datapine

MAY 14, 2019

“Big data is at the foundation of all the megatrends that are happening.” – Chris Lynch, big data expert. We live in a world saturated with data. Zettabytes of data are floating around in our digital universe, just waiting to be analyzed and explored, according to AnalyticsWeek. Wondering which data science book to read?

Data Science 350

Data Science Machine Learning Big Data Data-driven 350

DataKitchen

APRIL 13, 2021

Read the complete blog below for a more detailed description of the vendors and their capabilities. This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. Testing and Data Observability. Other Simple Orchestration Tools. Meta-Orchestration.

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Testing Machine Learning Consulting Data Science 304

DataKitchen

DECEMBER 30, 2020

We also want to thank all of the data industry groups that have recognized our DataKitchen DataOps Platform and Transformation Advisory Services throughout the year. Full disclosure: some images have been edited to remove ads or to shorten the scrolling in this blog post. DBTA’s 100 Companies That Matter Most in Data.

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Testing Big Data Statistics Manufacturing 241

O'Reilly on Data

MARCH 24, 2020

In a world focused on buzzword-driven models and algorithms, you’d be forgiven for forgetting about the unreasonable importance of data preparation and quality: your models are only as good as the data you feed them. Why is high-quality and accessible data foundational? Re-analyzing existing data is often very bad.”

Machine Learning 364

Machine Learning Statistics Data Quality Data Collection 364

datapine

NOVEMBER 27, 2019

Data exploded and became big. Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. The rise of self-service analytics democratized the data product chain. In 2020, BI tools and strategies will become increasingly customized.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

O'Reilly on Data

MAY 14, 2020

In the ideation phase, AI product managers should be able to use the same rapid innovation tools used by design experts, including UX mockups, wireframes, and user surveys. As a result, designing, implementing, and managing AI experiments (and the associated software engineering tools) is at times an AI product in itself.

Management 364

Management Experimentation B2B Machine Learning 364

DataKitchen

DECEMBER 7, 2021

Back by popular demand, we’ve updated our data nerd Gift Giving Guide to cap off 2021. We’ve kept some classics and added some new titles that are sure to put a smile on your data nerd’s face. Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI, by Randy Bean.

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Data-driven Data Governance Big Data Data Science 246

AWS Big Data

DECEMBER 4, 2024

This week on the keynote stages at AWS re:Invent 2024, you heard from Matt Garman, CEO, AWS, and Swami Sivasubramanian, VP of AI and Data, AWS, speak about the next generation of Amazon SageMaker , the center for all of your data, analytics, and AI. They aren’t using analytics and AI tools in isolation.

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O'Reilly on Data

SEPTEMBER 15, 2021

In June 2021, we asked the recipients of our Data & AI Newsletter to respond to a survey about compensation. The average salary for data and AI professionals who responded to the survey was $146,000. We didn’t use the data from these respondents; in practice, discarding this data had no effect on the results.

Machine Learning 363

Machine Learning Statistics Reporting Consulting 363

O'Reilly on Data

OCTOBER 13, 2020

We define debugging as the process of using logging and monitoring tools to detect and resolve the inevitable problems that show up in a production environment. From a technical perspective, it is entirely possible for ML systems to function on wildly different data. Proper AI product monitoring is essential to this outcome.

Management 317

Management Machine Learning Metrics Modeling 317

AWS Big Data

OCTOBER 21, 2024

Data is the most significant asset of any organization. However, enterprises often encounter challenges with data silos, insufficient access controls, poor governance, and quality issues. Embracing data as a product is the key to address these challenges and foster a data-driven culture.

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Sales Data-driven Data Processing Key Performance Indicator 116

DataKitchen

AUGUST 9, 2021

Below is our third post (3 of 5) on combining data mesh with DataOps to foster greater innovation while addressing the challenges of a decentralized architecture. We’ve talked about data mesh in organizational terms (see our first post, “ What is a Data Mesh? ”) and how team structure supports agility. Source: Thoughtworks.

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Testing Data Lake Metadata Publishing 246

datapine

SEPTEMBER 16, 2022

Previously, we discussed the top 19 big data books you need to read, followed by our rundown of the world’s top business intelligence books as well as our list of the best SQL books for beginners and intermediates. Data visualization, or ‘data viz’ as it’s commonly known, is the graphic presentation of data.

