Smart Data Collective

OCTOBER 17, 2022

This proliferation of data and the methods we use to safeguard it is accompanied by market changes — economic, technical, and alterations in customer behavior and marketing strategies , to mention a few. zettabytes in 2012. Reports and models stutter as they try to interpret the massive amounts of data flowing through them.

Big Data 105

Big Data Data-driven Recreation/Entertainment Data Governance 105

The Unofficial Google Data Science Blog

APRIL 23, 2024

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. It is also a sound strategy when experimenting with several parameters at the same time. And sometimes even if it is not[1].)

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

APRIL 17, 2017

In the first plot, the raw weekly actuals (in red) are adjusted for a level change in September 2011 and an anomalous spike near October 2012. Prediction Intervals A statistical forecasting system should not lack uncertainty quantification. Journal of Official Statistics 6.1 Specifically, see "1.4 2] Cleveland, Robert B.,

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

Domino Data Lab

APRIL 21, 2021

Auxiliary techniques like relying on card holders to report fraudulent transactions have unfortunately proven to be ineffective [1]. In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data. It can be implemented as either unsupervised (e.g.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

AWS Big Data

MARCH 21, 2024

Challenges Customers across industries today are looking to use data to their competitive advantage and increase revenue and customer engagement by implementing near real time analytics use cases like personalization strategies, fraud detection, inventory monitoring, and many more. The following diagram illustrates this architecture.

Data Warehouse 86

Data Warehouse Metrics Statistics Optimization 86

Domino Data Lab

AUGUST 8, 2019

Another key point: troubleshooting edge cases for models in production—which is often where ethics and data meet, as far as regulators are concerned—requires much more sophistication in statistics than most data science teams tend to have. It’s a quick way to clear the room. machine learning? Or something. Nothing Spreads Like Fear”.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

Occam's Razor

SEPTEMBER 21, 2015

The difference between a Reporting Squirrel and Analysis Ninja? Do you see how far away a Reporting Squirrel's job is from that of an Analysis Ninja? We send out our multi-tab spreadsheets, our best Google Analytics custom reports , our great dashboards full of data , and more to the tactical layer of data clients.

Visualization 160

Visualization Key Performance Indicator Slice and Dice Strategy 160

Sisense

DECEMBER 11, 2019

And he explained that the leap to visual-based data discovery put analytics in the hands of data experts working in different lines of business, rather than in IT, where bottlenecks could previously occur, and reporting could take much longer. From a startup in 2012, it is now valued at $3.2 The impact on customers. A true unicorn.

Data Lake 80

Data Lake Big Data Sales Data-driven 80

Domino Data Lab

MARCH 3, 2019

We had big surprises at several turns and have subsequently published a series of reports. Let’s look through some of the insights gained from those reports. Seriously, this entire article merely skims the surface of those reports. Mature practices reported 86%, and within financial services the numbers were somewhat higher.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Occam's Razor

JANUARY 10, 2011

10% of your time should be spent in implementing tools, not 15 months with an eye towards analysis in the middle of 2012. And possess at least some knowledge of the fundamentals of statistics. Here's the amazing thing: everyone else who got all those reports thought I was quite simply magnificent. Stop switching tools!

Analytics 120

Analytics Measurement Data-driven Optimization 120

CIO Business Intelligence

FEBRUARY 3, 2023

Major features: Open-source edition; tighter integration with AI for contextual and visual processing Major use cases: Supply chain management, financial transactions Fortra Automate The RPA tools from Fortra (formerly HelpSystems) tackle business tasks ranging from responding to inquiries to generating reports.

Data-driven 145

Data-driven Interactive Enterprise Statistics 145

Depict Data Studio

APRIL 10, 2023

Visualizing data in charts, graphs, dashboards, and infographics is one of the most powerful strategies for getting your numbers out of your spreadsheets and into real-world conversations. and implications of findings) than in statistical significance. Put your easiest-to-follow chart in your final presentation or report.

Visualization 105

Visualization Dashboards Testing Reporting 105

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. More people than ever are using statistical analysis packages and dashboards, explicitly or more often implicitly, to develop and test hypotheses. This question is statistical or methodological in nature. Know what matters.

Experimentation 61

Experimentation Testing Statistics Metrics 61

datapine

MAY 17, 2021

Businesses can benefit from improved data driven decision making as well as enhanced business processes and models and share insights across departments more fluently while propelling intelligent business strategies. Gartner released a full report discussing the importance of Augmented Analytics in the future. Does this sound familiar?

Visualization 161

Visualization Data-driven Business Intelligence Metrics 161