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How to Build an Empire with Robotic Process Automations

The quick pace of innovative change has had sweeping effect crosswise over business; introducing the time of advanced change. AI (ML), regular language preparing (NLP), man-made reasoning (AI), the Internet of Things (IoT), versatility, and distributed computing are a couple of instances of developing innovations that are affecting this transformational development.

This change is especially clear in the domain of client contact, as new advances hold the guarantee of improving both the client experience (CX) and representative experience (EX).

It has showed through the expansion of new computerized client a...

Four Steps To Make Your Business Intelligence Teams More Relevant

The world of analytics is changing. Self-Service Analytical tools like Tableau, Qlik, and Power BI are enabling business users to perform reporting and analytics on their own with little to no support from the IT organization.

This trend has evolved due to several factors including:

1) Organizations are flooded with data and IT organizations are not able to keep up.

2) Easier to use Business Intelligence tools make it more efficient for business users to directly create their reports rather than go through IT for a project.

3) IT organizations analytical projects can take several months when a busin...

4 Common Myths About The Big Data Industry.

With the confluence of growth in data, computing power to process that data and the democratization of AI technologies in the cloud, any organization can avail the benefits of Big Data, Analytics and AI to improve their business outcomes. But this should not be considered as a “magic” solution which can solve any business problem that an organization might have. This article addresses some of the common myths and misconceptions around these areas and presents a pragmatic approach and some best practices to apply Analytics & AI in today’s competitive world.

Myth-1: Big Data Technologies are better and cheap...

What's So Trendy About Data Analytics That Everyone Went Crazy Over It ?

It's that time of year again. Before you open up the presents under the tree, I've got some geekier gifts. In response to execs and luminaries from across the world of data and analytics sharing their predictions for the next year, I've dutifully compiled and stitched them together.

Also read: Analytics in 2018: AI, IoT and multi-cloud, or but Also read: Big Data's 2018: Can more meta thinking free us from current malaise?Also read: Big Data Predictions for 2019

Gather round, and soak up this year's batch, which focus on artificial intelligence, data regulation, data governance, the state of the Hadoop market...

Top 10 Business Intelligence Trends For 2019

Data is invaluable to all companies, from budding startups to global enterprises. This growing commodity is triggering organizations to deploy business intelligence solutions that will elevate and accelerate data-driven decisions.
Successful organizations are prioritizing a modern business intelligence approach, and in turn, priming their workforce to be the most analytically savvy generation ever seen. For a competitive edge in 2019, organizations must recognize the strategies, technologies, and business roles that can enhance their approach to business intelligence.
Here are some of the most critical trends to ...

Applying A Factory Model To Artificial Intelligence And Machine Learning

Advanced analytics techniques, such as artificial intelligence and machine learning, provide organizations with new insights not possible with traditional analytics. To take advantage of these technologies and drive competitive advantage, organizations need to design and build solutions that allow them to exponentially grow their capacity to create value from data.
The challenge is, how do you do that without also exponentially growing infrastructure costs and the number of data scientists needed to meet that business demand? The answer lies in industrializing the process using a data factory model.
Ms Offic...

The Next Big Thing in Simple Guidance For You In Business Intelligence.

Business intelligence plays a key role in the strategic planning of organizations and is used for multiple purposes, including measuring performance progress towards business goals, performing quantitative analysis, reporting and data sharing, and identifying customer insights.
Business intelligence involves the use of computing technologies to identify, discover and analyses business data to provide not only the current but also the historical and predictive views of data, Diseconomy says.
This makes it possible for decision-makers to access, analyses and understand business information so they can execute accor...

Master The Skills Of 4 Types Of Analytics And Be Successful.

Business Analytics is a phrase that can mean a lot of things to a lot of people. This article separates out the topic into four categories, and explain how each type can benefit HR. Those four types are Descriptive, Diagnostic, Predictive and Prescriptive. (Some definitions of business analytics focus on three types by eliminating Diagnostic.)
Businesses today are inundated with data from a wide range of sources: accounting, manufacturing, websites, CRM, etc. The larger the company, the more data. Too often management can be so overwhelmed with data and analytics that it begins to lose its value.

For CEOs, C...

Barclays Appoints Adam Kelleher as Chief Data Scientist for Research

Barclays has announced the appointment of Adam Kelleher as Director and Chief Data Scientist for Research. In this newly created role, he will be responsible for building a new global team of data scientists with expertise in sourcing, normalizing, and utilizing alternative data sets to support Barclaysâ?? Research franchise.

Dr. Kelleher joins Barclays from BuzzFeed, where he was Principal Data Scientist, responsible for advancing Buzzfeedâ??s machine learning and alternative data capabilities. During his tenure at Buzzfeed, Dr. Kelleher led various initiatives around data pipeline development, research metho...

Introduction to Non-parametric Statistical Significance Tests in Python

In applied machine learning, we often need to determine whether two data samples have the same or different distributions.

We can answer this question using statistical significance tests that can quantify the likelihood that the samples have the same distribution.

If the data does not have the familiar Gaussian distribution, we must resort to nonparametric version of the significance tests. These tests operate in a similar manner,

but are distribution free, requiring that real valued data be first transformed into rank data before the test can be performed.

In this tutorial, you will discover nonparam...