IBM Big Data Hub

JANUARY 10, 2023

Process mining tools automate and generate dashboards which illustrate an ‘at a glance’ view of adoption rates. They also allow you to quantify business value based on improvements and allows you to assign and track key metrics with business objectives. Making it stick: Driving continuous change.

Management 52

Management Data-driven Data mining Business Objectives 52

Domino Data Lab

MAY 20, 2021

Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. def get_neigbours(M, k): nn = NearestNeighbors(n_neighbors=k+1, metric="euclidean").fit(M) Here is a simplified version of the SMOTE algorithm: import random import pandas as pd import numpy as np.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

IBM Big Data Hub

SEPTEMBER 19, 2023

The data science lifecycle Data science is iterative, meaning data scientists form hypotheses and experiment to see if a desired outcome can be achieved using available data. The dedicated data analyst Virtually any stakeholder of any discipline can analyze data.

Data Science 78

Data Science Data Analytics Prescriptive Analytics Analytics 78

BizAcuity

APRIL 19, 2019

Several data mining and machine learning techniques are being developed that are able to foresee and predict high-risk players by tracking their actions while he or she is still engaged in gaming. We used similar metrics for bets and wins and pre-computed and aggregated them.

Machine Learning 75

Machine Learning Metrics Data mining Business Analytics 75

datapine

OCTOBER 10, 2019

And if you’re looking to make a dashboard to outline return on marketing investment (ROMI), your visualizations, design, and metrics would be very different from those featured in a marketing channel engagement report. Do they want to get more social reach on the blog posts your company is putting out?

Dashboards 146

Dashboards Visualization Sales Metrics 146

Occam's Razor

AUGUST 22, 2011

Here you'll find all my blog posts categorized into a structure that will hopefully make it easy for you to discover new content, find answers to your questions, or simply wallow in some excellent analytics narratives. " ~ Web Metrics: "What is a KPI? " + Standard Metrics Revisited Series. How to focus?"

KPI 124

KPI Metrics Measurement ROI 124

IBM Big Data Hub

JULY 6, 2023

Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics. appeared first on IBM Blog.

Machine Learning 55

Machine Learning Data Science Statistics Deep Learning 55

IBM Big Data Hub

JULY 24, 2023

It tracks four important pillars: metrics, events, logs and traces (MELT) to understand the behavior, performance, and other aspects of cloud infrastructure and apps. It aims to understand what’s happening within a system by studying external data. appeared first on IBM Blog. billion business.

IT 55

IT Analytics Cost-Benefit Predictive Analytics 55

IBM Big Data Hub

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. What is text mining? Validation and iteration It’s essential to make sure your mining results are accurate and reliable, so in the penultimate stage, you should validate the results.

Data mining 54

Data mining Machine Learning Deep Learning Marketing 54

datapine

MAY 14, 2019

This interdisciplinary field of scientific methods, processes, and systems helps people extract knowledge or insights from data in a host of forms, either structured or unstructured, similar to data mining.

Data Science 350

Data Science Machine Learning Data-driven Big Data 350

BizAcuity

APRIL 1, 2023

Data governance and security measures are critical components of data strategy. KPI Analysis: the process of evaluating the performance of an organization using a set of measurable metrics infrastructure: refers to the hardware, software, and other key resources that are used to manage, maintain and analyze data within an organization.

Business Intelligence 52

Business Intelligence Consulting KPI Data Warehouse 52

datapine

JULY 24, 2019

Data has never been more readily accessible. Approaches to communication are changing, and success in today’s technology-driven world correlates directly to the quantity rather than the quality of one’s information—metrics relating to the business, the client, the competitor, and the market. 3) Design data to avoid clutter.

Dashboards 148

Dashboards Business Intelligence Visualization KPI 148

datapine

MARCH 25, 2022

Predictive analytics : This method uses advanced statistical techniques coming from data mining and machine learning technologies to analyze current and historical data and generate accurate predictions. Tracking this metric in time can help you spot any issues related to cash conversion and find smart solutions to fix them.

Business Intelligence 264

Business Intelligence Analytics Statistics Dashboards 264

BizAcuity

JANUARY 31, 2023

Data governance and security measures are critical components of data strategy. KPI Analysis: the process of evaluating the performance of an organization using a set of measurable metrics infrastructure: refers to the hardware, software, and other key resources that are used to manage, maintain and analyze data within an organization.

