useful; ask what other data could be accessed.
Correct errors and missing, incomplete or corrupt records; convert to useable form.
Look for trends, maybe with visualisation and regression analysis to identify correlations.
Data mining, statistical modelling, machine learning, deriving algorithms.
Visualisation and storytelling to help users understand and action findings.
“Higher-value analysis, such as predictive or prescriptive analysis, demands more advanced analytics that usually requires the expertise of a data scientist.”
Data — Deluge and Decisions | Episode 1 | Welcome with Andrew Harding
Data analysis needs
How business challenges
are creating opportunities
The challenges businesses face
- There is a lack of people with the advanced analytical skills needed to be data scientists.
- Good data governance and data management are vital — otherwise, diverse practices and systems across different business units can make a large organisation’s data architecture very complex and data sets incompatible. Analysis based on such data can be misleading and discredit the analysts.
- Data tends to belong to whoever owns the processes and activities that generate it. Human nature means that if these owners report on it, the analysis is likely to be flattering. There is a clear need for the finance discipline’s professional objectivity and validation.
- Budgets are still important in setting expectations — but they’re subject to gaming and the level of detail in their preparation can be spurious. Driver-based rolling forecasts provide a more reliable and timelier basis for performance management.
- The drivers of value in most businesses are intangibles. There is a need for metrics, or quasi measures, that can help to monitor, manage and develop the intangibles that
- Too often, management is based on financial measures about outcomes. This is like painting by numbers. A management accountant’s commercial mindset has
How management accountants can seize the opportunity
- The planning and control cycle is often too cumbersome. The strategy > operating plans > budgets > reporting > analysis > forecasting phases need to be better aligned. This would enable better data planning including prior identification of the data that must be captured and curated properly to ensure it is fit to inform decision-making and performance management.
- The FP&A functions are often so busy gathering data and producing standard reports to support 'business as usual' that management accountants can fail to raise their game and achieve their potential. New technologies can streamline this reporting on dashboards, enabling business unit managers to interrogate and conduct analysis.
- Because data scientists are such a scarce resource, it can make sense to centralise them in multidisciplinary teams within finance, enabling their expertise to be better leveraged.
- Management accountants are well-positioned to work alongside data scientists. They can help to frame the business issues to be addressed and work with IT professionals to ensure data is made available for analysis.
- Management accountants can also work in partnership with business managers, cascading the CFO’s influence throughout the business. They are well positioned to help ensure that insights gained from Big Data are applied in how the business is managed.
“Management accountants are well-positioned to work alongside data scientists.”
What’s next: improving performance through business partnering
When it comes to Big Data and its analysis, business partnering is the area of greatest opportunity for management accountants. The Association’s ongoing research to update the professional syllabus shows the roles in data that employers are most keen for management accountants to take are not those of information technologists or of data scientists.
Rather, they require them as champions of evidence-based decision-making. That way, they’ll help to translate analytical insights into commercial insights which they’ll ensure are applied at business-unit level to improve the organisation’s prospects and performance.
Opportunity to learn more
Management accountants should be alert to the potential in all the data sources available to the business. That way, they can liaise with IT professionals, data scientists and business-unit managers as information brokers who ensure decision-making and performance management are properly supported.
Their roles give management accountants a broad overview of the business and the professional objectivity to be able to challenge assumptions or assess evidence.
Some, having an understanding of the business and its systems and being quantitative by nature, may take the opportunity to change careers to become data scientists. Others may prefer to develop their careers as project managers, change managers or IT professionals.
However, our discussions with employers clearly show that the biggest opportunity for management accountants lies in their combination of accounting and analysis skills with business understanding to support value creation as business partners.
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- i mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/distortions-and-deceptions-in-strategic-decisions
- ii cgma.org/resources/reports/insight-to-impact-big-data.html
- iii competency.aicpa.org/media_resources/206475-big-data-readying-business-for-the-big-data-revolu
- iv itproportal.com/2014/07/30/how-big-data-is-transforming-every-industry/
- v eweek.com/database/bmw-using-ibm-predictive-analytics-in-auto-production-repairs.html
- vi mckinsey.com/insights/organization/The_seven_traits_of_effective_digital_enterprises
- vii support.sas.com/resources/papers/proceedings12/383-2012.pdf
- viii datanami.com/2014/01/03/how_big_data_helps_airline_profitability/
- ix deloitte.com/content/dam/Deloitte/in/Documents/technology-media-telecommunications/in-tmt-opportunities-in-telecom-sector-noexp.pdf
- x computerweekly.com/news/2240210346/SAP-UK-US-companies-hit-automation-barrier-with-predictive-analytics
- xi tibco.com/blog/2011/11/03/how-predictive-analytics-turns-banks-into-fortune-tellers/
- xii nuim.ie/progcity/2014/06/predictive-analytics-in-the-city/
- xiii innovationexcellence.com/blog/2014/01/29/big-data-in-education-big-potential-or-big-mistake/?Itemid=92
- xiv sas.com/en_nz/insights/articles/analytics/4-ways-data-analytics-is-changing.html
- xv competency.aicpa.org/media_resources/206475-big-data-readying-business-for-the-big-data-revolu
- xvi sas.com/en_gb/offers/big-data-skills/register.html
- xvii cgma.org/resources/reports/rebooting-business.html
- xviii gartner.com/en/documents/2269516
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