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Big Data at 10: Did Bigger Mean Better?

Dataversity

If this time 10 years ago you were working in data and analytics, something was about to happen that would go on to dominate a large part of your professional life. Iā€™m talking about the emergence of ā€œbig data.ā€ The post Big Data at 10: Did Bigger Mean Better?

Big Data 246
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From Technical Writer to Lead Business Analyst and a $20K Salary Bump: Amelia McHenry

Bridging the Gap

Throughout the program, she did whatever she could to find projects and ultimately completed the program. Amelia McHenry: I had been a technical writer for about 15 plus years, had gone, in the last 10 years of that, gone through a bunch of mergers and acquisitions of companies and splits and all kinds of weird funky business stuff.

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Transformation, Business Architecture, and Scaling

Leading Agile

Okay, well, thatā€™s a big question, man, who is Mike Cottmeyer? Weā€™ve gotten pretty big over the last couple years, weā€™re about 160, almost 270 people, we focus almost exclusively on Agile Transformation. I play guitar and I did jiu-jitsu and Iā€™ve been kind of on a bit of a fitness journey for the last couple of years as well.

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Your Data Wonā€™t Speak Unless You Ask It The Right Data Analysis Questions

Data Pine

In our cutthroat digital age, the importance of setting the right data analysis questions can define the overall success of a business. That being said, it seems like weā€™re in the midst of a data analysis crisis. Your Chance: Want to perform advanced data analysis with a few clicks? Data Is Only As Good As The Questions You Ask.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

Data Pine

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes.

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A Guide To The Methods, Benefits & Problems of The Interpretation of Data

Data Pine

1) What Is Data Interpretation? 2) How To Interpret Data? 3) Why Data Interpretation Is Important? 4) Data Analysis & Interpretation Problems. 5) Data Interpretation Techniques & Methods. 6) The Use of Dashboards For Data Interpretation. Business dashboards are the digital age tools for big data.

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Navigating the Interplay between Project Management and Business Analysis for Project Success

Bridging the Gap

And then a variety of different data modeling techniques that articulate how information is stored and flows through the software systems. That means a lot of the skills are centered around creating a nice place to work and where you can get your team to pull together, and everyone’s empowered to do their best work.