IDG Contributor Network: Dawn of intelligent applications

Data remains a foundational element of computing. Recently, Hadoop and big data have been a central part of data progression, allowing you to capture data at scale. But companies now look to the expanding use of cloud computing and machine learning to create more intelligent applications.

This new generation of applications use all the data they can, including incoming real-time data, to respond in the moment to changing circumstances and formulate advantageous outcomes. This includes delivering on the digital transformation promise sought by companies to deliver rich customer experiences. Intelligent applications can converge database and data warehouse workloads, allowing companies to respond and react to changing conditions in real time.

This builds on a theme covered by nearly every large industry analyst firm regarding the merging of transactional and analytical functions. Gartner refers to this convergence as hybrid transaction analytical processing, or HTAP; 451 Research refers to it as hybrid operational analytical processing, or HOAP; and Forrester refers to it as translytical data platforms. According to Forrester[1]:

Analytics at the speed of transactions has become an important agenda item for organizations.

Translytical data platforms, an emerging technology, deliver faster access to business data to support various workloads and use cases. Enterprise architecture pros can use them to drive new business initiatives.

451 Research also calls out the idea of seizing the moment[2]:

Organizations are zeroing in on the so-called “transaction window” and realizing that it presents a significant opportunity–and once it’s gone, it’s gone for good.

Intelligent applications in finance, media, and energy sectors

The largest industry sectors are using these converged technologies for their intelligent applications. These applications collect and process data from a variety of sources, provide experiences in real time, and make use of the latest techniques in machine learning and artificial intelligence to push their usefulness forward.

Consider the following examples.

Finance

A popular intelligent application in finance is the new frontier of digital wealth management, including real-time portfolio analytics for clients across any platform. As one example, JP Morgan Chase highlighted[3] its investment in digital wealth management in an investor presentation last year. Behind the scenes, many of these digital wealth services are powered by digital startups such as InvestCloud, which states that its wealth management products “allow wealth managers to get a whole view of their clients–instantaneously and at scale.” Other companies in this space include SigFig, which showcases “experience integrating our platform with TD Ameritrade, Charles Schwab, Vanguard, E*Trade, among others.”

Energy

In the energy sector, intelligent applications include IoT data pipelines using sensor data.

Real-time capture and analysis of this data, with machine learning model scoring, provides helpful downtime mitigation and savings for global companies. Shell describes its advanced analytics for sensor collection[4] on its website:

Digital sensors installed in our operations around the world–from production fields to manufacturing complexes–produce a constant flow of data which we analyze to improve processes and take better business decisions. The technology can optimize the performance of plants by predicting when maintenance will be needed, to avoid unplanned downtime and lost productivity.

More than 5,000 machines globally connect to the system, which is thought to have saved more than 3.5 million barrels in lost production since its introduction.

Media

Perhaps no transformation is more visible in media than the shift from broadcast and cable television to real-time streaming. This change drives media companies to seek end user analytics and advertising opportunities tied to streaming, and key intelligent applications to drive revenue. This technology race led Disney to acquire a majority stake in BAMTech.

Disney said the following in a news release:

The media landscape is increasingly defined by direct relationships between content creators and consumers, and our control of BAMTech’s full array of innovative technology will give us the power to forge those connections, along with the flexibility to quickly adapt to shifts in the market.

4 steps towards intelligent applications

Nearly every company is charting its own digital transformation blueprint. Adding intelligent applications to the mix can jumpstart digital results. Here are four quick steps to get started:

1.

Identify corporate growth areas

Projects aligned with company objectives often get faster funding and resources.

2. Capture new and existing data sources

Showcase both to enhance existing value and demonstrate new data.

3. Design an architecture that supports a real-time feedback loop

Merge transactions and analytics where possible to incorporate real-time intelligence, including machine learning model scoring.

4.

Build intelligent applications using the latest best practices across industries

Track primary applications in high growth areas of finance, media, and energy to see how companies are putting technology to use. New intelligent applications have the power to transform businesses, drive new revenue, deepen customer engagement, and optimize operations. With a simple action plan and examples from industry leaders you can set your own company on a path to success.

This article is published as part of the IDG Contributor Network. Want to Join?[5]

References

  1. ^ According to Forrester (www.forrester.com)
  2. ^ the idea of seizing the moment (www.memsql.com)
  3. ^ highlighted (www.jpmorganchase.com)
  4. ^ advanced analytics for sensor collection (www.shell.com)
  5. ^ Want to Join? (www.infoworld.com)

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