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6 DataOps Best Practices to Increase Your Data Analytics Output AND Your Data Quality

Octopai

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.

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The top 15 big data and data analytics certifications

CIO Business Intelligence

Candidates are required to complete a minimum of 12 credits, including four required courses: Algorithms for Data Science, Probability and Statistics for Data Science, Machine Learning for Data Science, and Exploratory Data Analysis and Visualization. Candidates have 90 minutes to complete the exam.

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What is DataOps? Principles and Benefits

Octopai

Today, your business users have the same perspective on data analytics. Your dashboards, charts, visualizations… they’re all products. . A successful data analytics team is one that can increase the quantity of data analytics products they develop in a given time while ensuring (and ideally, improving) the level of data quality.

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Advanced Data Discovery and Augmented Analytics: Simple, Sophisticated Tools for Business Users

Smarten

The concept of Advanced Data Discovery allows business users to leverage advanced analytics and helps the organization to create Citizen Data Scientists. Empowers business users with access to meaningful data to test theories and hypotheses without the assistance of data scientists or IT staff.

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What Is DataOps? Definition, Principles, and Benefits

Alation

DataOps strategies share these common elements: Collaboration among data professionals and business stakeholders. Easy-to-experiment data development environment. Automated testing to ensure data quality. There are many inefficiencies that riddle a data pipeline and DataOps aims to deal with that. Simplicity.

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Success Stories: Applications and Benefits of Knowledge Graphs in Financial Services

Ontotext

Case studies The risk and opportunity event detection use case discussed above combines all of Ontotext’s capabilities: storing and managing large amounts of data adding meaning to it (e.g.,, Connected Inventory Ontotext’s Connected Inventory integrates data from various sources, which enables efficient reporting.

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AI Adoption in the Enterprise 2021

O'Reilly on Data

The biggest problems in this year’s survey are lack of skilled people and difficulty in hiring (19%) and data quality (18%). The biggest skills gaps were ML modelers and data scientists (52%), understanding business use cases (49%), and data engineering (42%). Bad data yields bad results at scale.