Smart Data

Armin Gögele
last updated on
30 August 2023

Smart data describes data sets that offer meaningful information and are obtained by processing large amounts of data (big data).

The term “smart” refers to the data entry point which has to be intelligent enough to make immediate decisions on incoming data. Data quality, security, and data protection are important criteria in this context. Shifting the focus from big data to smart data has become a relevant trend: Having lots of data is not enough, the data needs to offer valuable information that can be used in a specific area.

Advantages of Smart Data in Hotel Marketing

Frequently asked questions about smart data in hotel marketing:

What is the difference between Big Data and Smart Data?

While Big Data focuses on the mass and collection of data, Smart Data focuses on the targeted usability and extraction of relevant information.

Why has the focus changed from Big Data to Smart Data?

The focus has changed because it is no longer enough to just collect data. Actionable information is critical to making effective decisions and achieving optimal results.

How can hotels use smart data for their marketing?

Hotels can use smart data by collecting, analyzing and interpreting relevant information about their guests. This allows them to create accurate profiles of their guests and understand their preferences, booking habits and preferences.

Based on this data, personalized offers and services can be developed that are tailored to the individual needs of the guests. By targeting the right groups at the right time, hotels can optimize their marketing campaigns and use their resources more efficiently.Smart data also makes it possible to measure the success of marketing measures and continuously adjust them to achieve better results. This helps to improve guest loyalty, increase guest satisfaction and ultimately increase the hotel's revenue.

How is data processed into smart data?

Data is analyzed and prepared using algorithms and specialized software tools to filter out the wealth of information that is relevant to specific application areas. This preparation involves extracting patterns, trends and correlations from the data in order to gain concrete insights.

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