Smart Data Collective

SEPTEMBER 29, 2022

Optical character recognition is able to convert any text present on a medium into computer-readable textual data. To continue your document analysis, the second step extracts all the data present on the blue card. How does OCR work? However, at this stage, they are not structured.

Data-driven 145

Data-driven Data Quality Optimization Insurance 145

KDnuggets

MARCH 23, 2023

This article highlights the significance of ensuring high-quality data and presents six key dimensions for measuring it. These dimensions include Completeness, Consistency, Integrity, Timelessness, Uniqueness, and Validity.

Data Quality 117

Data Quality Measurement IT 117

CIO Business Intelligence

MARCH 13, 2024

But hearing those voices, and how to effectively respond, is dictated by the quality of data available, and understanding how to properly utilize it. “We We know in financial services and in a lot of verticals, we have a whole slew of data quality challenges,” he says. Traditionally, AI data quality has been a challenge.”

Data Quality 105

Data Quality Digital Transformation Enterprise Marketing 105

DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Ensuring Data Quality in New Data Sources (#1) Introduction to Data Evaluation in Data Observability Ensuring their quality and integrity before incorporating new data sources into production is paramount. Do I Have The Right Schema For The Data?

Data Quality 126

Data Quality Interactive Dashboards Testing 126

CIO Business Intelligence

OCTOBER 30, 2024

AI’s ability to automate repetitive tasks leads to significant time savings on processes related to content creation, data analysis, and customer experience, freeing employees to work on more complex, creative issues. A data mesh delivers greater ownership and governance to the IT team members who work closest to the data in question.

Enterprise 139

Enterprise Data Architecture Unstructured Data Insurance 139

Timo Elliott

NOVEMBER 7, 2024

In the digital era, data is the backbone of innovation and transformation. At IKEA, the global home furnishings leader, data is more than an operational necessity—it’s a strategic asset. The Strategy: A Greenfield Approach IKEA adopted a greenfield strategy with SAP, rethinking its processes, technology, and data from the ground up.

Data Transformation 84

Data Transformation Data Quality Digital Transformation Strategy 84

AWS Big Data

OCTOBER 10, 2023

Due to the volume, velocity, and variety of data being ingested in data lakes, it can get challenging to develop and maintain policies and procedures to ensure data governance at scale for your data lake. Data confidentiality and data quality are the two essential themes for data governance.

Data Quality 111

Data Quality Data Governance Data Lake Testing 111

David Menninger's Analyst Perspectives

MARCH 17, 2022

Organizations face various challenges with analytics and business intelligence processes, including data curation and modeling across disparate sources and data warehouses, maintaining data quality and ensuring security and governance.

Business Intelligence 207

Business Intelligence Data Warehouse Data Quality Dashboards 207

Octopai

JULY 11, 2022

And when business users don’t complain, but you know the data isn’t good enough to make these types of calls wisely, that’s an even bigger problem. How are you, as a data quality evangelist (if you’re reading this post, that must describe you at least somewhat, right?), Tie data quality directly to business objectives.

Data Quality 98

Data Quality Strategy Business Objectives Sales 98

O'Reilly on Data

JUNE 18, 2019

As model building become easier, the problem of high-quality data becomes more evident than ever. Even with advances in building robust models, the reality is that noisy data and incomplete data remain the biggest hurdles to effective end-to-end solutions. Data integration and cleaning.

Machine Learning 279

Machine Learning Data Quality Statistics Modeling 279

Alation

JANUARY 20, 2022

Several weeks ago (prior to the Omicron wave), I got to attend my first conference in roughly two years: Dataversity’s Data Quality and Information Quality Conference. Ryan Doupe, Chief Data Officer of American Fidelity, held a thought-provoking session that resonated with me. Step 2: Data Definitions.

Data Quality 94

Data Quality Data Governance Metrics Statistics 94

AWS Big Data

JUNE 6, 2023

Poor-quality data can lead to incorrect insights, bad decisions, and lost opportunities. AWS Glue Data Quality measures and monitors the quality of your dataset. It supports both data quality at rest and data quality in AWS Glue extract, transform, and load (ETL) pipelines.

