Two-thirds of digital marketing specialists consider data-driven decision-making to be far more accurate than relying on instinct. This is highlighted in a survey by Econsultancy in collaboration with Google, which also emphasizes that seven out of ten marketing experts already engage with a data-driven culture at every level within their organizations.

Companies are increasingly aware of the data collection and exploitation potential for the successful targeting of their business. However, to achieve the established growth objectives, it is necessary to break the silos and opt for transparency when accessing the companies information.

Data lakes and machine learning techniques are tools that contribute to achieving this goal. They help gather and classify varied and large amounts of data to be used by all departments (the c-suite, Marketing, Sales, Customer Service, etc.) and predict future trends with a high percentage of accuracy. Let’s see how business decision-making becomes easier and more reliable thanks to data lakes and automatic learning.

What are data lakes and what are their advantages

Data lakes are repositories that store raw data of all kinds, both structured and unstructured, from various sources, for preservation and analysis.

The use of data lakes has advantages over data warehouses and, overall, makes it possible to overcome the limitations of the isolated data exploitation source by source, responsible for the creation of disconnected information silos:

  • Adding new records to data lakes is simple and flexible: only a metadata-driven tagging scheme is required to simplify the extraction. In data warehouses, on the other hand, data must be previously transformed and normalized to fit a relatively rigid field structure, usually oriented to specific reporting needs.
  • Data lakes support data from many different sources: from structured files processed by third parties to raw documents, records collected by sensors and IoT devices, geolocation signals, activity on social networks and also content in audio, video and high-resolution images.
  • Data lakes open the door to self-service data access protocols within the company. Each department or international division can extract the set of insights that interest them and create their own reports and dashboards with minimal collaboration from IT specialists.

With the help of data lakes, it is easier to get a better picture the users’ buyer’s journey. By accepting a mix of sources in the data flow, it is possible to gather information about online and offline touchpoints in one place and accurately reconstruct the customer’s buying journey.

Alex Masip, Head of Data at Labelium Spain

Machine learning applied to marketing data lakes

As they are prepared to hold a huge volume of information, data lakes are the perfect site for the application of machine learning algorithms.

Data lakes are the favorite testing ground for data scientists when testing the potential of machine learning. Without it, it is necessary to conduct each analysis with a specific objective in order to draw conclusions. However, with machine learning, it is possible to develop valuable business intelligence and segmentation without having to define what you are looking for.

Alex Masip, Head of Data at Labelium Spain

In the digital marketing field, some of the most common uses of machine learning in data lakes marketing are:

  • The identification of the different consumer segments and their purchase patterns.
  • The anticipation of possible cancellations (churn rate). Through automatic learning, signals that indicate that a customer is going to end his or her service subscription contract are detected. In this way, there is room to offer you more favorable conditions before their departure becomes effective.
  • Hyper-personalization of the brand’s messages during the interaction with the user, providing a true all-round shopping experience.

How do data lakes and machine learning help businesses?

The implementation of a data lake and data analysis using machine learning techniques is the keystone of a data-driven marketing strategy and ultimately ensures that decision-making is supported by the possession of useful, valuable and up-to-date information.

The consolidation of all data collected by the organization, and its detailed processing of these, even in real time, leads to better management of the relationship with the customers. Predictive models based on machine learning give clues about future behavior, both global and individual:

  • At a general level, they are used to identify variations in demand and anticipate external conditions that may impact the business’  future.
  • One person at a time, they favor omnichannel, facilitate up-selling and cross-selling as well as anticipating possible incidents.

In large companies and multinationals, the use of cutting-edge technology in data analysis is essential in order not to lose ground in highly competitive markets. According to the forecasts by the International Data Corporation (IDC), in 2023 companies will invest 97.9 billion dollars in machine learning software, a figure 2.5 times higher than that recorded in 2019 (37.5 billion dollars). Therefore, turning our backs on data lakes and machine learning is not an option.

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