datapine

NOVEMBER 27, 2019

Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2020 will be the year of data quality management and data discovery: clean and secure data combined with a simple and powerful presentation. 1) Data Quality Management (DQM).

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

DataKitchen

APRIL 13, 2021

RightData – A self-service suite of applications that help you achieve Data Quality Assurance, Data Integrity Audit and Continuous Data Quality Control with automated validation and reconciliation capabilities. QuerySurge – Continuously detect data issues in your delivery pipelines. Data breaks.

Testing 300

Testing Machine Learning Consulting Data Science 300

CIO Business Intelligence

MARCH 25, 2025

Data debt that undermines decision-making In Digital Trailblazer , I share a story of a private company that reported a profitable year to the board, only to return after the holiday to find that data quality issues and calculation mistakes turned it into an unprofitable one.

Risk 140

Risk Cost-Benefit Data-driven Digital Transformation 140

AWS Big Data

MARCH 12, 2024

In recent years, data lakes have become a mainstream architecture, and data quality validation is a critical factor to improve the reusability and consistency of the data. In this post, we provide benchmark results of running increasingly complex data quality rulesets over a predefined test dataset.

Data Quality 115

Data Quality Measurement Testing Visualization 115

O'Reilly on Data

NOVEMBER 19, 2024

While RAG leverages nearest neighbor metrics based on the relative similarity of texts, graphs allow for better recall of less intuitive connections. decomposes a complex task into a graph of subtasks, then uses LLMs to answer the subtasks while optimizing for costs across the graph.

Unstructured Data 321

Unstructured Data Structured Data Statistics Modeling 321

AWS Big Data

JUNE 6, 2023

We are excited to announce the General Availability of AWS Glue Data Quality. Our journey started by working backward from our customers who create, manage, and operate data lakes and data warehouses for analytics and machine learning. It takes days for data engineers to identify and implement data quality rules.

Data Quality 98

Data Quality Statistics Data Lake Visualization 98

DataKitchen

MARCH 16, 2023

So it’s Monday, and you lead a data analytics team of perhaps 30 people. But wait, she asks you for your team metrics. Like most leaders of data analytic teams, you have been doing very little to quantify your team’s success. Where is your metrics report? What should be in that report about your data team?

Metrics 130

Metrics Data Analytics Analytics Measurement 130

AWS Big Data

NOVEMBER 22, 2024

Furthermore, you can gain insights into the performance of your data transformations with detailed execution logs and metrics, all accessible through the dbt Cloud interface. Cost management and optimization – Because Athena charges based on the amount of data scanned by each query, cost optimization is critical.

Data Lake 104

Data Lake Data Warehouse Cost-Benefit Data Transformation 104

datapine

JANUARY 21, 2020

The balance sheet gives an overview of the main metrics which can easily define trends and the way company assets are being managed. Operational optimization and forecasting. Cost optimization. Another important factor to consider is cost optimization. Enhanced data quality. It doesn’t stop here.

Business Intelligence 259

Business Intelligence Reporting Cost-Benefit Dashboards 259

CIO Business Intelligence

NOVEMBER 13, 2024

The company has already rolled out a gen AI assistant and is also looking to use AI and LLMs to optimize every process. One is going through the big areas where we have operational services and look at every process to be optimized using artificial intelligence and large language models. We’re doing two things,” he says.

IT 141

IT Consulting ROI Cost-Benefit 141

AWS Big Data

JUNE 6, 2023

Data consumers lose trust in data if it isn’t accurate and recent, making data quality essential for undertaking optimal and correct decisions. Evaluation of the accuracy and freshness of data is a common task for engineers. Currently, various tools are available to evaluate data quality.

Data Quality 98

Data Quality Data-driven Data Lake Metrics 98

O'Reilly on Data

JUNE 19, 2019

In a previous post , we noted some key attributes that distinguish a machine learning project: Unlike traditional software where the goal is to meet a functional specification, in ML the goal is to optimize a metric. Quality depends not just on code, but also on data, tuning, regular updates, and retraining.

