Smart Data Collective

AUGUST 21, 2019

Companies will place a greater emphasis on quantitative decision-making models than ever before, since new big data technology has made it more reliable. Global Executives Create Highly Sophisticated Big Data Decision Making Models. Companies are capturing more quantitative data than ever to get greater value from their models.

Big Data 81

Big Data Modeling ROI Metrics 81

Andrew White

JULY 5, 2023

One of the points of confusion is with catalogs – or data catalogs – or analytics catalogs or metrics stores. When a business analyst or data science leader is building a model, they will often start out by searching a catalog for data. Here is is: When is a Catalog No Longer a Catalog? This was from 2018.

Metrics 52

Metrics Metadata Modeling Data Quality 52

IBM Big Data Hub

FEBRUARY 22, 2024

Earning trust in the outputs of AI models is a sociotechnical challenge that requires a sociotechnical solution. There must be a concerted effort to make these principles a reality through consideration of the functional and non-functional requirements in the models and the governance systems around those models.

Metadata 94

Metadata Measurement Risk Modeling 94

Rocket-Powered Data Science

DECEMBER 15, 2018

Many unsupervised learning models can converge more readily and be more valuable if we know in advance which parameterizations are best to choose. If we cannot know that ( i.e., because it truly is unsupervised learning), then we would like to know at least that our final model is optimal (in some way) in explaining the data.

Machine Learning 171

Machine Learning Optimization Metrics Modeling 171

IBM Big Data Hub

JANUARY 25, 2024

Workers’ increasing usage of smart devices and apps such as ChatGPT or other black box public models, without proper approval, has become a persistent issue and doesn’t include the correct change management to inform workers about the associated risks.

Strategy 96

Strategy Risk Cost-Benefit Business Objectives 96

CIO Business Intelligence

JULY 5, 2022

Research firm Gartner defines business analytics as “solutions used to build analysis models and simulations to create scenarios, understand realities, and predict future states.”. Data analytics is used across disciplines to find trends and solve problems using data mining , data cleansing, data transformation, data modeling, and more.

Business Analytics 120

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 120

O'Reilly on Data

APRIL 19, 2021

Relatively few respondents are using version control for data and models. Tools for versioning data and models are still immature, but they’re critical for making AI results reproducible and reliable. The biggest skills gaps were ML modelers and data scientists (52%), understanding business use cases (49%), and data engineering (42%).

Enterprise 353

Enterprise Modeling Risk Manufacturing 353

BizAcuity

MARCH 8, 2019

Through workforce analytics, companies can get a comprehensive view of their employees designed to interpret historical trends and in creating predictive models that lead to insights and better decisions in the future. Facilitates the company to model, stimulate, report, compare and leverage the most important metrics of Human Resources.

Analytics 73

Analytics Prescriptive Analytics Cost-Benefit Metrics 73

Birst BI

AUGUST 23, 2018

For example, there are a plethora of software tools available to automatically develop predictive models from relational data, and according to Gartner, “By 2020, more than 40% of data science tasks will be automated, resulting in increased productivity and broader usage by citizen data scientists.” [1] Source: Gartner (April 2018).

Machine Learning 73

Machine Learning Data Science Testing Metrics 73

Alation

SEPTEMBER 30, 2022

Seeing this trend, Bessemer sought to define a new metric for assessing the success of a private SaaS company – achieving $100M of ARR (annual recurring revenue). They go on to explain that, at $100M in ARR, these companies have product-market fit, a scalable GTM model, and a growing customer base. Subscribe to Alation's Blog.

Measurement 92

Measurement Metrics Data Governance Sales 92

Corinium

JUNE 6, 2019

Beyond that, we recommend setting up the appropriate data management and engineering framework including infrastructure, harmonization, governance, toolset strategy, automation, and operating model. Measure user adoption and engagement metrics to not just understand products take-up, but also to enhance the overall product propositions.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

CIO Business Intelligence

AUGUST 12, 2022

in 2016, and BD Advanced Bioprocessing in 2018. We’re very much focused on the commercialization of acquisitions, making sure we don’t break the deal models and that things are running as they should be,” says John Stevens, vice president of IT at Thermo Fisher. in 2013, Alfa Aesar in 2015, Affymetrix and FEI Co. Catalyzing change.

