Pickl AI

OCTOBER 19, 2023

Data quality plays a significant role in helping organizations strategize their policies that can keep them ahead of the crowd. Hence, companies need to adopt the right strategies that can help them filter the relevant data from the unwanted ones and get accurate and precise output.

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Pickl AI

APRIL 26, 2024

This comprehensive blog outlines vital aspects of Data Analyst interviews, offering insights into technical, behavioural, and industry-specific questions. It covers essential topics such as SQL queries, data visualization, statistical analysis, machine learning concepts, and data manipulation techniques.

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Dataconomy

DECEMBER 17, 2024

This blog post explores effective strategies for gathering requirements in your data project. Whether you are a data analyst , project manager, or data engineer, these approaches will help you clarify needs, engage stakeholders, and ensure requirements gathering techniques to create a roadmap for success.

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Pickl AI

OCTOBER 18, 2023

How to Scale Your Data Quality Operations with AI and ML: In the fast-paced digital landscape of today, data has become the cornerstone of success for organizations across the globe. Every day, companies generate and collect vast amounts of data, ranging from customer information to market trends.

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The Data Administration Newsletter

OCTOBER 17, 2023

Where exactly within an organization does the primary responsibility lie for ensuring that a data pipeline project generates data of high quality, and who exactly holds that responsibility? Who is accountable for ensuring that the data is accurate? Is it the data engineers? The data scientists?

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Dataversity

AUGUST 2, 2022

Unfortunately, most organizations – across all industries – have Data Quality problems that are directly impacting their company’s performance. The post Why Data Quality Problems Plague Most Organizations (and What to Do About It) appeared first on DATAVERSITY.

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Pickl AI

SEPTEMBER 12, 2024

Summary : This article equips Data Analysts with a solid foundation of key Data Science terms, from A to Z. Introduction In the rapidly evolving field of Data Science, understanding key terminology is crucial for Data Analysts to communicate effectively, collaborate effectively, and drive data-driven projects.

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Flipboard

NOVEMBER 22, 2024

It serves as the hub for defining and enforcing data governance policies, data cataloging, data lineage tracking, and managing data access controls across the organization. Data lake account (producer) – There can be one or more data lake accounts within the organization.

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Tableau

MARCH 21, 2023

Data discovery and trust have been core principles of Tableau Catalog since its very inception. Learn about the latest features to help users find trusted data at the right time, so they can consume the data with confidence. We have also simplified how DQWs are displayed when viewing lineage in Tableau Catalog.

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Smart Data Collective

JANUARY 9, 2023

Here are a few ways in which a strategic approach of utilizing AI and machine learning technology can help organizations maximize their data’s value. Sorting Through Large Data Sets. Today, AI and machine learning software are able to function at extremely high levels and sort through huge data sets in short amounts of time.

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ODSC - Open Data Science

FEBRUARY 24, 2023

Its goal is to help with a quick analysis of target characteristics, training vs testing data, and other such data characterization tasks. Apache Superset GitHub | Website Apache Superset is a must-try project for any ML engineer, data scientist, or data analyst.

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Pickl AI

DECEMBER 25, 2024

Descriptive analytics is a fundamental method that summarizes past data using tools like Excel or SQL to generate reports. Techniques such as data cleansing, aggregation, and trend analysis play a critical role in ensuring data quality and relevance.

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Dataconomy

SEPTEMBER 4, 2023

Understand what insights you need to gain from your data to drive business growth and strategy. Best practices in cloud analytics are essential to maintain data quality, security, and compliance ( Image credit ) Data governance: Establish robust data governance practices to ensure data quality, security, and compliance.

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ODSC - Open Data Science

APRIL 3, 2023

As you’ll see below, however, a growing number of data analytics platforms, skills, and frameworks have altered the traditional view of what a data analyst is. Data Presentation: Communication Skills, Data Visualization Any good data analyst can go beyond just number crunching.

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Dataconomy

FEBRUARY 23, 2023

They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. This involves working closely with data analysts and data scientists to ensure that data is stored, processed, and analyzed efficiently to derive insights that inform decision-making.

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Pickl AI

MARCH 3, 2025

Business Requirements Analysis and Translation Working with business users to understand their data needs and translate them into technical specifications. Data Quality Assurance Implementing data quality checks and processes to ensure data accuracy and reliability.

Business Intelligence 52

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Alation

NOVEMBER 4, 2021

The practitioner asked me to add something to a presentation for his organization: the value of data governance for things other than data compliance and data security. Now to be honest, I immediately jumped onto data quality. Data quality is a very typical use case for data governance.

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Dataversity

FEBRUARY 19, 2021

Enterprises are modernizing their data platforms and associated tool-sets to serve the fast needs of data practitioners, including data scientists, data analysts, business intelligence and reporting analysts, and self-service-embracing business and technology personnel.

