Smart Data Collective

MARCH 13, 2020

It doesn’t matter what the project or desired outcome is, better data science workflows produce superior results. 5 Tips for Better Data Science Workflows. Data science is a complex field that requires experience, skill, patience, and systematic decision-making in order to be successful. Adding it All Up.

Data Science 101

Data Science Key Performance Indicator Knowledge Discovery Visualization 101

Cloudera

JULY 26, 2021

With this first article of the two-part series on data product strategies, I am presenting some of the emerging themes in data product development and how they inform the prerequisites and foundational capabilities of an Enterprise data platform that would serve as the backbone for developing successful data product strategies.

Strategy 117

Strategy Data Science Marketing Unstructured Data 117

Dataiku

MAY 20, 2020

Remote work is the ultimate litmus test of the data organization’s robustness. The Challenges of Remote Data Science. The Benefits of Remote-Ready Data Science. It makes business sense for three main reasons: It opens up doors to data talent that is not based where the company is based.

Data Science 105

Data Science IT Machine Learning Testing 105

Smart Data Collective

MARCH 3, 2022

There is no question that data has become a valuable asset to almost every organization. Companies use big data to optimize their marketing strategies, maintain better relationships with their customers, manage their financial strategies and improve human resources capabilities.

Strategy 111

Strategy Management Cost-Benefit Data Processing 111

CIO Business Intelligence

SEPTEMBER 27, 2023

“Most recently, our product teams have been piloting and successfully deploying new capabilities with gen AI in areas of content localization, content production, assessment creation, and assessment strategies, as well as within our search and recommendation engines,” says Orla Daly, CIO.

Strategy 118

Strategy Risk Data-driven Modeling 118

Rocket-Powered Data Science

SEPTEMBER 20, 2021

The way that I explained it to my data science students years ago was like this. The semantic layer delivers data insights discovery and usability across the whole enterprise, with each business user empowered to use the terminology and tools that are specific to their role. That’s data democratization. That’s empowering.

Data Science 286

Data Science Forecasting Business Intelligence Sales 286

Rocket-Powered Data Science

JULY 14, 2023

As I progressed in my career into management roles for enterprise data systems, I gained a deeper understanding and appreciation of the synergies and interdependencies between system and user requirements. Analytics products represent the user-facing and client-facing derived value from an organization’s data stores.

Analytics 166

Analytics Internet of Things Prescriptive Analytics Data Science 166

Alation

JULY 28, 2021

As data science processes continue to become operationalized and embedded within business processes, the importance of governing those processes continues to rise. While governance has been a major focus for many years when it comes to managing data, governance focused on data science processes is still far less mature.

Data Science 52

Data Science Data Governance Modeling Software 52

Cloudera

SEPTEMBER 23, 2022

Software upgrades and security patches need to be tested, scheduled, and delivered by the ops team. In short, just like on-premise deployments, a small team of operaitons personnel are required to successfully deploy and manage this type of data lakehouse deployment. .

Data Governance 107

Data Governance Strategy Enterprise Cost-Benefit 107

Domino Data Lab

JULY 9, 2019

Chris Wiggins , Chief Data Scientist at The New York Times, presented “Data Science at the New York Times” at Rev. Wiggins also indicated that data science, data engineering, and data analysis are different groups at The New York Times. Data science. Session Summary.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

DataKitchen

JUNE 12, 2023

We had been talking about “Agile Analytic Operations,” “DevOps for Data Teams,” and “Lean Manufacturing For Data,” but the concept was hard to get across and communicate. I spent much time de-categorizing DataOps: we are not discussing ETL, Data Lake, or Data Science.

Testing 130

Testing Data Lake Dashboards Data Science 130

Decision Management Solutions

MARCH 25, 2019

Nancy Casbarro and Deb Zawisa of Novarico recently published a new paper on Data Science in Insurance: Expansion and Key Issues subscription required) that was summarized in this nice little article on Dig-in 3 challenges facing insurers in data science implementation. 2 – Attracting talent.

Insurance 75

Insurance Data Science Testing Modeling 75

Cloudera

JANUARY 30, 2019

It’s official – Cloudera and Hortonworks have merged , and today I’m excited to announce the availability of Cloudera Data Science Workbench (CDSW) for Hortonworks Data Platform (HDP). Trusted by large data science teams across hundreds of enterprises —. What is CDSW?