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Visualization Dashboards Data-driven Statistics 304

Rocket-Powered Data Science

FEBRUARY 15, 2023

While generative AI has been around for several years , the arrival of ChatGPT (a conversational AI tool for all business occasions, built and trained from large language models) has been like a brilliant torch brought into a dark room, illuminating many previously unseen opportunities.

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

DataKitchen

SEPTEMBER 17, 2021

Below is our final post (5 of 5) on combining data mesh with DataOps to foster innovation while addressing the challenges of a data mesh decentralized architecture. We see a DataOps process hub like the DataKitchen Platform playing a central supporting role in successfully implementing a data mesh.

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Testing Data Architecture Measurement Visualization 237

datapine

MARCH 31, 2022

Does data excite, inspire, or even amaze you? Do you find computer science and its applications within the business world more than interesting? This all-encompassing branch of online data analysis is a particularly interesting field because its roots are firmly planted in two separate areas: business strategy and computer science.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

DataKitchen

AUGUST 3, 2021

The data mesh design pattern breaks giant, monolithic enterprise data architectures into subsystems or domains, each managed by a dedicated team. DataOps helps the data mesh deliver greater business agility by enabling decentralized domains to work in concert. . But first, let’s define the data mesh design pattern.

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Data Architecture Data Lake Cost-Benefit Data Warehouse 246

O'Reilly on Data

NOVEMBER 28, 2023

AI users say that AI programming (66%) and data analysis (59%) are the most needed skills. A healthy tools ecosystem has grown up around generative AI—and, as was said about the California Gold Rush, if you want to see who’s making money, don’t look at the miners; look at the people selling shovels. What’s Holding AI Back?

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Enterprise Testing Modeling Reporting 343

DataKitchen

AUGUST 13, 2021

If a company can use data to identify compounds more quickly and accelerate the development process, it can monetize its drug pipeline more effectively. DataOps automation provides a way to boost innovation and improve collaboration related to data in pharmaceutical research and development (R&D). Mastery of Heterogeneous Tools.

Testing 246

Testing Dashboards Marketing Measurement 246

DataKitchen

JANUARY 20, 2021

We’ll also discuss building DataOps expertise around the data organization, in a decentralized fashion, using DataOps centers of excellence (COE) or DataOps Dojos. A centralized team can promote DataOps adoption by building a common technical infrastructure and tools to be leveraged by other groups. Agile ticketing/Kanban tools.

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Metrics Experimentation Measurement Testing 243

AWS Big Data

DECEMBER 4, 2024

With the growing emphasis on data, organizations are constantly seeking more efficient and agile ways to integrate their data, especially from a wide variety of applications. In addition, organizations rely on an increasingly diverse array of digital systems, data fragmentation has become a significant challenge.

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Data Integration Data Lake Statistics Data-driven 115

AWS Big Data

NOVEMBER 22, 2024

However, the data migration process can be daunting, especially when downtime and data consistency are critical concerns for your production workload. As a backup strategy, snapshots can be created automatically in OpenSearch, or users can create a snapshot manually for restoring it on to a different domain or for data migration.

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Snapshot Metadata Recreation/Entertainment Data Processing 98

CIO Business Intelligence

NOVEMBER 19, 2024

Especially with companies like Microsoft, OpenAI, Meta, Salesforce and others in the news recently with announcements of agentic AI and agent creation tools and capabilities. For instance, If you want to create a system to write blog entries, you might have a researcher agent, a writer agent and a user agent.

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Testing Cost-Benefit Interactive ROI 135

AWS Big Data

DECEMBER 18, 2024

In this blog post, well dive into the various scenarios for how Cohere Rerank 3.5 uses a cross-encoder architecture where the input of the model always consists of a data pair (for example, a query and a document) that is processed jointly by the encoder. The Cohere Rerank 3.5

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Metrics Modeling Data Processing Machine Learning 97

O'Reilly on Data

JANUARY 22, 2019

In this blog post, we explore three types of errors inherent in all financial models, with a simple example of a model in TensorFlow Probability (TFP). Ever since then, economists have endeavored to make their discipline into a science like physics. An exploration of three types of errors inherent in all financial models.

