The digitization of business models, with the proliferation of cloud platforms, mobile applications, social platforms and smart objects, along with those of our private views, open up new scenarios on the collection, use and potential of huge volumes of data.
With the Internet of Things (IoT), each interconnected object provides data useful for companies’ decisions and choices so as to build an increasingly knowledge-based reality. Strategies of enhancement of data made available by the IoT are still poorly developed to their full potential and it is precisely this that will be one of the key aspects for development in the sector. Analysis of recent research by the IoT Observatory of the Politecnico di Milano shows clearly that the more the spread of solutions and IoT devices in different sectors is consolidated the more the focus shifts to how be able to derive value from data made available by the connected objects. In fact, these data can be used in internal corporate processes, for example to reduce costs and/or improve the effectiveness of services to customers, thanks to a better understanding of characteristics and needs. In this case, reference is made to internal enhancement of the datum.
There are many examples that can be considered a model in this regard: the reusable containers equipped with RFID (Radio Frequency Identification) tags for reducing control costs in rental processes to intelligent street lamps for optimizing energy consumption and maintenance, up to Kit15s in the Smart Home context that allow insurance companies to improve their competitiveness by offering new services such as 24 hours a day assistance by sending a technician in case of need, and reduce the cost of policies made possible by the reduction in the number of frauds and a lower level of damage.
But obviously these same data can also create value outside the company through the sale to third parties; in this case, it is a question of external enhancement of the data, which actually opens up new business opportunities that were not necessarily known at the time of realization of the IoT application. For example, data on energy consumption in households, used by Utilities to make the internal process of billing more efficient, could be re-sold to parties interested in improving forecasts of aggregate energy consumption. In many cases, the mode of use of these data are only partly known when designing an IoT application.
The ability to correlate data collected by IoT solutions with other information, internal or external to the company itself, it becomes fundamental for their full enhancement. Data are, in fact, linked more and more to company pricing strategies, in some cases enabling new sales logics based on actual use, in others justifying significant discounts on products that can be picked up. In the Smart Car context, for example, the data collected by sensors in the tires or the engine are beginning to be used to allow the user to pay on a periodic basis, depending on the mileage, and no longer once at the time of purchase. In other cases, for example with regard to some wearable devices (smart wearables), the sale of related products is encouraged in order to have access to the collected data, which are an effective source of value for the company.
These are some further examples that confirm the centrality of the datum in IoT applications: the value of which may be important enough to justify discounts or even free offers of products and services.
From analysis to action
But for transforming Analytics into Action, according to SAS Italy, an approach based on three pillars is necessary: Data, Discovery e Deployment. Data obviously underlie the analytical process and must be able to be managed in an efficient way, ensuring their maximum quality. Discovery, on the other hand, must be able to allow any type of user, even without technical skills, to explore data independently. Finally, through Deployment, the analytical models must be able to go into production for distribution within the organization. In this context, Data Management adds certainty to the analysis because it ensures that the provision of business processes and corporate roles are always effective and functional for the achievement of business objectives, with the growing number of data available and the best chances that Analytics provide the business with a strategic vision and decision-making process.
Data Management appears to be increasingly taking shape as a key resource for business management, both for small and medium enterprises (SMEs) and for large organizations that have to manage complexity and large amounts of data every day to transform them into knowledge and, above all, business opportunities. Data therefore need to have a certain form or starting structure to be able to effectively support the decisions that a company needs to take every day for the growth of its business. Good Data Management therefore need two key tools: Data Quality and Data Integration.
Data Quality is the set of processes that ensure the compliance of data value with business needs. Companies have always had two clear and precise objectives: increase sales and provide a better customer experience to their customers. With the advent of Big Data, these objectives have become more and more concrete but, at the same time, more and more subject to challenges.
Data integration, on the other hand, is the combination of processes and technologies for deployment in the company of business data from multiple sources, transforming and aggregating them into a single format with the objective of unifying the results and increasing their value.
Analyzing all the touchpoints of the customer journey makes it possible to identify the behavior and habits of consumers, making it possible, for example, to create customized marketing campaigns directed at specific targets, improving results and avoiding waste of resources and unnecessary costs. Access to and processing of data from different sources has run into many limitations over time, especially at the moment of choosing suitable tools and their degree of compatibility.
Standardizing is now the watchword, both in terms of the sharing of rules internal to the organization and of product. Effective data integration, combined with appropriate Data Quality, in fact helps to incorporate all types of unifying data in one system. This translates into lower costs and benefits in operational terms and support for business decisions. A wide variety of Data Integration programs and/or business initiatives can be followed, always taking the quality of data at each level of the integration strategy as the center of focus. This causes any new data that enter the company not only to be added to the amount of information already present, but to multiply their value by combining with current data. According to a recent SAS Italy study, the best performing organizations will be precisely those who already have in place a clear strategy which recognizes Data Integration as the base on which to establish their competitive advantage. This will allow greater simplicity, flexibility and speed of reaction.