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Alation

JULY 19, 2022

Instead of spending most of their time leveraging their unique skillsets and algorithmic knowledge, data scientists are stuck sorting through data sets, trying to determine what’s trustworthy and how best to use that data for their own goals. The Data Science Workflow. Closing Thoughts.

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Precisely

JANUARY 23, 2024

Respondents’ functional titles included everything from C-level executives to line-of-business managers, IT executives, data stewards, data architects, data managers, and data analysts. As a result, the data governance team could establish defined KPIs around data quality, data integration, and data enrichment.

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Dataconomy

SEPTEMBER 27, 2024

It was my first job as a data analyst. The time I spent at Renault helped me realize that data analytics is something I would be interested in pursuing as a full-time career. Sometimes, data analysts forget to ask themselves this question. But I think it’s crucial to have a business mindset.

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Tableau

MARCH 21, 2023

Data discovery and trust have been core principles of Tableau Catalog since its very inception. Learn about the latest features to help users find trusted data at the right time, so they can consume the data with confidence. We have also simplified how DQWs are displayed when viewing lineage in Tableau Catalog.

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phData

FEBRUARY 16, 2023

Some common data strategy goals of an ACE include improving data quality and governance, enhancing data-driven decision-making, increasing efficiency and productivity, and establishing a culture of data-driven thinking throughout the organization.

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Alation

MAY 27, 2021

As governance becomes a burden, analyst productivity decreases, which often results in diminished data quality. If the analyst and other data users are supported by governance policies that work with them in mind, data quality can be maintained throughout the cycle of gathering, storing, and analyzing.

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Alation

MAY 17, 2022

As a result, providing analysts with tools that increase their efficiency is critical to helping them do more work with less effort. An enterprise data catalog is one such key asset. The Data Analyst Workflow. The workflow of a data analyst consists of four key stages: Discovery. Preparation. See figure 1.).

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Pickl AI

OCTOBER 11, 2023

Data Observability and Data Quality are two key aspects of data management. The focus of this blog is going to be on Data Observability tools and their key framework. The growing landscape of technology has motivated organizations to adopt newer ways to harness the power of data. What is Data Observability?

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Alation

AUGUST 30, 2022

With Alation Anywhere launching in beta, we will meet people where they are, helping to deliver context and trustworthiness of data, from and across the modern data stack, starting with Tableau. With this integration, Alation descriptions and data quality flags of warnings and deprecations will propagate to Tableau.

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Pickl AI

AUGUST 31, 2023

However, analysis of data may involve partiality or incorrect insights in case the data quality is not adequate. Accordingly, the need for Data Profiling in ETL becomes important for ensuring higher data quality as per business requirements. Evaluate the accuracy and completeness of the data.

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Pickl AI

JANUARY 23, 2024

Job roles span from Data Analyst to Chief Data Officer, each contributing significantly to organisational success. They employ statistical methods, data visualisation techniques, and programming skills to dissect data, turning it into actionable intelligence. billion by 2025 and $118.7 to enhance your skills.

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Dataconomy

MARCH 29, 2023

It involves the creation of rules for collecting, storing, processing, and sharing data to ensure its accuracy, completeness, consistency, and security. Some key concepts related to data governance include: Data quality:  Ensuring that data is accurate, complete, and consistent.

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Dataconomy

MARCH 29, 2023

It involves the creation of rules for collecting, storing, processing, and sharing data to ensure its accuracy, completeness, consistency, and security. Some key concepts related to data governance include: Data quality:  Ensuring that data is accurate, complete, and consistent.

Data Governance 70

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Alation

OCTOBER 18, 2022

Alation is pleased to be named a dbt Metrics Partner and to announce the start of a partnership with dbt, which will bring dbt data into the Alation data catalog. In the modern data stack, dbt is a key tool to make data ready for analysis. Increase trust by granting data analysts and engineers.

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IBM Journey to AI blog

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.

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Pickl AI

DECEMBER 4, 2023

Data governance and security Like a fortress protecting its treasures, data governance, and security form the stronghold of practical Data Intelligence. Think of data governance as the rules and regulations governing the kingdom of information. It ensures data quality , integrity, and compliance.

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Alation

MARCH 17, 2021

Now, MLDCs make it possible to understand how data is being used internally, where it came from, who is using it, and how it’s related to other data sets. Tracking and Scaling Data Lineage. MLDCs remove pressure from data consumers by taking on responsibility for data quality, find-ability, and reliability.

Machine Learning 52

Machine Learning Machine Learning Data Governance Data Quality 52

ODSC - Open Data Science

APRIL 26, 2023

Data Analyst When people outside of data science think of those who work in data science, the title Data Analyst is what often comes up. What makes this job title unique is the “Swiss army knife” approach to data. But this doesn’t mean they’re off the hook on other programs.

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IBM Journey to AI blog

OCTOBER 2, 2024

These professionals encounter a range of issues when attempting to source the data they need, including: Data accessibility issues: The inability to locate and access specific data due to its location in siloed systems or the need for multiple permissions, resulting in bottlenecks and delays.

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