One of the biggest benefits of implementing the Datasynthesis Platform is increased trust in the quality of enterprise data. Increased trust naturally leads to less resources being used on data reconciliation, data validation and encourages more data collaboration and innovation. This in turn means greater efficiency in finding and accessing good data, better decisions taken and increased profitability from new ideas.
In addition to these people and process benefits, data architecture is simplified, and costly point-to-point data tools can incrementally be rationalized in favour of a single set of enterprise-scalable data quality services. The result is a data architecture that is dramatically simpler, less costly and built for whatever your business wants to do next.
How does the Datasynthesis Platform scale to meet new requirements?stuk2021-01-28T14:54:03+00:00
The platform uses open-source data technology within a modern, serverless cloud-native design. In case that may be too many hyphenated technology terms for some, lets break that down some more.
The open-source data technology part is what some journalists might refer to as “big data” technologies. Given the original designers, ongoing development contributions and internet-scale usage patterns of technologies such as Apache HBase and Apache Kafka, the Datasynthesis Platform is built using technology components that are proven and reliable.
These data technologies are used within our platforms serverless, cloud-native design. A serverless architecture is a way to run and build applications without having to manage infrastructure, and is closely aligned concept of cloud-native design. Many vendors claim that their tools and applications are available as cloud services, but more accurately such vendors should describe their products as “cloud-based” since the underlying architecture has not been changed significantly, and as such are still limited by their on-premise, typically client-server, design. In contrast, a cloud-native design has been built only for the cloud, to take full advantages of its effectively infinite processing and storage capabilities.
Putting the technical descriptions to one side, then in summary all this means that the Datasynthesis Platform automatically scales to perform well regardless of processing needs, storage requirements and the number of simultaneous users. And with scalability removed as a constraint, its possible to deliver services to all users that are more complete in scope, more timely and less complex to use.
Why is scalability so important to data management?stuk2021-01-28T14:53:37+00:00
The lack of scalability in many legacy systems promotes a defensive approach to data operations. Processes take longer because they are batched into separate sequential jobs rather than run in parallel. Data quality has to be checked after a process has finished, allowing data to enter the data ecosystem and delaying how quickly problems can be fixed. Business users are not allowed to look directly at operational data so as to prevent them from slowing production processes. New departments and data needs mean multiple instances of data management software, increasing software licensing costs and the complexity of reconciling between systems.
Even within the field of data management, lack of scalability naturally biases vendor solutions towards segmentation by data type and data operation, whether that be data governance, data integration, data quality, data mastering or data collaboration. If the constraint of scalability were effectively removed, data management processes could be designed that were enterprise in scope, that operated in real-time, that were simpler and that encouraged much greater interaction and collaboration around data from all departments and all users, regardless of technical knowledge and ability.
Why was Datasynthesis Platform created?stuk2021-01-28T14:53:12+00:00
The founders of Datasynthesis thought the timing was right for a new data quality management platform due to a variety of factors. Firstly, their fundamental belief that data should be managed by data experts. Secondly, that the size of the data management problem was accelerating beyond the capabilities of current vendor solutions and traditional data architectures. And thirdly that open-source data technologies and serverless, cloud-native design had matured to the extent that would enable data, rules and transformations to managed at an enterprise level, effectively without limits on processing power, storage capacity or concurrency of users.
What are the main concepts behind the Datasynthesis Platform?stuk2021-01-28T14:52:45+00:00
The main concept behind the platform is our belief that control of enterprise data should reside with data experts, and that this can be enabled through providing no-code control of the entire data manufacturing process. Put another way, data experts should be able to govern and manage data without having to wait on technology resources, and technology experts should be able to concentrate on strategic deliveries without the ongoing burden of ad-hoc data requests from business users.
What is the Datasynthesis Platform?stuk2021-01-28T14:54:53+00:00