Data Quality 96

Data Quality Data Lake Visualization Data-driven 96

CIO Business Intelligence

JANUARY 7, 2025

Defining policies and other AI governance was a priority at many organizations trying to channel how employees used copilots while protecting sensitive data from leaking to public LLMs. For AI to deliver safe and reliable results, data teams must classify data properly before feeding it to those hungry LLMs.

Digital Transformation 143

Digital Transformation Experimentation Cost-Benefit Strategy 143

Rocket-Powered Data Science

MARCH 20, 2023

Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science. Live online presentations, demos, and customer testimonials were complemented with new content posted at sap.com/datasphere.

Data Warehouse 325

Data Warehouse Metadata Digital Transformation Machine Learning 325

Smarten

APRIL 25, 2024

How Can I Ensure Data Quality and Gain Data Insight Using Augmented Analytics? There are many business issues surrounding the use of data to make decisions. One such issue is the inability of an organization to gather and analyze data.

Data Quality 62

Data Quality Analytics Machine Learning Visualization 62

CIO Business Intelligence

JANUARY 9, 2025

This shift challenges retailers to rethink how they present and position their offerings. The Data Consistency Challenge However, this AI revolution brings its own set of challenges. In just the past few months, weve seen AI-powered shopping agents redefine product discovery and understanding, explains Fouache.

B2B 105

B2B Cost-Benefit Data-driven Sales 105

TDAN

OCTOBER 18, 2022

Last week, we presented a webinar in our Data Governance — Best Practices series on data quality. Obviously, we’re a […].

Data Quality 52

Data Quality Data Governance Data Strategy Strategy 52

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

Cloudera

NOVEMBER 13, 2024

This includes having full visibility into the origin of the data, the transformations it underwent, its relationships, and the context that was added or stripped away from that data as it moved throughout the enterprise. This guarantees data quality and automates the laborious, manual processes required to maintain data reliability.

Metadata 90

Metadata Management Data Governance Data-driven 90

datapine

JANUARY 24, 2021

This can include a multitude of processes, like data profiling, data quality management, or data cleaning, but we will focus on tips and questions to ask when analyzing data to gain the most cost-effective solution for an effective business strategy. 4) How can you ensure data quality?

IT 317

IT Statistics KPI Data-driven 317

Ontotext

NOVEMBER 16, 2023

In the first part of this series of technological posts, we talked about what SHACL is and how you can set up validation for your data. Tacking the data quality issue — bit by bit or incrementally There are two main approaches to validating your data, which would be dependent on the specific implementation.

Data Quality 69

Data Quality Reporting Testing Technology 69

datapine

JANUARY 21, 2020

Utilization of real-time and historical data. With traditional means of reporting, it is difficult to utilize and comprehend the vast amount of gathered data. Creating a simple presentation out of voluminous information can challenge even the most experienced managers. Enhanced data quality. Enhanced data quality.

Business Intelligence 259

Business Intelligence Reporting Cost-Benefit Dashboards 259

Smart Data Collective

FEBRUARY 23, 2021

To help you identify and resolve these mistakes, we’ve put together this guide on the various big data mistakes that marketers tend to make. Big Data Mistakes You Must Avoid. Here are some common big data mistakes you must avoid to ensure that your campaigns aren’t affected. Ignoring Data Quality.

Big Data 142

Big Data Visualization Data Quality Data-driven 142

DataKitchen

MAY 10, 2021

Make sure the data and the artifacts that you create from data are correct before your customer sees them. It’s not about data quality . In governance, people sometimes perform manual data quality assessments. It’s not only about the data. Data Quality. Location Balance Tests.

Testing 214

Testing Manufacturing Data Quality Statistics 214

O'Reilly on Data

JUNE 19, 2019

First, the machine learning community has conducted groundbreaking research in many areas of interest to companies, and much of this research has been conducted out in the open via preprints and conference presentations.

