Astera

MARCH 20, 2023

As data warehousing technologies continue to grow in demand , creat ing effective data models has become increasingly important. However, creating an OLTP data model presents various challenges. Firstly, OLTP systems carry large volumes of transactional data every day. We suggest the latter!

Data Modelling 52

Data Modelling Data Warehouse Visualization Business Intelligence 52

Sisense

JULY 8, 2020

Dealing with Data is your window into the ways Data Teams are tackling the challenges of this new world to help their companies and their customers thrive. In recent years we’ve seen data become vastly more available to businesses. This has allowed companies to become more and more data driven in all areas of their business.

Data Warehouse 90

Data Warehouse Embedded Analytics Real-time Data Documentation 90

Insight Software

JANUARY 20, 2021

Organizations that can effectively leverage data as a strategic asset will inevitably build a competitive advantage and outperform their peers over the long term. In order to achieve that, though, business managers must bring order to the chaotic landscape of multiple data sources and data models.

Data Management 95

Data Management Data Analytics Data Analytics E-Commerce 95

Astera

NOVEMBER 28, 2022

Data warehouses have long served as a single source of truth for data-driven companies. But as data complexity and volumes increase, it’s time to look beyond the traditional data ecosystems. Does that mean it’s the end of data warehousing? Does that mean it’s the end of data warehousing?

Data Warehouse 52

Data Warehouse Data Modelling Data Management Big Data 52

Sisense

JANUARY 15, 2021

And rather than answering prescriptive questions — something that BI teams excel at — data teams are able to model future events and understand how changing a past variable could have affected the present. So why do data teams reach out to their business intelligence colleagues to implement a new BI and analytics tool?

Skillset 75

Skillset Data Warehouse Data Modelling Business Intelligence 75

Astera

JANUARY 3, 2023

Dimensional modeling is among the most preferred design approaches for building analytics-friendly data warehouses. First introduced in 1996, Kimball ’ s dimension al data models have now bec o me cornerstones of modern data warehouse design and development. Dimensional Data Model.

Data Warehouse 52

Data Warehouse Data Modelling Business Intelligence Business Intelligence 52

Astera

FEBRUARY 14, 2024

Data vault is an emerging technology that enables transparent, agile, and flexible data architectures, making data-driven organizations always ready for evolving business needs. What is a Data Vault?  A data vault is a data modeling technique that enables you to build data warehouses for enterprise-scale analytics.

Data Warehouse 52

Data Warehouse Data Modelling Agile Data Quality 52

Sisense

OCTOBER 14, 2020

These increasingly difficult questions require sophisticated data models, connected to an increasing number of data sources, in order to produce meaningful answers. Therein lies the power of your data team: Armed with know-how, they connect with the end user teams (internal users, product teams embedding insights, etc.)

Data Modelling 98

Data Modelling Embedded Analytics Data Warehouse Innovation 98

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 Profiling.

Data Analytics 98

Data Analytics Data Analytics Data Warehouse Data Modelling 98

Whizlabs

APRIL 22, 2021

Cloud services are being used for storing and using more data from various sources to help business organizations grow. But, the major concern for most of the companies in the present era is to make the data work seamlessly and efficiently after the infrastructure is built.

Data Warehouse 88

Data Warehouse Data Automation Data Modelling Planning 88

Astera

DECEMBER 14, 2022

Kimball-style dimensional modeling has been the go-to architecture for most data warehouse developers over the past couple of decades. The questions that arise, however, are the following: How easy is it to load and maintain data in fact and dimension tables? And Is it worth the effort?

Data Warehouse 52

Data Warehouse Data Modelling Business Intelligence Business Intelligence 52

Astera

JANUARY 25, 2024

Reverse ETL (Extract, Transform, Load) is the process of moving data from central data warehouse to operational and analytic tools. How Does Reverse ETL Fit in Your Data Infrastructure Reverse ETL helps bridge the gap between central data warehouse and operational applications and systems.

E-Commerce 52

E-Commerce Data Warehouse Retail Customer Experience 52

Sisense

MAY 27, 2019

This is usually done when the data scientist hands their findings and insights to a business analyst. To extend our analogy, if the data scientist is the diamond cutter, then they pass the material on to the last expert in the chain – the jeweler (business analyst) – to create something valuable for a non-expert audience.

