O'Reilly on Data

FEBRUARY 11, 2020

We suspected that data quality was a topic brimming with interest. The responses show a surfeit of concerns around data quality and some uncertainty about how best to address those concerns. Key survey results: The C-suite is engaged with data quality. Data quality might get worse before it gets better.

Data Quality 299

Data Quality Metadata Data Governance Publishing 299

erwin

APRIL 29, 2020

Data lineage is the journey data takes from its creation through its transformations over time. It describes a certain dataset’s origin, movement, characteristics and quality. Tracing the source of data is an arduous task. Data Lineage Use Case: From Tracing COVID-19’s Origins to Data-Driven Business.

Metadata 111

Metadata Data Governance Key Performance Indicator Data Quality 111

IBM Big Data Hub

NOVEMBER 28, 2022

Metadata management performs a critical role within the modern data management stack. It helps blur data silos, and empowers data and analytics teams to better understand the context and quality of data. This, in turn, builds trust in data and the decision-making to follow. Improve data discovery.

Metadata 57

Metadata Data Quality Data-driven Data Governance 57

FineReport

MARCH 24, 2024

Rapid technological advancements and extensive networking have propelled the evolution of data analytics, fundamentally reshaping decision-making practices across various sectors. In this landscape, data analysts assume a pivotal role, tasked with interpreting data to drive informed decision-making.

Statistics 52

Statistics Data mining Visualization Reporting 52

DataKitchen

JUNE 7, 2021

This is a common question that we hear from our conversations with data scientists, engineers and analysts. DataOps includes four key objectives: Measure Your Process – As data professionals, we advocate for the benefits of data-driven decision making. What can you do? How can one get started given these limitations?

Testing 130

Testing IT Measurement Data-driven 130

CIO Business Intelligence

JUNE 14, 2023

Data and big data analytics are the lifeblood of any successful business. Getting the technology right can be challenging but building the right team with the right skills to undertake data initiatives can be even harder — a challenge reflected in the rising demand for big data and analytics skills and certifications.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

Cloudera

SEPTEMBER 15, 2022

Cloudera Contributor: Mark Ramsey, PhD ~ Globally Recognized Chief Data Officer. July brings summer vacations, holiday gatherings, and for the first time in two years, the return of the Massachusetts Institute of Technology (MIT) Chief Data Officer symposium as an in-person event. Luke: What is a modern data platform?

Data Lake 85

Data Lake Data Architecture Data-driven Data Warehouse 85

Anmut

OCTOBER 29, 2020

Data governance - who's counting? The role of data governance. This large gap between reported figures raises tough questions on the reliability of COVID-19 tracking data. In dealing with situations like pandemic data, how important are aspects of data governance such as standardised definitions?

Data Governance 52

Data Governance Data Collection Data-driven Statistics 52

Sisense

SEPTEMBER 3, 2020

We live in a constantly-evolving world of data. That means that jobs in data big data and data analytics abound. The wide variety of data titles can be dizzying and confusing! The growth in the range of data job titles is a testament to the value that these experts bring to their organizations.

Statistics 97

Statistics Metrics Visualization Finance 97

Alation

MAY 24, 2022

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

Data Quality 98

Data Quality Data Governance Metadata Metrics 98

Ontotext

JUNE 22, 2023

Recent statistics shed light on the realities in the world of current drug development: out of about 10,000 compounds that undergo clinical research, only 1 emerges successfully as an approved drug. The current process involves costly wet lab experiments, which are often performed multiple times to achieve statistically significant results.

Metrics 52

Metrics Statistics Visualization Metadata 52

O'Reilly on Data

JULY 28, 2020

It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Fair warning: if the business lacks metrics, it probably also lacks discipline about data infrastructure, collection, governance, and much more.) Agreeing on metrics. This is particularly true for AI products.

Marketing 361

Marketing Experimentation Metrics Testing 361

O'Reilly on Data

APRIL 19, 2021

During the first weeks of February, we asked recipients of our Data & AI Newsletter to participate in a survey on AI adoption in the enterprise. The second-most significant barrier was the availability of quality data. Relatively few respondents are using version control for data and models. Respondents.

