datapine

MARCH 20, 2019

“Vision without action is merely a dream. Action without vision just passes the time. Vision with action can change the world.” – Joel A. Barker. Financial analysis and reporting are one of the bedrocks of modern business. While you may already know that financial reporting is important (mainly because it’s a legal requirement in most countries), you may not understand its untapped power and potential.

Reporting 248

Reporting Finance Snapshot Dashboards 248

David Menninger's Analyst Perspectives

MARCH 20, 2019

I am happy to share some insights gleaned from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Mobile Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research.

Business Intelligence 145

Business Intelligence Analytics ROI Software 145

DataRobot

MARCH 20, 2019

The 2019 NCAA March Madness tournament has arrived! This is one of the most famous annual sports events in the United States, bringing together the best Division 1 men’s and women’s college basketball teams from 68 schools to compete against each other for the NCAA Champion title.

91

91

Jet Global

MARCH 20, 2019

If it seems as though we were recapping the largest Microsoft Dynamics NAV, Business Central, and GP partner event just months ago, you are right. New for 2019, Directions North America is now a Spring event, and we’re excitedly preparing for one of our favorite shows of the year, taking place in Las Vegas, Nevada May 5th – May 8th. As a Gold Sponsor, we’re going all in on what has led us to success – our loyal partners!

Reporting 75

Reporting Business Intelligence Publishing Strategy 75

Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

Experimentation

Sisense

MARCH 20, 2019

As a company that touts the benefits of a full end-to-end BI solution, we certainly know the value of a data engineer. The data engineer’s job is to extract, clean, and normalize data, clearing the path for data scientists to explore that data and build models. To do that, a data engineer needs to be skilled in a variety of platforms and languages. In our never-ending quest to make BI better, we took it upon ourselves to list the skills and tools every data engineer needs to tackle the ever-grow

Big Data 72

Big Data Machine Learning Data Warehouse Unstructured Data 72

Dataiku

MARCH 20, 2019

Data science is a team sport that needs to be collaborative to be successful, but data leaders and practitioners often disagree on where exactly responsibility for data quality and data science activation is housed. Without clearly defined and communicated data responsibilities, frustration (and low data ROI) is inevitable.

ROI 65

ROI Data Quality Data Science Enterprise 65

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. A recent flourish of posts and papers has outlined the broader topic, listed attack vectors and vulnerabilities, started to propose defensive solutions, and provided the necessary framework for this post.

Modeling 226

Modeling Machine Learning Predictive Modeling Consulting 226

Domino Data Lab

MARCH 20, 2019

Our mission at Domino is to enable organizations to put models at the heart of their business. Models are so different from software — e.g., they require much more data during development, they involve a more experimental research process, and they behave non-deterministically — that organizations need new products and processes to enable data science teams to develop, deploy and manage them at scale.

Management 97

Management Experimentation Data Science Modeling 97