CIO Business Intelligence

NOVEMBER 20, 2023

The US Bureau of Labor Statistics has projected that the number of software developers will grow 25% from 2021-31. Well-known metrics, such as deployment frequency, are useful when it comes to tracking teams but not individuals. The results are not definitive, but they are promising. ROI and Metrics, Software Development

Measurement 126

Measurement Metrics Software Testing 126

datapine

JUNE 7, 2022

From political issues to sports statistics and the recent report you received on the ROI of your company blog, the internet as well as informational reports are flooded with examples of misleading data visualization. But, by knowing what to look for, you can avoid connecting with metrics that will lead your organization down the wrong path.

Visualization 267

Visualization Metrics Statistics Dashboards 267

Occam's Razor

FEBRUARY 6, 2018

Your digital performance dashboard has 16 metrics along 9 dimensions, and you know that the font-size 6 text and sparkline sized charts make them incomprehensible. Focus only on KPIs, eliminate metrics. Here are the definitions you'll find in my books: Metric : A metric is a number. Time on Page is a metric.

Strategy 263

Strategy Metrics KPI Dashboards 263

Occam's Razor

APRIL 8, 2013

To win in business you need to follow this process: Metrics > Hypothesis > Experiment > Act. We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. That metric is tied to a KPI.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

IBM Big Data Hub

APRIL 13, 2023

SELECT count (*) FROM FLIGHT.FLIGHTS_DATA — — — 99879 Look into the scheme definition of the table. Explore data In this step, I’ll look at both sample records and the summary statistics of the training dataset to gain insights into the dataset. Check the summary statistics of the numeric column.

Modeling 90

Modeling Statistics Machine Learning Testing 90

Alation

JANUARY 20, 2022

Step 2: Data Definitions. In this step, you connect data integrity to business results in shared definitions. This is done by collecting data statistics. Step 7: Data Quality Metrics. With data quality rules completed and firing, you can collect data quality metrics. With this step, you create a glossary for CDEs.

Data Quality 94

Data Quality Data Governance Metrics Statistics 94

Occam's Razor

SEPTEMBER 24, 2012

FBe's recommendation was (paraphrasing a 35 min talk): Don't invent new metrics, use online versions of Reach and GRPs to measure success. Because we don't understand the uniqueness, we fall back on profoundly sub-optimal old world metrics like Reach or Online GRP equivalents. Metrics are a problem.

Advertising 165

Advertising ROI Metrics Marketing 165

IBM Big Data Hub

DECEMBER 18, 2023

Does that imply that there are “fake” user metrics as well? Synthetic data is a statistical representation of reality. By definition, anomalous network performance is unpredictable. Are there alternatives to RUM data? Why “real”? Actually, yes!

IT 70

IT Metrics Optimization Testing 70

AWS Big Data

JUNE 6, 2023

They have to gather detailed data statistics, such as minimums, maximums, averages, and correlations. They have to then review the data statistics to identify data quality rules, and write code to implement these checks in their data pipelines. For an up-to-date list, refer to Data Quality Definition Language (DQDL).

Data Quality 92

Data Quality Statistics Data Lake Visualization 92

AWS Big Data

MARCH 12, 2024

AWS Glue Data Quality reduces the effort required to validate data from days to hours, and provides computing recommendations, statistics, and insights about the resources required to run data validation. We provide the plain text definition for each ruleset in the following table. Add the ruleset-5 definition under Ruleset editor.

Data Quality 85

Data Quality Measurement Testing Visualization 85

Sisense

SEPTEMBER 3, 2020

Besides strong technical skills (for instance, use of Hadoop, programming in R and Python , math, statistics), data scientists should also be able to tackle open-ended questions and undirected research in ways that bring measurable business benefits to their organization. See an example: Explore Dashboard.

