Digital transformation and data-driven operations and decision making are consistently increasing their presence in a broad spectrum of sectors and are high on the strategic agenda of organisations and institutions.
The need to transform data into assets that can produce actionable information and ultimately increased or new value, several technological and operational challenges must be efficiently met. A major one is the challenge of managing and processing the vast amounts of data that are required to maximise the value of analytical processes. Such data include not only those available in legacy persistence solutions, but also data that are dynamically generated or streamed from IoT devices, relevant third party collections, simulated data, etc.
Problems of scale and heterogeneity are intensifying and overwhelm data analysts and decision makers, hindering the process of adapting to the new reality.
To harvest the advantages of Big Data analytics and maximise value, an effective data scaling strategy along with corresponding technical solutions must be adopted.
The Data Fabric approach, i.e. the notion that discrepant, remote and heterogeneous resources can be federated and integrated in such a way to provide a single entry point to access multiple endpoints, is in our view integral for achieving economy of scale and high quality of analytics.
At the same time, Data Fabric simplifies data management by homogenising cloud and on-premises storage, as well as distinct repositories with their own management and access mechanisms (remote databases, API-exposing repositories and data portals, graph databases, etc.).
Such novel architectures will help moving past isolated data silos, monolithic platforms and cumbersome, error-prone data exchange processes. They will be fundamental in the effort to build connected data ecosystems, where cloud-based, in-premise and hybrid solutions for Big Data can co-exist and cooperate, under a simple management paradigm that promotes privacy, provenance and intellectual property.
Using large-scale harvesting, data linking, semantic harmonization and big data technologies, we offer bespoke data fabric systems with the following capabilities:
- distributed data cataloguing
- data transformation
- semantic alignment
- heterogeneous data integration
- large-scale data analytics