Modeling 256

Modeling Machine Learning Testing Metrics 256

Alation

DECEMBER 7, 2021

Alation and Soda are excited to announce a new partnership, which will bring powerful data-quality capabilities into the data catalog. Soda’s data observability platform empowers data teams to discover and collaboratively resolve data issues quickly. Does the quality of this dataset meet user expectations?

Data Quality 52

Data Quality Metadata Data Governance Testing 52

Alation

AUGUST 5, 2021

What is Data Quality? Data quality is defined as: the degree to which data meets a company’s expectations of accuracy, validity, completeness, and consistency. By tracking data quality , a business can pinpoint potential issues harming quality, and ensure that shared data is fit to be used for a given purpose.

Data Quality 52

Data Quality IT Data Governance Sales 52

datapine

MAY 28, 2019

From Fortune 100 companies to small business owners, BI tools and technology are becoming the standard to oversee historical, present, and future data of business operations. Maximum security and data privacy. But what makes these projects successful and what to look out for? Reducing the reporting time.

Business Intelligence 317

Business Intelligence KPI Dashboards Reporting 317

CIO Business Intelligence

FEBRUARY 20, 2025

However, it is often unclear where the data needed for reporting is stored and what quality it is in. Often the data quality is insufficient to make reliable statements. Insufficient or incorrect data can even lead to wrong decisions, says Kastrati. Subsequently, the reporting should be set up properly.

Big Data 84

Big Data Measurement Visualization Machine Learning 84

Dataiku

JULY 25, 2023

In a survey of 400 global data leaders , nearly a quarter of respondents (24%) cited a “lack of quality data or ability to easily access the right data” as their most present barrier to delivering more value from analytics and AI.

Data Quality 52

Data Quality Analytics 52

datapine

APRIL 16, 2019

8) Present the data in a meaningful way. With the help of a great data visualization software , you don’t need to be an IT crack to build and customize a powerful online dashboard that will tell your data story and assist you, your team, and your management to make the right data driven business decisions.

Data-driven 287

Data-driven Dashboards Visualization Cost-Benefit 287

datapine

AUGUST 21, 2019

From sales performance and targets through to customer acquisition costs, sales cycle, and beyond — here, we’ll present sales graphs and charts that matter to the ongoing growth of your business, in turn considering the essential question, what type of charts are used for sales? How Do You Know Which Graph To Use? 1) Sales Performance.

Sales 235

Sales Dashboards Visualization KPI 235

AWS Big Data

APRIL 8, 2025

In modern data architectures, Apache Iceberg has emerged as a popular table format for data lakes, offering key features including ACID transactions and concurrent write support. Consider a common scenario: A streaming pipeline continuously writes data to an Iceberg table while scheduled maintenance jobs perform compaction operations.

Snapshot 138

Snapshot Management Metadata Big Data 138

DataKitchen

MAY 10, 2024

Data Evaluation Before new data sets are introduced into production environments, they must be thoroughly evaluated and cleaned. This initial stage of data observability ensures that data quality is maintained from the start, preventing errors that could affect downstream processes and decisions.

Data Quality 144

Data Quality Dashboards Strategy Testing 144

O'Reilly on Data

OCTOBER 13, 2020

Therefore, the PM should consider the team that will reconvene whenever it is necessary to build out or modify product features that: ensure that inputs are present and complete, establish that inputs are from a realistic (expected) distribution of the data, and trigger alarms, model retraining, or shutdowns (when necessary).

Management 308

Management Machine Learning Metrics Modeling 308

Juice Analytics

APRIL 6, 2021

If you are going to lean on data, you want to understand its quality. Here’s an overview article from Neil Patel about assessing data quality. Alberto Cairo is a preeminent advocate for truth in presentation of data his book, How Charts Lie is a must-read on this topic.

Metrics 132

Metrics Dashboards Data-driven Data Quality 132

DataKitchen

OCTOBER 20, 2023

In today’s data-driven landscape, Data and Analytics Teams i ncreasingly face a unique set of challenges presented by Demanding Data Consumers who require a personalized level of Data Observability. Deploying a Data Journey Instance unique to each customer’s payload is vital to fill this gap.

Insurance 173

Insurance Metadata Data-driven Data Quality 173