Statistics 97

Statistics Metrics Visualization Finance 97

FineReport

MARCH 7, 2024

Data Analysis Report (by FineReport ) Based on my understanding of this definition, I summarize big data analysis as the process of acquiring data, breaking down silos, integrating information, identifying patterns, and promptly deriving actionable insights for decision-making. Try FineReport Now 1.1

Dashboards 52

Dashboards Reporting Advertising Statistics 52

O'Reilly on Data

NOVEMBER 13, 2018

There are also many important considerations that go beyond optimizing a statistical or quantitative metric. As we deploy ML in many real-world contexts, optimizing statistical or business metics alone will not suffice. Models will need to be customized (for specific locations, cultural settings, domains, and applications).

Machine Learning 220

Machine Learning Risk Management Statistics 220

datapine

APRIL 2, 2019

Tracking the success metrics based on your needs, and the time frame you select while comparing your values can be done with simple yet effective scorecards. Let’s start with a simple definition. A KPI scorecard is a term used to describe a statistical record that measures progress or achievement towards a set performance indicator.

Scorecard 226

Scorecard KPI Dashboards Reporting 226

CIO Business Intelligence

OCTOBER 20, 2022

That’s a staggering statistic, and it means that people want to buy from companies that have similar values as theirs, and they’ll also refuse to bring their talents to places that don’t engage in meaningful change. This is where Schneider shines. We have bigger and bigger data centers because we rely on more and more data to get things done.

Snapshot 98

Snapshot Statistics Metrics Publishing 98

Occam's Razor

MARCH 8, 2010

Like good little Reporting Squirrels we collect and stack metrics as if preparing for an imminent ice age. In this case its making right choices about the web metrics we knight and sent to the battle to come back with insights for our beloved corporation to monetize. You run a report and notice a trend for this metric.

Metrics 117

Metrics Testing Key Performance Indicator KPI 117

datapine

JANUARY 6, 2022

First of all, let’s find a definition to understand what lies behind data interpretation meaning. More often than not, it involves the use of statistical modeling such as standard deviation, mean and median. Let’s quickly review the most common statistical terms: Mean: a mean represents a numerical average for a set of responses.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

datapine

JULY 17, 2019

Whether utilizing HR KPIs or any other department and industry, it is crucial to understand these basic definitions so that the creation and analysis can improve a company’s bottom line. What Is A KPI Report? They are customizable and thus offer a powerful means of drilling down deep into very specific pockets of information.

KPI 223

KPI Reporting Key Performance Indicator Dashboards 223

datapine

NOVEMBER 13, 2019

Fortunately, we live in a digital age rife with statistics, data, and insights that give us the power to spot potential issues and inefficiencies within the business. With so many areas to consider, deciding which KPIs to focus on while defining metric measurement periods can prove to be a challenge at the initial stages.

Reporting 158

Reporting KPI Cost-Benefit Metrics 158

datapine

NOVEMBER 15, 2022

1) What Are Product Metrics? 2) Types Of Product Metrics. 3) Product Metrics Examples You Can Use. 4) Product Metrics Framework. The right product performance metrics will give you invaluable insights into its health, strength and weaknesses, potential issues or bottlenecks, and let you improve it greatly.

Metrics 141

Metrics KPI Measurement Dashboards 141

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature. Among these, only statistical uncertainty has formal recognition.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

Occam's Razor

AUGUST 22, 2011

" ~ Web Metrics: "What is a KPI? " + Standard Metrics Revisited Series. "Engagement" Is Not A Metric, It's An Excuse. Defining a "Master Metric", + a Framework to Gain a Competitive Advantage in Web Analytics. Overview & Importance of Qualitative Metrics. How to focus?"

KPI 124

KPI Metrics Measurement ROI 124

datapine

MAY 14, 2019

For those embarking on a journey to master the art of the ‘R’ language – a statistical computing program and framework for increased business intelligence-based success – Advanced R is intuitive, easy to follow, and will give you a well-rounded overview of this invaluable area of data science. click for book source**.

Data Science 350

Data Science Machine Learning Data-driven Big Data 350

FineReport

AUGUST 17, 2021

Digital dashboard: definition & benefits. Definition. Digital dashboards enable users to easily analyze and gain insights into important metrics and insights, thereby facilitating faster data-driven decisions in all key aspects of the business. The goal of data is to assist decision-making and clarify business goals.

