Self-service data quality
Data quality without the need for programming skills
For many data experts, it is frustrating that many organizations use data quality tools that need programming skills to monitor and improve data quality. And for IT experts, it is frustrating to receive seemingly ad-hoc requests from data professionals to correct some data issue, particularly when these requests compete for time with the project deliverables you have been tasked with.
Adopting a self-service approach, the Datasynthesis platform offers no-code data governance and data quality management. This frees the data expert from the dependency on IT time and resources needed to address data issues and increases the productivity of the IT expert through leaving data issues to the people who know the data best. Combining this approach with the scalability of the platform enables data experts to ask questions such as “Tell me about any data issues right now” and receive enterprise-scope responses in real-time, delivering a new level of transparency for enterprise data quality.
Data governance directly driving data operations
Your data policies driving innovation
Many organizations lack enterprise data standards and policies. For those that have implemented a data governance program, the outcome can prove to be disappointing. Stale policy documentation and data catalogs that are out of date in reflecting data operations.
The Datasynthesis Platform takes the concept of metadata management to the next level, using metadata to automate data integration, transformation and validation. This approach moves data governance from being a passive observer of data operations, to an active control centre that is directly driving data operations.
Adoption of industry data standards
Better understanding of your data and reduced reconciliation effort
Whilst data locally within a specific tool might be of good quality, enterprise data quality can still be heavily compromised by poor consistency across systems, departments and geographies. Inconsistent data leads to confusion over what data means in one system when compared to another and drives the need for resources to be employed on reconciliation between different silos of data.
The Datasynthesis Platform uses a Common Data Model (alternatively known as a Canonical Data Model) to represent all data entities and relationships in their simplest possible form. For each operational system only one transformation is needed from Canonical form to that of the system’s local data model, allowing enterprise consistency to be maintained through this common understanding used by all systems. Combine this enterprise level understanding with industry standards such as FIBO and you have a platform for increased data collaboration and reduced reconciliation.
Structured and unstructured data
High data quality regardless of data type
Whilst structured data is still of vital and fundamental operational importance, newer sources of data are not always tabular in nature. Data quality management is important for unstructured and semi-structured data too, and this is not possible if your data quality tools are designed to only support traditional structured data.
Graph database technology enables the Datasynthesis Platform to manage complex relationships between data, regardless of data type. Enabling unstructured data to be brought into the capabilities of your data quality initiatives means you can control any type of data more efficiently and more consistently.
Real-time data quality
Pro-active prevention of the spread of data issues
Many errors are only identified downstream in the destination systems that need the data. Without the propagation of the correction back to the data source, then at best this results in multiple data teams identifying and duplicating corrective action. At worst, data errors are corrected locally in just one system, leaving the other users and systems unaware of the issue. This siloed approach to data quality, often with multiple data quality tools used across multiple departments, leads to inefficiency, expense and inconsistent data quality at an enterprise level.
The cloud-native scalability of the Datasynthesis Platform enables a much more pro-active approach to be taken to enterprise data quality. Rather that manage data quality downstream on a local value-by-value exception basis, data quality can be managed upstream as an enterprise process. So no waiting for batch processes to complete, data quality for the entire enterprise can be monitored in real-time on data quality dashboards and issues mitigated before data errors have spread to downstream systems and users.
As at historic data versioning
Reproduce your universe of data at any point in time
Being able to reproduce the data used in reports at any point in time is a key capability for dealing with regulatory, risk and audit reporting requests. Due to architectural constraints, many data tools lack this capability, but even for those that do have it there are often significant issues. One key issue is simply the scope of the data, rules and transformations that the data tool manages is constrained to its own local world of data, rather than flows and transformations across and between downstream systems and operational data stores.
With the Datasynthesis Platform, your policies automatically define how much history is stored, and the raw data feeding in and out of all systems in your data ecosystem is available wherever and whenever you need it. So you can keep the regulators happy by being able to reproduce the state of data at any point, in time for any part of your data architecture.
Lineage for the entire lifetime of data
Track your data flows across all your operational systems
Being able to understand the flow of your data is vital for increased efficiency and security in how data is procured, ingested and used. Due to constraints in scale and scope, many data tools can only tell you how data flows locally into the tool, how it is transformed and where the tool exports it to. You are left guessing where the data came from, and more concerningly, where it flows to.
The Datasynthesis Platform tracks all data flows throughout the lifetime of data, enabling clear understanding of which systems are dependent on what sources, and whether data is being used in compliance with its licensing terms. Given that the platform is used to define operational data flows, all data lineage reporting is an accurate and direct reflection of your live data architecture, so lineage reports will automatically update as your data architecture changes.
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