Data

Eight common analytic issues and mistakes in Tableau 8

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Understand them, avoid them and correct them December 23rd, 2013 — 11 AM Central, Noon Eastern, 9 AM Pacific, 5 PM London Click here to register Synopsis In this webinar, Stephen reviews common shortcomings and misunderstandings that can prevent effective use of Tableau. These issues can result in misleading or just plain wrong answers being … Read more

A spin-free explanation of data warehouse versus big data

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“It should be no surprise that many Hadoop (big data) systems sit side by side with data warehouses. These systems serve different purposes and complement one another.”
– Joint quote from CTO of cloudera and General Manager at Teradata

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As you may have heard, big data is all the craze and at the top of the technology hype cycle! In my opinion, it is thoroughly confusing business execs, overwhelming IT teams and being used to market a massive range of new startups, sometimes justifiably. The reality is that big data will most likely NOT replace your data warehouse and the data scientist will most likely NOT replace your business analyst teams.

In a recent white paper,

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Top News – Data, Data Warehousing, Analytics & BI

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November 18th, 2013
Stephen McDaniel
Chief Data Officer Advisor at Freakalytics, LLC

Finding it hard to make time to keep up with the rapidly changing world of data, data warehousing, analytics, data science, business intelligence and visual analytics? We understand! Here’s our curated summary of relevant news that could help with your future data and analytic projects. We also add commentary on the topic, a summary of the article and the link to read the full article.

There are four articles in this update:
     Amazon wades into big data streams with Kinesis
     Top 10 Trends in Text Analytics
     Effective Customer Analytics Call for Data Integration, Culture Shifts
     Your Car Is a Data Platform, What Can It Tell About You?

Missed our last issue of Top News, November 15th? Stories included RapidMiner (free and premium data mining), big data not top CFO priority, the DATA Act passes Senate, SAS replacing PowerPoint and big data sources to consider at your company.
 
 
 
 
 
1_1Amazon wades into big data streams with Kinesis

Amazon adds another layer to data storage and streaming options-Kinesis. Kinesis is all about real-time data collection and aggregation in a hosted cloud-scalable from Megabytes to Terabytes per hour! As such, it is a service that keeps your data for a maximum of 24 hours, by which time you presumably used it or stored it in a data warehouse (like Amazon Redshift), Hadoop system (like Amazon Elastic Map Reduce), NoSQL system (like Amazon Dynamo DB) or file store (like Amazon S3!) Do you notice a trend here?

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Top News – Data, Data Warehousing, Analytics & BI

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November 11th, 2013
Stephen McDaniel
Chief Data Officer Advisor at Freakalytics, LLC

i5_2Finding it hard to make time to keep up with the rapidly changing world of data, data warehousing, analytics, data science, business intelligence and visual analytics?  We understand! Here’s our curated summary of relevant news that could help with your future data and analytic projects. We also add commentary on the topic, a summary of the article and the link to read the full article.

There are seven articles in this update:
     How Big Data Is Changing Science (and Society)
     Big data blues: The dangers of data mining
     2014 INFORMS Conference on the Business of Big Data
     Facebook System for Massive Big Data (Hadoop FS) Offered Free to World
     Paxata Launches Industry’s First Adaptive Data Preparation Platform
     C-Suite and Trust Both Affect Financial Returns on Analytics, Big Data
     Meeting a VAST challenge – Lincoln Laboratory staff create winning visualization
 
 
 
 
 
i6How Big Data Is Changing Science (and Society)

Traditional statistical approaches that long dominated scientific research are being challenged and augmented by new approaches from the fields of big data and data science.

HOW CAN YOU PREDICT something without understanding it? Simple:

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Data scientists & the data warehouse team-building success

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Data-Scientist-Tech-200Whether you are a CIO, data architect, or a data management professional, it is imperative to understand the different approaches, attitudes and needs of the next generation of data warehouse consumers. Traditional data warehouse users include reporting teams, BI teams (who created reports for the rest of the company), statisticians and others. In the past few years, this has been rapidly changing with the new roles of data scientists, the rise of Data Enthusiasts and the burgeoning population of Accidental Analysts. In Part 1 of this series, we focus on successful collaboration between data scientists and data warehouse teams.

