Operational Data Store
Are your operational processes complicated and slowed down by a fragmented data base? Are you receiving key documents late? Get a data storage solution that really simplifies your life.
Let's talkDo you need to improve and speed up your operational processes?
The right ODS can simultaneously support both the operational and operational-analytical aspects of your processes, allowing you to focus on the business. It will work with your existing processes and systems, so it won't constrain you in the short or long term.
Field-proven operational data warehouse solution – for supporting operational processes and downstream analytical tasks
Our ODS solution consists of proven methodologies, practices, design patterns, data models, technology solutions and tools that ensure maximum efficiency. It also places a strong emphasis on metadata.
This approach ensures the long-term sustainability of the solution and enables the use of Model Driven Development techniques where the majority of the program code is generated.
This ensures maximum development productivity (and thus lower cost), a high degree of standardization and the use of all our experience gained from many practical ODS implementations.
From an application point of view, ODS can be used to integrate customer or product data. Not to forget the potential of ODS solutions as a database for campaign management or master data management that can be easily linked to ODS.
80 %
faster critical operative processes
85 ms
(milliseconds) is the average response time of our
ODS to user requests
Build data warehouses according to uniform rules and standards
Operational Data Store (ODS) can fulfill the following roles:
- Data storage for operational processes
- Data storage of master data
- Shared cache of other systems (data integrated or non-integrated)
- Support layer of the data warehouse (stage layer)
- Data store for operational reporting
- Replacement for legacy applications (ODS provides data from defunct/extinct systems)
Regarding data integration, ODS can be built on various data-mining methods:
- Replication of tables from primary systems
- Replication of transactions from primary systems
- Integration of transaction from primary systems
- Deriving data from the data warehouse
Regarding the frequency of performance and data updating, ODS can be divided into the following groups:
- Adhoc performance or performance upon demand
- Monthly performance
- Daily performance
- Performance several times a day
- Performance in almost-real-time
- Performance in real-time
Regarding the logical architecture, our solution consists of several components:
- Data storage
- Transformation layer
- Communication layer
- Tools for data storage administration and development
- Tools for transformation and communication of metadata development
- Tools for other metadata and documentation administration
Data storage
Data storage is the cornerstone of the entire solution. It preserves the data, as well as some selected metadata, necessary for a fully-functioning solution. As a repository it uses proven, tested relational databases. The data model can be designed according to customer needs, or, we can use one of our tailor-made industry models, such as those used in banking and in financial institutions.
Tools
We place great emphasis on our solution tools. Adastra tools ensure easy central administration of metadata solutions, administration of data modules, administration of data transformations and accurate data flows, including their timing, creation of documentation, solutions monitoring as a functional entity and integration of the communication layer. We understand the importance of metadata and their follow-up usage for development and operations.
Transformation layer
The transformation layer is the main carrier of application logistics in the ODS solution. It enables transformation between the outside world and data storage. Primarily, it is the API which enables the processing of individual data records or ETL/ELT, which processes entire groups of data sets.
This principle of the filtering layer screens the outside world from the internal data module enabling modifications to the model according to actual requirements and without the necessity of rebuilding everything. The transformation layer can carry out data transformations in real-time, almost-real-time or in batches, according to the customer needs and requirements.
Communication layer
The communication layer connects the ODS with the outside world ensuring the transformation of input and output communication. Essentially, this layer technologically translates communications into a format that ODS can process internally and vice versa.
This modular attitude enables us to implement our solution effortlessly into environments that require a specific way of communication, such as web services, specific connectors or specific formats of communicated message. Moreover, the possible communication tools can be easily modified when required.
Case studies
Equa Bank clients were fully migrated to Raiffeisenbank in 12 hours
When Equa Bank was being mergedintoRaiffeisenbank in November 2022, we handled the migration of Equa Bank’s client data into Raiffeisenbank’s CRM system. We also ensured the client master data was propagated to the bank’s core systems.
ŠKODA AUTO: data transfers on analytics platform are comprehensively managed and monitored by Adastra’s Adoki
In 2018-19, Adastra built an on-premise Data Analytics Platform (DAP) at ŠKODA AUTO. Its purpose? To visualize data and use advanced analytics and artificial...
Automatic categorization for 98.5% of card transactions
With millions of clients conducting millions of operations every day, the bank needed to automatically assign a unique category to every banking transaction (card...
Just-in-time loan offers: a 10x higher conversion rate
Together with the bank, we used several years’ worth of transaction descriptions and a number of transactions of a specific type to identify a...
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