Decision Management Solutions

JUNE 4, 2021

Like many enterprises, you’ve likely made a hefty investment in analytic technology—from interactive dashboards and advanced visualization tools to data mining, predictive analytics, machine learning (ML), and artificial intelligence (AI). 1 MIT Sloan Management Review September 06, 2017. Focusing on decision-making changes everything.

Analytics Technologies 52

Analytics Technologies Technology Modeling Key Performance Indicator 52

CIO Business Intelligence

MAY 16, 2023

higher [in 2022] than in 2017.” Metaverse experiences enable new ways of interacting Metaverses are persistent, connected virtual spaces where users or visitors can immerse themselves in work, play, commerce, and socialization. McKinsey & Company’s 2022 Global Survey on AI says , “AI adoption globally is 2.5x

Advertising 98

Advertising Recreation/Entertainment Experimentation Enterprise 98

Cloudera

APRIL 7, 2021

A 2017 PWC survey of over 100 global insurance company CEOs revealed that the number one objective of these CEOs was to “get closer to their customers and to better understand their evolving needs.” Changing Business Models. Data — Too much and Too little. The Hunt for Talent. Yet they need to address the above issues to make this a reality.

Insurance 59

Insurance Digital Transformation Internet of Things IoT 59

Smarten

JANUARY 7, 2020

Disparate data sources are integrated into a uniform view, so users can drag and drop data and interact with the tools. Every business understands the value of objective metrics and accurate analysis and the modern BI environment is designed to support these goals at every level of the organization.

IT 52

IT Business Intelligence ROI Reporting 52

BizAcuity

APRIL 28, 2019

is delinquent as of June 30th, 2017. An improvement of 50% in debt collection was seen in just 3 months time, that too without any loss on customer interaction. Also, we noticed that, earlier, the data consisted of only a few metrics, based on which the team classified its customers. This goes on to show the potential it holds.

Predictive Analytics 98

Predictive Analytics Analytics Big Data Data mining 98

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

TF Lattice offers semantic regularizers that can be applied to models of varying complexity, from simple Generalized Additive Models, to flexible fully interacting models called lattices, to deep models that mix in arbitrary TF and Keras layers. The drawback of GAMs is that they do not allow feature interactions.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

Rita Sallam

NOVEMBER 11, 2016

What’s on Deck for 2017? This new engine will replace the.TDE by end of 2017. Nearer-term in 2017, Tableau’s Live Query Agent features will be the most important. Users will also be able to define and monitor metrics and alerts to changes based on defined or dynamic thresholds. Enterprise Features. Tableau for Linux!!

Visualization 49

Visualization Machine Learning Metrics Enterprise 49

Domino Data Lab

MAY 8, 2019

Finale Doshi-Velez, Been Kim (2017-02-28) ; see also the Domino blog article about TCAV. Adrian Weller (2017-07-29). “ If your “performance” metrics are focused on predictive power, then you’ll probably end up with more complex models, and consequently less interpretable ones. Challenges for Transparency ”. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Data Insight

APRIL 15, 2019

We reward and punish based on our last interaction with an organisation and are not afraid to take to social channels to articulate our experiences – whether it be good or bad – to also influence our peer’s choices. That’s a massive shift in quite a short time frame from traditional metrics without us even realising it.

Data-driven 40

Data-driven Metrics Measurement Strategy 40

AWS Big Data

AUGUST 7, 2023

The OBJECTIVE parameter specifies a metric to minimize or maximize the objective of a job. For this post, we use a modified version of the individual household electric power consumption dataset. For more information, see ElectricityLoadDiagrams20112014 Data Set (Dua, D. and Karra Taniskidou, E. UCI Machine Learning Repository [ [link] ].

Forecasting 93

Forecasting Data Warehouse Machine Learning Modeling 93

Peter James Thomas

AUGUST 12, 2018

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. Again see Using BI to drive improvements in data quality for further details. Some of these are based on the data available.

