Becoming a data-driven organisation

Important checkpoints in your journey

From October to December 2017, ISCA collaborated with PwC to roll out a three-part ISCA Breakfast Talk series on data & analytics. More than 220 participants benefited from the insights presented by the subject matter experts over the three sessions. Noting that data can be transformed into a very powerful tool, and that more and more organisations are utilising analytics to augment decision-making, it would be beneficial to extend the content of the three sessions to a wider audience so that more can benefit from these timely recommendations and advice. The following article articulates the points organisations should note on their journey towards becoming a data-driven organisation.

As the world drives towards artificial intelligence (AI), there are increased expectations for all organisations to change. Underpinning this is data, which ultimately enables AI. Data is a critical asset, and here, we outline three key considerations for an organisation to reap its value.

1) Data governance: Laying the foundation for analytics

Data is increasingly becoming an integral part of corporate strategy. Companies are rapidly deploying data-enabled solutions, robotics, machine learning and analytics to drive their business. Data is also coming from multiple sources and increasingly, there is use of unstructured data that is external to the organisation. Key decisions and actions are made on this data at speed; we have less and less time to question the data and therefore, trust is critical.

Trust means answering basic questions such as, “Where is the data coming from?”; “Is the data accurate?”; “How will it be stored and who will have access to it?” and, importantly, “How is my data being used?” Increasingly, these are critical questions as data drives more decisions in the organisation and the risk of bad data materially harming the organisation becomes more acute.

Many companies are only just now waking up to the complexity of the aforementioned questions, more often than not as a response to incidents that have resulted in significant losses. In responding, some companies have set up data governance programmes to maintain and update data for selected systems. These progammes are well intended but often tactical as data is not an asset for system – it is a business asset. Fundamentally, a shift is needed within the organisation to view and treat data not in isolation but as a strategic, corporate asset, where ensuring data integrity becomes a shared, enterprise-wide objective.

Data governance is the enablement of a controlled data environment across an organisation in which the full potential of data can be realised and its risks minimised. This includes the establishment of an enterprise-wide governance framework including standards, policies and procedures, assignment of ownership of data, and the creation of new functions and roles within the organisation to manage data.

Fundamentally, good data governance will be the foundation of future enterprises.

Figure 1 PwC’s data governance framework to embed trust in data

Case study

Data quality and standards do not often seem to be at the forefront of an organisation’s priorities, but in one extreme case, it caused a NASA spacecraft to get lost in space. Inconsistent data definition led to a navigation team using a metric system of millimetres and metres, while the aircraft designer had used an English system of inches, feet and pounds. The speed of the aircraft inflight was miscalculated, and the vessel was lost.1

Down on earth, many commercial organisations face similar challenges of a common definition of data across functions. When different stakeholders in a bank talk about a “balance”, some will gravitate towards the opening balance, others to the closing balance, and more may even interpret it as gross balance or net balance.

Proper governance with data standards and glossaries is needed to ensure that data is fit for purpose and ready for analytics.

1 Source: CNN, 30 September 1999

2) Application of analytics across the entire value chain: Be business-led, not tool-focused

Once organisations have begun to put robust procedures in place over governance of their key data assets, another question is what tools to use to benefit from the world of data analytics, big data and AI. No doubt, there is a myriad of technology in the market to generate insights and competitive advantage, but cutting through all various possibilities and budget considerations, one should first take the approach of formulating desired business outcomes, then consider how data analytics can help to solve it. An organisation’s business value chain can stretch from strategy and growth to marketing and customer experience, and finance function, risk and compliance (Figure 2). Each one of these areas requires decisions to be made at varying levels, from strategic to operational ones.

Enduring questions for businesses such as, “What markets do I enter to achieve 25% growth in three years?” to “How do I better anticipate inventory requirements?” can now be answered quickly and confidently with the use of data analytics.

Case study

Many Internal audit functions are looking to data analytics to analyse 100% of their organisation’s transactions, rather than traditional sampling methods. This has resulted in greater levels of assurance, as well as value delivered to a company’s management. Increasingly, the analytics scripts are becoming operationalised into management control systems. This allows continuous controls monitoring of transactions in near real-time, and acts as a preventative control mechanism.

