There is enormous potential in the data. Today, more than ever before, companies are trying to use this potential to improve their market position and are increasingly bold in their use of Artificial Intelligence (AI) and Data Science to secure their leadership position.
The success of digital champions such as Amazon, Uber and Netflix is almost entirely based on customer data – skillfully used by them to generate added value and expand into new areas of the digital ecosystem. It is becoming increasingly clear that “money follows data”. Maintaining competitiveness requires companies to make full use of their digital data not only to be more efficient, but above all to identify and exploit new business opportunities.
Why should business strategies be data-based?
Today, companies are acquiring large amounts of unstructured data, such as text documents, audio and video files, emails and graphics. Analysts predict that by 2022, more than 50% of a company’s data will be created and processed outside the data center or cloud, and by 2025, this percentage will rise to 75%. Data is at the heart of every digital transition process.
So how can organizations use their oceans of data to create innovation and grow their business and generate new revenue? Unstructured data is little more than a stream of unconnected information. The real challenge, however, is to make sense of it: You can’t tap into the potential of data without streamlining and integrating data flow and processing across the organization and its supply chain. For Artificial Intelligence and Data Science to work, you need a flexible, agile and efficient structure.
Why is monetization of data difficult?
Analytics is commonly considered to be a magic wand that can miraculously turn data into a revenue stream. However, it soon turns out that it is not that simple, and transformation projects implemented by organizations begin to get bogged down in unforeseen difficulties. Despite being aware of the potential of information resources, most organizations are initially poor at identifying interdependencies between data, developing an information management model for all locations (physical objects, clouds or both), protecting data against loss or implementation of appropriate security measures, and using AI and Data Science to obtain the information the organization needs.
Building an organization based on data with Fujitsu
The data-driven digital transition project with Fujitsu consists of four key areas and is focused on increasing the value of the company. Each layer is crucial to the success of the transformation process. Economic value is a direct result of the right choices and the application of data science and AI, while the choices themselves depend on the right strategy and architecture.
1. Determining the starting point for the data transformation
In undertaking the cooperation, Fujitsu tries to assess the current situation of the organization first. To this end, together with all stakeholders from across the organization, we conduct a workshop where we jointly analyze the available data and the current use of the data, and closely observe the environments in which the organization operates. At this stage it is also important to discuss the business strategy and expected project results. The result is a document describing the starting point for the whole process, future data architecture, data protection measures and Data Science and AI technologies necessary to achieve the expected value.
2. Creating data architecture
The challenge here is to create an architecture that allows full access and control of data in peripheral devices, company infrastructure and the cloud. No single solution can provide an immediate deployment of a distributed data architecture. This requires extensive hardware and software integration and collaboration with cloud service providers. Fujitsu defines the target architecture together with the client, analyzing the hybrid landscape for platforms, storage media, workloads, and data management.
3. Data protection
Real-time data analysis and the use of AI is now a key element for monetizing data. This stage of the transformation aims to define methods in the area of Data Science and Machine Learning that will cost-effectively support dynamic data models and handle data of different formats and volumes, while allowing for real-time analysis. In this phase of the process solutions are being developed that will allow to obtain previously unavailable information hidden in the data held by the organization, which the organization already bears the storage costs.
4. Generating economic value
The digital transition process means greater data protection and security requirements. It’s not just about making backups, it’s about protecting data integrity and ensuring that it’s available every time a company needs it. Fujitsu is working with its customers to analyze existing issues, helping them to develop solutions for data protection and security systems that provide effective protection against external threats on a continuous basis. Importantly, data protection must also be implemented on data collection platforms to create information sources that can be used securely by analysts and AI.
Success built on data
Fujitsu works with clients in every aspect of the transition to a data-driven enterprise and at all stages of this project, from initial consultation to the development and implementation of solutions. Many organizations have already used Fujitsu’s experience to unlock the potential of data and use it to improve their performance.
Learn more: fujitsu.com/data-transformation
Dariusz Kwieciński, managing director, Fujitsu Eastern Europe