Achieving Sustainable Enterprise Data Consistency

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Achieving Sustainable Enterprise Data Consistency

Moving from reactive problem solving to real time data driven insights through living data maps, smart data connectors on native cloud platform

Achieving data consistency and a higher degree of trust in the data driving enterprise decision-making begins with a good understanding of these data assets and how they are being used within the enterprise, currently. However, this can prove to be challenging and expensive exercise unless a standardized approach supported by helpful tools are used. Additionally, this exercise is best executed in a distributed manner within the organization where people who are closest or understand their data directly contribute towards capturing of this information.

Rapidly evolving business applications landscape and the deployment of Big Data technology stacks in organizations clearly requires the ability of to keep up with change and the ability to configure new data sources efficiently. For example, there is a significant interest in enterprises towards implementing data lakes to centrally manage the data assets and serve end users in a controlled manner. Such initiatives can help leverage data locked up in silos while meeting the overall data governance objectives of the organization. However, unless these data lakes initiatives are combined with initiatives for ensuring high quality, consistent data being made available to the users across the enterprise – the risk of the situation deteriorating back to the original state are high. Hence, it is important to have a data strategy that not only leads to establishing high quality data repositories but also establishes processes to keep them consistently and continuously updated and current.

The approach to achieving enterprise-grade data consistency requires capturing rich metadata with respect to the data being stored and used in the organization. Details with respect to various data sources needs to be captured – streaming / batch, SQL / NoSQL / File, location (cloud, inpremise, hybrid), and so on. The next level of detail should include defining the data lifecycle elements, data criticality, data replication details, backup / restore strategy including RPO (Recovery Point Objective) and Recovery Time Objective) parameters, and so on. Finally, data schemas, data validation requirements, frequency of updates, details regarding data users and their respective access rights, mapping data flows including any transformations and enrichment, etc. Employing tools that simplify and automate the initial data metadata capture process can help reduce errors and make it more acceptable to various stakeholders.

Based on the details captured, data management strategies will need to be finalized in order to address potential data quality and consistency issues for each data source. Specific strategies will be required to deal with anomaly detection, duplicates detection and resolution, tools to support record linkages, ability to identify and address any eventual consistency issues (typically associated with distributed and cloud-based systems), identify and address issues arising from late arriving data in streaming real-time systems, reconciliation mechanisms, and so on.Using ML / DL techniques to detect potential data inconsistencies can help improve data quality within the enterprise and reduce data reconciliation efforts, significantly.

Additionally, tooling for continuous monitoring across the enterprise data assets coupled with real-time alarms and alerts (in response to data anomalies and inconsistencies) will help make data consistency initiatives sustainable and successful over the longer term.

Advisory Services

Strategy Mapping and Assessment

In this stage, Veristorm sets the stage with key stakeholders in your organisation to define the scope, objectives and deliverables fir the business, based on our expertise in the data integration use cases in your industry. A strategic program map and project plan are developed to support interim and ultimate business objectives.

Business Case Development

In collaboration with program sponsors, this phase involves Veristorm's consultative approach of identifying and verifying the business justification for proposed solutions as well as an appraisal of the associated costs, risks, and returns in the recommended investments. This allows for the program feasibility and value determination.

Program Maturity Analysis

Veristorm then benchmarks the existing data integration platform against industry best standards to examine its inherit functional capabilities, so as to ascertain and make calculated recommendations with opportunities for any potential performance improvement program expansion as well as related investments.

Pilot Project Implementation

After the scope has been defined and benchmarking against existing technology and methods has been completed, Veristorm applies a combination of proprietary (vStorm Enterprise Platform) and open source technology platforms to fit your organization's requirements, targeted to optimal business outcomes.

Functional Performance Validation

Veristorm will lead a thorough performance validation to ensure system functionality and efficacy against anticipated program performance. We help define metrics source directly form the vStorm Enterprise platform and identify alternative means for data mining as needed, so as to ultimately create a plan of ongoing analytics that will bring the greatest value to your business.

Blueprint for Operationalization

The final step is the handoff to Veristorm's Implementation team, your organization's own internal Operations team, or an established service partner to deploy the program into production to be sustained and managed on an ongoing basis. We deliver a long-term strategy to operationalize the blueprint created for your business for technology, process and support.