In the last decade we have seen a major change in consumer profiles, with a migration from 100% in-person purchase journeys to 100% digital or hybrid (‘phygital’) journeys, with interaction at both physical and digital points. Retaining this audience has become a great challenge for companies, as today the customer has a greater quantity of choices available for online purchasing – regardless of the market analyzed – and values the experience during the product purchase journey much more than the benefits of the product itself.

To succeed in a business in the digital era, it is crucial that companies provide their audiences with a real differentiation in their product or service offering, taking into consideration the improvement of the experience that their customer has during all stages of the purchase journey, from searching for their product on the internet, to using the product and recommending new customers on social media, for example. All of this, always taking into consideration digital, sales platforms with their audiences, and the computerized systems that your company needs to operate. There are several ways to improve the experience of consumers or collaborators, and they all start from a fundamental question: who is your user and how do they behave?

By understanding user behavior, your company can make adjustments to the experience journey of your digital platform in order to generate a positive impression of your consumer that enhances positive reviews and consequently the recommendation of new customers. Only by understanding the desires, pains, and ways of thinking of your user will it be possible to adapt your product to the real needs of your audience. Therefore, data analysis takes on a role of extreme importance that should be prioritized in the roadmap of your business evolution.

Do you know what a Data Lake is?

A Data Lake is a repository that unifies all structured and unstructured data that can be collected from your platform. The advantage of the Data Lake is that we can divide data storage into several layers, performing different processing and treatments to generate insights and relevant information for the business area.

From a Data Lake your company can extract metrics and reports that will provide valuable insights that will guide the business team to set goals and priorities to increase your product’s competitiveness.

The more data is captured and analyzed, the more assertive the prioritization of your product’s backlog will be. Your business planning becomes data-driven, understanding exactly the needs of your audience and optimizing their experience. It is even possible to segment data by audience type or business areas and use them not only to trace a roadmap of features, but also to direct your marketing and sales team for a closer and more positive communication with your user.

Since a Data Lake supports structured and unstructured data, your company has the ability to plug in different data sources in order to extract metrics.

What can be connected to a Data Lake?

With a Data Lake, data analysis is not limited only to structured data in traditional databases. It is possible to ingest access logs, Google Analytics data, sensor data, and any other data sources specific to your business. By unifying different data sources, data processing can cross-reference sources to build intelligent models, capable of understanding your customer’s behavior and needs. Thus, your platform is able to offer personalized experiences according to different behaviors of your audience.

How to create a Data Lake?

There are several best practices and methodologies for creating a Data Lake. In general terms, Data Lakes can be divided into four pillars:

• Data sources: It is the set of sources that can generate data for the Data Lake. They can be structured data such as corporate databases or unstructured data such as social media information and analytics.
• Storage and processing: Data is processed and stored within the Data Lake. Here it is possible to create different data treatments and divide the Data Lake into layers so that cataloged and treated data serve different analyses and purposes.
• Advanced analytics: This pillar implements advanced algorithms of Machine Learning, artificial intelligence, and statistics for more complex analyses such as behavior prediction models.
• Business Intelligence: Data is consumed by analysis tools to generate reports, metrics, KPIs and serve as triggers for marketing, sales, and backlog prioritization actions.

How do I start building and using a Data Lake?

To start building the Data Lake, some questions are important to answer, such as:

• What are the known data sources?
• How often is the data updated?
• What analysis expectations do I have?

It is necessary to define the architecture of your Data Lake well before starting the construction process.

What is the cost of a Data Lake?

Fortunately, cloud service providers such as AWS, GCP, and Azure offer services for creating your Data Lake with costs often based on data ingestion, that is, on-demand usage.

This way, you can avoid unexpected memory or computing power costs by using cloud tools.

Have any questions, or need help?

Zappts can help your company build a scalable and robust Data Lake that brings relevant insights to your business team. Since 2014, Zappts has been delivering business results through technology, delivering high-relevance cases using agile processes, best development practices, and software quality, as well as an unparalleled ability to understand and solve the day-to-day pains of technology development projects.

We offer solutions for all phases of digital solution development, from discovery processes to solution support. With defined processes and agile management, our deliveries are always based on expectation alignment, quality, and technical robustness.

Contact us by clicking here, and learn more about Zappts.

Continue your studies, check out our material on “Data Lake: creating a searchable document repository“.