CIO Business Intelligence

DECEMBER 10, 2024

Align data strategies to unlock gen AI value for marketing initiatives Using AI to improve sales metrics is a good starting point for ensuring productivity improvements have near-term financial impact. When considering the breadth of martech available today, data is key to modern marketing, says Michelle Suzuki, CMO of Glassbox.

Sales 143

Sales Metrics Data-driven Measurement 143

CIO Business Intelligence

JANUARY 5, 2025

Research from Gartner, for example, shows that approximately 30% of generative AI (GenAI) will not make it past the proof-of-concept phase by the end of 2025, due to factors including poor data quality, inadequate risk controls, and escalating costs. [1] Reliability and security is paramount.

Enterprise 124

Enterprise Cost-Benefit Unstructured Data Data Quality 124

CIO Business Intelligence

FEBRUARY 26, 2025

conducted a survey of more than 1,000 enterprise technology professionals and found 90% of enterprises say integration with organizational data is critical to success, but 86% say theyll need to upgrade their existing tech stack to deploy AI agents. Ashok Srivastava, chief data officer at Intuit, agrees with that sentiment.

IT 141

IT Enterprise Interactive Data Quality 141

AWS Big Data

JUNE 6, 2023

AWS Glue Data Quality allows you to measure and monitor the quality of data in your data repositories. It’s important for business users to be able to see quality scores and metrics to make confident business decisions and debug data quality issues. An AWS Glue crawler crawls the results.

Data Quality 78

Data Quality Metrics Visualization Dashboards 78

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

O'Reilly on Data

SEPTEMBER 24, 2018

If this dirty data proliferates and propagates to other systems, we open Pandora’s box of unintended consequences. The DataOps team needs to watch out for data issues and fix them before they get copied around. These data quality issues bring a new level of potential problems for real-time systems.

Enterprise 252

Enterprise Big Data Data Quality Unstructured Data 252

Octopai

MARCH 23, 2022

Fortunately, this is far simpler to do for a data asset than for a can of meat. Data lineage tools give you exactly that kind of transparent, x-ray vision into your data quality. Data Supervision. Having the right data intelligence tools can be a make-or-break for data responsibility success.

Data Quality 98

Data Quality Reporting Modeling Interactive 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

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

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 64

Data Quality Analytics Machine Learning Visualization 64

O'Reilly on Data

MARCH 24, 2020

You may picture data scientists building machine learning models all day, but the common trope that they spend 80% of their time on data preparation is closer to the truth. This definition of low-quality data defines quality as a function of how much work is required to get the data into an analysis-ready form.

Machine Learning 364

Machine Learning Statistics Data Quality Data Collection 364

datapine

JANUARY 21, 2020

One additional element to consider is visualizing data. Since humans process visual information 60.000 times faster than text , the workflow can be significantly increased by utilizing smart intelligence in the form of interactive, and real-time visual data. Enhanced data quality. Source: newgenapps.com *.

Business Intelligence 259

Business Intelligence Reporting Cost-Benefit Dashboards 259

Jim Harris

DECEMBER 24, 2015

Have you ever experienced that sinking feeling, where you sense if you don’t find data quality, then data quality will find you? These discussions are a critical prerequisite for determining data usage, standards, and the business relevant metrics for measuring and improving data quality.

Data Quality 56

Data Quality Enterprise Business Intelligence Data Governance 56

CIO Business Intelligence

FEBRUARY 6, 2025

They promise to revolutionize how we interact with data, generating human-quality text, understanding natural language and transforming data in ways we never thought possible. From automating tedious tasks to unlocking insights from unstructured data, the potential seems limitless. And guess what?

Unstructured Data 115

Unstructured Data Manufacturing Data Governance Sales 115

datapine

MAY 21, 2019

Odds are, businesses are currently analyzing their data, just not in the most effective manner. It is time to save valuable staff resources and walk away from static spreadsheets by using interactive dashboards. These tools allow for a wide range of users to easily connect to, interact with, visualize and communicate their data.

Business Intelligence 183

Business Intelligence Cost-Benefit Dashboards ROI 183

Smart Data Collective

AUGUST 11, 2022

Research conducted by the Harvard Business Review found that the interaction between machines and humans significantly improves firms’ performance. How Artificial Intelligence is Impacting Data Quality. Data quality is crucial in the age of artificial intelligence. Assessment of Data Types for Quality.

Data Quality 139

Data Quality Machine Learning Digital Transformation Big Data 139

CIO Business Intelligence

OCTOBER 31, 2024

BPM as a driver of IT success Making a significant contribution to Norma’s digital transformation, a BPM team was initiated in 2020 and its managers support all business areas to improve and harmonize the understanding of applications and processes, as well as data quality.

IT 89

IT Digital Transformation Machine Learning Data Quality 89

AWS Big Data

NOVEMBER 22, 2024

The need for streamlined data transformations As organizations increasingly adopt cloud-based data lakes and warehouses, the demand for efficient data transformation tools has grown. This enables you to extract insights from your data without the complexity of managing infrastructure.

