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Big Data Analytics has evolved considerably over the last decade. Now, organizations need practical solutions for their problems, instead of relying on past to reach their goals. That’s why Big Data needs talented individuals! read to know more.

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Thanks for coming to Dallas Data Brewery monthly meetup and sharing your stories and views. We had a great talk about data quality or "fitness of data for intended purpose" if you like. There is much more to talk about in more details within this topic and we will definitely dive deeper during our future meetups. Here are the slides for short topic introducion...

#data   #datawarehouse   #dataquality   #dallas  

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Here are the introductory slides for Dallas Data Brewery meetups... What is it about? What are the goals?

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Dallas Data Brewery monthly meetup is approaching – June 10th.

What would you like to talk or hear about? Here are some suggested topics:

Topic 1: Perception of Data Quality – What is "quality data"? How business side sees it? How data engineers see it? Any stories of misunderstandings?

Topic 2: Troubles of Getting Data – What are the issues of getting the data to begin with? Existence, structure, security, laws, automation, digitization, scraping, quality, volume, speed ... How do you deal with it? What tricks and tools to use and why?

Topic 3: ??? – you choose

Bring your stories, questions and worries. You are welcome to show few slides if you have to share.

#dallas   #data   #datawarehouse   #businessintelligence  

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Thank you all for coming and launching the Dallas Data Brewery Meetup. The balance of backgrounds ranging from technical to business was great. I hope that the Meetup will give you opportunity to share knowledge, give answers to your questions or connect with people that can provide additional insights.

Topics that we touched yesterday that might be of interest for a further discussions:

Business oriented

* Understanding of requirements and end-user expectations.
* Data modeling – why and how to do it?
* Understanding datamarts – from purpose, through design to their use
* Report utilization and "over-reporting"
* Data governance and master data management - "why?"
* Missing documentation and knowledge of existing data
* Mistaken perception of data quality
* Misinterpretation of data
* Visualization

Technology oriented

* Data governance and master data management - "how?"
* Ad-hoc queries
* Surrogate keys
* Natural language processing for data analysis
* Data preservation
* Numerical vs. categorical data
* Analytics on transactional processing (in real time)

#dallas   #data   #datawarehouse   #businessintelligence  

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Data Processing Pipeline – Map of stages and processes within data processing pipeline. From discovery, through cleansing, transformations, conformation to data driven decisions. PDF download (A3 format). Comments suggestions are welcome.
#data   #etl   #datascience   #datawarehouse  
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