How to Create a Data Quality Dashboard

The main purpose of a dashboard is to provide a comprehensive snapshot of performance, which means that you should incorporate a large amount of detail without using too many drill-downs. It uses data from the past to identify trends and patterns that can help design future process improvements.
Data Quality Dashboard is an information management tool that visually tracks, analyzes, and displays key performance indicators metrics It`s highlighting key data points to monitor the health of a business, department, or specific process. They can be customized to meet the specific needs of a business and it shows how much trust you can put in your data.

#dashboard #data-governance #data-quality #master-data-management #data-science

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How to Create a Data Quality Dashboard
Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Sid  Schuppe

Sid Schuppe

1617988080

How To Blend Data in Google Data Studio For Better Data Analysis

Using data to inform decisions is essential to product management, or anything really. And thankfully, we aren’t short of it. Any online application generates an abundance of data and it’s up to us to collect it and then make sense of it.

Google Data Studio helps us understand the meaning behind data, enabling us to build beautiful visualizations and dashboards that transform data into stories. If it wasn’t already, data literacy is as much a fundamental skill as learning to read or write. Or it certainly will be.

Nothing is more powerful than data democracy, where anyone in your organization can regularly make decisions informed with data. As part of enabling this, we need to be able to visualize data in a way that brings it to life and makes it more accessible. I’ve recently been learning how to do this and wanted to share some of the cool ways you can do this in Google Data Studio.

#google-data-studio #blending-data #dashboard #data-visualization #creating-visualizations #how-to-visualize-data #data-analysis #data-visualisation

How to Create a Data Quality Dashboard

The main purpose of a dashboard is to provide a comprehensive snapshot of performance, which means that you should incorporate a large amount of detail without using too many drill-downs. It uses data from the past to identify trends and patterns that can help design future process improvements.
Data Quality Dashboard is an information management tool that visually tracks, analyzes, and displays key performance indicators metrics It`s highlighting key data points to monitor the health of a business, department, or specific process. They can be customized to meet the specific needs of a business and it shows how much trust you can put in your data.

#dashboard #data-governance #data-quality #master-data-management #data-science

Trevor  Russel

Trevor Russel

1618483320

Data Quality Dashboard: How to Create

Your cornerstone in improving data quality

The main purpose of a dashboard is to provide a comprehensive snapshot of performance, which means that you should incorporate a large amount of detail without using too many drill-downs. It uses data from the past to identify trends and patterns that can help design future process improvements.

Data Quality Dashboard is an information management tool that visually tracks, analyzes, and displays key performance indicators metrics It`s highlighting key data points to monitor the health of a business, department, or specific process. They can be customized to meet the specific needs of a business and it shows how much trust you can put in your data.

Improvement of Data Quality is a long-term process and the best outcomes of such initiatives are bulletproof processes that will serve you in the future instead of just on time cleaned data. If You want to be effective you should get your process in shape monitor variations and control it instead of performing periodically data cleansing exercises. Correcting data is time-consuming so try thinking ahead when designing and implementing new processes. Investing time into quality assurance can save you a lot of later work.

#ai & machine learning #dashboard #data governance #data quality #data science #information management tool #master data management

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management