Data Lake 83

Data Lake Data Warehouse Cost-Benefit Data Transformation 83

O'Reilly on Data

JULY 15, 2019

Regulators behind SR 11-7 also emphasize the importance of data—specifically data quality , relevance , and documentation. While models garner the most press coverage, the reality is that data remains the main bottleneck in most ML projects.

Machine Learning 51

Machine Learning Management Enterprise Risk Management 51

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

DataKitchen

SEPTEMBER 21, 2023

These layers help teams delineate different stages of data processing, storage, and access, offering a structured approach to data management. In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

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

datapine

MAY 28, 2019

Maximum security and data privacy. Facing the challenges of poor data quality, dispersed through a number of spreadsheets and databases, this financial company was unable to track financial data in real-time and generate valuable insights needed to ensure their vendor payment, managed by the accounts payable department, is accurate and fast.

Business Intelligence 317

Business Intelligence KPI Dashboards Reporting 317

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

DataKitchen

APRIL 11, 2024

Statistical Process Control in Data Operations: Gil touched upon applying statistical process control techniques to data operations to monitor and control data quality and process performance.

Statistics 152

Statistics Manufacturing Data Processing Data Quality 152

DataKitchen

NOVEMBER 10, 2023

Extrinsic Control Deficit: Many of these changes stem from tools and processes beyond the immediate control of the data team. Unregulated ETL/ELT Processes: The absence of stringent data quality tests in ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes further exacerbates the problem.

Data Quality 136

Data Quality Testing Data Lake Data Integration 136

datapine

DECEMBER 17, 2019

By understanding your core business goals and selecting the right key performance indicator ( KPI ) and metrics for your specific needs, you can use an information technology report sample to visualize your most valuable data at a glance, developing initiatives and making pivotal decisions swiftly and with confidence.

Reporting 173

Reporting IT Key Performance Indicator Dashboards 173

Ontotext

NOVEMBER 23, 2023

Keep in mind how named graphs interact with your validation: The SHACL shapes graph will validate the union of all graphs. The next step is to get out there and challenge your data quality dragons. The post SHACL-ing the Data Quality Dragon III: A Good Artisan Knows Their Tools appeared first on Ontotext.

Data Quality 69

Data Quality Reporting Metadata IT 69

DataKitchen

AUGUST 3, 2021

Secure and permissioned – data is protected from unauthorized users. Governed – designed with data quality and management workflows that empower data usage. Clear accountability – users interact with a responsive, dedicated team that is accountable to them.

Data Architecture 246

Data Architecture Data Lake Cost-Benefit Data Warehouse 246

DataKitchen

FEBRUARY 27, 2024

Your LLM Needs a Data Journey: A Comprehensive Guide for Data Engineers The rise of Large Language Models (LLMs) such as GPT-4 marks a transformative era in artificial intelligence, heralding new possibilities and challenges in equal measure.

Data Quality 117

Data Quality Unstructured Data Testing Data-driven 117

AWS Big Data

APRIL 8, 2025

Concurrent UPDATE/DELETE on overlapping partitions When multiple processes attempt to modify the same partition simultaneously, data conflicts can arise. For example, imagine a data quality process updating customer records with corrected addresses while another process is deleting outdated customer records.

Snapshot 116

Snapshot Management Metadata Big Data 116

datapine

AUGUST 21, 2019

8) Revenue And Sales Interactive Management Overview. This is a really fun interactive sales graph, as it lets you see your revenue and sales according to different time periods that you select. In particular, the monthly view is extremely helpful. A versatile dashboard for use on a daily, weekly, and monthly basis.

Sales 235

Sales Dashboards Visualization KPI 235

Smart Data Collective

OCTOBER 4, 2021

There is no question that big data is very important for many businesses. Unfortunately, big data is only as useful as it is accurate. Data quality issues can cause serious problems in your big data strategy. You need to recognize that both data quality and quantity are important. Better Connections.

Big Data 133

Big Data Data Quality Advertising Interactive 133

O'Reilly on Data

NOVEMBER 28, 2023

Few nonusers (2%) report that lack of data or data quality is an issue, and only 1.3% AI users are definitely facing these problems: 7% report that data quality has hindered further adoption, and 4% cite the difficulty of training a model on their data.

Enterprise 339

Enterprise Testing Modeling Reporting 339

erwin

FEBRUARY 19, 2021

A strong data management strategy and supporting technology enables the data quality the business requires, including data cataloging (integration of data sets from various sources), mapping, versioning, business rules and glossaries maintenance and metadata management (associations and lineage).

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

datapine

APRIL 16, 2019

First and foremost, the main reason usually invoked is data quality. Data quality is the condition of a set of qualitative or quantitative variables, that should be “fit for [its] intended uses in operations, decision making and planning”, according to an article written by author Thomas C. 1) General management.

Data-driven 287

Data-driven Dashboards Visualization Cost-Benefit 287

datapine

DECEMBER 4, 2019

Augmented analytics will help in providing unbiased material to make better decisions and a more impartial context comprehension, and transform the way we interact with data. Each has its foundation in artificial intelligence solutions developed to make human-computer interaction easier and more efficient. Graph Analytics.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212