Modeling 258

Modeling Machine Learning Testing Metrics 258

O'Reilly on Data

MARCH 31, 2020

At measurement-obsessed companies, every part of their product experience is quantified and adjusted to optimize user experience. These companies eventually moved beyond using data to inform product design decisions. Without large amounts of good raw and labeled training data, solving most AI problems is not possible.

Management 363

Management Machine Learning Experimentation Metrics 363

AWS Big Data

JULY 29, 2024

Despite their advantages, traditional data lake architectures often grapple with challenges such as understanding deviations from the most optimal state of the table over time, identifying issues in data pipelines, and monitoring a large number of tables. It is essential for optimizing read and write performance.

Metadata 126

Metadata Snapshot Data Lake Metrics 126

AWS Big Data

SEPTEMBER 11, 2024

The application supports custom workflows to allow demand and supply planning teams to collaborate, plan, source, and fulfill customer orders, then track fulfillment metrics via persona-based operational and management reports and dashboards. The data quality (DQ) checks are managed using DQ configurations stored in Aurora PostgreSQL tables.

Data Architecture 111

Data Architecture Optimization Data Warehouse Metadata 111

DataKitchen

SEPTEMBER 21, 2023

L1 is usually the raw, unprocessed data ingested directly from various sources; L2 is an intermediate layer featuring data that has undergone some form of transformation or cleaning; and L3 contains highly processed, optimized, and typically ready for analytics and decision-making processes. What is Data in Use?

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

DataKitchen

APRIL 26, 2021

At Workiva, they recognized that they are only as good as their data, so they centered their initial DataOps efforts around lowering errors. Hodges commented, “Our first focus was to up our game around data quality and lowering errors in production. Multiple Metrics for Success. At GSK, success is all about adoption.

Measurement 246

Measurement Metrics Data-driven Dashboards 246

AWS Big Data

DECEMBER 4, 2024

Domain ownership recognizes that the teams generating the data have the deepest understanding of it and are therefore best suited to manage, govern, and share it effectively. This principle makes sure data accountability remains close to the source, fostering higher data quality and relevance.

Metadata 105

Metadata Data Governance Data Quality Data-driven 105

CIO Business Intelligence

OCTOBER 23, 2024

For the first time, we’re consolidating data to create real-time dashboards for revenue forecasting, resource optimization, and labor utilization. Data literacy across the company was a challenge because, as is often the case, we were all describing our business data a little differently. How is the new platform helping?

Enterprise 119

Enterprise Dashboards KPI Data Lake 119

AWS Big Data

JANUARY 15, 2025

For container terminal operators, data-driven decision-making and efficient data sharing are vital to optimizing operations and boosting supply chain efficiency. The data science and AI teams are able to explore and use new data sources as they become available through Amazon DataZone.

IoT 111

IoT Machine Learning Metadata Data-driven 111

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. In most cases, this can prove detrimental to the business.

Data-driven 287

Data-driven Dashboards Visualization Cost-Benefit 287

CIO Business Intelligence

JANUARY 23, 2024

For example, McKinsey suggests five metrics for digital CEOs , including the financial return on digital investments, the percentage of leaders’ incentives linked to digital, and the percentage of the annual tech budget spent on bold digital initiatives. As a result, outcome-based metrics should be your guide.

Measurement 141

Measurement Digital Transformation KPI Metrics 141

Jet Global

MARCH 4, 2020

A manufacturing Key Performance Indicator (KPI) or metric is a well defined and quantifiable measure that the manufacturing industry uses to gauge its performance over time. Manufacturing companies specifically use KPIs to monitor, analyze, and optimize operations, often comparing their efficiencies to those of competitors in the same sector.

Manufacturing 80

Manufacturing Metrics Reporting KPI 80

CIO Business Intelligence

FEBRUARY 20, 2025

While data tends to be used in tactical-operational areas such as HR reporting and controlling, there is still room for improvement in the strategic area of people analytics. Most use master data to make daily processes more efficient and to optimize the use of existing resources.

