BizAcuity

MARCH 18, 2019

Credit scoring systems and predictive analytics model attempt to quantify uncertainty and provide guidance for identifying, measuring and monitoring risk. The consumer lending business is centered on the notion of managing the risk of borrower default. Benefits of Predictive Analytics in Unsecured Consumer Loan Industry.

Predictive Analytics 83

Predictive Analytics Analytics Cost-Benefit Data mining 83

CIO Business Intelligence

JUNE 22, 2022

Oxford Economics, a leader in global forecasting and quantitative analysis, teamed up with Huawei to develop a new approach to measuring the impact of digital technology on economic performance. Ongoing innovation in digital technologies is now essential to support its expansion.

Digital Transformation 88

Digital Transformation Internet of Things Manufacturing Unstructured Data 88

IBM Big Data Hub

APRIL 20, 2023

A Process Mining exercise drawing data from enterprise SAP has helped measure KPI performance and define the transformation roadmap.

KPI 67

KPI Consulting Finance Enterprise 67

IBM Big Data Hub

OCTOBER 18, 2022

Most admit uncertainty around ROI and nearly half struggle with adequate insights from their data. Fewer than 1 in 4 agree that sustainability requires financial tradeoffs, but many acknowledge the need to redefine and re-calibrate how they measure performance.

Uncertainty 52

Uncertainty Strategy Measurement ROI 52

datapine

MAY 31, 2022

Since we live in a digital age, where data discovery and big data simply surpass the traditional storage and manual implementation and manipulation of business information, companies are searching for the best possible solution for handling data. It is evident that the cloud is expanding. Cost management and containment.

Risk 237

Risk Cost-Benefit Business Intelligence Data-driven 237

AWS Big Data

AUGUST 7, 2023

Many businesses use different software tools to analyze historical data and past patterns to forecast future demand and trends to make more accurate financial, marketing, and operational decisions. Forecasting acts as a planning tool to help enterprises prepare for the uncertainty that can occur in the future.

Forecasting 86

Forecasting Data Warehouse Machine Learning Modeling 86

Sisense

OCTOBER 6, 2020

Most commonly, we think of data as numbers that show information such as sales figures, marketing data, payroll totals, financial statistics, and other data that can be counted and measured objectively. This is quantitative data. It’s “hard,” structured data that answers questions such as “how many?”

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

Sisense

DECEMBER 13, 2021

Many financial institutions face common challenges when it comes to turning their big data sets into actionable business intelligence. This insight can inform future partnerships, and reduce uncertainty about which services will be most relevant and useful. Where are they spending their money, when, and how often?

Data-driven 59

Data-driven IT Business Intelligence Cost-Benefit 59

Cloudera

NOVEMBER 19, 2018

One of Cloudera’s partners offers “Sustainability Services” with a goal of assisting organizations in turning costs and risks associated with changing regulatory and workforce environments, as well as supply chain uncertainties and volatile markets, into business opportunities.

Digital Transformation 40

Digital Transformation IoT Cost-Benefit Uncertainty 40

The Unofficial Google Data Science Blog

JANUARY 14, 2016

Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Occam's Razor

APRIL 8, 2013

First, you figure out what you want to improve; then you create an experiment; then you run the experiment; then you measure the results and decide what to do. For each of them, write down the KPI you're measuring, and what that KPI should be for you to consider your efforts a success. Measure and decide what to do.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

IBM Big Data Hub

APRIL 18, 2024

Beyond cost savings, organizations seek tangible ways to measure gen AI’s return on investment (ROI), focusing on factors like revenue generation, cost savings, efficiency gains and accuracy improvements, depending on the use case. The AGI would need to handle uncertainty and make decisions with incomplete information.

Cost-Benefit 91

Cost-Benefit Modeling Manufacturing Interactive 91

Domino Data Lab

JUNE 2, 2019

ans from Nick Elprin, CEO and co-founder of Domino Data Lab, about the importance of model-driven business: “Being data-driven is like navigating by watching the rearview mirror. If your business is using big data and putting dashboards in front of analysts, you’re missing the point.”. I consider that a healthy trend.

Data-driven 110

Data-driven Data Science Machine Learning Modeling 110

Andrew White

APRIL 22, 2020

Emergency measures are undertaken with little planning. In this second phase executive leaders will need to make critical business decisions with even less data and with more uncertainty. Big data; small data. Real data; synthetic data. Things stop working. This phase is all about survival.

Software 52

Software Uncertainty Measurement Marketing 52

Peter James Thomas

AUGUST 12, 2018

This piece was prompted by both Olaf’s question and a recent article by my friend Neil Raden on his Silicon Angle blog, Performance management: Can you really manage what you measure? Data may be perfectly valid, but still not represent reality. It is hard to account for such tweaking in measurement systems.

Metrics 49

Metrics Statistics Data Quality Measurement 49

The Unofficial Google Data Science Blog

JULY 22, 2020

For this reason we don’t report uncertainty measures or statistical significance in the results of the simulation. From a Bayesian perspective, one can combine joint posterior samples for $E[Y_i | T_i=t, E_i=j]$ and $P(E_i=j)$, which provides a measure of uncertainty around the estimate.

Experimentation 95

Experimentation Statistics Testing Strategy 95

Domino Data Lab

JUNE 23, 2019

This was all enabled and accelerated by an earlier wave of big data technologies ushered in by Google’s MapReduce paper and the open source project Hadoop. These measurement-obsessed companies have an advantage when it comes to AI. One of the most important jobs of PMs is to collect the right data at the right time.

Management 110

Management Machine Learning Experimentation Metrics 110

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

The result is that experimenters can’t afford to be sloppy about quantifying uncertainty. These typically result in smaller estimation uncertainty and tighter interval estimates. And since the metric average is different in each hour of day, this is a source of variation in measuring the experimental effect.

Experimentation 40

Experimentation Metrics Statistics Measurement 40

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

Note also that this account does not involve ambiguity due to statistical uncertainty. As you can see from the tiny confidence intervals on the graphs, big data ensured that measurements, even in the finest slices, were precise. We addressed #1 with an observational study and #2 with a randomized experiment, as follows.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45