Skillset 75

Skillset Cloud Computing Visualization Big Data 75

Astera

MAY 31, 2024

Two key disciplines have emerged at the forefront of this approach: data science vs data analytics. While both fields help you extract insights from data, data analytics focuses more on analyzing historical data to guide decisions in the present. Data warehouses and data lakes play a key role here.

Data Analytics 52

Data Analytics Data Analytics Data Warehouse Healthcare 52

Insight Software

JANUARY 15, 2021

The company has offered up “bring your own database” (BYOD) as a workaround and has presented data entities as a long-term approach to data access for reporting. A non-developer can build a custom data warehouse with Jet Analytics in as little as 30 minutes.

Data Warehouse 72

Data Warehouse Supply Chain Finance Data Visualization 72

Actian

MAY 23, 2019

Because data is separated and fragmented, decision makers (at all levels of the organization) are prevented from seeing the holistic big picture of how their actions and decisions impact the company. Data silos and company culture. The next step to break down data silos is to establish a unified company-wide data model.

Data Warehouse 40

Data Warehouse Data Modelling Agile Planning 40

Sisense

MARCH 14, 2021

For starters, the rise of the Internet of Things (IoT) has created immense volumes of new data to be analyzed. IoT sensors on factory floors are constantly streaming data into cloud warehouses and other storage locations. Teams also use text analytics to benchmark their performance against their competitors.

Big Data 52

Big Data Data Analytics Data Analytics Supply Chain 52

Actian

MAY 21, 2019

The refinement process starts with the ingestion and aggregation of data from each of the source systems. This is often done in some sort of data warehouse. Once the data is in a common place, it must be merged and reconciled into a common data model – addressing, for example, duplication, gaps, time differences and conflicts.

Big Data 40

Big Data Data Warehouse Artificial Intelligence Digital Transformation 40

Astera

SEPTEMBER 20, 2024

his setup allows users to access and manage their data remotely, using a range of tools and applications provided by the cloud service. Cloud databases come in various forms, including relational databases, NoSQL databases, and data warehouses. There are several types of NoSQL databases, including document stores (e.g.,

Data Warehouse 40

Data Warehouse Cloud Computing E-Commerce Documentation 40

Astera

APRIL 23, 2024

Data quality metrics are not just a technical concern; they directly impact a business’s bottom line. million annually due to low-quality data. Furthermore: 41% of data warehouse projects are unsuccessful, primarily because of insufficient data quality.

Data Quality 52

Data Quality Monitoring Data Modelling Data Warehouse 52

Astera

MAY 30, 2024

Statistical Analysis : Using statistics to interpret data and identify trends. Predictive Analytics : Employing models to forecast future trends based on historical data. Data Visualization : Presenting data visually to make the analysis understandable to stakeholders.

Data Analytics 52

Data Analytics Data Analytics Data Mining Supply Chain 52

Data Pine

MARCH 31, 2022

To simplify things, you can think of back-end BI skills as more technical in nature and related to building BI platforms, like online data visualization tools. Front-end analytical and business intelligence skills are geared more towards presenting and communicating data to others. Business Intelligence Job Roles.

Business Intelligence 95

Business Intelligence Business Intelligence Data Visualization Visualization 95

Astera

DECEMBER 22, 2022

It’s one of the three core data types, along with structured and semi-structured formats. Examples of unstructured data include call logs, chat transcripts, contracts, and sensor data, as these datasets are not arranged according to a preset data model. This makes managing unstructured data difficult.

Data Quality 52

Data Quality Data Mining Data Warehouse Data Governance 52

Astera

AUGUST 16, 2024

The presence of diverse data assets requires organizations to plan, implement, and validate the source data during migration. Improper planning can lead to data corruption or loss. This can lead to the organization losing a lot of valuable data after migration.

Planning 52

Planning Digital Transformation Government Cloud Computing 52

MindsMapped

JULY 18, 2020

Business Analytics mostly work with data and statistics. They primarily synthesize data and capture insightful information through it by understanding its patterns. Business Analysts and Business Analytics – Differences. Business Analytics.

Business Analysis 46

Business Analysis Business Analysis Requirements Gathering Documentation 46

Astera

AUGUST 22, 2023

Reusable Artifacts: Astera’s data preparation scripts are reusable across various artifacts (dataflow, workflow, analytics workbench), promoting efficient and consistent data processing throughout multiple workflows. It offers an instant preview and feedback on data quality, helping you ensure the accuracy and integrity of your data.

