Building a Dynamic Data Architecture to Deliver Data as a Service

by July 7, 2020 0 comments

Data ArchitectureWith a flexible data architecture, businesses can drive data-driven decisions.

Over the years, data has been a crucial foundation for organizations across almost every industry. But businesses would not have much techniques and tools to extract meaningful insights from the data they collect. As business leaders these days have realized the significance of data virtualization and effective data management, they must embrace the right data architecture that can help them glean, store, analyze, process and model data.

In order to create an effective data architecture, McKinsey has identified six foundational shifts organizations are making to their data architecture blueprints that enable more rapid delivery of new capabilities and vastly simplify existing architectural approaches. Those six shifts include: from on-premise to cloud-based data platforms; from batch to real-time data processing; from pre-integrated commercial solutions to modular, best-of-breed platforms; from point-to-point to decoupled data access; from an enterprise warehouse to domain-based architecture; and from rigid data models toward flexible, extensible data schemas.

 

Flexible Data Architecture to deliver Data as a Service 

Data-as-a-Service, an open-source software solution that provides critical capabilities for different data sources, manages businesses’ data and their tools to assess, visualize, and process data for diverse data consumer applications. To power data analytics, Data-as-a-Service platforms take a different approach. Despite shifting data into a single repository, the platforms access the data where it is managed and perform entailed transformations and integrations of data dynamically.

As volumes of data are set to grow further, Data-as-a-Service platforms enable companies to optimize the physical access to data which is independent of the schema that is used to organize and facilitate access to the data. However, most businesses are challenged today to harness and derive value from all the data they are collecting over the years.

According to a recent report from MIT Technology Review Insights, having the right architecture for storing and analyzing data is critical for higher levels of capability. This can help develop new products and services, solve business complexities, and deliver value to internal and external customers. The report, titled “Data on demand: Dynamic architecture for a high-speed age,” written in association with TIBCO, looks at distinct architectures and approaches, and the goals that data executives have to deliver data as a service in the years ahead. The key findings of the report include:

• Chief data officers (CDOs) and heads of data and analytics around the world are developing architectures and platforms that are aligned with their current business models, goals, and key performance indicators (KPIs).

• Data leaders are finding new ways to assess existing and new data sets for hidden value. They are exploring ways to integrate and connect data sets to solve business problems, create new product capabilities, and offer deeper insights.

• Data executives are making decisions and trade-offs regarding data architecture that usually go through several evolutions. There is no one-size-fits-all, and choices must be made around what data sets to integrate and how to provide access.

• Data analytics teams must strike a balance between providing access and maintaining control. Data governance must deliver transparency and access for those who need it, and provide robust controls that safeguard compliance.

• To become data-driven organizations, data executives are increasingly part of change management efforts, such as increasing workforce data literacy and designing appropriately pitched analytics tools.

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