Data analytics: don t forget the human element

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1 Author John Farrelly Director, Data Analytics, Advisory, EY Data analytics: don t forget the human element Data analytics tools and methods have an increasingly important influence on decision-makers choices. However, in order to be truly effective, organisations need to consider the human element in the adoption of data analytics. The ability to align data analytics with the business strategy and decisionmaking processes in the organisation may depend on an effective change management programme, which incentivises adoption and minimises barriers to success. Data and analytics are disruptive. They can change the way that executives think about problems and can open their eyes to opportunities of which they may not previously have been aware. They can provide insights that could challenge the status quo and move the organisation in entirely new and more innovative directions. Data and analytics have been business strategy tools for a long time, but it is only in recent years that they have come to the attention of executives and managers, as a powerful way to create competitive advantage. That s partly because there has been an explosion of data, coming from every corner of the enterprise; from consumers across the globe; and also from a dizzying array of devices such as telematics, sensors, geospatial devices, video and audio. The availability of computing power on demand, at dramatically reduced costs has added enormous new capabilities to the equation, making many applications commercially feasible. Despite this, many organisations are struggling to derive value from their data and analytics initiatives. The recent Analytics: Don t Forget the Human Element survey carried out by EY and Forbes, surveyed over 50 executives in large enterprises operating across the globe. Most respondents admitted that they still do not have an effective and aligned business strategy for competing in a digital, analytics-enabled world. Further, they continued to struggle with change-management issues when getting business-users to adopt analytics insights and to embed them in their decision-making process.

2 ISSUE 4 What separates the leaders in data analytics excellence from those organisations struggling with their adoption? The survey identified that the top 10% of enterprises meet three criteria: They use data analytics in their decision-making process, all of the time, or most of the time. They report a significant shift in their company s ability to meet competitive challenges. They indicate that their enterprises are advanced or leading in applying data analytics to business issues and opportunities. An increasing number of organisations across all industries are taking advantage of increasingly ubiquitous and low-cost technology, combining it with a growing awareness of the power of data analytics. The winning formula is to combine four factors - strategy, leadership, production and consumption. While the first three factors draw a great deal of attention, it must be noted that the consumption side is ultimately what delivers business value. This is the point at which the human element - the analytics-based decisions and business processes of the data s end users is of utmost importance. For companies whose decision-making processes were formed before the digital era enterprises that were not born digital the transformation into an analytics-driven enterprise is a journey. As this journey progresses, the success stories begin to get noticed and analytics become a greater part of executive decision-making. On the other hand, the process can be impeded by organisational resistance, mismatched processes, misaligned incentive systems, skills mismatches and other factors that are essential to achieving greater acceptance of analytics-driven approaches. Enterprises are addressing these challenges by recognising the importance of change-management and forming teams or centres of excellence to elevate these initiatives and to provide centralised governance across the entire organisation. In the months and years ahead, organisations will need to do more to close the gap between the production and consumption of analytics. I believe that organisations need to focus on assessing the current return on investment of their analytics investment; they need to understand which parts are working and where the barriers to improvement lie. Analytics can provide insights that could challenge the status quo and move the organisation in entirely new - and more innovative - directions.

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4 ISSUE 4 The vision to energise and sustain data and analytics activities needs to come from the top of the organisation. I would propose that, for many companies, the challenge to creating value is not data, technology or even skill sets; it s a question of applying the insights in a consistent way across the enterprise. Ultimately, the attainment of success requires people to make decisions, change their processes or behaviours, based on the insights generated. The continuum from analytics to insights to organisational change involves many different aspects, whether business processes, incentive systems, or simply the implicit assumptions and biases of individuals. This last step is often where the breakdowns occur. While the journey to becoming an analytics-enabled enterprise is not a quick or easy one, organisations should consider the following key factors: Strategy and leadership Data and analytics must become a strategic imperative for the organisation, because the vision to energise and sustain data and analytics activities needs to come from the top of the organisation. It is not enough to introduce an analytics strategy; that strategy must be aligned with and embedded in the enterprise business strategy. Analytics leaders must be identified, credible and trusted leaders whose task it will be to embed data and analytics best practices throughout the enterprise. Dedicated executives or executive teams should be appointed, on an enterprise, unit and team basis. These leaders will be the bridge between the business and analytics teams. Analytics production The analytics backbone must be built and maintained. Data and technology are key enablers for creating an analytics-driven enterprise. Accordingly, an enterprise-wide architecture must be implemented to provide easy access to data and to ensure that it s of high quality. From a skills perspective, it s important to define and manage data and analytics competencies. While the data and technology are crucial enablers, people are at the core of every initiative. This means nurturing the skill sets to gather and analyse data, in order to create analytics-based business insights. Analytics skills need to be recognised, effective, efficient, monitored, and demonstrably used to support decisions.

5 Analytics consumption: organisational The resources of the organisation must be aligned around data and analytics. The value of analytics comes from the behavioural alignment required to consume analytics, the ability to move from insights to action to value, which is a combination of culture, organisational processes, business users skills and incentives. While there is no perfect model to suit all organisations, it is advisable to have a central team playing a leadership, coordination and enablement role in areas where units have common needs. However, analytics delivery resources need to be close to the business units and functions where analytics are applied. It is important to create an analyticsdriven culture. This involves promoting the idea of analytics-based decisionmaking and enabling the organisation s capacity to implement change, based on analytics-driven insights. Analytics-driven insights must be embedded into internal business processes and customer interactions. Leaders in the organisation should encourage a culture of collaboration, including informal networks, as well as organisational structures such as centres of analytics excellence. Analytics consumption: decision-makers It s important to start with the decisionmaking end-user in mind. Analytics for analytics sake will remain simply that. As a first step, the business problem or opportunity needs to be identified. Subsequently, appropriate analytics solutions need to be developed, which take into account the changes a user will make in decision-making or business processes. It s also important to align businessuser incentives and capabilities. Employees should be motivated to participate in the analytics-driven enterprise, by ensuring that incentives are aligned with the actions you want people to take. Training in data and analytics should be organised for the business teams to encourage them to become consumers of analytics. Finally, it s important to measure results. This is imperative to improve management s awareness of the benefits of using data and analytics by monitoring business outcomes. Formal key performance indicators (KPIs) will help ensure that analytics efforts are focused in the right areas and delivering tangible results. For enterprises that were not born digital the transformation into an analyticsdriven enterprise is a journey.