Data-driven services are disruptive extensions of the existing business model and a logical consequence of today’s pervasive digitalisation. They often emerge from big data technologies and are the operationalised form of prototypical use cases. To identify and design these services, professional big data engineering is needed.
Not only does digitalisation enrich our everyday lives and is noticeable in every aspect of life, it also supplies an unprecedented amount of data of any structure. This data opens up new potential for companies. Big data engineering can help companies make this data usable. This focuses on the use of modern technologies for the distributed processing of polystructured data sets, often through the construct of the data lake. As a result, there are no limits to the integration and processing of data or the discovery of new data-driven services.
Whether it’s an enterprise data platform or an innovative data lab, your organisation and your requirements have a significant impact on choosing the right big data strategy.
From the technical description to valid source code, big data engineering can bring your data-driven services to life.
From the functional description to the right technology, the abstract functional requirements need to be determined before suitable technologies can be chosen and your big data architecture defined.
No one knows your current business model, challenges, processes and potential better than you. In a joint Interaction Room:analytics with the specialist department, IT, management and even the end customer, we concentrate this knowledge and fill a backlog with data-driven services, forming the basis for your data journey.
Companies are faced with the challenge of gaining new insights from data and managing the step from descriptive analysis to innovative and data-driven services. adesso can advise you on these projects and, through a multitude of customer projects, has identified two proven strategies. By starting with the Interaction Room, value-driving requirements are recorded in qualitative terms. The decision as to which strategy is the right one for you is based on the requirements and is an important aspect in anchoring your big data strategy.
We differentiate between a data lab and a data platform. A data lab meets the need for fast and measurable results. With compatible and cloud-enabled technologies, implementation can begin quickly and specialist expertise can be brought into focus. A specific implementation strategy, technology selection and source integration are defined for each use case. The lab is separated from the operative IT landscape to avoid dependencies. This practice is called bimodal IT.
The data platform is all about standardisation. The goal is to create a company-wide platform for the uniform use of all data, from operative systems to data-driven service. The data platform enables synergies between data-driven services to be leveraged by selecting homogeneous technology.
We define your big data architecture together with you based on the strategy. In doing so, we focus on a goal-oriented selection of technology at the beginning of your data journey, while also considering the scalability and operationalisation of your data-driven services and defining meaningful metadata and processes for setting up an Enterprise Data Catalog right from the start.