Chapter 2

4 trends that support this vision

  • 1

    Data governance becomes priority

    Data governance, or the proper management of critical data assets including processes, architecture and required infrastructure, is high on many priority lists. We see that many companies are in the process of properly investing the responsibility for data and related processes. Indispensable if you want to process data in a good and safe way. At the same time, data governance is a challenging topic, because it has an impact on all layers and processes in an organisation.

  • 2

    Cloud Analytics provide the necessary speed and flexibility

    We expect the use of SaaS Analytics to continue to grow exponentially and to mature. SaaS Analytics are services such as Microsoft Azure Machine Learning Services, Azure Cognitive Services, Microsoft Pureview and of course Power BI. Plus Azure Synapse as a full SaaS Enterprise Data Management Platform. These as-a-service analytics services offer flexibility in use and speed. You can trust it with your data.

  • 3

    Companies are building a strong data culture

    There is a data culture in an organisation when everyone is convinced of the importance of data, employees work with data with confidence and use data to make informed decisions.

    In organisations with a strong data culture, data discovery, data visualisation, storytelling and insights leading to improvement actions come together. For example, these companies rely on user-friendly data and on storytelling and visualisations to tell a clear message and provide better insights. So that everyone can make better decisions and take immediate action every day.

  • 4

    Bringing hidden information to light with Artificial Intelligence

    'Data influence' is about using algorithms to find connections that people cannot find themselves. The aim is to use these algorithms to benefit the company's ambitions and strategy. Data mining, machine learning and AI are being used to exert influence. This way, you make decisions based on forecasts and predictions revealed by data patterns combined with algorithms.

From data collection to data application

Inherent to these trends is the shift in focus from collecting and storing as much data as possible to looking at its value and applicability. At the same time, this means that we are becoming more critical about our investments in data and the added value we can realistically expect.