Data scientists have been with us for many years. However, the moniker “data scientist” is a recent change. The same role existed (and still exists) with titles such as statisticians, mathematicians, computer scientists or systems analysts; however, having one of these alternate titles doesn’t necessarily imply that one is a data scientist, although a wide range of techniques may be used by both groups.

Traditional training for people now in data science focused on

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My experience with data analytics-Strata Conference

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Please join me at the Strata Conference/Hadoop World in New York next Tuesday, October 29th, as I talk about my years of experience with data analysis alongside the founders and executives of ClearStory Data. ClearStory Data is delivering a new solution that makes it simple for business users to find, combine and interactively analyze data … Read more

A Share the Data client project plan

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Stephen McDaniel, Chief Data Officer AdvisoryHere’s an actual Share the Data™ project.  This is a simplification of a plan developed with a real client, with details changed to remove any identifying information.  While every project is unique, this is not an unusual engagement. Share the Data™ engagements range in length from 3 days to several weeks, depending on project complexity and client resource availability.

Background 

The client is approaching a key turning point in their operational history- going beyond traditional reporting with mainframe technologies to an advanced data warehouse with web-based reporting, dashboards, self-service analytics and even advanced analytics to optimize customer recruitment, operational management and improve the overall customer experience.

All of these areas are a dynamic, iterative undertaking with each area interlinked and interdependent.  It is critical that the management team at the client work to prioritize, fund and support each iterative phase of these projects.  Likewise, it is imperative that each phase of these projects show an acceptable rate of return (or greater!) for the time and money invested in these initiatives.  Better data and analytics are not an end in themselves, but rather a means to a more profitable and competitive company.

Objective

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Free Webinar—Quick & dirty analysis with Tableau
in 13 lucky steps!

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6_Manual_Rearrange_Items_Freakalytics_1_Tableau
July 31st, 2013, Noon Pacific, 3 PM Eastern, 8 PM London
 
 
So much data, so little time!
–Stephen McDaniel
Co-founder of Freakalytics
 
 
Synopsis
Let’s face it: in the daily world of work, you often are asked to provide an answer to a new problem in less than a day. Of course, your boss tends to forget about the other three project deadlines you are currently facing, so you really have only 10 or 20 minutes to squeeze in a quick and dirty analysis.

If this sounds familiar to you, this webinar will walk you through the thirteen flexible steps that can take you from being clueless to looking smart with Tableau in just a few minutes. Hopefully you’ll be able to obtain enough information to come up with ideas for an e-mail update or talking points for the unexpected meeting that is looming large over your day, showing your boss and colleagues that you can deliver great results in time to be useful.

So, if you’re already a user of Tableau, this webinar will guide you in the critical path of many analyses in Tableau. If you are totally new to Tableau, you can see the possibilities of what you can accomplish in a short amount of time, once you get started and practice these techniques.
 
 
A preview of the first few steps

1 What question will you examine?

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Okay, in reality this step might take hours or even days! But let’s assume you have your question, and if it is complex, break it down into several, simpler questions.

2 Grab the closest, readily available dataset

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Bullet charts and simple enhancements to maximize value

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Bullet charts were added to Tableau in version 5.1. They are an original idea designed and advocated for by Stephen Few, at the University of California at Berkeley. The bullet chart is intended to enable easy examination of attainment relative to a target for categorical items.
 
According to Stephen’s original specification, “The bullet graph was developed to replace the meters and gauges that are often used on dashboards. Its linear and no-frills design provides a rich display of data in a small space, which is essential on a dashboard.”

I have shown the standard Tableau bullet chart and a wide array of variants in our public training course. Based on extensive attendee feedback, I will share how just a few minutes spent enriching your bullet charts will yield powerful enhancements for your dashboard audience.
 

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