IT 104

IT Data Lake Sales Interactive 104

Occam's Razor

JULY 24, 2018

work (collection, processing, reporting, analysis), processes, org structure, governance models, last-mile gaps , metrics ladders of awesomeness , and… so… much… more. Remember, tools, work, processes, org structure, governance models, last-mile gaps, metrics ladders of awesomeness, and… so… much… more. The Impact Matrix.

Analytics 93

Analytics Metrics Strategy Measurement 93

IBM Big Data Hub

MARCH 8, 2024

This makes 5G’s Block Error Rate (BER)—a metric of error frequency—much lower. The 5G rollout began in 2018 with a global initiative known as the 3rd Generation Partnership Project (3FPP) that defined a new set of standards to steer the design of devices and applications for use on 5G networks.

IoT 100

IoT Broadcasting Internet of Things Technology 100

CIO Business Intelligence

NOVEMBER 3, 2022

This reflection pushed Nikolic to make changes to how technology teams worked across the organisation, and support a new target operating model. By 2018, the Trainline platform was hosting more than 80 million customer visits a month, with more than 80% coming through mobile devices. The company sold more than 204 tickets every minute.

Sales 98

Sales Digital Transformation Data Processing Technology 98

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

Others argue that there will still be a unique role for the data scientist to deal with ambiguous objectives, messy data, and knowing the limits of any given model. With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

datapine

OCTOBER 21, 2020

Now that we live longer, treatment models have changed and many of these changes are namely driven by data. and could provide a model for the EU to follow. Both descriptive and predictive analytics models can enhance decisions for negotiating pricing, reducing the variation in supplies, and optimizing the ordering process as a whole.

Big Data 364

Big Data Data Analytics Analytics Cost-Benefit 364

Insight

MARCH 18, 2019

Bias in Machine Learning Algorithms (Bottom Photos Source: ProPublica ; Top Photos Source: Pexels.com) Biases in predictive modeling are a widespread issue Machine learning and AI applications are used across industries, from recommendation engines to self-driving cars and more.

Machine Learning 77

Machine Learning Predictive Modeling Modeling Metrics 77

datapine

NOVEMBER 27, 2019

Marketers determine customer responses or purchases and set up cross-sell opportunities, whereas bankers use it to generate a credit score – the number generated by a predictive model that incorporates all of the data relevant to a person’s creditworthiness.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Peter James Thomas

AUGUST 12, 2018

Some systems (even in 2018) can still make it harder to capture good data than to ram in bad. Pertinence and fidelity of metrics developed from Data. Metrics are seldom reliant on just one data element, but are often rather combinations. There are often compromises to be made in defining metrics.

Metrics 49

Metrics Statistics Data Quality Measurement 49

Domino Data Lab

MARCH 3, 2019

O’Reilly Media published our analysis as free mini-books: The State of Machine Learning Adoption in the Enterprise (Aug 2018). What’s been the impact of using ML models on culture and organization? Who builds their models? What metrics are used to evaluate success?

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Cloudera

MAY 22, 2018

With CDSW, organizations can research and experiment faster, deploy models easily and with confidence, as well as rely on the wider Cloudera platform to reduce the risks and costs of data science projects. track model metrics, performance, and any model artifacts the user specifies. Experiments.

Data Science 41

Data Science Snapshot Machine Learning Metadata 41

Domino Data Lab

FEBRUARY 10, 2019

Metrics can also increase a lot in the early days of a project and then suddenly hit a wall. I once ran a Kaggle competition where thousands of people competed around the world to model my data. It’s possible, even common, for a week spent on modeling data to result in no improvement whatsoever. We might expect that.

Machine Learning 63

Machine Learning Modeling Deep Learning Metrics 63

IBM Big Data Hub

OCTOBER 16, 2023

While the US doesn’t have a single data privacy law at the federal level, the California Consumer Privacy Act of 2018 is one of the most ambitious data privacy legislation ever enacted in the US, much like European Union’s GDPR. Data sovereignty in the EU is an evolving field.

Cost-Benefit 67

Cost-Benefit Data Governance Risk Data-driven 67

Insight

JANUARY 14, 2020

Lauren Holzbauer was an Insight Fellow in Summer 2018. a “pre-trained” model) to be recycled and reused for many different tasks. Just like Lasso and Ridge are regularization techniques that can be applied to linear regression models, dropout can be applied to neural networks to prevent overfitting.

