Bring robust analytics to your JSON data

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Today’s Internet of Things (IoT), web, and mobile apps are generating treasure troves of user data. The sheer volume, velocity, and variety (the three Vs) of such data present analytics challenges that can overwhelm most organizations.

This is partly because semistructured data — including JSON docs from web and mobile apps — is resistant to traditional analytics. Customer data that could otherwise generate meaningful insights for a business often sits unused.

Ongoing obstacles to analyzing JSON

A new white paper from IBM Cloud Data Services, “Analyze JSON Data for Customer Insights,” examines some of the underlying obstacles to getting actionable, real-time results from cloud data sources. Some observations it makes include:

  • Planning, building, and maintaining an infrastructure for semistructured data like JSON consumes resources better spent engaging customers.
  • The lack of compatibility between JSON data from systems of engagement and relational data forms in systems of record is an obstacle to holistic analytics that could otherwise provide a “360-degree view” of the customer.
  • While traditional, on-premises systems of record tend to grow and change at a predictable and stable rate, JSON data produced by systems of engagement is more fluid and unpredictable.

There are also cultural, practical, and logistical barriers to JSON analytics:

  • Cultural: Data from systems of engagement belongs to web developers, while systems of record are the domain of traditional IT.
  • Practical: Trying to merge semistructured data with a flexible schema in a NoSQL database into structured data with rigid schemas in an SQL database.
  • Logistical: Servers supporting systems of engagement could be located anywhere, while data from systems of record is often stored in highly secure locations, like in an on-premises data center or private cloud.

Bridging the analytics gap for cloud-born data

The white paper further makes some assertions about what’s necessary to bridge the gap and bring more robust analytics to JSON data:

  • JSON data generated by systems of engagement should be managed, maintained, and analyzed in its native environment (the cloud) rather than moved to on-premises systems and analytic architectures.
  • While data generated by systems of engagement lacks the sophisticated schema of traditional systems of record and is difficult to store for analytics using a relational data warehouse, it is ideal for storing in cloud-native solutions that leverage both SQL and NoSQL database systems.
  • With the right platform, the data of the future — including JSON data created by new, disruptive technologies — can be not only accommodated but also fully exploited within a cloud-based data warehouse.

A cloud-native solution

To get the most out of all data, organizations must reverse traditional thinking of relying upon on-premises data warehouses, and instead embrace the concept of keeping cloud data (like JSON) online, bringing analytics to where the data already resides.

A new hybrid solution from IBM Cloud Data Services, using a simple integration between IBM Cloudant® and IBM dashDB™, enables this very thing — cloud-based analytics for JSON — with minimal data movement or transformation required.

This solution utilizes Cloudant’s schema discovery process (SDP) to scan a Cloudant JSON data store and intuit the implicit structures in the data. It then creates a corresponding schema in the relational dashDB cloud data warehouse. Now, the semistructured data originally stored as JSON in Cloudant can be easily understood in dashDB by the BI and analytics tools of your choice.

JSON analytics

Cloud data is only going to grow as disruptive technologies, such as IoT, become more ingrained within business processes. With cloud-based analytics, the semistructured data generated by IoT, social media, and mobile devices becomes a rich source of immediate, real-time insights into customers and partners.

Read the free white paper “Analyze JSON Data for 360-Degree Customer Insights” now, and discover how to unlock the analytical richness in your JSON data stores. You’ll learn:

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  • Why data from mobile, web, and IoT based systems of engagement should be maintained in the cloud for analysis.
  • How a simple integration between a JSON database and a relational cloud data warehouse can yield quick customer insights.
  • How managed cloud service providers can remove the usual DBA and IT pains from this process.
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