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Competent work with data brings business to a leading position. Incorporating data-driven innovation into operational business processes provides up-to-date information across all enterprise directions, leading to more efficient operations. During the 4th industrial revolution, data is the fuel for Artificial Intelligence, Machine Learning, robotics, the Internet of things, etc. According to forecasts, by 2025 the digital transformation wave will reach 3.7 trillion US dollars.

Data by itself has no value. It acquires value with the right working approach: having a data strategy, skills to work with it, a management process and an infrastructure that includes software and technical tools for collecting, storing, processing and transmitting data.

Although infrastructure is an important element of the data workflow, starting with infrastructure is not entirely correct. Sure, it will be necessary to invest into devices, applications, platforms and services that will provide efficient work with data. But the work begins with setting goals and developing a strategy, tailoring the tools to the strategy, challenges, and business issues.

With the increasing desire to capture data value and the growing demand for the technical means to enable it, the market for platform and solution providers has expanded significantly. This situation made it possible to reduce the entry barrier to work with advanced technologies and analytical solutions. Some of these offerings are called infrastructure as a service. Selecting the right product from a wide range of products requires doing a lot of research, understanding business needs, and identifying questions that need to be answered by introducing a new product.

Key functions that the infrastructure should provide:

1. Data collection. Injecting internal (transactional data, customer feedback, cross-departmental data) and external data (data from social media, public sources, purchased third-party data) into the infrastructure stack. The process of collecting streaming data in real time must also be provided, which requires a reliable collection infrastructure.
2. Data storage. Depending on data privacy level it’s possible to store it locally in own storage or in the cloud. Cloud storage providers provide free access to data for business users from anywhere. It also reduces the initial cost of setting up own servers, energy, and security.
3. Data processing and analysis. At this stage work begins with machine learning, computer vision, speech processing, neural networks, etc. The main task here is to find a solution for preparing and cleaning data, building analytical models and extracting valuable information from unprocessed information.
4. Obtaining information and disseminating it to business users. It is the stage of data visualization and reports creation with the help of which business users can make decisions, share information, improve internal processes efficiency, create improved products or services.