DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Fast, Safe Development and Deployment (#4) Introduction The integrity and functionality of new code, tools, and configurations during the development and deployment stages are crucial. This process is critical as it ensures data quality from the onset. Historically?

Testing 100

Testing Cost-Benefit Data Quality Dashboards 100

DataRobot Blog

MAY 26, 2022

Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. In summary, to ensure that they have built a robust model, modelers must make certain that they have designed the model in a way that is backed by research and industry-adopted practices.

Risk 52

Risk Modeling Metrics Business Objectives 52

CIO Business Intelligence

APRIL 16, 2024

While warp speed is a fictional concept, it’s an apt way to describe what generative AI (GenAI) and large language models (LLMs) are doing to exponentially accelerate Industry 4.0. By leveraging a GenAI-fueled edge with small language models (SLMs), this lengthy work will be streamlined and simplified. Data quality dependence.

Manufacturing 142

Manufacturing Digital Transformation Risk Dashboards 142

Octopai

OCTOBER 26, 2022

DataOps is an approach to best practices for data management that increases the quantity of data analytics products a data team can develop and deploy in a given time while drastically improving the level of data quality. Automated workflows for data product creation, testing and deployment.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

DataKitchen

JUNE 19, 2023

Another way to look at the five pillars is to see them in the context of a typical complex data estate. Initially, the infrastructure is unstable, but then we look at our source data and find many problems. Our customers start looking at the data in dashboards and models and then find many issues.

Testing 130

Testing Cost-Benefit Metrics Data Quality 130

Alation

JUNE 2, 2022

These include tracking, documenting, monitoring, versioning, and controlling access to AI/ML models. Currently, models are managed by modelers and by the software tools they use, which results in a patchwork of control, but not on an enterprise level. How Data Lineage Is a Use Case in ML. from cognilytica.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

Ontotext

NOVEMBER 18, 2021

And all of them are asking hard questions: “Can you integrate my data, with my particular format?”, “How well can you scale?”, “How many visualizations do you offer?”. Nowadays, data analytics doesn’t exist on its own. You have to take care of data extraction, transformation and loading, and of visualization.

Visualization 98

Visualization Reporting Metadata Enterprise 98

AWS Big Data

FEBRUARY 29, 2024

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Data governance is a critical building block across all these approaches, and we see two emerging areas of focus.

Data Governance 91

Data Governance Unstructured Data Metadata Data Lake 91

CIO Business Intelligence

JUNE 14, 2023

The certification focuses on the seven domains of the analytics process: business problem framing, analytics problem framing, data, methodology selection, model building, deployment, and lifecycle management. The exam consists of 65 multiple-choice or multiple-response questions, which the candidate has 180 minutes to complete.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

FineReport

APRIL 9, 2024

Evolving BI Tools in 2024 Significance of Business Intelligence In 2024, the role of business intelligence software tools is more crucial than ever, with businesses increasingly relying on data analysis for informed decision-making.

Business Intelligence 52

Business Intelligence Software Data-driven Predictive Modeling 52

Laminar Security

DECEMBER 1, 2023

DSPM is a term coined by Gartner to describe solutions that focus on discovering, classifying, and protecting sensitive data in cloud environments. DSPM solutions help organizations achieve data security compliance, reduce data breach risks, optimize cloud costs, and improve data quality, all while enabling data-driven innovation.

Modeling 52

Modeling Cost-Benefit Risk Data-driven 52

CIO Business Intelligence

APRIL 8, 2024

Risk Mitigation Modeling can then be used to analyze training data and determine a player’s ideal training volume while minimizing injury risk. Like other data-driven initiatives, Souza says Digital Athlete uses data rather than hunches and instinct to understand what’s happening on the field during games and practices. “One

Analytics 118

Analytics Broadcasting Predictive Analytics Metrics 118

CIO Business Intelligence

APRIL 24, 2024

In fact, AMA collects a huge amount of structured and unstructured data from bins, collection vehicles, facilities, and user reports, and until now, this data has remained disconnected, managed by disparate systems and interfaces, through Excel spreadsheets.

Digital Transformation 105

Digital Transformation Business Intelligence Unstructured Data Data Lake 105

CIO Business Intelligence

JUNE 23, 2023

The focus here should be on considering all ways your customers currently consume data as well as new ways they might want to achieve better results. By incorporating data into your deliverables, you can often turn this into an annuity revenue stream where these projects become software products to monetize via a SaaS business model.

Data Strategy 116

Data Strategy Strategy Visualization Consulting 116

CIO Business Intelligence

MARCH 24, 2023

It encompasses the people, processes, and technologies required to manage and protect data assets. The Data Management Association (DAMA) International defines it as the “planning, oversight, and control over management of data and the use of data and data-related sources.”

Data Governance 142

Data Governance Management Metadata Data Quality 142

erwin

FEBRUARY 28, 2024

It actually reverse engineers the ETL/ELT technical code sets and transforms and visualizes it on a logical level. Finally, automated data quality scoring from erwin Data Quality is completely integrated within erwin Data Intelligence data lineage. Does it exist at the initial data entry/data source?

Metadata 52

Metadata Data Governance Data Quality Technology 52

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

AUGUST 14, 2019

Data science has become an extremely rewarding career choice for people interested in extracting, manipulating, and generating insights out of large volumes of data. To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

Alation

DECEMBER 21, 2022

Master Data Management (MDM) and data catalog growth are accelerating because organizations must integrate more systems, comply with privacy regulations, and address data quality concerns. What Is Master Data Management (MDM)? Data Catalog and Master Data Management. Assess Data Quality.

