ElegantJ BI

SEPTEMBER 30, 2021

Suitable For: Use by business units, departments or specific roles within the organization that have a need to analyze and report and require high quality data and good performance. Advantages: Can provide secured access to data required by certain team members and business units. Data Warehouse.

Data Management 130

Data Management Data Warehouse Data Governance Government 130

ElegantJ BI

OCTOBER 1, 2020

The average business user does not have a full grasp of Advanced Data Discovery or Data Preparation methods, and most organizations would not want business users to waste precious time trying to navigate the complexities of a manual data preparation process. Why is Augmented Data Preparation Important?

Data Discovery 130

Data Discovery Data Warehouse Business Intelligence Business Intelligence 130

The BAWorld

NOVEMBER 24, 2023

The blog discusses key elements including tools, applications, future trends, and fundamentals of data analytics, providing comprehensive insights for professionals and enthusiasts in the field. Key Features: User-friendly interface for data manipulation and visualization. Rich visualization options for effective communication.

Data Analytics 52

Data Analytics Data Analytics E-Commerce Healthcare 52

ElegantJ BI

OCTOBER 1, 2020

The average business user does not have a full grasp of Advanced Data Discovery or Data Preparation methods, and most organizations would not want business users to waste precious time trying to navigate the complexities of a manual data preparation process. Why is Augmented Data Preparation Important?

Data Discovery 130

Data Discovery Data Warehouse Government Business Intelligence 130

Astera

JANUARY 23, 2024

Businesses need scalable, agile, and accurate data to derive business intelligence (BI) and make informed decisions. Their data architecture should be able to handle growing data volumes and user demands, deliver insights swiftly and iteratively.

Agile 52

Agile Data Warehouse Data Quality Government 52

Astera

APRIL 24, 2024

Key Features: Data collection Data processing and presentation Integration with various sources User-friendly interface Multi-server support, backup and recovery, and maintainability. Its data modeling layer helps users integrate data from disparate databases, CRMs, and systems into a single view.

IBM Support 52

IBM Support IBM Maintenance IBM cost E-Commerce 52

Data Pine

MAY 2, 2023

To work effectively, big data requires a large amount of high-quality information sources. Where is all of that data going to come from? The future is bright for logistics companies that are willing to take advantage of big data. Now’s the time to strike.

Supply Chain 110

Supply Chain Logistics Big Data Customer Experience 110

Tableau

JUNE 6, 2022

Seamlessly integrating dashboards, visualizations, and reports into end users’ products, apps, and web portals expedites decision-making by putting data and insights where people are already working. The data they did have access to was disparate, static, and unreliable. June 6, 2022 - 11:36pm. June 7, 2022.

Embedded Analytics 98

Embedded Analytics Innovation Real-time Data Visualization 98

ElegantJ BI

DECEMBER 11, 2019

If we look at the idea of data agility and delivering Augmented Analytics Tools to business users, we can encourage the use of self-serve tools with auto-suggestions and guidance to help users see the best way to visualize their data or to use a Self-Serve Data Prep tool or an Assisted Predictive Modeling tool.

Data Quality 130

Data Quality Agile Innovation Data Requirement 130

ElegantJ BI

DECEMBER 11, 2019

If we look at the idea of data agility and delivering Augmented Analytics Tools to business users, we can encourage the use of self-serve tools with auto-suggestions and guidance to help users see the best way to visualize their data or to use a Self-Serve Data Prep tool or an Assisted Predictive Modeling tool.

Data Quality 130

Data Quality Agile Innovation Data Requirement 130

Astera

OCTOBER 19, 2023

While all data transformation solutions can generate flat files in CSV or similar formats, the most efficient data prep implementations will also easily integrate with your other productivity business intelligence (BI) tools. Manual export and import steps in a system can add complexity to your data pipeline.

IBM Support 40

IBM Support Data Quality IBM Maintenance Real-time Data 40

Astera

APRIL 28, 2023

Final Verdict: Intelligent Systems are Changing the Game Intelligent systems are revolutionizing data management by providing new and innovative ways to analyze, process, and interpret vast amounts of data. Data management throughout its entire lifecycle, from acquisition to disposal, is a complex process.

Data Management 93

Data Management Artificial Intelligence Predictive Analytics Data Quality 93

Astera

MAY 21, 2024

A database schema, or DB schema, is an abstract design representing how your data is stored in a database. Data Analysis and Reporting Data analysis and report generation become easier thanks to the structured format that database schemas provide. It also mitigates the risk of breaches and misuse.

