Exploring Analytics & AI in 2018: A Detailed Primer

It is often said that data is the most valuable asset a business can have; the oil of a digital era. But data itself, while interesting, often leaves out a variety of important details – creating a need for analytics. And how we complete these analytics has evolved – and will continue to do so; particularly with the rapid proliferation of AI tools and technologies.

Some benefits associated with AI and analytics include:

  • Increased productivity via instant access to the right data through better classification processes
  • Improved speed, accuracy, and efficiency throughout analytics and data management practices
  • Facilitating clear compliance with legal and similar requirements
  • Cost saving by finding the best ways to achieve a wide range of tasks

To better understand data, analytics, AI, and how they all go together, we will answer the following questions: Read more

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Improving Understanding with Data Visualizations

As we become better at collecting and analyzing data, we also have to become better at explaining what it all means. Data visualization is the use of algorithms to create images (graphical and pictorial) from data so that humans can more effectively understand and respond to that data. It is important because it enables us to digest large amounts of complex data that would otherwise be overwhelming or difficult to understand.

To better understand data visualization, there are several questions that we will answer:

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Integrating IoT Analytics for a Smarter Future

By 2020, it is expected that there will be between 20 and 30 million IoT units in the marketplace, according to a study conducted by Gartner. With this much data being collected, the need for a way to analyze it grows exponentially. Many of the enterprise applications for IoT analytics, such as in manufacturing, finance, telecom, healthcare, and others have unlimited potential when data is managed and analyzed correctly.

To meet this need, IoT analytics has emerged as the broader category of uses and applications designed to help analyze the data obtained by IoT sensors. Once this data has been properly analyzed it can then be used to help make better, data-driven decisions for organizations that are in search of a competitive edge.

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Marketing analytics with AI: Complete guide [2018 update]

Martech tools increased from 150 to 5000 in last 6 years

There were 150 marketing tools in the market in 2011, now that number is >5,000. Customers were a single data point in 2000s while now we have rich time series data on every user. Artificial intelligence powered tools are required to harmonize data and complete automated predictive analytics.

Why is AI relevant for marketing analytics now?

You need too many tools!

Marketers have access to more tools and channels than ever. It would take a genius to be up to date on all the tools and technologies that is becoming available.

The proliferation of tools creates the complex task of unifying their output for analytics. Normally this involves taking CSV backups, writing scripts and other data janitor work. Read more

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Max Yankelevich, CEO of WorkFusion, Explains RPA &Automation

We cut out all the intros and other small talks from our podcasts but I should begin by thanking Max Yankelevich for his time. Max is the founder, CEO and Chief Architect of WorkFusion and one of the pioneers in applying Artificial Intelligence to enterprise processes. Though I had a very high-level idea of cognitive robots at the beginning of the talk, Max explained everything a knowledge company executive needs to know about automation: the concept, the industry, automation potential for enterprises, pricing, ROI and how enterprises should implement automation solutions. We also had a quick discussion on how this all affects future of work. Below you can find our podcast edited for clarity and brevity. Read more

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