CIO Business Intelligence

JUNE 23, 2023

The focus here should be on considering all ways your customers currently consume data as well as new ways they might want to achieve better results. Of course, no set of imperatives for a data strategy would be complete without the need to consider people, process, and technology.

Data Strategy 112

Data Strategy Strategy Visualization Consulting 112

datapine

JANUARY 24, 2021

This can include a multitude of processes, like data profiling, data quality management, or data cleaning, but we will focus on tips and questions to ask when analyzing data to gain the most cost-effective solution for an effective business strategy. 4) How can you ensure data quality?

IT 317

IT Statistics KPI Data-driven 317

CIO Business Intelligence

JUNE 14, 2023

Candidates are required to complete a minimum of 12 credits, including four required courses: Algorithms for Data Science, Probability and Statistics for Data Science, Machine Learning for Data Science, and Exploratory Data Analysis and Visualization. Candidates have 90 minutes to complete the exam.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

erwin

FEBRUARY 28, 2024

Examples of complementary technologies include technology for data warehouse automation, change data capture and master data management. For data governance, it’s more complex. It actually reverse engineers the ETL/ELT technical code sets and transforms and visualizes it on a logical level. How cool is that?

Metadata 52

Metadata Data Governance Data Quality Technology 52

datapine

SEPTEMBER 27, 2022

Business leaders, developers, data heads, and tech enthusiasts – it’s time to make some room on your business intelligence bookshelf because once again, datapine has new books for you to add. We have already given you our top data visualization books , top business intelligence books , and best data analytics books.

Business Intelligence 292

Business Intelligence Data Warehouse Data Processing Data mining 292

Octopai

FEBRUARY 20, 2022

Deciding to migrate your data from legacy, on-prem databases to a cloud-based data warehouse like Snowflake is often a step in the right direction. How exactly does data flow through your legacy systems? Which of the pipelines carry data effectively, which are redundant, and which dead end? Where can it improve?

Data Warehouse 92

Data Warehouse Optimization Measurement Visualization 92

AWS Big Data

NOVEMBER 16, 2023

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

Enterprise 80

Enterprise Data Warehouse Snapshot Cost-Benefit 80

CIO Business Intelligence

NOVEMBER 22, 2022

External data sharing gets strategic Data sharing between business partners is becoming far easier and much more cooperative, observes Mike Bechtel, chief futurist at business advisory firm Deloitte Consulting. CIOs should first understand the different approaches to observing data and how it differs from quality management,” he notes.

Data Strategy 145

Data Strategy Strategy Enterprise Consulting 145

Alation

FEBRUARY 7, 2023

Back then, visualizing impact analysis seemed futuristic with great promise. It wouldn’t be until 2013 that the topic of data lineage would surface again – this time while working on a data warehouse project. The business analysts were dealing with a problem that may sound familiar to folks in the data management space.

Metadata 52

Metadata Data Warehouse Visualization Data Quality 52

datapine

MAY 21, 2019

In addition to increasing the price of deployment, setting up these data warehouses and processors also impacted expensive IT labor resources. These tools allow for a wide range of users to easily connect to, interact with, visualize and communicate their data. 7) Dealing with the impact of poor data quality.

Business Intelligence 183

Business Intelligence Cost-Benefit Dashboards Slice and Dice 183

AWS Big Data

MARCH 7, 2023

Data lakes are more focused around storing and maintaining all the data in an organization in one place. And unlike data warehouses, which are primarily analytical stores, a data hub is a combination of all types of repositories—analytical, transactional, operational, reference, and data I/O services, along with governance processes.

Analytics 68

Analytics Data Warehouse Data Lake Metadata 68

erwin

AUGUST 7, 2019

A data catalog benefits organizations in a myriad of ways. With the right data catalog tool, organizations can automate enterprise metadata management – including data cataloging, data mapping, data quality and code generation for faster time to value and greater accuracy for data movement and/or deployment projects.

