Predictive analytics: shortcut to tomorrow’s business
5 insights for executives series
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.
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.
Many leading organizations have started to regard their information as a corporate asset.
Business can benefit by creating systems that can convert information into actionable insights, all within the context of key priorities. Some of these include:
- 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.
- Improving business performance — enabling agile planning, more accurate forecasting, better budgeting and trusted decision-making support.
- 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.
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 spending.
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. 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, 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.
We operate in a digital world, with vast — and constantly increasing — volumes of data being generated. By 2020, some 450 billion online transactions are expected to take place every day.
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.
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.
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 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, 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.
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.
Bringing technology-driven analytics to bear on a project involves several key steps:
- 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.
- 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.
- 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.
As such steps become embedded, you should develop a portfolio of analytics projects. With limited resources, the most important initiatives need to be pushed to the front, charting the likely impacts a given project might have.
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
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
- Guaranteeing a deep understanding of how process performance drives financial performance