Data Science 74

Data Science Machine Learning Experimentation Cost-Benefit 74

CIO Business Intelligence

MARCH 14, 2024

The UK government could improve productivity through widespread and systematized uptake of generative AI, but only if it takes steps to build its expertise and come up with an adoption strategy, a new study has found.

Testing 97

Testing Risk Digital Transformation Statistics 97

Rocket-Powered Data Science

OCTOBER 16, 2020

Be sure to listen to the full recording of our lively conversation, which covered Data Literacy, Data Strategy, Data Leadership, and more. The data age has been marked by numerous “hype cycles.” We build models to test our understanding, but these models are not “one and done.” The Age of Hype Cycles.

Digital Transformation 142

Digital Transformation Experimentation Data Strategy Strategy 142

CIO Business Intelligence

DECEMBER 19, 2023

This year’s technology darling and other machine learning investments have already impacted digital transformation strategies in 2023 , and boards will expect CIOs to update their AI transformation strategies frequently. Luckily, many are expanding budgets to do so. “94%

Digital Transformation 137

Digital Transformation Unstructured Data Machine Learning Risk Management 137

CIO Business Intelligence

OCTOBER 13, 2023

The data architect also “provides a standard common business vocabulary, expresses strategic requirements, outlines high-level integrated designs to meet those requirements, and aligns with enterprise strategy and related business architecture,” according to DAMA International’s Data Management Body of Knowledge.

Data Architecture 122

Data Architecture Data Warehouse Statistics Visualization 122

CIO Business Intelligence

AUGUST 1, 2023

As many CIOs prepare their 2024 budgets and digital transformation priorities, developing a strategy that seeks opportunities to evolve business models, targets near-term operational impacts, prioritizes where employees should experiment, and defines AI-related risk-mitigating plans is imperative.

Digital Transformation 96

Digital Transformation Unstructured Data Strategy Experimentation 96

DataKitchen

MAY 10, 2021

And the worst part – data errors take the fun out of data science. Remember your first data science courses? You probably imagined your career would be about helping drive insights with data instead of having to sit in endless meetings discussing analytics errors and painstaking corrective actions.

Testing 214

Testing Manufacturing Data Quality Statistics 214

Rocket-Powered Data Science

SEPTEMBER 25, 2022

All phases of the MVT process are discussed: strategy, designs, pilot, implementation, test, validation, operations, and monitoring. In order to help an organization get started with a DT (digital twin), this book outlines the process of building the MVT (Minimum Viable Twin).

Diagnostic Analytics 216

Diagnostic Analytics Prescriptive Analytics Internet of Things IoT 216

CIO Business Intelligence

SEPTEMBER 14, 2023

They’re often responsible for building algorithms for accessing raw data, too, but to do this, they need to understand a company’s or client’s objectives, as aligning data strategies with business goals is important, especially when large and complex datasets and databases are involved.

Analytics 128

Analytics Data Science Unstructured Data Data mining 128

CIO Business Intelligence

NOVEMBER 7, 2022

CIOs seeking big wins in high business-impacting areas where there’s significant room to improve performance should review their data science, machine learning (ML), and AI projects. Are data science teams set up for success? Establish MLOps, ModelOps, and infrastructure-monitoring capabilities.

Machine Learning 127

Machine Learning Data Science Experimentation IoT 127

CIO Business Intelligence

APRIL 2, 2024

Over the past decade, CIOs have invested significantly in digital transformation initiatives in an effort to improve customer experiences, build data analytics capabilities, and deliver productivity enhancements with automation. It’s like trying to get a jazz quartet, a rock band, a classical orchestra, and a DJ to play in harmony.”

Management 113

Management Digital Transformation Testing Experimentation 113

O'Reilly on Data

NOVEMBER 28, 2023

Unexpected outcomes, security, safety, fairness and bias, and privacy are the biggest risks for which adopters are testing. Programmers have always developed tools that would help them do their jobs, from test frameworks to source control to integrated development environments. We’d like to see more companies test for fairness.

