In the era of Big Data, Data Driven Marketing and the synergy between physical and digital touchpoints, the topic of information management and data integration is becoming increasingly crucial.
According to data released by the Big Data Observatory of the Politecnico di Milano, in 2021, 78% of large Italian companies had adopted data integration strategies, but, of these, only 18% were able to reap the full benefits of these strategies, showing themselves to be competent and proactive, not only in protecting and maintaining these data, but also in fully exploiting their information assets. In fact, it is precisely on this last aspect that the game of digital transformation and the enhancement of the customer experience, which is one of its founding pillars, is played.
But, as we have seen from the data, it is not always easy: the integration and management of heterogeneous data, coming from multiple sources, is a daily challenge: it requires trained professionals, technological solutions that are up to the task and a management that is ready for change.
Data Integration: the key to omnichannel marketing
The term ‘data integration’ usually refers to the process of merging and integrating data from multiple sources. It may involve various stages ranging from data collection, data normalisation and mapping to processing by means of complex systems aimed at making such data easily understandable to those accessing it.
The main advantage of a data integration strategy thus lies in the possibility of building a 360°, complete, unambiguous and up-to-date view of the customer, the starting point for effective marketing strategies oriented towards the personalisation of the customer experience in an omnichannel perspective.
Information from multiple sources, but which data specifically? Usually, when we talk about data integration, we refer to five different types of data:
- machine-to-machine data: this includes data generated as a result of the interaction between electronic devices;
- people-to-machine data: this is data that is generated by the interaction between people and electronic devices (an example of this is data concerning online purchases);
- people-to-people data: this is information that is generated by interaction between people and therefore concerns more specific channels such as social networks, blogs, forums;
- public admin data: these comprise data present within public databases and therefore available without any restrictions;
- enterprise data: these are those present in corporate databases and data warehouses.
Improving decision-making processes, devising strategies to strengthen one’s competitive advantage: data, if well exploited and analysed, represent a real gold mine for companies today, but the right mindset must be adopted.
Data lake and data warehouse: from silos model to integration
Whereas in the past, each department and business function collected data in separate repositories that were difficult to link together, today this approach seems to be outdated and many companies have shown signs of openness towards models that allow data integration in single environments.
In the context of data integration, two distinct storage models and approaches are usually referred to, but they can easily coexist. Let us analyse them in detail.
- Data warehouse: these are storage systems for large quantities of data from multiple sources. Here, the data are structured and normalised in order to make them available to the various business functions by means of relational databases that allow for rather complex queries and searches. The data, however, in this case, are not updated in real time….
- Data lake: in this second case, data are collected and stored, at least in an initial phase, in their original format. Data lakes make it possible, precisely by virtue of the fact that they store data in their original format, to collect large quantities of data.
What if the focus was on the customer and their customer journey?
Data integration and Customer Data Platform: user centricity
Dealing with the subject of data integration, we could not fail to refer to the Customer Data Platform. In numerous previous articles, we have examined its characteristics and peculiarities also in comparison with other solutions offered by the martech universe; in the field of data integration,
a customer data platform, certainly, represents one of the most powerful technological solutions to collect and normalise customer and user data from multiple sources.
Collection and normalisation of data in a single repository, but that’s not all: the richness represented by user data can, in this case, be exploited to the full, thanks to the possibility of deploying effective profiling and audience segmentation activities and, consequently, customisation activities of the online and offline customer experience.
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