Bridging the Gap

NOVEMBER 17, 2023

Learn more about use cases in this video: The Business Analyst Blueprint® Framework: Information Level The Information-Level addresses how data and information are stored and maintained by an organization. Data modeling is critical on all kinds of projects, but especially data migration and system integration projects.

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BizAcuity

APRIL 1, 2023

Among other differences between the two options, data storage is a main factor – depending on the data requirement, you can choose which option of the tool to use. With a Power BI Pro license, you can upload up to 10 GB of data to the Power BI Cloud.

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BizAcuity

SEPTEMBER 21, 2022

Among other differences between the two options, data storage is a main factor – depending on the data requirement, you can choose which option of the tool to use. With a Power BI Pro license, you can upload up to 10 GB of data to the Power BI Cloud.

Data Modelling 130

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Astera

NOVEMBER 1, 2023

Trusted by Fortune 1000 companies, the flexible and scalable data warehousing solution comes with automation capabilities to fast-track design, development, and implementation phases. It provides a tailored set of data warehouse automation features to meet your specific data requirements.

Data Warehouse 52

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Marutitech

APRIL 7, 2022

This is where data cleaning comes in. . Data cleaning involves removing redundant and duplicate data from our data sets, making them more usable and efficient. . Converting data requires some data manipulation and preparation, allowing you to uncover valuable insights and make critical business decisions.

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Bridging the Gap

OCTOBER 12, 2022

Whether you are working with SAP, Microsoft SharePoint, Salesforce.com, Archer, Service Now, or another tool, these requirements will help you leverage these powerful tools to lead a successful project. I’ll be sharing specific techniques for business process analysis , use cases , and data modeling , as well as success stories from ACBAs.

Skillset 52

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Astera

JANUARY 23, 2024

Their data architecture should be able to handle growing data volumes and user demands, deliver insights swiftly and iteratively. Traditional data warehouses with predefined data models and schemas are rigid, making it difficult to adapt to evolving data requirements.

Agile 52

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Astera

NOVEMBER 13, 2023

Style Validators: This feature allows users to maintain design consistency across multiple APIs through standard naming conventions, data models, and other design elements. Domains: Domains enable the definition of reusable components like data models, security schemes, and servers, reducing duplication and enhancing efficiency.

Documentation 52

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Sisense

AUGUST 14, 2020

In the case of a stock trading AI, for example, product managers are now aware that the data required for the AI algorithm must include human emotion training data for sentiment analysis. It turns out that emotional reaction is an important variable in stock market behavior! .

Healthcare 98

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Astera

JANUARY 18, 2024

So, in case your data requires extensive transformation or cleaning, Fivetran is not the ideal solution. Fivetran might be a viable solution if your data is already in good shape, and you need to leverage the computing power of the destination system.

Data Warehouse 52

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Astera

DECEMBER 21, 2022

Unlike on-premise data warehouses, cloud data warehouses can be accessed from anywhere in the world. What’s more, modern data warehouses come with access control features to ensure that the data required for business intelligence is only visible to relevant personnel.

Data Warehouse 52

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Astera

MAY 25, 2023

However, businesses can also leverage data integration and management tools to enhance their security posture. How is big data secured? Big data is extremely valuable, but also vulnerable. Protecting big data requires a multi-faceted approach to security. Access Control Controlling access to sensitive data is key.

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The BAWorld

APRIL 11, 2022

However, these critical responsibilities of a data analyst vary from organization to organization. . Convert business needs into data requirements. Clean, transform, and mine data from primary and secondary sources. Collaborate with business teams to establish business needs. Collaborate with team members.

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The BAWorld

APRIL 11, 2022

However, these critical responsibilities of a data analyst vary from organization to organization. . Convert business needs into data requirements. Clean, transform, and mine data from primary and secondary sources. Collaborate with business teams to establish business needs. Collaborate with team members.

IBM Maintenance 52

IBM Maintenance IBM cost IBM Support Data Analytics 52

Smart Data Collective

JULY 9, 2023

ETL allows for the creation of data models that support complex queries and calculations. These models provide a solid foundation for data analysis, allowing decision-makers to explore trends, patterns, and correlations. Scalability and Future-Proofing As businesses grow, so does their data volume and complexity.

