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April 10, 2020

Streaming Analytics: The Silver Bullet for Predictive Business Decisions

Bernd Gross

(Hamara/Shutterstock)

Streaming analytics has become an indispensable tool for companies to gain precise insights into their current business information from their ever-growing volumes of data. But is the high-performance streaming technology really relevant for every business? How does streaming analytics work? And what capabilities set a high-performance technology apart from the rest?

Organizations in nearly every industry are confronted with growing volumes of data – and the streams of data that need to be analyzed are also increasing rapidly. To meet these high demands, streaming analytics enables businesses to maintain an overview of the integration and analysis of their data.

When organizations use different mobile devices or multiple sensors that feed data into the network, managing the whole environment can often be complex. Streaming analytics can help them break down all the relevant parameters and enables profitability comparisons, among other things. Perhaps different departments also use different internal transaction systems, or an organization wants more transparency in its supply chain. From ambitious digitalization goals like automated retail or predictive maintenance to fraud recognition and prevention, gaining solid knowledge from an organization’s own data requires high-performance streaming analytics.

What Can Streaming Analytics Actually Do for a Company?

Streaming analytics helps businesses gain real-time insights into huge volumes of fast-paced data that can help them predict what will happen next. With real-time insight, organizations can recognize risks and opportunities immediately, allowing them to  respond in an optimal way.

Until a few years ago, complex software could only be operated by experienced developers and was managed by an organization’s IT department. Nowadays, there’s a strong trend toward direct interaction with employees who don’t work in the development department. Thanks to intuitive user interfaces, people such as business analysts can modern tools, including APIs with visual design tools, to develop applications quickly without needing deep programming knowledge. The use of solution libraries to accelerate application deployment is on the rise and promotes the development of custom approaches for organizations to develop suitable solutions as needed.

There are many use cases for streaming analytics across departments and even across industries. At a high level, here are some of the specific applications streaming analytics can be leveraged for:

  • Retail: Streaming analytics can help marketers better understand the customer’s purchase experience by registering real-time data on customer behavior and using it accordingly to improve their customer journey.
  • Finance: Streaming analytics can help brokers in the financial industry always be one step ahead of the fast-paced exchange markets. For example, credit card companies use streaming analytics to detect cases of fraud across several channels and millions of transactions every day with precision.
  • Sales Department: Likewise, streaming analytics can improve a company’s sales channels by enabling greater transparency with orders and ensuring the product that customers want can be found through every channel in the sales network and tracked.

Furthermore, streaming analytics deliver greater process efficiency without the need for programming. A streaming analytics platform with an intuitive user interface gives non-IT experts the tools they need to create complex but precise analysis scenarios.

Moreover, streaming analytics increases the operational transparency of business processes. The analyzed data can be especially helpful with predicting and avoiding possible errors in service level agreements between clients and service providers. This prevents the formation of organizational silos and enables the correlation of events from many sources.

The Criteria Needed to Succeed with Streaming Analytics

In-depth analyses and a wide bandwidth of analyzable formats for streaming data are crucial for delivering a meaningful image of an organization’s processes. But even the widest bandwidth of analysis types won’t help if its streaming analytics tools aren’t able to scale and grow with the business.

Organizations need to have sufficient interfaces available to be able to expand the system at any time. The ability to accommodate many data formats is also relevant so the organization stays agile and the software remains suitable for new business models. Furthermore, a streaming analytics should have the capability to be implemented in the most important places for a company – e.g., local, in the cloud, or on the edge.

The right streaming analytics tool also provides the ability to react quickly to real-time data. The software should allow organizations to fully understand their customers so they can gain loyalty over the long term. Finally, a streaming analytics tool should enable businesses to boost their operational efficiency to help them save money and avoid problems.

The most successful streaming analytics tools will also be able to:

  • Recognize and analyze patterns in real time;
  • Aggregate data and perform fast analytics, filtering and correlating;
  • Enrich streaming data with cached static data;
  • Enable access to analytics libraries of third-party providers and perform detailed quantitative analyses;
  • Connect streaming and static data with low-latency and high-throughput connectivity;
  • Develop continuous real-time analysis scenarios;
  • Create business dashboards for end users to monitor data streams;
  • Enable simple visualization of key performance indicators.

Big data keeps organizations all over the world on their toes—but with the help of streaming analytics tools, businesses can put the data to work for them. With the help of a powerful streaming analytics technology, companies can keep an eye on where they can optimize their processes while generating added value from their own treasure trove of data to maximize their competitive advantage.

About the author: Bernd Gross is CTO for Software AG. He was previously responsible for the IoT Business at Nokia Siemens Networks and CEO of Cumulocity. Bernd is a globally oriented executive with more than 20 years of work experience in Germany, Finland, UK, Asia and the US (Silicon Valley). Bernd holds a master degree in Information & Communication Technology and an MBA from the London Business School, UK.

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