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The Role of a Data Platform in Maximizing Data Analytics Benefits

group of colleagues analyzing data in real-time

Analytics anywhere, insights everywhere

Organizations with a critical need for fast access to new insights to transform performance and unlock competitive advantage require innovative data solutions.

The result is that businesses are now focusing on how to drive greater value from data and add additional capabilities, but without disturbing existing systems. There was a time when having an operational system along with a data warehouse with month-old data was sufficient to be competitive. In most industries, those days are over.

Today, people need greater insights from current data to make better decisions and drive immediate actions in response to events and opportunities. Companies able to do so have a major competitive advantage.

This generates numerous conversations about how to maximize the benefits of data analytics, but the important point to focus on is the insight that can be gleaned. We must always remember the purpose of computing is insight not numbers. The increased power and lower cost of computing has enabled businesses to amass large volumes of data. Yet that is only a first step. The question people ask is: “What is useful in all this data?” They certainly have a point. How do enterprises consume and organize data? How do they leverage advances in technology to enable new capabilities and business value without disturbing existing systems?

We need to go back to first principles and consider how analytics is the scientific process of discovering and communicating meaningful patterns in data. In business, however, many users think analytics refers to queries and reporting, with some statistical analysis on top.

The many types of analytics

There are in fact many forms of analytics. Simple SQL queries and reporting enable us to understand what happened, and if we want a deeper understanding, we use business intelligence. Predictive analytics will give insight into what is likely to happen in the future, while prescriptive analytics looks forward, using predictive models to recommend actions that will provide good outcomes or help us avoid bad ones. This enables businesses to see around corners and be highly prepared for what arrives.

It’s important to be ready to adopt these new types of analytics as they are created. But businesses may not be able to drive these insights from their existing data silos, which begs the question: “How can you create an environment where these insights can be obtained and allow for the future evolution of analytics?”

A new approach to provide insights now and for the future

One approach is to provide real-time data feeds of transactions from operational systems, silos, and other sources to create a data fabric. This gives accurate visibility from a single pane of glass across an entire business, without the problems associated with data warehouses and data lakes. Data fabrics can transform and harmonize data from multiple sources on demand to make it usable and actionable.

The smart data fabric takes this further by incorporating the full armory of analytics capabilities, including data exploration, business intelligence, natural language processing, and machine learning, enabling organizations to gain critical new insights and power intelligent prescriptive services and applications.

It may sound simple but in practice it often is not. Organizations have data in many locations, a legacy of previous approaches to effective data-management. Data is often in data lakes, data warehouses or data marts. Data lakes are low-cost but require data science expertise and perform poorly when organizations want insights from live or operational data in real, or near-real time. Data warehouses are an improvement, being a structured version of the lake, but are costly and still require high levels of data skill. Data marts are faster and easier to maintain, but serve a specific function and struggle to support multiple use cases across an organization.

These prior approaches leave businesses with the problems of stale data and high latency, undermining the accuracy of the data and the trust of end users in the insights offered, unable to realize the full value of their data analytics.    

How InterSystems helps

InterSystems IRIS resolves these limitations, providing a single, unified data management platform that is at the core of the smart data fabric. It simplifies the data architecture, bringing organizations the wealth of capabilities that are required in a single product, all built from the ground up. It reduces the complexity, accelerates development and simplifies maintenance and operations. InterSystems IRIS eliminates the need to integrate many different technologies, lowering the total cost of ownership.

And by embedding analytics directly within the platform, InterSystems IRIS delivers the advanced capabilities in machine learning, business intelligence, data exploration and natural language processing where data resides, eliminating the need to move data to different environments to perform analytics. This enables accurate, real-time insights and actions from current data through a high performance transactional-analytic data management engine. It powers the extreme performance at scale that real-time and low latency use cases demand in today's fast-paced business environments.

InterSystems IRIS enables organizations to implement the smart data fabric with minimal disruption and without the need to extract and replace existing systems. When organizations need to know they can trust the data, InterSystems IRIS gives them vital confidence to trust the insights and to trust their partners.

Learn more about a smart data fabric.