Big Data 84

Big Data Measurement Visualization Machine Learning 84

AWS Big Data

JULY 13, 2023

Data quality for account and customer data – Altron wanted to enable data quality and data governance best practices. Goals – Lay the foundation for a data platform that can be used in the future by internal and external stakeholders.

Optimization 98

Optimization B2B Data Quality Sales 98

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 125

Modeling Metrics Data Quality Data-driven 125

CIO Business Intelligence

AUGUST 13, 2024

Unlike traditional approaches, deep automation is holistic, adaptive, and evolutive, prioritizing human-machine partnership and customer experience for optimal efficiency and impact. AI-integrated tractors, planters, and harvesters form a data-driven team, optimizing tasks and empowering farmers.

Data-driven 130

Data-driven Metrics Optimization Deep Learning 130

AWS Big Data

AUGUST 1, 2024

Migrating to Amazon Redshift offers organizations the potential for improved price-performance, enhanced data processing, faster query response times, and better integration with technologies such as machine learning (ML) and artificial intelligence (AI).

Data Warehouse 115

Data Warehouse KPI Optimization Cost-Benefit 115

Anmut

JULY 12, 2021

Data without context is just meaningless noise, and any effort to improve or extract value from your data without considering the larger business context is doomed to fall short.? Unfortunately, traditional approaches to data remediation often focus on technical data quality in isolation from the broader data and business ecosystem.

Data Quality 52

Data Quality Measurement Metrics Dashboards 52

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

DataKitchen

MAY 10, 2021

Some will argue that observability is nothing more than testing and monitoring applications using tests, metrics, logs, and other artifacts. That’s a fair point, and it places emphasis on what is most important – what best practices should data teams employ to apply observability to data analytics. It’s not about data quality .

Testing 214

Testing Manufacturing Data Quality Statistics 214

AWS Big Data

JULY 20, 2023

Prior to the creation of the data lake, Orca’s data was distributed among various data silos, each owned by a different team with its own data pipelines and technology stack. Moreover, running advanced analytics and ML on disparate data sources proved challenging.

Data Lake 98

Data Lake Analytics Snapshot Data Quality 98

Alation

OCTOBER 5, 2021

The main reasons that a company’s data strategy and governance protocols fail to deliver are somewhat universal, regardless of the industry sector. Without a doubt, no company can achieve lasting profitability and sustainable growth with a poorly constructed data governance methodology. Incomplete data. Lack of commitment.

Data Governance 96

Data Governance Strategy Data Quality Metrics 96

CIO Business Intelligence

SEPTEMBER 10, 2024

Result: 40%-50% fewer UAT issues Streamlining workflows: GenAI analyzes post-deployment metrics to optimize SDLC workflows for faster, more reliable development. Invest in data quality: GenAI models are only as good as the data they’re trained on -with GenAI, mistakes can be amplified at speed.

Software 124

Software Digital Transformation Testing Advertising 124

AWS Big Data

MAY 1, 2024

To provide a variety of products, services, and solutions that are better suited to customers and society in each region, we have built business processes and systems that are optimized for each region and its market. Foundation – This role encompasses the data steward and governance team. Each role has sub-roles.

Dashboards 124

Dashboards Publishing Data-driven Cost-Benefit 124

BizAcuity

NOVEMBER 13, 2024

Beyond mere data collection, BI consulting helps businesses create a cohesive data strategy that aligns with organizational goals. This approach involves everything from identifying key metrics to implementing analytics systems and designing dashboards.

Consulting 52

Consulting Data-driven Cost-Benefit Business Intelligence 52

CIO Business Intelligence

NOVEMBER 22, 2022

The next step in every organization’s data strategy, Guan says, should be investing in and leveraging artificial intelligence and machine learning to unlock more value out of their data. CIOs should first understand the different approaches to observing data and how it differs from quality management,” he notes.

Data Strategy 145

Data Strategy Strategy Enterprise Consulting 145