Metadata 90

Metadata Data Governance Data Quality Data Warehouse 90

IBM Big Data Hub

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. Ensuring data quality is made easier as a result.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

CIO Business Intelligence

AUGUST 9, 2022

Data engineers are often responsible for building algorithms for accessing raw data, but to do this, they need to understand a company’s or client’s objectives, as aligning data strategies with business goals is important, especially when large and complex datasets and databases are involved. Data engineer job description.

Analytics 128

Analytics Data Science Statistics Unstructured Data 128

Alation

DECEMBER 21, 2022

Master Data Management (MDM) and data catalog growth are accelerating because organizations must integrate more systems, comply with privacy regulations, and address data quality concerns. What Is Master Data Management (MDM)? Data Catalog and Master Data Management. Assess Data Quality.

Management 52

Management Data Quality Metadata Testing 52

Octopai

DECEMBER 28, 2022

For most data analysts and end-users, the vast majority of data activity is spent in the zoomed-in view of their data environment: analytics and reporting. Power BI is a robust data analytics and reporting platform, with the ability to draw in data from a variety of sources, then model, visualize and derive insights.

Reporting 52

Reporting Data Quality Visualization Data Warehouse 52

Ontotext

MAY 2, 2024

Storage-centric approach In the storage-centric approach, people try to address data silos by throwing everything in a data lake or a data warehouse. But, although, this helps somewhat in terms of architecture, soon these data lakes become unwieldy.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

FineReport

MAY 31, 2023

A financial dashboard, one of the most important types of data dashboards , functions as a business intelligence tool that enables finance and accounting teams to visually represent, monitor, and present financial key performance indicators (KPIs). Inaccurate or inconsistent data can lead to erroneous conclusions and decisions.

Dashboards 52

Dashboards Key Performance Indicator Metrics Visualization 52

Jedox

OCTOBER 22, 2020

Other challenges to data analytics include data storage, data quality, and a lack of knowledge and tools necessary to make sense of the data and generate those critical insights. Limited real-time analytics and visuals. Typically, we take our multiple data sources and perform some level of ETL on the data.

Data Analytics 69

Data Analytics Analytics Unstructured Data Data mining 69

AWS Big Data

JUNE 6, 2023

Data consumers lose trust in data if it isn’t accurate and recent, making data quality essential for undertaking optimal and correct decisions. Evaluation of the accuracy and freshness of data is a common task for engineers. Currently, various tools are available to evaluate data quality.

Data Quality 72

Data Quality Data Lake Data-driven Metrics 72

bridgei2i

NOVEMBER 8, 2021

‘Transformation isn’t restricted to people, processes, or technology, but there’s an integral fourth dimension: Data.’. For example, the availability of a data warehouse, the method of disseminating information/communication, or the tools required for data visualization for all the projects coming in.

Enterprise 52

Enterprise B2B Sales Cost-Benefit 52

DataKitchen

SEPTEMBER 21, 2023

Data in Place refers to the organized structuring and storage of data within a specific storage medium, be it a database, bucket store, files, or other storage platforms. In the contemporary data landscape, data teams commonly utilize data warehouses or lakes to arrange their data into L1, L2, and L3 layers.

Testing 176

Testing Data Quality Predictive Modeling Metrics 176

Ontotext

MARCH 8, 2023

As such, most large financial organizations have moved their data to a data lake or a data warehouse to understand and manage financial risk in one place. Yet, the biggest challenge for risk analysis continues to suffer from lack of a scalable way of understanding how data is interrelated.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Howard Dresner

JANUARY 4, 2015

So here’s the list we compiled — Some of the worst mistakes organizations make in BI initiatives Technology/tools: " Thinking the BI toolset will make up for not understanding the business " Thinking BI tools will solve the business problems instead of using BI to solve the problems " Generalizing solutions or tools for all types of users – BI is not (..)

Business Intelligence 45

Business Intelligence Recreation/Entertainment Sales Data Quality 45

AWS Big Data

MARCH 26, 2024

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data.

