Pickl AI

JULY 3, 2023

What is Business Intelligence? Business Intelligence (BI) refers to the technology, techniques, and practises that are used to gather, evaluate, and present information about an organisation in order to assist decision-making and generate effective administrative action. billion in 2015 and reached around $26.50

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

Tableau

JUNE 8, 2021

Domain experts, for example, feel they are still overly reliant on core IT to access the data assets they need to make effective business decisions. In all of these conversations there is a sense of inertia: Data warehouses and data lakes feel cumbersome and data pipelines just aren't agile enough.

Tableau 52

Tableau Data Lakes Data Warehouse SQL 52

phData

DECEMBER 12, 2023

A complete view of the fan, rather than pieces of information spread across various departments, means less guesswork and more data insights. Step 2: Analyze the Data Once you have centralized your data, use a business intelligence tool like Sigma Computing , Power BI , Tableau , or another to craft analytics dashboards.

Data Warehouse 52

Data Warehouse Machine Learning Machine Learning Tableau 52

IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. What does a modern data architecture do for your business?

Data Quality 52

Data Quality Data Lakes Data Warehouse Business Intelligence 52

ODSC - Open Data Science

JANUARY 18, 2024

This individual is responsible for building and maintaining the infrastructure that stores and processes data; the kinds of data can be diverse, but most commonly it will be structured and unstructured data. They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Alation

MARCH 31, 2022

In the breakneck world of data, which I have been privy to since the mid 1990s, business intelligence remains one of the most enduring terms. The writer Richard Millar Devens used “business intelligence” to describe how a banker had the foresight to gather and act on information thus getting the jump on his competition.

Business Intelligence 52

Business Intelligence Business Intelligence Data Warehouse Data Scientist 52

The MLOps Blog

MAY 17, 2023

We also discuss different types of ETL pipelines for ML use cases and provide real-world examples of their use to help data engineers choose the right one. What is an ETL data pipeline in ML? Xoriant It is common to use ETL data pipeline and data pipeline interchangeably.

ETL 59

ETL Data Pipeline ML ML 59

IBM Journey to AI blog

OCTOBER 16, 2023

How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools. A data store lets a business connect existing data with new data and discover new insights with real-time analytics and business intelligence.

AI 78

AI AI Data Warehouse ML 78

IBM Journey to AI blog

SEPTEMBER 19, 2023

Data analytics is a task that resides under the data science umbrella and is done to query, interpret and visualize datasets. Data scientists will often perform data analysis tasks to understand a dataset or evaluate outcomes. Watsonx comprises of three powerful components: the watsonx.ai

Data Science 85

Data Science Analytics Analytics Data Scientist 85

IBM Journey to AI blog

DECEMBER 16, 2022

It uses metadata and data management tools to organize all data assets within your organization. It synthesizes the information across your data ecosystem—from data lakes, data warehouses, and other data repositories—to empower authorized users to search for and access business-ready data for their projects and initiatives.

Data Quality 52

Data Quality Data Governance Data Wrangling Data Scientist 52

Alation

JANUARY 17, 2023

It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse. Data ingestion/integration services. Data orchestration tools. Business intelligence (BI) platforms. Better Data Culture.

Data Warehouse 52

Data Warehouse ETL Cloud Data Tableau 52

Smart Data Collective

SEPTEMBER 8, 2021

The ETL process is defined as the movement of data from its source to destination storage (typically a Data Warehouse) for future use in reports and analyzes. The data is initially extracted from a vast array of sources before transforming and converting it to a specific format based on business requirements.

ETL 94

ETL Hadoop Data Warehouse Data Pipeline 94

Alation

JANUARY 26, 2023

That’s why many organizations invest in technology to improve data processes, such as a machine learning data pipeline. However, data needs to be easily accessible, usable, and secure to be useful — yet the opposite is too often the case. Low quality In many scenarios, there is no one responsible for data administration.

Data Governance 52

Data Governance Data Engineering Data Engineer Data Engineering 52

ODSC - Open Data Science

JANUARY 30, 2024

So let’s do a quick overview of the job of data engineer, and maybe you might find a new interest. Building and maintaining data pipelines Data integration is the process of combining data from multiple sources into a single, consistent view. Think of data engineers as the architects of the data ecosystem.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

phData

SEPTEMBER 5, 2023

Google Analytics 4 (GA4) is a powerful tool for collecting and analyzing website and app data that many businesses rely heavily on to make informed business decisions. It enables us to create, schedule, and monitor the data pipeline, ensuring seamless movement of data between the various sources and destinations.

Azure 52

Azure Analytics Analytics Data Pipeline 52

ODSC - Open Data Science

FEBRUARY 24, 2023

Great Expectations provides support for different data backends such as flat file formats, SQL databases, Pandas dataframes and Sparks, and comes with built-in notification and data documentation functionality. You can even connect directly to 20+ data sources to work with data within minutes.