Metrics 49

Metrics Statistics Data Quality Measurement 49

Cloudera

MAY 18, 2021

These interactions are captured and the resulting synthetic data sets can be analysed for a number of applications, such as training models to detect emergent fraudulent behavior, or exploring “what-if” scenarios for risk management. Value-at-Risk (VaR) is a widely used metric in risk management. Intraday VaR. Citations. [1]

Risk 97

Risk Marketing Risk Management Data-driven 97

Jet Global

MAY 27, 2021

However, fear of the unknown has left many companies afraid to implement a new reporting tool, yet the risk of staying with Discoverer increases day by day: Discoverer extended support ended June 2017. Hubble delivers significant benefits to the team, helping us understand key spend metrics.”. Chrome: September 2015. Cairn Energy.

Reporting 52

Reporting Cost-Benefit Dashboards Finance 52

Domino Data Lab

OCTOBER 23, 2019

Knowing that the ultimate goal is to compare the social-media influence and power of NBA players, a great place to start is with the roster of the NBA players in the 2016–2017 season. One of the things this data set doesn’t have, however, is a single metric to rank both offensive and defensive performance in a single statistic.

Statistics 59

Statistics Machine Learning Testing Modeling 59

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? Katherine Twomey, Gert Westermann (2017). This is not that.

Data Science 66

Data Science Experimentation Machine Learning Data-driven 66

Business Over Broadway

DECEMBER 20, 2018

Using Predictive Analytics and Artificial Intelligence to Improve Customer Loyalty – As users/customers engage with a company (their products, services, surveys), they generate a lot of data about their behaviors and interactions with the brand. Data Science/Analytics Tools, Technologies and Languages used in 2017.

Machine Learning 74

Machine Learning Data Science Deep Learning Predictive Analytics 74

Domino Data Lab

AUGUST 1, 2021

The need for interaction – complex decision making systems often rely on Human–Autonomy Teaming (HAT), where the outcome is produced by joint efforts of one or more humans and one or more autonomous agents. In IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI), pages 24–30, Melbourne, Australia, 2017.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Occam's Razor

JUNE 7, 2017

If someone bothers to interact with the post, the posted comment is a spam or totally useless. Which in turn lead to even fewer customer interactions for content posted by brands. To give you some context as to how insanely lame these numbers are, Expedia.com received 59,400,000 Visits in May 2017. They keep posting.

Advertising 162

Advertising Marketing B2B ROI 162

Smarten

APRIL 24, 2019

Augmented Analytics allows organizations to integrate data from numerous data sources and to use that data to analyze and display results in a clear manner so the business can make unbiased decisions and establish objective metrics. Users can compare results against plans and forecasts.

Business Intelligence 0

Business Intelligence Analytics IT Metrics 0

datapine

NOVEMBER 27, 2019

Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. Today, managers and workers need to interact differently as they face an always-more competitive environment. from 2017 , and this is one of the business analytics topics we will hear even more in 2020.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Occam's Razor

JULY 13, 2017

4: Interactivity With Insightful End-Points. Oh, or your main traffic sources and the visitor acquisition metrics? Strategy 4: Interactivity With Insightful End-Points. Interactive visualization are great, only when packaged with insights for actions at logical end-points in exploration. 2: If Complex, Focus! Tweet that.

Visualization 125

Visualization Strategy Interactive Statistics 125

Alation

AUGUST 26, 2021

HBR Review May/June 2017. Finally, data catalogs leverage behavioral metadata to glean insights into how humans interact with data. A common BI application is the BI dashboard, which displays key metrics so that leaders have a “big picture view” to inform wise goals and decisions. Source: “What’s Your Data Strategy?”

Metadata 64

Metadata Data Governance Dashboards Software 64

O'Reilly on Data

JANUARY 25, 2021

We’ve explored usage across all publishing partners and learning modes, from live training courses and online events to interactive functionality provided by Katacoda and Jupyter notebooks. Our data shows that Chef and Puppet peaked in 2017, when Kubernetes started an almost exponential growth spurt, as Figure 4 shows.

Machine Learning 363

Machine Learning Software Testing Technology 363

Domino Data Lab

SEPTEMBER 9, 2019

For example, with those open source licenses we can download their text, parse, then compare similarity metrics among them: In [12]: pairs = [?. ["mit", "asl"],?. ["asl", "bsd"],?. ["bsd", "mit"] ?]? ?for …. One common use case for natural language work is to compare texts. for a, b in pairs:?.

Deep Learning 110

Deep Learning Machine Learning Data Science Visualization 110