Zakary  Goyette

Zakary Goyette


Top 7 Tableau Alternatives for Visualizing and Analyzing Data

Since its inception in 2003, Tableau has been the favourite software amongst business leaders to drive business intelligence. It is a visual analytics platform transforming the way businesses use data to solve problems. It allows the user to explore, manage data, discover and share insights that can alter the business outcomes.

However, over the past few years, the deployment of Tableau amongst businesses has significantly reduced. Business intelligence guides an organization to make informed decisions by transforming data into meaningful insights through leveraging software and services. Many companies are now coming up with software that has the potential to deliver a positive outcome in business intelligence and can be utilized as an alteration to Tableau.

In this article, we will navigate through the top business intelligence software that will drive business intelligence in the future.

1. Domo

Domo is a powerful business intelligence software with a wide dataset and connector support. Its unique collaboration capabilities make it one of the widely used software to assist the business in finding effective solutions in the competitive business world. With Domo, the user can deliver high-quality data insights in a few minutes or seconds which was not possible earlier. Having more than 500 data connections to update in real-time and extensibility through SKD, the connected data is analyzed with the help of visual editor without any need for special coding knowledge. Data visualization in Domo can be done through the mobile application, and without any need for a broader screen.

2. GoodData

GoodDataoffers two products to its users: End-to-end data platform and embedded analytics. With the help of embedded analytics, the company can deliver a wide range of customizations, create product tiers to deliver custom analytics to different customer segments, and frequently release new analytical features and distribute changes across customers. Its centrally managed analytics distribution includes betas, early access launches, and more-to each product audience. Due to the GoodData’s fault-tolerant data pipeline, the software is capable to ingest terabytes of data for thousands of users at regular intervals. With its comprehensive monitoring and alert integration, the software reduces the risk of data mishandling. This is particularly incorporated in smaller corporations, who are planning to improve the data analytics quality, but are unable to commit full data management system

#business intelligence #latest news #uncategorized #data-science

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Top 7 Tableau Alternatives for Visualizing and Analyzing Data

Top Tableau Data Analytics Companies Reviews 2020 –

An extensively researched list of best Tableau data analytics service providers with ratings & reviews to help find the best Tableau analytics companies around the world.

#list of best tableau data analytics companies #top tableau data analytics service providers #top tableau data analytics companies #expert big data developers #tableau #big data

 iOS App Dev

iOS App Dev


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


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

Gerhard  Brink

Gerhard Brink


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.


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

Tia  Gottlieb

Tia Gottlieb


How to Dynamically Swap Metrics and Change Data Points in Tableau

“ The key is, no matter what story you tell, make the Buyer, the Hero” — Chris Brogan

One of the most attractive characteristics of Tableau is its intuitive and user-friendly features that allow users to gain control of their reports. One such powerful feature is that of Parameters.

Parameters allow the creator or user to prepare for varied scenarios, allowing users to pass in values to modify the visualization in question. Here the user or audience is in the position to control the input to see the parameter effect on the visualization.

Tableau defines parameters as_ ‘a global placeholder value such as a number, date, or string that can replace a constant value in a calculation, filter, or reference line’_

Outlined below are two different ways in which you can use parameters in your visualizations and reports to make them more user friendly and efficient.

Dynamically Swapping Measures or Metrics:

Many times, if a dashboard or a report caters to multiple audiences/users, you would notice, each of them individually is concerned with different metrics and different data points.

For such cases, swapping of measures or metrics plays an intelligent yet elegant role of showcasing multiple metrics in a single report or dashboard, furthering efficient use of space and analysis of several measures at a glance.

Follow the steps below to create a swapping measures feature in your report using a parameter -

In the example below I wish to switch the **_Sales _**and the Profit metrics or measures over time.

Additionally, I have used the Sample Superstore dataset in all of the examples. (

Step 1 — Create a viz/chart for first measure/metric

Open up a new worksheet and create a visualization or chart for your first measure. In the image below, I have created a time-series, or a line chart based on sales, indicating a trend of sales over time.

![Image for post](*ogA0JVG0TGNbN0GPkcStkQ.png

Step 2 — Create a Parameter

Click on the dropdown alongside the Dimensions pane and select the Create Parameter option.

Image for post

Provide a meaningful name, change the Data Type to Integer and change the Allowable values to List

Under the **List of Values **pane, add the values in a linear fashion starting with 1 and under the Display As column, provide a meaningful, metric indicative name, such as Sales over Time and Profit over Time.

You can add multiple such measures or metrics based on your preference and use case, since I want to switch between sales and profit, I have added just the two.

Image for post

Step 3 — Create a Calculated Field

Click on the dropdown alongside the Dimensions pane and select Create a Calculated Field option.

#data-visualization #data #tableau #data-analysis #data-science #data analysisa