Management 52

Management Data Quality Metadata Testing 52

O'Reilly on Data

APRIL 19, 2021

Relatively few respondents are using version control for data and models. Tools for versioning data and models are still immature, but they’re critical for making AI results reproducible and reliable. It’s gratifying to note that organizations starting to realize the importance of data quality (18%).

Enterprise 356

Enterprise Modeling Risk Manufacturing 356

AWS Big Data

DECEMBER 21, 2023

OpenSearch Service also has vector database capabilities that let you implement semantic search and Retrieval Augmented Generation (RAG) with large language models (LLMs) to build recommendation and media search engines. AWS Glue provides both visual and code-based interfaces to make data integration effortless.

Analytics 92

Analytics IT Data Lake Visualization 92

DataKitchen

NOVEMBER 18, 2022

An effective DataOps observability solution requires supporting infrastructure for the journeys to observe and report what’s happening across your data estate. Logs and storage for problem diagnosis and visualization of historical trends. Data and tool tests. Observability includes the following components. Alerting paths.

Testing 130

Testing Statistics Measurement Metrics 130

Timo Elliott

JANUARY 4, 2021

With advanced analytics, flexible dashboarding and effective data visualization, FP&A storytelling has become both an art and science. Business people want more data than ever. We’re no longer talking about tinkering at the margins of a stable business model. Second, the stakes are higher than ever.

Dashboards 139

Dashboards Visualization Data-driven Forecasting 139

IBM Big Data Hub

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

FineReport

MARCH 24, 2024

They analyze, interpret, and manipulate complex data, track key performance indicators, and present insights to management through reports and visualizations. Data analysts interpret data using statistical techniques, develop databases and data collection systems, and identify process improvement opportunities.

Statistics 52

Statistics Data mining Visualization Reporting 52

CIO Business Intelligence

JUNE 9, 2023

Data d ependenc y Regardless of your industry, data is central to almost every business today. Leveraging that data, in AI models, for example, depends entirely on the accessibility, quality, granularity, and latency of your organization’s data. Consider wind turbine operation.

Strategy 116

Strategy Management Key Performance Indicator ROI 116

IBM Big Data Hub

NOVEMBER 24, 2023

Generative AI becomes very handy here through its ability to correlate domain/functional capabilities to code and data and establish business capabilities view and connected application code and data—of course the models need to be tuned/contextualized for a given enterprise domain model or functional capability map.

Cost-Benefit 98

Cost-Benefit Testing Data-driven Enterprise 98

Smarten

MAY 13, 2022

Tech research and analysis firm, Gartner predicts that, ‘Through 2024, 50% of organizations will adopt modern data quality solutions to better support their digital business initiatives,’ and that prediction applies to all types of industries and vertical business sectors, including finance and accounting.

Analytics 76

Analytics Cost-Benefit Finance Software 76

datapine

AUGUST 7, 2019

SaaS tools enable you to choose the best delivery model that corresponds with your business requirements and adapt it as your business changes. Moreover, as a SaaS metrics dashboard serves all of your data in one centralized space, you don’t need to waste time logging into different applications or platforms to source the insights you need.

Dashboards 168

Dashboards Management KPI Cost-Benefit 168

IBM Big Data Hub

MAY 9, 2022

Others have come into sharper focus relatively recently: a global effort to create new data privacy laws, a post-pandemic expectation by customers to know them individually across all touchpoints, and increased attention on any racial, gender-based, or socioeconomic bias in AI models. million each year [1] and $1.2 28, 2021. [2].

Management 57

Management Data Quality Metadata Data Integration 57

DataRobot

JULY 16, 2021

The first is trust in the performance of your AI/machine learning model. They all serve to answer the question, “How well can my model make predictions based on data?” So, we ask, what recommendations and assessments can you use to verify the origin and quality of the data used? How large is the data set?

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

CIO Business Intelligence

APRIL 25, 2022

An education in data science can help you land a job as a data analyst , data engineer , data architect , or data scientist. It culminates with a capstone project that requires creating a machine learning model. Data Science Dojo. NYC Data Science Academy.

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

erwin

DECEMBER 3, 2020

The answer is radical transformation, made possible by an intelligent, data-driven approach to: New business models. One Customer’s Journey to Controlling Data Chaos. Being able to integrate all data touchpoints, including erwin DM for data modeling, Denodo for data visualization, and Jira for ticketing, has been key.

Metadata 141

Metadata Data Governance Data-driven Digital Transformation 141

Ontotext

MAY 2, 2024

Every new dataset and new user adds a little more friction that hits the core metric of the velocity of data and brings it down to zero. Another challenge is the data models that represent how insights and analytics teams see the world. The other half of our value proposition is Ontotext GraphDB.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

FineReport

MAY 31, 2023

A financial dashboard, one of the most important types of data dashboards , functions as a business intelligence tool that enables finance and accounting teams to visually represent, monitor, and present financial key performance indicators (KPIs). Inaccurate or inconsistent data can lead to erroneous conclusions and decisions.

Dashboards 52

Dashboards Key Performance Indicator Metrics Visualization 52

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.

Measurement 52

Measurement Visualization Advertising Metrics 52

CIO Business Intelligence

JUNE 30, 2023

It’s well acknowledged that data, when used correctly, has the potential to be a strategic growth asset driving innovation – and with the recent developments in large language models (LLM) for AI, data is really having its day in the sun. And we’ll let you in on a secret: this means nailing your data strategy.

Internet of Things 104

Internet of Things Strategy Data Strategy Risk 104