In a previous article, we covered the topic of the emergence of interconnected devices at the heart of our society and the challenges that the Internet of Things might bring. Today, we’re going to focus on two other terms, both of which are highly topical and up-to-the minute: the concepts of Big Data and Predictive Analytics… And above all, clarify for you the implications of these two concepts.
Let’s start with a brief introduction to Big Data. It’s important for all of us to take an interest in these technical terms: they underpin the technological revolution that we’re experiencing today and will live through over the coming years.
Big Data and data processing
NASA researchers Michael Cox and David Ellsworth coined the term for the first time in October 1997 in an article that explained that massive amounts of information would require the adaptation of methods of analysis.
In fact, with the incredible expansion that the Internet has witnessed since the year 2000, we’re now facing an explosion of quantitative digital data.
The concept of Big Data brings with it a three-fold challenge, generally known as the “Rule of the 3Vs”
Here, we’re talking about the vast Volume of data that must be processed, alongside a huge Variety of formats (from diverse sources, some organized, some unstructured…), with the goal of achieving a certain level of Velocity. The velocity (or speed) refers to the pace at which data can be generated and analyzed.
The information available for processing comes from everywhere: shared videos, SMS, GPS signals, climate information, bank transactions…
Not that there’s a right or wrong way to process this data: it all depends on the end goal and the desired value. In a business environment, Big Data can be used to address very different objectives, such as the enhancement of customer experience and the optimization of operational performance and processes, as well as strengthening and diversifying the business model.
In recent years, with the development of analytics capacity through progressive technological advancements, Big Data has expanded its scope, moving beyond the “Rule of the 3Vs” to the “Rule of the 5Vs”:
- Veracity: which refers to the trustworthiness or reliability of the collected data;
- But above all Value, which is associated with the notion of the benefit that can be taken from the use of Big Data.
In this way, Big Data, currently being widely used for statistical ends, now has the ability to be harnessed within a wider dimension: the predictive domain.
Predictive analytics and its outcomes
Predictive analytics, artificial intelligence or “machine learning” as it’s known, are still abstract concepts for many – they’re even frightening to some of us. And yet, predictive analytics are simply the combined outcome of Big Data and Business Intelligence (BI).
The quantity and quality of processed data are now sufficient to detect emerging trends, to give predictions that allow us to get closer to reality. These predictive analyses today offer us new decision-making mechanisms that can be used company-wide, for increased visibility.
In practical terms, this capacity for predictive analysis is already present within companies and it can be tapped into in different ways:
- Predictive Scores: largely known today for their ability to predict the performance of a marketing campaign. They’re now being employed successfully at the service of financial analysis. Sidetrade, global leader of predictive solutions for the Sales-to-Cash cycle, has introduced a predictive scoring process called Sidetrade Payment Intelligence solution or SPi. This predictive solution empowers financial directors with real visibility over potential areas for improvement across their client cycle. Its purpose is to provide the means to predict cash flow generation via intelligent analysis of client payment behavior. Sidetrade processes enough financial information and data, in quantity and quality, to enable it to reliably analyze the past payment behavior of your customers. The predictive score dashboard serves as a decision-making support tool.
- Benchmarking models enable the identification and acquisition of customers with similar profiles to those already within the loyal customer base. These models prove highly useful for the goals of upselling and cross selling, as well as for winning new customers. British start-up BrightTarget is one of the most advanced B2B predictive marketing platforms in this field. Its versatile software allows you to predict the Customer Lifetime Value of each of your customers – in other words, the forecast of the net profit that is expected to occur from the entire future relationship between your business and a customer. But it also permits you to assess the potential of each of your existing customers and to predict which is the most likely to leave.
- Automated segmentation is used to effect to deliver advanced customer segments so that personalized messages can be sent to them. IKO System, founded in 2010, is a French company that specializes in predictive solutions to automate sales prospecting. Via its platform, sales automation email software can be harnessed to engage leads and trigger meetings. Target email scenarios can be controlled according to multiple criteria, such as your prospect’s sector of activity and his or her role in the company, while tapping into numerous communication media, including, of course, LinkedIn.
If you’ve got this far, you’ll now have grasped that predictive analytics is simply one of many possible developments that has come from Big Data. We’ve moved from being able to process data on a large scale to now extending that ability to sort information and analyze it efficiently to produce reliable indicators for our organizations.
This is just the start. Given the exponential growth in data and its variety, combined with the expansion of connected devices, new rich seams of data to mine will be continually opened up ahead. In the coming years, we are going to see yet more technological innovations surrounding data. They’re set to transform society as we know it.