Enterprise 353

Enterprise Modeling Risk Manufacturing 353

Alation

SEPTEMBER 28, 2022

The term has been used a lot more of late, especially in the data analytics industry, as we’ve seen it expand over the past few years to keep pace with new regulations, like the GDPR and CCPA. In essence, DataOps is a practice that helps organizations manage and govern data more effectively. What exactly is DataOps ?

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

erwin

JANUARY 11, 2019

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. But the attempts to standardize data across the entire enterprise haven’t produced the desired results.

Data Governance 40

Data Governance Risk Metadata Management 40

O'Reilly on Data

MARCH 31, 2020

AI products are automated systems that collect and learn from data to make user-facing decisions. All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. Machine learning adds uncertainty.

Management 327

Management Machine Learning Experimentation Metrics 327

Sisense

APRIL 15, 2021

Your company is gathering data (and has likely been doing so for years), and you’ve probably got a system or two to glean insights from that data to make smarter decisions. Whatever you do and however you do it, augmented analytics serve up deeper intelligence from data with less heavy lifting. Analytics is the future.

Analytics 98

Analytics Data Science Modeling Machine Learning 98

Sisense

JANUARY 14, 2021

Especially when dealing with business data, trust in the figures is an essential element of every transaction. A reputation for stability and accuracy is critical in the fintech industry, dealing as it does with sensitive, high-impact data and security challenges. For analytics teams, trust is hard to gain and easy to lose.

Dashboards 85

Dashboards Testing Business Intelligence Interactive 85

DataRobot

APRIL 20, 2021

In this installment, I’ll cover four key elements of trusted AI that relate to the performance of a model: data quality, accuracy, robustness and stability, and speed. Quality Input Means Quality Output. Trustworthy AI is built on a foundation of high-quality data. Get Useful Conclusions Quickly.

Measurement 52

Measurement Visualization Advertising Metrics 52

Occam's Razor

AUGUST 22, 2011

Be data driven?" Six Rules For Creating A Data Driven Boss! Be data driven?" Slay The Analytics Data Quality Dragon & Win Your HiPPO's Love! Web Data Quality: A 6 Step Process To Evolve Your Mental Model. The Ultimate Web Analytics Data Reconciliation Checklist.

KPI 124

KPI Metrics Measurement ROI 124

AWS Big Data

JULY 13, 2023

Altron is a pioneer of providing data-driven solutions for their customers by combining technical expertise with in-depth customer understanding to provide highly differentiated technology solutions. This is a guest post co-authored by Jacques Steyn, Senior Manager Professional Services at Altron Group.

Optimization 93

Optimization B2B Data Quality Sales 93

O'Reilly on Data

NOVEMBER 28, 2023

AI users say that AI programming (66%) and data analysis (59%) are the most needed skills. Given the cost of equipping a data center with high-end GPUs, they probably won’t attempt to build their own infrastructure. Few nonusers (2%) report that lack of data or data quality is an issue, and only 1.3%

Enterprise 267

Enterprise Modeling Testing Reporting 267

CIO Business Intelligence

DECEMBER 22, 2022

DataOps (data operations) is an agile, process-oriented methodology for developing and delivering analytics. It brings together DevOps teams with data engineers and data scientists to provide the tools, processes, and organizational structures to support the data-focused enterprise. What is DataOps?

Analytics 130

Analytics Machine Learning Software Data mining 130

Smarten

APRIL 19, 2024

Benefits include customized and optimized models, data, parameters and tuning. It must be integrated with business systems to leverage available data. This approach does demand skills, data curation, and significant funding, but it will serve the market for third-party, specialized models.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

Occam's Razor

MARCH 12, 2012

Is there anything in the analytics space that is so full of promise and hype and sexiness and possible awesomeness than "big data?" So what is big data really? As I interpret it, big data is the collection of massive databases of structured and unstructured data. It is great that we have big data.