Dashboards 52

Dashboards Cost-Benefit Key Performance Indicator Sales 52

AWS Big Data

MAY 16, 2023

The AWS Glue Data Catalog contains the table definitions for the smart sensor data sources stored in the S3 buckets. Amazon Athena runs queries using a variety of SQL statements on data stored in Amazon S3, and QuickSight is used for business intelligence and data visualization.

Dashboards 74

Dashboards Statistics Data Collection Business Intelligence 74

Cloudera

JUNE 28, 2022

A dashboard feeds from the Kudu database to show fraud summary statistics. NiFi is integrated with Schema Registry and it will automatically connect to it to retrieve the schema definition whenever needed throughout the flow. You can simply connect to the CDF console, upload the flow definition, and execute it.

Dashboards 84

Dashboards Machine Learning Statistics KPI 84

datapine

MARCH 16, 2023

Here, you’ll learn the definition, its advantages in a business context, common types and their use cases as well as an insightful list of examples for different functions and industries. Let’s dive in with the definition. Since the goal of this metric is comparison, the bar chart is a great choice to represent the data.

Visualization 196

Visualization KPI Cost-Benefit Dashboards 196

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. Because individual observations have so little information, statistical significance remains important to assess.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Anmut

JULY 12, 2021

Facts, events, statements, and statistics without proper context have little value and only lead to questions and confusion.?This We’ll start with basic definitions and then take an in-depth look at the characteristics, strengths, and weaknesses of data quality and data condition. Context is everything.?Facts,

Data Quality 52

Data Quality Measurement Metrics Dashboards 52

Alation

AUGUST 18, 2022

It’s important to realize that we need visibility into lineage and relationships between all data and data-related assets, including business terms, metric definitions, policies, quality rules, access controls, algorithms, etc. The challenge today is to think more broadly about what these data things could or should be.

Metadata 69

Metadata Visualization Statistics Data Architecture 69

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 Implications of Complexity.

Analytics 93

Analytics Metrics Strategy Measurement 93

Sisense

APRIL 10, 2020

R is a tool built by statisticians mainly for mathematics, statistics, research, and data analysis. Here, we will implement the XG-Boost algorithm, an algorithm that learns on the basis of training data (which we loaded earlier in both R and Python programming languages) with the help of probability and statistics.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

Sisense

AUGUST 11, 2019

Typically, this involves using statistical analysis and predictive modeling to establish trends, figuring out why things are happening, and making an educated guess about how things will pan out in the future. What About “Business Intelligence”? But on the whole, BI is more concerned with the whats and the hows than the whys.

Business Intelligence 75

Business Intelligence Business Analytics Analytics OLAP 75

Cloudera

JULY 18, 2022

We will also compute some summary statistics on the fly so that we can have a real-time dashboard of what is happening. A dashboard feeds from the Kudu database to show fraud summary statistics. In the first part of this blog we covered steps one through to five in the diagram below. Apache Flink.

Analytics 90

Analytics Dashboards Statistics Visualization 90

Data Science 101

OCTOBER 31, 2019

A definition from the book ‘Data Mining: Practical Machine Learning Tools and Techniques’, written by, Ian Witten and Eibe Frank describes Data mining as follows: “ Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Regression.

Data mining 77

Data mining KDD Data Science Forecasting 77

Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. Data science is an interdisciplinary field within STEM that combines math and statistics, computer science, and business acumen to find patterns in complex datasets that can be used to improve decision making.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

Occam's Razor

APRIL 4, 2011

Any application of algorithmic intelligence , weighted sort , expected range for metric values ( control limits ), or anything that even remotely smells of ever so slightly advanced statistics is a good sign. Also mere existence of statistics is not sufficient. Unknown unknowns are what it's all about! Look for that. #9.

Reporting 127

Reporting Metrics Dashboards Statistics 127