What You Need to Know About Building a Dynamic Data Architecture

TIBCO MIT Research Report
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Businesses today generate massive amounts of data. Data seems to flow endlessly from digital products and services, operations, supply chains, and even cloud computing technology. And it’s only going to keep growing. With the rise of 5G, increased connectivity and large-scale IoT deployments will add to the large volumes of data businesses must collect, process, and put into use. 

With this rising tide of data, the ongoing challenge for businesses today is harnessing and extracting value from all that data. Organizations must have the right architecture in place to store, structure, and analyze data to deal with greater and greater amounts of data.

With such a dynamic data architecture, businesses can develop new products and services, solve business problems, and deliver greater value to internal and external customers. Here’s what leading organizations are doing to build data infrastructures, services, and use cases that drive business value:

  • Building enterprise data strategies along business value chains. Around the world, chief data officers and heads of data and analytics are working to develop architectures and platforms that are tailor-made to align with their current business models, goals, and KPIs. That way their data integration strategy will support their wider business strategy.  
  • Analyzing existing and new data sets for hidden value. Data leaders are finding new ways to integrate and connect data sets to solve business problems, create new product capabilities, and offer deeper insights. Often this means breaking down organizational silos and encouraging information sharing, making sense of unstructured data, and even looking to external data sets. 
  • Making decisions and trade-offs regarding data architecture. Data architectures 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.
  • Adapting data architectures to be flexible and support multiple use cases. Centralized approaches to data management are no longer feasible. Standing up another warehouse or lake is both an expensive proposition and time-consuming. “Everyone talks about this magic data lake. In my experience, every time people have tried and attempted these data lakes, they’ve ended up with data swamps,” says Samik Chandarana at J.P. Morgan.
  • Striking a balance between providing access and maintaining control. Data governance teams must both provide transparency and access for those who need it and put in place robust controls that safeguard compliance. 
  • Shifting to a data-driven culture. To embed insights and analytics capabilities into the business, organizations must democratize access to data. Data executives need to lead the charge to increase workforce data literacy and enable teams with the right analytics tools.  

Businesses need to find a way to put all of their data to use. With a robust data foundation and a dynamic data architecture that flexibly delivers on emerging needs and priorities, organizations can create a truly data-driven business. 

Read this MIT Technology Review Insights report, “Data on Demand: Dynamic Architecture for a High-speed Age,” sponsored by TIBCO to learn more about how to put data at the core with the right data architecture.