Big Data 127

Big Data Data-driven Unstructured Data Marketing 127

Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. At Rev’s “ Data Science, Past & Future” , Paco Nathan covered contextual insight into some common impactful themes over the decades that also provided a “lens” help data scientists, researchers, and leaders consider the future.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

datapine

DECEMBER 17, 2019

As in many other industries, the information technology sector faces the age-old issue of producing IT reports that boost success by helping to maximize value from a tidal wave of digital data. The purpose is not to track every statistic possible, as you risk being drowned in data and losing focus. Let’s get started.

Reporting 173

Reporting IT Key Performance Indicator Dashboards 173

Peter James Thomas

AUGUST 12, 2018

These normally appear at the end of an article, but it seemed to make sense to start with them in this case: Recently I published Building Momentum – How to begin becoming a Data-driven Organisation. A number of factors can play into the accuracy of data capture. Honesty of Data that is captured. Timing issues with Data.

Metrics 49

Metrics Statistics Data Quality Measurement 49

Grooper

AUGUST 19, 2019

Read on to learn how data literacy, information as a second language, and insight-driven analytics take digital strategy to a new level. C-level executives and professionals alike must learn to speak a new language - data. The benefit of speaking data, a.k.a. Increasing data literacy is the answer.

Analytics 54

Analytics Machine Learning Data Science Data-driven 54

FineReport

JULY 18, 2022

In an era full of data, data analysis allows us to discover the most useful information and make more scientific decisions for business operations. Data analysis tools are widely used by data analysts as well as non-professional business people to achieve better performance and higher efficiency. FineRepor t.

Forecasting 52

Forecasting Dashboards Visualization Statistics 52

datapine

AUGUST 29, 2022

The saying “knowledge is power” has never been more relevant, thanks to the widespread commercial use of big data and data analytics. The rate at which data is generated has increased exponentially in recent years. Companies, both big and small, are seeking the finest ways to leverage their data into a competitive advantage.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Domino Data Lab

JUNE 15, 2021

Over the next decade, the companies that will beat competitors will be “model-driven” businesses. These companies often undertake large data science efforts in order to shift from “data-driven” to “model-driven” operations, and to provide model-underpinned insights to the business. Why Snowflake UDFs.

Modeling 69

Modeling Data Science Data-driven Data Warehouse 69

Domino Data Lab

JUNE 23, 2019

Skomoroch advocates that organizations consider installing product leaders with data expertise and ML-oriented intuition (i.e., 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. A few highlights from the session include.

Management 110

Management Machine Learning Experimentation Metrics 110

Andrew White

JANUARY 9, 2022

On Thursday January 6th I hosted Gartner’s 2022 Leadership Vision for Data and Analytics webinar. – In the webinar and Leadership Vision deck for Data and Analytics we called out AI engineering as a big trend. I would take a look at our Top Trends for Data and Analytics 2021 for additional AI, ML and related trends.

Analytics 64

Analytics Measurement Modeling Data-driven 64

BI-Survey

NOVEMBER 24, 2020

In particular, the integration of strategic planning and company-wide operational planning, as well as its integration with analytics and business intelligence (BI), are becoming increasingly important to making comprehensive and well-founded decisions based on data. The study is based on a worldwide online survey of 424 companies.

Forecasting 94

Forecasting Predictive Modeling Statistics Data-driven 94

Alation

JANUARY 25, 2022

Data fabric is now on the minds of most data management leaders. In our previous blog, Data Mesh vs. Data Fabric: A Love Story , we defined data fabric and outlined its uses and motivations. The data catalog is a foundational layer of the data fabric.

Metadata 52

Metadata IT Metrics Data-driven 52

datapine

APRIL 16, 2019

The term ‘big data’ alone has become something of a buzzword in recent times – and for good reason. By implementing the right reporting tools and understanding how to analyze as well as to measure your data accurately, you will be able to make the kind of data driven decisions that will drive your business forward.

Data-driven 287

Data-driven Dashboards Visualization Cost-Benefit 287