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Integrating Structured and Unstructured Data in the Cloud

Authors Photo Precisely Editor | March 28, 2022

Historically, when business users talked about data and data analytics, they were referring to structured data. There are two reasons for this. The first is that in the past, the most important information available to businesses was generally structured. It included things like customer master records, demographic data, sales orders, inventory records, purchasing transactions, and so on. The second reason is that until just a few years ago, the technological capabilities that we have today for processing and making sense of unstructured data did not exist.

All that has changed dramatically in recent years, as cloud computing has powered a revolution in big data analytics, and natural language processing (NLP) capabilities have evolved to add meaning and context to the human-generated language inherent in unstructured data such as product reviews, social media posts, and customer service notes.

unstructured data - Digitally created human.

Be integrating structured and unstructured data in the cloud, enterprises can set the stage for unleashing the full potential of data analytics in their organizations. That, in turn will drive better strategic and tactical decisions, and can power the AI and machine learning initiatives that are creating stand-out business value for the most innovative companies across every industry.

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Combining Disparate Data Sources in the Cloud

Before we get to the question of unstructured data, let’s first consider some of the challenges enterprises face when dealing with structured data sources such as ERP databases and CRM systems. Most modern relational databases are designed for open integration; the process of reading, writing, or updating data from external sources is relatively simple because from a technical point of view, it is fairly well standardized. Structured Query Language (SQL), Open Database Connectivity (ODBC), and Java Database Connectivity (JDBC) have provided a means of relatively simple communication between different platforms.

unstructured data - Bar graphs.

The key word here is “relatively.” It still involves a good deal of effort to get data from point A to point B. The devil is in the details, and there are plenty of things that can go wrong. As you begin to add more data sources into the mix, things can begin to get complicated fairly quickly.

Consider, for example, the fact that many organizations still depend upon mainframe computers running IBM z/OS or z/VSE, Unisys OS 2200 or MCP, or similar technologies that were designed long before modern relational databases were in widespread use. In most cases, these systems serve a core need, processing some of the organization’s most business-critical transactions. Omitting this data from data analytics systems is not really a viable option. Yet for many, integrating it with other forms of data can be quite a challenge.

Organizations working with mainframe data will commonly encounter technologies like VSAM, IMS, COBOL copybooks, or fixed and sequential files. Many existing systems are still storing data in flat files, including fixed length, variable length, or delimited formats. Translating hierarchical data for use in a relational context and dealing with COBOL copybooks or other anomalies of mainframe data is somewhat foreign to most modern technology platforms and frameworks.

Precisely Connect helps companies take control of their data, seamlessly integrating their mainframe with the cloud. Connect offers real-time or batch mode integration to support advanced analytics, AI and machine learning, and seamless data migration.

Connect leverages the expertise that Precisely has built over decades as a leader in mainframe sort and IBM i data availability and security to lead the industry in accessing and integrating complex data.

Unstructured Data

Unstructured data may include natural-language text, but it can also incorporate media such as audio, video, and images. Semi-structured data may take the form of JSON or XML, and although it typically contains text, these formats may also incorporate binary objects such as images that have been encoded as text strings. As the vast potential of modern data analytics platforms has become apparent and the cost of storage has plummeted, many organizations are simply collecting these kinds of data for potential use in the future.

As customer reviews and social media have taken off, smart business leaders have seen the potential in monitoring sentiment, tracking customer responses to their marketing campaigns, and evaluating customer service experiences at scale. As NLP has evolved, technology has gotten better at understanding the nuances of human communication, distinguishing positive comments from negative ones, and detecting sarcasm or similar anomalies.

For this reason, unstructured data is a critically important component of most companies’ data analytics strategies.

Bringing Structured and Unstructured Data Together in the Cloud

Cloud analytics is driven by highly scalable products like Splunk. It is supported by big data platforms such as AWS, Azure, Databricks, Confluent, and Snowflake. For full flexibility and future-proof assurance, you need an enterprise integration platform that can work with all of these.

This calls for an enterprise-grade platform designed to operate across heterogeneous environments that include modern relational databases, mainframe sources, and semi-structured and unstructured data. Precisely Connect brings all of your data together using a single platform that links those disparate systems in real-time or in batch mode, making it possible for your organization to take full advantage of today’s powerful cloud analytics platforms.

An effective integration strategy begins with a clear framework designed around business value and a holistic understanding of the enterprise and its various information systems. Integration processes should be designed to be repeatable but flexible, with a clear understanding as the business cases that will drive value for the organization. Reliability and data quality are essential; information that is incomplete or which arrives too late will result in lost opportunities or bad decisions. Finally, an integration framework must be scalable, with adequate attention to data quality and adaptable to change as the organization grows and changes.

Precisely Connect delivers world-class integration capabilities for leading companies around the globe. To learn more about how your organization can leverage the power of structured and unstructured data in the cloud, read our free e-book today, A Data Integrator’s Guide to Successful Big Data Projects.