Data Strategy 67

Data Strategy Strategy Data Warehouse Prescriptive Analytics 67

Jet Global

JANUARY 20, 2021

As the volume of available information continues to grow, data management will become an increasingly important factor in effective business management. Lack of proactive data management, on the other hand, can result in incompatible or inconsistent sources of information, as well as data quality problems.

Data Analytics 95

Data Analytics Management Analytics Data Warehouse 95

CIO Business Intelligence

DECEMBER 21, 2022

BI software helps companies do just that by shepherding the right data into analytical reports and visualizations so that users can make informed decisions. To gain employee buy-in, Stout’s team builds BI dashboards to show them how they can easily connect to and interact with their data, as well as visualize it in a meaningful way.

IT 130

IT Business Intelligence Sales Key Performance Indicator 130

Alation

MAY 16, 2023

Data Pipeline Use Cases Here are just a few examples of the goals you can achieve with a robust data pipeline: Data Prep for Visualization Data pipelines can facilitate easier data visualization by gathering and transforming the necessary data into a usable state.

Data Lake 69

Data Lake Data Governance Data Warehouse Data Processing 69

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. OwlDQ — Predictive data quality.

Testing 307

Testing Machine Learning Consulting Data Quality 307

Alation

JULY 19, 2022

The traditional data science workflow , as defined by Joe Blitzstein and Hanspeter Pfister of Harvard University, contains 5 key steps: Ask a question. Get the data. Explore the data. Model the data. Communicate and visualize the results. A data catalog can assist directly with every step, but model development.

Metadata 59

Metadata Data Quality Statistics Data Science 59

BizAcuity

APRIL 1, 2023

Thanks to the recent technological innovations and circumstances to their rapid adoption, having a data warehouse has become quite common in various enterprises across sectors. This also applies to businesses that may not have a data warehouse and operate with the help of a backend database system.

Business Intelligence 52

Business Intelligence Consulting KPI Data Warehouse 52

BizAcuity

JANUARY 31, 2023

Thanks to the recent technological innovations and circumstances to their rapid adoption, having a data warehouse has become quite common in various enterprises across sectors. This also applies to businesses that may not have a data warehouse and operate with the help of a backend database system.

Business Intelligence 52

Business Intelligence Consulting KPI Data Warehouse 52

Smart Data Collective

SEPTEMBER 26, 2021

There’s not much value in holding on to raw data without putting it to good use, yet as the cost of storage continues to decrease, organizations find it useful to collect raw data for additional processing. The raw data can be fed into a database or data warehouse. If it’s not done right away, then later.

Metadata 98

Metadata Visualization Unstructured Data Data mining 98

Alation

MAY 16, 2023

Data Pipeline Use Cases Here are just a few examples of the goals you can achieve with a robust data pipeline: Data Prep for Visualization Data pipelines can facilitate easier data visualization by gathering and transforming the necessary data into a usable state.

Data Lake 40

Data Lake Data Governance Data Warehouse Data Processing 40

Smarten

NOVEMBER 26, 2023

You may be interested to know that TechJury reports seven out of ten businesses rate data discovery as very important, and that the top three business intelligence trends are data visualization, data quality management and self-service business intelligence. or What is happening?

Marketing 40

Marketing Business Intelligence Key Performance Indicator Prescriptive Analytics 40

Alation

OCTOBER 21, 2021

Just as lakes benefit from the filtering power of surrounding rocks, roots, and soil to sift out incoming impurities, data lakes benefit from a diligent effort to prevent them from becoming a dumping ground for all and any data. Ungoverned data. Data governance helps keep data quality high and data literacy efforts on track.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

BI-Survey

FEBRUARY 17, 2020

From operational systems to support “smart processes”, to the data warehouse for enterprise management, to exploring new use cases through advanced analytics : all of these environments incorporate disparate systems, each containing data fragments optimized for their own specific task. .

Data Governance 89

Data Governance Data Warehouse Data Quality Data Strategy 89