Business Intelligence 52

Business Intelligence Consulting KPI Data Warehouse 52

Cloudera

MAY 15, 2019

American Water leverages NiFi to track metrics against a simulated truck, showing the initial values in capturing this type of data. Walmart will be sharing about how its construction of a Finance stream in its data lake helped reduce and eliminate efforts on data mining and cleansing.

Enterprise 48

Enterprise Data Lake Data mining IoT 48

Sisense

APRIL 10, 2020

In Talking Data , we delve into the rapidly evolving worlds of Natural Language Processing and Generation. Text data is proliferating at a staggering rate, and only advanced coding languages like Python and R will be able to pull insights out of these datasets at scale. Every library has its own purpose and benefits.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

BizAcuity

APRIL 28, 2019

With Big Data, it is possible to acquire and segregate data with laser sharp focus with respect to one singular debtor. By clubbing various techniques like data mining, machine learning, artificial intelligence and statistical modelling, it makes predictions about events in the future.

Predictive Analytics 98

Predictive Analytics Analytics Big Data Data mining 98

Occam's Razor

SEPTEMBER 4, 2012

I'm sure you are impressed at the data mining and intent targeting efforts of TripIt. That would have been an easy thing to do based on data mining ( lame data mining!). When I got this email, as a Marketer and Data Mining connoisseur, I had to smile. Let me explain. It was just so smart.

Marketing 144

Marketing Data mining Measurement Strategy 144

Sisense

AUGUST 11, 2019

The sheer quantity and scope of data produced and stored by your company can make it incredibly hard to peer through the number-fog to pick out the details you need. This is where Business Analytics (BA) and Business Intelligence (BI) come in: both provide methods and tools for handling and making sense of the data at your disposal.

Business Intelligence 75

Business Intelligence Business Analytics Analytics OLAP 75

datapine

NOVEMBER 27, 2019

Accordingly, predictive and prescriptive analytics are by far the most discussed business analytics trends among the BI professionals, especially since big data is becoming the main focus of analytics processes that are being leveraged not just by big enterprises, but small and medium-sized businesses alike. And it’s completely free!

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Sisense

MAY 11, 2021

You can’t talk about data analytics without talking about data modeling. The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. Reporting Reporting contains the flattest and most cleaned version of our data.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

Domino Data Lab

AUGUST 1, 2021

Because of its architecture, intrinsically explainable ANNs can be optimised not just on its prediction performance, but also on its explainability metric. Conference on Knowledge Discovery and Data Mining, pp. def create_model(): sgd = optimizers.SGD(lr=0.01, decay=0, momentum=0.9, Guestrin, C., Why should I trust you?:

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

IBM Big Data Hub

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

Data Science 54

Data Science Machine Learning Cost-Benefit Deep Learning 54

datapine

MARCH 31, 2022

S/He is responsible for providing cost-effective solutions to achieve business objectives, comparing operational progress against project development while assisting in planning budgets, forecasts, timelines, and developing reports on performance metrics. SAS BI: SAS can be considered the “mother” of all BI tools.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

Occam's Razor

NOVEMBER 13, 2009

I now have several full time jobs, plus this blog, plus speaking around the world, plus a family, plus… so much more. " This blog post is in three parts: The pitch. Chapter 3 The Awesome World of Clickstream Analysis: Metrics. No metrics, data pukes, guidance on creating every more reports.

Analytics 71

Analytics Metrics Measurement Testing 71

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

Nevertheless, A/B testing has challenges and blind spots, such as: the difficulty of identifying suitable metrics that give "works well" a measurable meaning. In this blog post, we summarize that paper and refer you to it for details. This is essentially the same as finding a truly useful objective to optimize. 2] Ron Kohavi, Randal M.

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

The Unofficial Google Data Science Blog

JANUARY 14, 2016

The LSOS may do this by exposing a random group of users to the new design and compare them to a control group, and then analyze the effect on important user engagement metrics, such as bounce rate, time to first action, or number of experiences deemed positive. In addition to a suitable metric, we must also choose our experimental unit.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Alation

AUGUST 26, 2021

Today, BI represents a $23 billion market and umbrella term that describes a system for data-driven decision-making. BI leverages and synthesizes data from analytics, data mining, and visualization tools to deliver quick snapshots of business health to key stakeholders, and empower those people to make better choices.

Metadata 64

Metadata Data Governance Dashboards Software 64

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

Variance reduction through conditioning Suppose, as an LSOS experimenter, you find that your key metric varies a lot by country and time of day. And since the metric average is different in each hour of day, this is a source of variation in measuring the experimental effect. Obviously, this doesn’t have to be true.

Experimentation 40

Experimentation Metrics Statistics Measurement 40