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Modeling Finance Uncertainty Data mining 216

datapine

DECEMBER 4, 2019

Predictive analytics is the practice of extracting information from existing data sets in order to forecast future probabilities. Applied to business, it is used to analyze current and historical data in order to better understand customers, products, and partners and to identify potential risks and opportunities for a company.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

Smart Data Collective

MARCH 5, 2022

Are you a data scientist ? Even if you already have a full-time job in data science, you will be able to leverage your expertise as a big data expert to make extra money on the side. Ways that Data-Savvy People Can Make Money with Side Hustles This Year. It uses complex data analytics features.

Data mining 145

Data mining Big Data Data-driven Data Science 145

DataKitchen

MAY 10, 2021

Data errors impact decision-making. Data errors infringe on work-life balance. Data errors also affect careers. If you have been in the data profession for any length of time, you probably know what it means to face a mob of stakeholders who are angry about inaccurate or late analytics.

Testing 214

Testing Manufacturing Data Quality Statistics 214

Smarten

FEBRUARY 19, 2025

It must be based on historical data, facts and clear insight into trends and patterns in the market, the competition and customer buying behavior. With these tools, users can explore patterns in data and receive suggestions to help them gain insight on their own without dependence on IT or data scientists.

Predictive Analytics 62

Predictive Analytics Analytics Predictive Modeling Forecasting 62

CIO Business Intelligence

JUNE 19, 2024

The company on Wednesday unveiled the release of Generative Chemistry and Accelerated DFT, which together expand how scientists in the chemicals and materials science industry can use its Azure Quantum Elements platform to help drastically shorten the time it takes them to do research, the company said in a blog post.

Strategy 137

Strategy Consulting Optimization Technology 137

datapine

JANUARY 31, 2022

Because things are changing and becoming more competitive in every sector of business, the benefits of business intelligence and proper use of data analytics are key to outperforming the competition. BI software uses algorithms to extract actionable insights from a company’s data and guide its strategic decisions.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Data Science 101

JANUARY 22, 2020

Microsoft has updated the Data Science Certification exam. It should now match the current focus and tools on Azure. Those blog posts were for the old exam which focused on the legacy Azure Machine Learning Studio interface and general data science knowledge. DP 100 Updated! Offical, DP 100 Updated.

Data Science 115

Data Science Machine Learning Optimization Modeling 115

DataKitchen

AUGUST 19, 2021

Below is our fourth post (4 of 5) on combining data mesh with DataOps to foster innovation while addressing the challenges of a decentralized architecture. We’ve covered the basic ideas behind data mesh and some of the difficulties that must be managed. Below is a discussion of a data mesh implementation in the pharmaceutical space.

Data Warehouse 246

Data Warehouse Data Lake Manufacturing Testing 246

datapine

JANUARY 6, 2022

1) What Is Data Interpretation? 2) How To Interpret Data? 3) Why Data Interpretation Is Important? 4) Data Analysis & Interpretation Problems. 5) Data Interpretation Techniques & Methods. 6) The Use of Dashboards For Data Interpretation. Business dashboards are the digital age tools for big data.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

O'Reilly on Data

MARCH 20, 2019

The objective here is to brainstorm on potential security vulnerabilities and defenses in the context of popular, traditional predictive modeling systems, such as linear and tree-based models trained on static data sets. Data poisoning attacks. Data poisoning attacks have also been called “causative” attacks.)

Modeling 278

Modeling Machine Learning Predictive Modeling Consulting 278

datapine

APRIL 14, 2022

“You can have data without information, but you cannot have information without data.” – Daniel Keys Moran. When you think of big data, you usually think of applications related to banking, healthcare analytics , or manufacturing. However, the usage of data analytics isn’t limited to only these fields. Discover 10.

Big Data 244

Big Data Recreation/Entertainment Analytics Data-driven 244

Domino Data Lab

AUGUST 6, 2021

This includes: Model lineage, from data acquisition to model building Model versions in production, as they are updated based on new data Model health in production with model monitoring principles Model usage and basic functionality in production Model costs. First is the data the model is using. How Model Governance Works.

Machine Learning 145

Machine Learning Modeling Testing Data Science 145