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Insights for executives Predictive analytics
The C-suite’s shortcut to the business of tomorrow

Of special interest to

Chief executive officer
Chief financial officer
Chief marketing or sales officer
Chief information officer

In the era of big data, companies across a range of industries are recognizing the need for better intelligence and insight about their business. They want to work out how to make the best decisions, drawing on the right information, at the right time.

• Finding and accelerating growth opportunities — drawing on internal and external data to help model and predict business outcomes, identify the most profitable opportunities and differentiate the business from its rivals.

One organization that has been pioneering in its use of predictive analytics has been the
United States Postal Service. Using an analytical approach, it predicted which workers’ compensation claims and payments were unwarranted — and saved some US$9.5 million during 2012 alone. This is not an isolated example: many leading organizations have started to regard their information as a corporate asset.

• Improving business performance — enabling agile planning, more accurate forecasting, better budgeting and trusted decision-making support.

Business benefit can be gained by creating systems that can convert information into actionable insights, all within the context of key business priorities. Some of these include:

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• Managing risk and regulatory pressures — improving reporting processes through the exploitation of more robust data, while also identifying potential risk areas, such as compliance violations, fraud or reputational damage.
• Exploiting emerging technologies — continually identifying new opportunities to gain insights from data.

What’s the issue?
Very few companies are fully exploiting the potential of predictive analytics.
And these new aspirations also often collide with the struggle to contain and cut IT spend.
The key questions start with the ability to quantify the value of available information within the context of an organization, a department or business function — working out which data sources might help to strengthen the generation of new insights.

At the outset, issues range from working out how to derive benefits from information and in which context such data might be relevant. There is also a need to prioritize those activities, not least as views may differ sharply between what IT considers important versus what the rest of the business does. Finally, there is the challenge of determining how to perform and operationalize analytics, while taking the impact on processes and behaviors into account.

Why now?
We operate in a digital world, with vast — and constantly increasing — volumes of data being generated. Every minute of the day, more than
200 million emails are sent globally, while Google receives more than 2 million search queries. The retailer Walmart now handles about 1 million transactions every hour, adding to a database that is nearly 2.5 petabytes in size. By 2020, some 450 billion online transactions are expected to take place every day. Sources of data, both structured and unstructured, are multiplying rapidly, from external social networking interactions to internal call center transcripts. Indeed, organizations now embrace data as a fourth factor of production, alongside capital, people and materials. They use it to help sharpen their business performance by differentiating their offerings, uncovering new opportunities and minimizing their risk exposure.

Predictive analytics is not brand new, but the technologies that help firms make sense of their data have only recently become available. This is allowing firms to uncover and exploit patterns in their historical data, identifying both risks and opportunities ahead. In short, businesses can use data to look forward, rather than at past performance.
Leading organizations increasingly recognize that predictive analytics can deliver more than just customer insight; it can also have a positive impact on compliance, security, fraud detection and risk management.

Predictive analytics is not brand new, but the technologies that help firms make sense of their data have only recently become available.

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How does it affect you?
Many businesses report a disconnect between their desire to capitalize on data and their ability to do so. It becomes even more imperative for business and IT to develop a joint model and terminology for valuing information, which is directly linked to the organization’s key performance indicators.
Efficient analytics-enabled business processing measurably impacts performance by supporting better planning, forecasting and decisionmaking. It can help boost revenues, reduce risks and increase agility. But getting this right often demands that traditional IT and operational roles, structures and culture adapt to a new way of working, not least through the introduction of specialist positions, such as data scientists.
The adoption of analytics also brings with it new risks. Traditional levels of comfort around data quality, privacy, intellectual property and reputation management must evolve. This, in turn, impacts people’s behavior.

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Efficient analytics-enabled business processing measurably impacts performance.
Ensuring that all parts of the business are engaged, so that the full picture is captured and information gaps are minimized, is another challenge.
Customer data is a typical example: sales holds some data, such as billing addresses and transaction records, while marketing may hold customer feedback insights, and logistics holds physical delivery details. Information is often either duplicated or inconsistent, if it is available at all.
Nevertheless, correctly navigating such challenges is worth the effort. In an era of consumerization, those organizations able to monitor and predict customer behavior and preferences closely, without crossing the line on privacy, can gain significant advantage.

What’s the fix?
Bringing technology-driven analytics to bear on a project involves several key steps:

As such steps become embedded, a portfolio of analytics projects should be developed. Quite simply, with limited resources, the most important initiatives need to be pushed to the front. As part of this, an implementation roadmap needs to be set out that also charts the likely impacts a given project might have.

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1.
Understand the problem and address it in a way that it becomes clear which insights need to be discovered through predictive analytics. Collect the information needed to tackle the problem. This demands an analysis of which data is most needed, what is already available and where any key gaps lie, along with an assessment of data quality and a sense of where missing data might be sourced.

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Act on the findings — even if they imply a major shift — by adapting processes and behaviors to capitalize fully on the transformative potential of predictive analytics.

Perform the analytics, using mathematical algorithms to help uncover patterns within the data.
These findings need to be translated back to the business problem to help interpret the outcomes in the most useful context.

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What’s the bottom line?
The information war has already started. From here on in, business performance will depend to a great extent on an organization’s ability to gain access to the right information and to exploit it fully.
At a high level, predictive analytics can help companies to:
• Move from a retroactive and intuitive decision-making process to a proactive data-driven one
• Build models that more closely predict future real-world scenarios and their related problems and opportunities
• Uncover hidden patterns and relationships in the firm’s data

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More specifically, information-led companies will be able to sharpen their competitive edge. There are numerous examples of what this might deliver: attracting more valuable and loyal customers, charging prices closer to the market rate, ensuring more focused and relevant marketing campaigns, running more-efficient and less-risky supply chains, ensuring the best product or service quality levels, ensuring highly individualized customer service and guaranteeing a deep understanding of how process performance drives financial performance.

Want to learn more?
The answers in this issue are supplied by:

Andy Rusnak
Enterprise Intelligence Leader
Americas Advisory Services
Ernst & Young LLP
+1 215 448 5029
Andy.Rusnak@ey.com

Christer A. Johnson
Principal
Americas Advisory Services
Enterprise Intelligence - Analytics
Ernst & Young LLP
+1 703 747 0628 christer.johnson@ey.com Gary Angel
Principal
Americas Advisory Services
Enterprise Intelligence - Digital Analytics
Ernst & Young LLP
+1 415 894 8255 gary.angel@ey.com For related thought leadership, visit www.ey.com
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