Innovation 52

Innovation Documentation Data Analytics Data Analytics 52

Data Pine

SEPTEMBER 29, 2022

With quality data at their disposal, organizations can form data warehouses for the purposes of examining trends and establishing future-facing strategies. Industry-wide, the positive ROI on quality data is well understood. Business/Data Analyst: The business analyst is all about the “meat and potatoes” of the business.

Data Quality 96

Data Quality Monitoring Big Data Government 96

Astera

MAY 13, 2024

It prepares data for analysis, making it easier to obtain insights into patterns and insights that aren’t observable in isolated data points. Once aggregated, data is generally stored in a data warehouse. Some of these features include reporting tools, dashboards, and data modeling.

Supply Chain 52

Supply Chain E-Commerce Healthcare Data Quality 52

Sisense

JUNE 30, 2020

However, a lot of people are not data oriented and can feel overwhelmed by data (especially too much data). Everything we’re doing is trying to make BI more accessible to more people, really to people who don’t even care but could use the insights from their data to do better work. JC: How do enterprises embed?

Embedded Analytics 59

Embedded Analytics Visualization Data Modelling Data Warehouse 59

Insight Software

AUGUST 27, 2021

With the advent of cloud ERP, software publishers have been forced to abandon that direct access approach because it presents potential security threats in a multi-tenant environment. On a shared database server, that kind of capability could impact other customers’ data as well. We designed Jet Analytics for operational efficiency.

IBM Maintenance 98

IBM Maintenance IBM Support IBM cost Data Warehouse 98

Insight Software

MAY 3, 2021

Jet Analytics enables you to pull data from different systems, transform them as needed, and build a data warehouse and cubes or data models structured so that business users can access the information they need without having to understand the complexities of the underlying database structure.

IBM Support 98

IBM Support IBM Maintenance Finance Data Warehouse 98

Insight Software

MAY 1, 2023

Discuss, don’t present. Present your business case. To support your case, present findings from the State of Embedded Analytics study. Information Delivery The main reason software providers take on an embedded analytics project is to improve how data is presented. It is now most definitely a need-to-have.

Embedded Analytics 52

Embedded Analytics IBM Support IBM Maintenance IBM cost 52

Insight Software

JULY 15, 2021

A better solution is to use a tool that enables you to work with a shared, single source of truth for your planning data, model an unlimited number of scenarios quickly and easily, and work within an environment that is as familiar and flexible as Excel. There are, of course, situations that present both crisis and opportunity.

IBM Maintenance 98

IBM Maintenance IBM cost Supply Chain IBM Support 98

Insight Software

JULY 24, 2023

Here are the burdens facing your team with on-premises ERP solutions: Too complex: ERP data models are complex and difficult to integrate with other ERPs, BI tools, and cloud data warehouses. Too inflexible: Financial processes such as month-end close require flexibility and access to up-to-date data.

IBM Maintenance 52

IBM Maintenance IBM cost IBM Support Data Warehouse 52

Insight Software

SEPTEMBER 30, 2022

What are the best practices for analyzing cloud ERP data? Data Management. How do we create a data warehouse or data lake in the cloud using our cloud ERP? How do I access the legacy data from my previous ERP? How can we rapidly build BI reports on cloud ERP data without any help from IT?

IBM Maintenance 52

IBM Maintenance IBM Support IBM cost IBM Expert 52

Insight Software

JULY 13, 2023

Streamline Your Monthly Reporting Manual data processes kill organizational agility, greatly reducing the time your finance team can invest in generating business insights to help you get ahead of the competition. The point-and-click data warehouse automation allows for BI customization that’s five times faster than manual coding.

IBM Maintenance 52

IBM Maintenance IBM Support IBM Expert Finance 52

Insight Software

JULY 24, 2023

Organizations must understand how to extract complex data on a regular cadence and present the reporting to end users to manipulate through an interactive BI tool. Angles for Oracle delivers a powerful data model, library of pre-built, no code business reports and robust process analytics engine. Reporting is inflexible.

Supply Chain 52

Supply Chain IBM Support IBM Maintenance Vision 52