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Sisense

AUGUST 11, 2019

Ultimately, these questions will help you establish the level of self-service you need, and whether your data requirements are geared more towards descriptive or predictive analytics, leading your business in the right direction – regardless of the terminology behind the tool.

Business Intelligence 75

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Astera

DECEMBER 22, 2022

Enterprises will soon be responsible for creating and managing 60% of the global data. Traditional data warehouse architectures struggle to keep up with the ever-evolving data requirements, so enterprises are adopting a more sustainable approach to data warehousing. Use flexible data schemas .

Data Warehouse 52

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The BAWorld

NOVEMBER 22, 2021

Data Modeling. Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Conceptual Data Model. Logical Data Model : It is an abstraction of CDM.

Data Analytics 98

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Bridging the Gap

NOVEMBER 2, 2023

When I got to Module 3, which was the data modeling , that was a little bit challenging for me because I had to juggle more time with allowing more time to commit to the actual workbook as well as not having an impact on my work schedule. I would say for me, data modeling, the third module in the course, was completely new to me.

Business Analysis 52

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Argon Digital

AUGUST 4, 2022

Creating a Business Data Diagram. I found the exercise of creating a Data Flow Diagram for a block walk/canvass so interesting that I decided to play with the same use case to create another data model, the Business Data Diagram (BDD). The BDD is one of the most important and useful models we use.

Banking 52

Banking Documentation Visualization Data Modelling 52

Argon Digital

AUGUST 4, 2022

I found the exercise of creating a Data Flow Diagram for a block walk/canvass so interesting that I decided to play with the same use case to create another data model, the Business Data Diagram (BDD). The BDD is one of the most important and useful models we use.

Banking 52

Banking Documentation Visualization Data Modelling 52

Astera

JANUARY 2, 2024

Data Governance Data governance provides strategic oversight and a framework to ensure that data is treated as a valuable asset and managed in a way that aligns with organizational goals and industry best practices. It ensures data quality, consistency, and compliance with regulations.

Data Management 52

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Cprime

MAY 4, 2023

Data Warehouse Automation The data warehouse automation process accelerates the availability of analytics-ready data by automating the data warehouse lifecycle—from data modeling and real-time ingestion to data marts and governance.

Data Warehouse 52

Data Warehouse Big Data Finance Data Quality 52

Marutitech

DECEMBER 17, 2021

You must be wondering what the different predictive models are? What is predictive data modeling? This blog will help you answer these questions and understand the predictive analytics models and algorithms in detail. What is Predictive Data Modeling? Applying the learning to different cases.

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Astera

OCTOBER 27, 2023

Here are more benefits of a cloud data warehouse: Enhanced Accessibility Cloud data warehouses allow access to relevant data from anywhere in the world. What’s more, they come with access control features to ensure that the data required for BI is only visible to the relevant personnel. We've got both!

Data Warehouse 52

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Astera

APRIL 24, 2024

Google Looker Google Looker is a cloud-based business intelligence platform designed to support businesses in collecting, analyzing, and visualizing data from various sources. Its data modeling layer helps users integrate data from disparate databases, CRMs, and systems into a single view.

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Astera

JANUARY 12, 2024

It was developed by Dan Linstedt and has gained popularity as a method for building scalable, adaptable, and maintainable data warehouses. Data Vault includes mechanisms for data quality control within the centralized data repository, while Data Mesh promotes data product quality through decentralized ownership.

Data Architecture 52

Data Architecture Data Warehouse Data Quality Government 52

Sisense

SEPTEMBER 3, 2020

It’s important that the analytics and BI team clearly indicate their needs and that the data team understand what the BI platform will be used for and how they can build the right data model(s) to suit the analytics and BI team’s requirements. Jennah says.

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Insight Software

JULY 13, 2023

Without deep insights into your organization’s operations, your stakeholders lack a clear understanding of company-wide performance and data analysis to shape the future. Key challengers for your Oracle users are: Capturing vast amounts of enterprise data requires a powerful and complex system.

IBM Maintenance 52

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Insight Software

MAY 1, 2023

Strategic Objective Create an engaging experience in which users can explore and interact with their data. Requirement Filtering Users can choose the data that is important to them and get more specific in their analysis. Drilling Users can dig deeper and gain greater insights into the underlying data.

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