Sisense

SEPTEMBER 3, 2020

Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization. Data analysts in one organization might be called data scientists or statisticians in another. Database design is often an important part of the business analyst role.

Statistics 97

Statistics Metrics Visualization Finance 97

AWS Big Data

MARCH 26, 2024

In 2022, AWS commissioned a study conducted by the American Productivity and Quality Center (APQC) to quantify the Business Value of Customer 360. The following figure shows some of the metrics derived from the study. We recommend building your data strategy around five pillars of C360, as shown in the following figure.

Data Strategy 68

Data Strategy Strategy Data Warehouse Prescriptive Analytics 68

IBM Big Data Hub

SEPTEMBER 11, 2023

A pain point tracker (a repository of business, human-centered design and technology issues that inhibit users’ ability to execute critical tasks) captures themes that arise during the data collection process. The pain point tracker clusters the foundational data in which value metrics are then applied.

Data-driven 86

Data-driven IT Measurement Cost-Benefit 86

Occam's Razor

AUGUST 13, 2012

The questions reveal a bunch of things we used to worry about, and continue to, like data quality and creating data driven cultures. That means: All of these metrics are off. I can use that to hypothesize what an optimal budget allocation might look like. EU Cookies!)

Modeling 124

Modeling Metrics Data Quality Data-driven 124

Occam's Razor

DECEMBER 12, 2016

Data quality plays a role into this. And, most of the time, regardless of the size of the size of the company, you only know your code is not working post-launch when data is flowing in (not!). You got me, I am ignoring all the data layer and custom stuff! This is sub-optimal. All that is great.

Analytics 141

Analytics Data-driven Strategy Consulting 141

Domino Data Lab

JUNE 23, 2019

Companies with successful ML projects are often companies that already have an experimental culture in place as well as analytics that enable them to learn from data. Ensure that product managers work on projects that matter to the business and/or are aligned to strategic company metrics. That’s another pattern.

Management 110

Management Machine Learning Experimentation Metrics 110

Occam's Razor

APRIL 21, 2014

The mistake we make is that we obsess about every big, small and insignificant analytics implementation challenge and try to fix it because we want 99.95% comfort with data quality. We wonder why data people are not loved. :). I believe these two posts with a collection of some of my favorite metrics will inspire you: 1.

Marketing Analytics 131

Marketing Analytics Marketing Analytics Measurement 131

Domino Data Lab

JULY 22, 2019

One is data quality, cleaning up data, the lack of labelled data. The problems down in the mature bucket, those are optimizations, they aren’t showstoppers. Machine learning is a subset of mathematical optimization. How can you trace that all the way back into the data collection?

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Domino Data Lab

MARCH 3, 2019

Then, when we received 11,400 responses, the next step became obvious to a duo of data scientists on the receiving end of that data collection. Over the past six months, Ben Lorica and I have conducted three surveys about “ABC” (AI, Big Data, Cloud) adoption in enterprise. What metrics are used to evaluate success?

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Andrew White

JANUARY 9, 2022

This is the same for scope, outcomes/metrics, practices, organization/roles, and technology. Check this out: The Foundation of an Effective Data and Analytics Operating Model — Presentation Materials. Much as the analytics world shifted to augmented analytics, the same is happening in data management.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Domino Data Lab

FEBRUARY 4, 2019

These two points provide a different kind of risk management mechanism which is effective for science, specifically data science. Of course, some questions in business cannot be answered with historical data. Instead they require investment, tooling, and time for data collection. You’ll need to read the papers.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

datapine

JANUARY 31, 2022

As Dan Jeavons Data Science Manager at Shell stated: “what we try to do is to think about minimal viable products that are going to have a significant business impact immediately and use that to inform the KPIs that really matter to the business”. Business intelligence and analytics allow users to know their businesses on a deeper level.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Alation

NOVEMBER 11, 2021

Let’s take a look at some of the key principles for governing your data in the cloud: What is Cloud Data Governance? Cloud data governance is a set of policies, rules, and processes that streamline data collection, storage, and use within the cloud. This framework maintains compliance and democratizes data.

Data Governance 52

Data Governance Data Quality Metrics Metadata 52

datapine

FEBRUARY 22, 2022

In the article, he pointed to a pretty fascinating trend: “Experian has predicted that the CDO position will become a standard senior board-level role by 2020, bringing the conversation around data gathering, management, optimization, and security to the C-level.” We love that data is moving permanently into the C-Suite.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

datapine

APRIL 16, 2019

While sometimes it’s okay to follow your instincts, the vast majority of your business-based decisions should be backed by metrics, facts, or figures related to your aims, goals, or initiatives that can ensure a stable backbone to your management reports and business operations. 3) Gather data now. 6) Analyze and understand.

Data-driven 287

Data-driven Dashboards Visualization Cost-Benefit 287

Alation

AUGUST 26, 2021

Data intelligence first emerged to support search & discovery, largely in service of analyst productivity. For years, analysts in enterprises had struggled to find the data they needed to build reports. This problem was only exacerbated by explosive growth in data collection and volume. Data lineage features.

Metadata 64

Metadata Data Governance Dashboards Software 64

Jet Global

MAY 9, 2024

Monitoring can include tracking performance metrics such as execution time and resource usage, and logging errors or failures for troubleshooting and remediation. It also includes data validation and quality checks to ensure the accuracy and integrity of the data being processed. How is ELT different from ETL?

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52