Enterprise 267

Enterprise Modeling Testing Reporting 267

datapine

AUGUST 29, 2022

Best for : the new intern who has no idea what data science even means. An excerpt from a rave review : “I would definitely recommend this book to everyone interested in learning about data from scratch and would say it is the finest resource available among all other Big Data Analytics books.”.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

CIO Business Intelligence

SEPTEMBER 29, 2023

First, a clear vision and strategy for integrating GenAI is essential. Developers and code-writers for example will also benefit from GenAI because it will allow them to describe entire applications through natural language conversations, generate code, refine application logic, and create unit tests.

Machine Learning 116

Machine Learning Finance Strategy Testing 116

CIO Business Intelligence

MAY 27, 2024

According to the Ponemon Institute’s 2023 Cost of Data Breach Report , organizations with extensive incident response planning and testing programs saved $1.49 Here are some key strategies: 1. INE’s suite of learning paths offers an incomparable depth of expertise across cybersecurity, cloud, networking, and data science.

Cost-Benefit 94

Cost-Benefit Statistics ROI Measurement 94

bridgei2i

MAY 11, 2020

Catalysing Digital CX using Data Science Capabilities in the Post COVID World. The strategies for digital CX in this regard should revolve around: Business Goals. The strategies for digital CX in this regard should revolve around: By Widespread Use of Chatbots. Ensuring uniform CX for customers. Click here.

Data Science 52

Data Science Interactive Enterprise Strategy 52

AWS Big Data

APRIL 16, 2024

In the following test, we simulate a high load scenario, use the CloudWatch observability metrics to identify common problems, and make an informed decision to plan scaling to larger environments to mitigate the issues. From the list of environments, we select the one in use for this test.

Metadata 98

Metadata Metrics Testing Management 98

CIO Business Intelligence

JULY 6, 2022

Laying the foundation for its cloud strategy, BNY Mellon undertook a multi-year application modernization effort. “In On BNY Mellon’s cloud strategy: First and foremost, we view cloud as a journey, not a destination. Today, our strategy is to have a multi-cloud approach. What follows are edited excerpts of that conversation.

Experimentation 81

Experimentation Testing Strategy Risk 81

IBM Big Data Hub

FEBRUARY 13, 2024

Teamwork: Assemble a team with expertise in AI, data science and your industry. Data: High-quality, relevant data is the fuel that powers generative AI success. Invest in data hygiene and collection strategies to keep your engine running smoothly. Garbage in, garbage out.

Enterprise 120

Enterprise Modeling Testing Data-driven 120

CIO Business Intelligence

AUGUST 9, 2022

Data engineers are often responsible for building algorithms for accessing raw data, but to do this, they need to understand a company’s or client’s objectives, as aligning data strategies with business goals is important, especially when large and complex datasets and databases are involved.

Analytics 116

Analytics Data Science Statistics Unstructured Data 116

TDAN

DECEMBER 1, 2021

Migrating data to the public cloud offers a wide range of benefits for enterprises; data teams can more easily access their data, write, and test data science models, evaluate new data platforms and test applications, run POCs, and deploy in production.

Data Governance 83

Data Governance Testing Data Science Enterprise 83

O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. There’s a substantial literature about ethics, data, and AI, so rather than repeat that discussion, we’ll leave you with a few resources.

Marketing 361

Marketing Experimentation Metrics Testing 361

O'Reilly on Data

MAY 14, 2020

This includes tools for model development (such as the Cloudera Data Science Workbench ) and production serving infrastructure (such as Seldon and TFX ). According to VentureBeat , fewer than 15% of Data Science projects actually make it into production. This responsibility falls to executive leadership.

Management 363

Management Experimentation B2B Machine Learning 363

DataKitchen

JANUARY 3, 2022

For example, teams working under the VP/Directors of Data Analytics may be tasked with accessing data, building databases, integrating data, and producing reports. Data scientists derive insights from data while business analysts work closely with and tend to the data needs of business units.

Business Analytics 245

Business Analytics Analytics Testing Dashboards 245

CIO Business Intelligence

APRIL 15, 2024

In this case, while we have the same roles involved that many of our product teams have, such as product, experience design, engineering, and data science, we worked differently by keeping the team small and isolated from all the operational stuff that gets in the way.” The first was safety and data privacy testing.

Testing 72

Testing Experimentation Data Quality Machine Learning 72