Extracting value from your data

Many companies have a large volume of data, which they don’t use sufficiently to their advantage. However, it is often possible to extract the information required to manage a company or use it to anticipate certain future events, to stimulate the company’s growth.

A few years ago, to meet its own needs, DEEP implemented a powerful data analysis platform and developed advanced expertise in Data Intelligence . To offer our customers all the benefits of a Big Data approach, we now give them access to our tools and the support of our teams, in particular to respond to certain problems they face.

Creating value through data

The concept of Big Data covers approaches to data analysis that meet the “5 V” principle: Volume, Velocity, Variety, Veracity, Value. In other words, Big Data aims for the real-time analysis of large quantities of widely varied data. In order to extract value from this data, we need to be sure it is relevant and of high quality.

The challenge of making better use of available data through technological analysis, data science expertise, artificial intelligence and even machine learning, seeks to identify new sources of value creation.

Because good examples are always better than long-winded explanations, below we’ve provided some concrete examples on creating value from a data analytics approach.

Optimising your marketing budgets

A traditional communication campaign is relatively ineffective. In order to reach its target, it has to be deployed over a very wide audience, with most people being unlikely to buy into the message. It is generally estimated that 50% of the marketing budget is spent fruitlessly. In particular, the analysis of the available data must make it possible to better understand who the targets are, to identify precisely who to address the offer to and those most likely to respond to it, in order to engage with them directly. This way, we can concentrate efforts on the right people and the right channels, and considerably reduce the communication budget. Leveraging data therefore contributes directly to improving marketing performance.

In the same vein, refining our understanding of the customer and his or her needs based on available data also makes it possible to define offers that are better suited to market expectations and, therefore, more convincing.

Minimising customer attrition

Telecommunications providers, energy suppliers, banks and insurance companies are making major efforts to limit the attrition of their customer base. Analytical approaches to all customer and behavioural data enable us to develop, among other things, predictive models of those most likely to seek greener pastures elsewhere, with other providers. Specific measures can then be taken with these customers to encourage them to stay. Since the cost of acquiring a customer is higher than the cost of retaining them, this approach creates value.

Controlling and minimising risks

While Big Data contributes to more efficient marketing approaches and an improved customer experience, there are other examples of how such approaches create value.

In finance or insurance, for example, real-time data analysis plays a part in better risk management, for example in the analysis of credit applications or setting insurance premiums.

Improving business processes

The platform we provide can also be used by waste management services to improve their collections, for example. By analysing container fill rates, the regular rounds of the lorries that empty them can be optimised. This not only improves the operational process of this service, but also helps avoid an unnecessary waste of resources, such as travel time or fuel needed to complete a collection round.

On an industrial scale, data analysis can be used to improve production processes, reduce scrap rates, optimise resource management, and implement preventive maintenance for machinery.

Preventing road accidents

Other examples of use for public service could be cited. For example, the DEEP platform has been selected for a European road safety project. The goal is to halve the number of deaths on the roads. To achieve this, the platform aggregates and analyses a large amount of data transmitted anonymously by vehicles in traffic to better understand the circumstances that lead to incidents.

Real-time data processing helps to prevent certain dangers. Cross-referencing weather data with information from cars on the road, such as the fact that several cars have activated their emergency braking systems, can warn other drivers of a risk. Information can be generated which, when shared, can prevent accidents.

Optimising water and energy management

In energy management, a data analytics approach can offer significant savings or drive the transition towards more efficient or renewable energy models. Real-time processing of production and consumption data, provided by smart meters, for example, makes it possible to optimise the distribution of available energy and avoid excessive losses. This can be applied to other increasingly precious resources, such as water.

Data analysis in a healthcare setting

Lastly, Big Data offers huge potential in healthcare. The analysis of large volumes of anonymised medical data helps improve our understanding of the probable causes of a disease, design predictive models for better targeted preventive approaches, as well as to identify risky drug interactions. Finally, large-scale data analysis is essential for the development of personalised medicines, and enables a set of rules to be drawn up for use in telemedicine.