Documentation 52

Documentation Data Modelling Visualization Data Requirement 52

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. Best Practices to Build Your Data Warehouse .

Data Warehouse 52

Data Warehouse IBM Maintenance IBM Support Data Architecture 52

Astera

FEBRUARY 1, 2024

Data models help us understand and utilize data within any system. Data modeling involves creating a detailed visual representation of an information system or its components. It is designed to communicate the connections between various data points and structures.

Data Modelling 40

Data Modelling Retail Documentation Visualization 40

Astera

DECEMBER 21, 2022

In comparison to cloud data warehouses, on-premise data warehouses pose certain challenges that affect the efficiency of the organizations’ analytics and business intelligence operations. Moreover, when using a legacy data warehouse, you run the risk of issues in multiple areas, from security to compliance.

Data Warehouse 52

Data Warehouse Supply Chain Business Intelligence Business Intelligence 52

Juice Analytics

MARCH 11, 2021

However, the data was essentially stored in old copies of the paper magazine, not a format that was conducive to delivering insights to their target audience. (3) That isn’t to say we haven’t seen many companies that believe that a massive data extract represents a useful solution to their customers. Just kidding!

Product Management 128

Product Management Product Management Vision Healthcare 128

The BAWorld

APRIL 11, 2022

Collaborate with business teams to establish business needs. Convert business needs into data requirements. Clean, transform, and mine data from primary and secondary sources. Database Tools : Any data analyst’s toolbox should include Microsoft Excel and SQL. CCA Data Analyst.

IBM Maintenance 52

IBM Maintenance IBM cost IBM Support Data Analytics 52

The BAWorld

APRIL 11, 2022

Collaborate with business teams to establish business needs. Convert business needs into data requirements. Clean, transform, and mine data from primary and secondary sources. Database Tools : Any data analyst’s toolbox should include Microsoft Excel and SQL. CCA Data Analyst.

IBM Maintenance 52

IBM Maintenance IBM cost IBM Support Data Analytics 52

Sisense

OCTOBER 6, 2020

As quantitative data is always numeric, it’s relatively straightforward to put it in order, manage it, analyze it, visualize it, and do calculations with it. Spreadsheet software like Excel, Google Sheets, or traditional database management systems all mainly deal with quantitative data. Why is quantitative data important?

Visualization 98

Visualization Data Warehouse Innovation Predictive Analytics 98

Insight Software

MAY 1, 2023

Learn how embedded analytics are different from traditional business intelligence and what analytics users expect. Embedded Analytics Definition Embedded analytics are the integration of analytics content and capabilities within applications, such as business process applications (e.g., that gathers data from many sources.

Embedded Analytics 52

Embedded Analytics IBM Support IBM Maintenance IBM cost 52

Data Pine

OCTOBER 30, 2019

Usually created with past data without the possibility to generate real-time or future insights, these reports were obsolete, comprised of numerous external and internal files, without proper data management processes at hand. The rise of innovative report tools means you can create data reports people love to read.

Visualization 51

Visualization Retail Data Visualization Government 51

Astera

NOVEMBER 28, 2023

These could be to enable real-time analytics, facilitate machine learning models, or ensure data synchronization across systems. Consider the specific data requirements, the frequency of data updates, and the desired speed of data processing and analysis.

Data Warehouse 52

Data Warehouse Monitoring Data Security Healthcare 52

Insight Software

NOVEMBER 18, 2022

For enterprise reporting globally, Oracle Essbase does a great job maintaining the underlying financial data. But when it comes to making sense of this data – organizing, visualizing, and finding the narrative – Essbase has limited capabilities. And without the need for expensive business intelligence tools or IT projects.

IBM Maintenance 52

IBM Maintenance IBM cost IBM Support IBM Expert 52

Insight Software

JULY 11, 2022

Even with its out-of-the-box reporting, it’s likely you’ll find yourself unable to quickly compile all your critical business data into an agile, customizable report. Generating queries to pull data requires knowledge of SQL, then manual reformatting and reconciling information is a time-consuming process.

IBM Maintenance 52

IBM Maintenance IBM Support Finance Supply Chain 52

Insight Software

MAY 4, 2021

SAP Analysis for Office is a reasonably good tool for getting a surface-level view of ERP data, but it is simply not flexible enough to provide for the sophisticated needs of analysts in finance and accounting. BusinessObjects cannot support real-time data changes, making it unwieldy for ad hoc reporting.

IBM Maintenance 52

IBM Maintenance IBM Support IBM cost Finance 52