See your customers clearly with analytics for JSON

Program Director, Digital Experience, IBM

Modern businesses can study customers and their needs and habits with a level of granularity once thought impossible. Using a wide array of data generated by social media, mobile devices and the Internet of Things (IoT), companies are now harvesting terabytes of information, most of it semi-structured. But with this mounting wave of data come challenges:

  • Where do you put it?
    Data stores from systems of engagement can be unpredictable and immense, outstripping organizational attempts to keep pace.
  • How do you analyze it?
    The thin structure of data from systems of engagement, whether in JSON or similar forms, presents difficulties for most analytical engines—particularly for analysts who are accustomed to constructing their queries in SQL.
  • How do you give it context?
    So long as you can effectively analyze data from systems of engagement, the next logical step is to query that data with systems of record—customer data in a CRM system, for example.

Analyze JSON Data for Customer Insights, a whitepaper from IBM Cloud Data Services, offers a startling solution: Keep semi-structured data on the cloud, where it belongs—then build your data warehouse there. A traditional data warehousing approach, for example, would export data from systems of engagement to an on-premises data center where systems of record are located—but now organizations can bring systems of record data to the cloud for analysis alongside cloud-native data, including JSON. Moreover, use of cloud data services such as Cloudant and dashDB to convert semi-structured JSON data from systems of engagement into a relational database format can help provide robust analytics when, for example, data from back-office systems is imported to the cloud, allowing detailed queries of multiple databases.

Many companies, thinking that data generated by IoT, web and mobile services is stored temporarily on the cloud, believe that using such data requires exporting it into a local repository—and thus absorbing the costs of transferring that data. However, a cloud data warehouse is well suited to accommodate the growth of systems of engagement as they generate ever more born-on-the-cloud data. By conducting analytics in the cloud, where such data already resides, a company can reduce network costs while also paying only for the storage its uses—and enjoying a virtually unlimited capacity to expand. the growth of IoT, web and mobile applications begins to strain the capabilities of on-premises data warehouses, the use of formats such as JSON documents to manage goes hand in hand with the cloud service providers for whom it is a core competency. By exploring born-on-the-cloud data using a managed cloud data warehouse, companies can shift their focus from managing analytics infrastructure to exploring activities that can give them a competitive advantage in the marketplace.

Find out how cloud-based analytics can give you an eye-opening view of your customers when you read the free whitepaper Analyze JSON Data for 360-Degree Customer Insights.