Exploratory Data Analysis 126

Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

Dataversity

APRIL 1, 2021

In the data-driven world we live in today, the field of analytics has become increasingly important to remain competitive in business. In fact, a study by McKinsey Global Institute shows that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and nine times […].

Analytics 69

Analytics Analytics Data Silos Data Lakes 69

phData

AUGUST 10, 2023

The implementation of a data vault architecture requires the integration of multiple technologies to effectively support the design principles and meet the organization’s requirements. Data Acquisition: Extracting data from source systems and making it accessible. as well as calculating business keys.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Smart Data Collective

SEPTEMBER 26, 2021

The raw data can be fed into a database or data warehouse. An analyst can examine the data using business intelligence tools to derive useful information. . To arrange your data and keep it raw, you need to: Make sure the data pipeline is simple so you can easily move data from point A to point B.

Database 99

Database Data Visualization Data Warehouse Data Mining 99

Tableau

DECEMBER 7, 2022

But good data—and actionable insights—are hard to get. Traditionally, organizations built complex data pipelines to replicate data. Those data architectures were brittle, complex, and time intensive to build and maintain, requiring data duplication and bloated data warehouse investments.

Tableau 96

Tableau Data Warehouse Data Pipeline Data Visualization 96

Tableau

DECEMBER 7, 2022

But good data—and actionable insights—are hard to get. Traditionally, organizations built complex data pipelines to replicate data. Those data architectures were brittle, complex, and time intensive to build and maintain, requiring data duplication and bloated data warehouse investments.

Tableau 94

Tableau Data Warehouse Data Pipeline Data Visualization 94

phData

JANUARY 5, 2023

There’s a multitude of different reasons why an organization may consider a business intelligence (BI) platform migration. On-Premises to The Cloud This type of migration involves moving an organization’s BI platform from an on-premises environment (such as a local server or data center) to a cloud-based environment.

Analytics 52

Analytics Analytics Tableau Database 52

Precisely

APRIL 3, 2023

It simply wasn’t practical to adopt an approach in which all of an organization’s data would be made available in one central location, for all-purpose business analytics. To speed analytics, data scientists implemented pre-processing functions to aggregate, sort, and manage the most important elements of the data.

ETL 52

ETL Database Analytics Analytics 52

phData

MAY 25, 2023

How to Optimize Power BI and Snowflake for Advanced Analytics Spencer Baucke May 25, 2023 The world of business intelligence and data modernization has never been more competitive than it is today. Microsoft Power BI has been the leader in the analytics and business intelligence platforms category for several years running.

Power BI 84

Power BI Analytics Analytics Azure 84

Tableau

DECEMBER 7, 2022

But good data—and actionable insights—are hard to get. Traditionally, organizations built complex data pipelines to replicate data. Those data architectures were brittle, complex, and time intensive to build and maintain, requiring data duplication and bloated data warehouse investments.

Tableau 53

Tableau Data Warehouse Data Pipeline Data Visualization 53

IBM Journey to AI blog

JULY 17, 2023

It is supported by querying, governance, and open data formats to access and share data across the hybrid cloud. Through workload optimization across multiple query engines and storage tiers, organizations can reduce data warehouse costs by up to 50 percent.

AI 68

AI AI Machine Learning Machine Learning 68

IBM Journey to AI blog

OCTOBER 12, 2023

Securing AI models and their access to data While AI models need flexibility to access data across a hybrid infrastructure, they also need safeguarding from tampering (unintentional or otherwise) and, especially, protected access to data.

AI 84

AI AI Data Scientist Data Governance 84

Alation

APRIL 14, 2022

Data pipeline orchestration. Moving/integrating data in the cloud/data exploration and quality assessment. Similar to a data warehouse schema, this prep tool automates the development of the recipe to match. On-premises business intelligence and databases. Collaboration and governance.

Data Governance 59

Data Governance ML ML Cloud Data 59

phData

NOVEMBER 9, 2023

This data mesh strategy combined with the end consumers of your data cloud enables your business to scale effectively, securely, and reliably without sacrificing speed-to-market. What is a Cloud Data Warehouse? For example, most data warehouse workloads peak during certain times, say during business hours.

Data Warehouse 98

Data Warehouse Data Lakes Clustering Cloud Data 98

phData

JULY 18, 2023

With the birth of cloud data warehouses, data applications, and generative AI , processing large volumes of data faster and cheaper is more approachable and desired than ever. First up, let’s dive into the foundation of every Modern Data Stack, a cloud-based data warehouse.

Data Warehouse 52

Data Warehouse SQL Cloud Data Analytics 52

IBM Journey to AI blog

AUGUST 4, 2023

Data fabric Data fabric architectures are designed to connect data platforms with the applications where users interact with information for simplified data access in an organization and self-service data consumption. This lets users across the organization treat the data like a product with widespread access.

Data Lakes 65

Data Lakes AI AI Data Governance 65