Metrics 58

Metrics Optimization Modeling Testing 58

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. 2018-06-21). Introduction.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Occam's Razor

JANUARY 8, 2018

Media mix modeling. If you are a reader of my newsletter, The Marketing < > Analytics Intersect , you’ve seen me apply it to metrics (last TMAI was on Bounce Rate), reports, frameworks and more. I tried to click on “January 2018” hoping it pops up a calendar. 2018: the year of doing the unsexy fundamentals well!

Sales 133

Sales Predictive Analytics Insurance Data Warehouse 133

Alation

MARCH 21, 2023

They may also suffer from data duplication, which undermines their analytics models. When it embarked on a digital transformation and modernization initiative in 2018, the company migrated all its data to AWS S3 Data Lake and Snowflake Data Cloud to provide accessibility to data to all users.

Data Analytics 52

Data Analytics Analytics Data-driven Big Data 52

Jet Global

AUGUST 14, 2019

Chances are good that your average non-technical sales agent or purchasing representative will have difficulty joining multiple tables together with a standard report, but with Business Intelligence cubes, all that is required drag and drop the metrics and dimensions that matter to them into their own personalized dashboard.

Enterprise 70

Enterprise OLAP Business Intelligence Cost-Benefit 70

Domino Data Lab

SEPTEMBER 9, 2019

We have configured the default Compute Environment in Domino to include all of the packages, libraries, models, and data you’ll need for this tutorial. That nlp variable is now your gateway to all things spaCy and loaded with the en_core_web_sm small model for English. Getting Started. for a, b in pairs:?. print(a, b, lic[a].similarity(lic[b])).

Deep Learning 110

Deep Learning Machine Learning Data Science Visualization 110

Depict Data Studio

SEPTEMBER 15, 2020

Paul presented new metrics of success: How much time do you have with your family? I went for it in Spring of 2018. I’ve got this perfect demonstration I did at a conference in 2018 on how to survey children. I didn’t want to be this role model for women in data. One time, we read Company of One by Paul Jarvis together.

Visualization 105

Visualization Consulting Dashboards Reporting 105

Jet Global

OCTOBER 2, 2019

Chances are good that your average non-technical sales agent or purchasing representative will have difficulty joining multiple tables together with a standard report, but with Business Intelligence cubes, all that is required drag and drop the metrics and dimensions that matter to them into their own personalized dashboard.

Enterprise 40

Enterprise OLAP Business Intelligence Cost-Benefit 40

Business Over Broadway

MAY 22, 2019

For those of you who are interested, here is Gartner’s latest (2018) hype cycle on emerging technologies. Remember that digital transformation is about transforming your business and operating models with technology. ’ Also, research shows that CX is a priority but technology adoption lags. IoT Artificial Intelligence.

Machine Learning 40

Machine Learning Technology Digital Transformation Data Science 40

Domino Data Lab

FEBRUARY 4, 2019

People who attended JupyterCon 2017–2018 can attest, an “industry poster session” includes an open bar, catered hors d’oeuvres, lots of mingling … to paraphrase feedback from JupyterCon, “As a tech person, would I get up extra early to meet strangers for coffee at 8:00 am? Problems with training ML models efficiently.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Domino Data Lab

JULY 22, 2019

how “the business executives who are seeing the value of data science and being model-informed, they are the ones who are doubling down on their bets now, and they’re investing a lot more money.” and drop your deep learning model resource footprint by 5-6 orders of magnitude and run it on devices that don’t even have batteries.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

bridgei2i

MAY 19, 2021

Because once in 2018, I stepped out of my, you know, classical corporate journey, I, as you probably know, I also joined as an advisor with Deloitte. That’s it; it’s amazing to see how these models keep on evolving. And what are the changes you’ve seen in the last couple of decades? So both are a problem.

Enterprise 75

Enterprise Digital Transformation Marketing Cost-Benefit 75

Domino Data Lab

OCTOBER 23, 2019

This chapter will explore the numbers behind the numbers using ML and then creating an API to serve out the ML model. This means covering details like setting up your environment, deployment, and monitoring, in addition to creating models on clean data. Phrasing the Problem. questions to ask. Gathering the Data. In Figure 6.1,

Statistics 59

Statistics Machine Learning Testing Modeling 59