Figure 2 Capabilities to support all business areas

Shining the spotlight on the finance function, PwC’s paper, “Finance Function of the Future”, suggests a provocative view of the finance function of 2030 (Figure 3). The focus on four key pillars, powered by data analytics, will define the repositioning of the finance function.

Figure 3 A provocative view of the finance function of 2030

  • Navigation The finance function is responsible for creating the conditions to steer the organisation to success. Imagine a function which does not spend time on data-gathering but instead, data is available in real time to all stakeholders, from a robust data source. In addition, financial forecasting is real time and performance decisions are made, facilitated by predictive analytics of business opportunities.
  • Mediation External stakeholders are increasingly asking more questions of any organisation, outside of the core financial metrics. Singapore, recently following suit with other countries, requires sustainability reporting for Singapore Exchange-listed companies. In the United Kingdom, many companies by law are required to report on figures relating to gender pay. The ability to collect, analyse, report and mediate such data will broaden the role of Finance, as stakeholder engagement becomes broader and more varied.
  • Resilience Continuously monitoring risks on a real-time basis through analytics will make the organisation stronger in defence. Embedding predictive characteristics to this will add greater value and a preventative layer to control, not just detect.
  • Connectivity Connecting transactions automatically and seamlessly will add precision and free up time spent on transaction processing. Manual invoice processing will be a thing of the past, and finance functions will focus on connecting processes, departments, the business and customers together through real-time reports, dashboards and a view of what is happening in the present, not the past. Routine processes will be automated, and the finance function will apply emotional intelligence, judgement, and perception to add value.

3) Typical challenges and how to overcome them

The route to successful implementation of advanced analytics tools typically faces the following challenges:

  • Lack of alignment with strategic goals and direction Organisations that start with board-level support for digital and data transformation will progress more effectively. Tone from the top, along with a vision and supported by adequate investment funding, will enable a quicker speed of adoption.
  • Poor integration with business as usual Using data analytics should not be seen as an add-on. Rather, it should be embedded with current “business-as-usual” processes, adopted in the frontline. Appropriate change management needs to be considered and planned; the softer elements of culture and behaviours can often become a bottleneck in speedy adoption.
  • Poor data quality and accessibility Effective data governance procedures need to be in place from the onset, along with data being made available for analysis. Data security is a concern, but a pragmatic, risk-focused approach is required to prevent it being a barrier to analytics.

Conclusion

Here are the next steps towards a data-driven finance function:

  • Set goals and objectives Making your goals specific and measurable will already be a step towards being data-driven; challenging yourselves to make the KPIs reportable on a real-time basis will force yourself to embed data-driven processes.
  • Start small, think big Perform pilot projects, prepare for failure, and learn lessons from these to refine your future projects, and achieve success.
  • Focus on business problems, not tools Embed a mindset to go through a problem-solving process powered by data and technology rather than relying on technical resources without the business knowledge and expertise; the former will automatically upskill your people.
  • Collaborate with the rest of the organisation Your organisation may be applying data analytics centralised, supported by a data analytics centre of excellence, or de-centralised, with data specialists dotted across domains. Whichever the model, leverage the learnings across the organisations. Often, the same analytical tools and techniques can be used to answer different problem statements.
  • Share success The culture of change is difficult to tackle. Celebrate success, and also allow it to enthuse others to innovate.

Case study

At GE, the company is repositioning itself from a heavy equipment manufacturer to a solutions provider that adds value to its products through data and analytics. Finance is partnering with the company’s data professionals to identify, quantify, and maximise the value of data. They have approached it both from the use of data & analytics to be a more effective finance function, and further, how to work with the frontline and commercialise the data assets. They aim to use data & analytics to make GE Oil & Gas’ finance function a capability-based organisation, complete with shared services, centres of excellence, clear roles for FP&A and the controller’s office, and acumen in commercial and supply chain finance, along with deep specialist expertise.

Reference: PwC’s finance effectiveness benchmark report 2017

James Larmer is Head of Data & Analytics, PwC Southeast Asia; Mark Jansen is Technology, Media and Telecommunication Industry Leader and Singapore Data & Analytics Leader, PwC Singapore, and Andre Tan is Director, Data & Analytics, PwC Singapore.

This article was first published by the Institute of Singapore Chartered Accountants. You can read the original article here