277385_l_srgb_s_glEvery day, we are surrounded by data. Millions of email and search queries. Hundreds of thousands of pieces of content shared. Tens of thousands of apps downloaded. You get the picture. We are virtually swimming in it. For most companies, this data represents an opportunity to run effective business processes, report past performance, and react to market dynamics in real time.

Although this is a good start, the world moves at such a pace that companies must look ahead to protect their brand and operations while taking hold of opportunities before anyone else does. They need to use data to predict the future.

Is your organization getting the most of its data? Most likely not. In fact, recent research from McKinsey & Company revealed that less than 1% of all business data is regularly used worldwide. And when it is being accessed, decision makers are most likely setting alarms and real-time control with it.

To explore this finding further, I had the great fortune of talking to Timo Elliott, Innovation Evangelist for SAP. During our discussion, he shared his perspective on why organizations may be missing out on the potential of 99% of their data – and how they can tap into it now.

Why are so many companies missing out on the remaining 99%+?

Elliott: Organizations can now collect vast amounts of information at high speed. But even as the technology continues to improve, the biggest barrier remains how decision makers can derive insight from that data. By offering the potential to automate some critical steps of analysis, predictive technology can help organizations make better use of their data assets.

At the end of the day, sound business decisions are not about the quantity of data – it’s about the quality of the decision-making process. It’s not just about “reporting” – data is now an essential foundation for building business models of the future.

For example:

  1. Social media. This is not just a new channel for customer engagement. It’s also a powerful mechanism for transparency that can lead to strategic, fundamental shifts in the vendor-consumer relationship. In this digital age, your product or service is your marketing – good offerings are discovered and recommended to others while bad offerings are hounded out of the market.
  1. Customer experience. The competitors of tomorrow may have a completely different business model and value proposition that can ultimately disrupt your entire enterprise. As a firm defense, you must take a look at each customer’s lifestyle, buying behavior, and perspectives to figure out whether you are addressing a relevant need and how to create a personalized end-to-end experience. For example, Vodafone Netherlands is applying predictive analytics to help ensure that customers only receive offers that interest them, expose opportunities for acquiring new customers or cross-selling, and pinpoint underserved areas that are ripe for a new store location.
  1. Greater agility. The only reliable prediction about the future is that it will be unexpected. The new core competency of every organization is to become increasingly flexible to adapt to fast-changing marketing conditions.
  1. Business and technical expertise that leads to good decisions. Predictive analytics augment smart decision making, but the expertise of data scientists is required to get the most out of this technology. Because there is a small pool of such talent, a new generation of predictive technologies – commonly referred as Big Data discovery tools – are encouraging the rise of the citizen data scientist to drive insight from data.

Is predictive analytics the answer to move from reporting the past to predicting the future?

Elliott: Predictive capabilities have always helped optimize complex decisions. However, today’s exponential growth in available data and processing power means that predictive algorithms can be used outside of traditional niches, such as fraud management, logistics, and manufacturing, and expand across the entire business network.

Organizations need to establish a reliable infrastructure to gather the data they need. This typically requires a mix of the latest technologies – such as sensors enabled by the Internet of Things, mobile devices, cloud, and social data – with traditional business process information including financial figures and logistics. Often, it is the difficulty of finding and merging data across different silos with sufficient quality that is the biggest barrier to meaningful predictive analysis. Then comes the next hurdle: changing organizational processes, incentives, and even business models to act on that insight.

Predictive technology is not really about predicting the future – or at least only in a very limited way. Predictive technology is ideally adapted to optimize complex, repeatable decisions to “predict,” for example, the likely power consumption of different types of consumers. Ultimately, no single person can truly predict the future. The unexpected will happen, but predictive analytics can help you react faster and adapt faster.