We live in an information-driven world and in a global economy that is increasingly propelled by complex and technology-based transactions. Based on the Analytics Playbook published by EY (Ernst and Young), 90% of the world’s data was created only in the last few years, and many are already overwhelmed with the amount of information available. This increasing volume of accumulated data is called “Big Data.” Given the remarkable speed at which data is produced and accumulated by various organizations, businesses are more focused on how they can benefit from Big Data. One way is through predictive data analytics, or the gathering, analysis and evaluation of historical data to predict future outcomes or behaviors.
Listed below are five key insights that can help the C-Suite understand the challenges, and subsequently uncover the power of predictive data analytics for their organizations.
WHAT ARE THE COMMON CHALLENGES?
Most companies are not yet fully leveraging predictive analytics, especially since many companies are focusing on IT cost reductions rather than on investing in infrastructure, people and processes to compile and generate accurate data and turn them into actionable insights. We should also note that, unless the data gathered can be fully quantified in the context of the organization’s operations, it loses much of its value. This can be a matter of perspective — what the IT team considers important compared to what management sees as relevant data. Additionally, management has to develop processes in order to determine how to process and operationalize analytics, and how this will impact overall operations.
WHY ONLY NOW?
These are challenging times and with disruption and potential data overload becoming part of our daily lives, the use of predictive data analytics can help company leadership better manage the risks of doing business. The recent rise of new tools to manage and analyze data has also made this objective much easier. Though it is not meant to provide absolute assurance in predicting outcomes, predictive data analytics nonetheless offer some insight into what could happen based on available historical data. This can help management strategize, minimize business risks, enhance compliance, strengthen fraud detection, formulate solutions to potential problems before these arise, and even identify best practices.
HOW DOES IT AFFECT YOU?
Some attempts to maximize the benefits of predictive data analytics have failed as the desire to capitalize on data and the ability to do so are not aligned. In applying predictive analytics, the relevant parts of the business operations must also be engaged to properly interpret data and ensure that gaps are minimized. Take, for example, customer data. A company’s sales department has the customer information, billing addresses and other transaction records (returns); marketing has customer feedback insights; logistics with physical delivery details and returns; and accounting holds the collection history and transaction records. Because of this, information can be duplicated or inconsistent.
All data need to be considered holistically to come up with strategic business decisions and meaningful insights on how to increase customer sales. Getting these perspectives to work together requires a top-down management approach that is echoed in all parts of the business. It may also require that traditional IT and operational roles and culture evolve, such as through the implementation of data-focused specialist positions. Furthermore, the adoption of predictive data analytics may bring in new risks around data quality, privacy, and intellectual property so it is also important that the company’s data management policy should evolve accordingly.
HOW TO DO IT?
Companies that decide to use predictive data analytics should conduct a thorough cost-benefit analysis. Accurate and complete data collection, filtering, and analysis entail considerable cost and this should be weighed against the benefits that could be derived. When an organization is trying to maximize the benefits of predictive analytics, it should have the following implementation roadmap:
1. Understand first the problem and see how predictive analysis can yield the most useful and actionable information. For example, is the problem about sales, purchasing, or regulatory compliance? Having a clear idea can help you filter down to the vital information you need. At the same time, management will need to identify which business units can offer input and insights in order to better interpret the analysis.
2. Do you have the right data or information? Once you know what you’re looking for, the next step is to collect the data from available sources. This can help you identify the data you need most and where to get it, any information gaps in current data, as well as the actual quality of data gathered – whether the information is sufficient to help you generate meaningful and valuable insights.
3. Once the data are collected, you can leverage analytics tools and processes to look for patterns in the data. Afterwards, any findings should then be related back to the business issues to help generate useful outcomes.
4. Take concrete action, even if it requires a major shift in current business processes and behaviors. Make the most of the transformative power of predictive data analytics to implement corrective actions, modify business plans and formulate new strategies.
WHAT’S THE BOTTOM LINE?
Using data intelligently and effectively is now a new business paradigm. In order for companies to stay ahead of the curve, the use of data, analyzing and turning it into actionable insights has become a powerful strategic tool in a highly competitive environment. The use of data should change a company’s approach to business from being reactive and intuitive to proactive. Predictive data analytics tend to build models that will closely predict what’s bound to happen based on past real-life scenarios, as well as identify new risks and issues in business operations that may only surface following data generation, structure and analysis.
Data analytics, as a business discipline, has been around for decades, but the insufficiency of data in the past did not yield meaningful conclusions. Technology was not as sophisticated to facilitate accessing and processing of data.
With today’s advanced technology, management teams are more equipped to maximize the benefit of voluminous data. It can help uncover any untapped business opportunities — such as new products to develop, new customers to grow, and new markets to explore. Moreover, predictive analytics can be a source of vital information on identifying processes and areas of improvement to help a company be more competitive, nimble and profitable.
This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinion expressed above are those of the author and do not necessarily represent the views of SGV & Co.
Benigno F. Leongson is a Senior Director of SGV & Co.