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Enterprise analytics leveraging AI: Analytics trends in 2019

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Last Updated on December 26, 2018
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With businesses transforming into data-driven enterprises, there is a need for investments in data technologies and analytics strategies to start delivering value. It’s interesting to note that by 2019, 40% of digital transformation initiatives will be supported by cognitive/AI capabilities, providing timely critical insights for new operating and monetization models (Source: IDC FutureScape: Worldwide Digital Transformation Predictions.)

Artificial Intelligence (AI) and Machine Learning Predictions 2018

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LatentView Analytics is an advanced analytics firm delivering solutions that help companies drive digital transformation and use data to gain a competitive advantage — we work with some of the most reputed Fortune 500 brands globally, giving us insights on analytics and market trends. Here are five analytics trends to watch out for in 2019:

AI gaining traction through Man + Machine solutions: The future as we see it continues to be collaborative – man + machine, not man-machine competition, is clearly gaining ground as the way to implement AI. According to Gartner, by 2020 AI is poised to create more jobs than it eliminates: 2.3 million to 1.8 million. By 2025, job creation related to AI will top two-million net-new jobs, which includes specialized skills, senior management, and a small percentage of entry-level as well as low-skilled variety, Gartner says.

While man + machine is the trend at present, this does not rule out specific scenarios where autonomous AI might be the solution. We are now seeing implementable solutions which have translated this vision into reality. Simply put, the machines will do all the grunt work, allowing humans to focus on higher-level, strategic or creative functions, or those relating to fundamental business decisions rather than execution.

For example, using anonymized location data from smartphones, Google Maps can analyze the speed of movement of traffic at any given time. Access to vast amounts of data being fed to its proprietary algorithms means Maps can reduce commutes by suggesting the fastest routes to and from work.

Predictive analytics for Industry 4.0 (IIoT): Increasingly, robotics, artificial intelligence, and big data analytics are coming together, creating potential for enormous development and progress when it comes to productivity, efficiency, and cost savings. The market opportunity of the IIoT is huge. According to IndustryARC research, the industrial IoT (IIoT) market is estimated to reach $125 billion by 2021. It is fair then to say that IIoT creates a universe of intelligent sensors that empowers accelerated deep learning of existing operations, and looks to further augment and streamline processes. The insights from sensor data allow for rapid contextualization, automatic pattern, and trend detection. Predictive analytics, previously based on subjectivity and qualitative decisions/judgements, is now objective and powers data-driven decision making.

For example, in an award-winning case study, LatentView Analytics worked with a leading automobile manufacturer to develop a scalable, self-serving analytics platform that helped the client understand usage patterns for their products and test product related hypotheses. This resulted in a significant reduction of warranty costs through customer behavioral segmentation analysis leveraging real-time IoT analytics from connected vehicles. Read how we built a scalable, self-serving analytics platform that helped our automobile client reduce warranty costs.

Conversational AI for the intelligent workplace: With machine-learning trained systems gaining the ability to make sense of speech, the idea of automated chatbots is no longer just hype, it is now being used increasingly across applications. Voice-based conversational systems such as Siri and Alexa are already commonplace in the consumer world. It is only natural that business leaders expect to see similar intelligent conversational systems at the workplace. Chatbots were initially employed to listen in and provide call-to-action for a one off customer care complaint. Now, chatbots find their place in a far bigger, organization-wide strategy which uses them for business process automation, cost-savings and empowering teams with additional and more granular insights.

For example, LatentView Analytics’ proprietary solution SNAP is an interactive, voice-based analytics platform that can help visualize data on the fly and validate simple hypotheses. It can be used across platforms and devices for convenience. Using the SNAP chatbot, you get a deep-dive analysis about customers, employees, sales, and more. Aside from giving you information, the right enterprise chatbot solution goes beyond just answering questions posed– it recommends insights that other business teams are looking for, helping you find new lines of inquiry.

Image Analytics for social listening: We live now in an era of Instagram, where images are taking over social media. There are well over three billion photos shared daily on social media. It is interesting to note that a majority of those photos contain a brand’s products and logos, but 85% of them don’t include a text reference to the brand. According to a survey conducted by Hootsuite, top brands on Instagram are seeing a per-follower engagement rate of 4.21%, which is 58 times higher than on Facebook and 120 times higher than on Twitter. These channels have now become powerful platforms for marketers, the potential of which cannot be underestimated.

While image analytics were previously only employed extensively by tech giants like Facebook and Apple, they’ve now become more mainstream with CPG and Media and Entertainment companies, whose brand messaging has a higher image quotient. These companies are starting to deploy algorithms to remove subjectivity through machines, increasing accuracy and scalability. Image analytics enables brands to analyze the logos, objects, scenes and actions in social media posts. By analyzing social media text and images together, brands can get the full picture of consumer conversation.

Variety in data to provide incremental insights: Traditionally, companies primarily used structured and internal data insights to understand the consumer. However, in today’s rapidly-evolving tech-driven market, unstructured and external data is increasingly being tapped into to leverage consumer insights. This variety in data insights is an opportunity for incremental insight. Digitally-savvy businesses need to get creative in identifying relevant and impactful data sources, putting together partnerships to source data and stitch together insights from a combination of internal, external, structured, unstructured, syndicated and proprietary data.

Sources of external data not only include social media platforms such as Facebook, Twitter, and WhatsApp, but also searches in Google, data from smart devices (IoT), and geo info used by companies such as Uber. This multitude of sources, as well as many others (such as publicly listed government data like census information), can be extremely insightful, providing income and demographic details that a brand might not have access to within its own big data repositories. The inclusion and combining of multiple data sources into a big data analytics enterprise will expand scope and also provide granular insights about customers and their needs.

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