10 Big Data Implementation Best Practices
This is a great article and list of topics to remember when working on big data projects. Here is the list.
- Gather business requirements before gathering data
- Implementing big data is a business decision not IT
- Use Agile and Iterative Approach to Implementation
- Evaluate data requirements
- Ease skills shortage with standards and governance
- Optimize knowledge transfer with a center of excellence
- Embrace and plan your sandbox for prototype and performance
- Align with the cloud operating model
- Associate big data with enterprise data
- Embed analytics and decision-making using intelligence into operational workflow/routine
See the original article, 10 Big Data Implementation Best Practices, for details.
The inaugural issue of Big Data was published a few weeks ago. The journal is excellent. The articles are relevant, readable, and free. In the first issue, most of the articles were not super technical (meaning there was not a lot of equations or algorithms). I would like to highlight just 5 of the articles (feel free to read the others as well).
- Making Sense of Big Data – A nice brief discussion of the term big data and some goals for the journal.
- Big Data For Development - This is an introduction to United Nations Global Pulse, an initiative to use data to better understand human well-being.
- Broad Data: Exploring the Emerging Web of Data – This article is all about dealing with the explosion of open data becoming available.
- Data Science and Its Relationship to Big Data and Data-Driven Decision Making – The title is pretty self-explanatory. The article points out 7 fundamental concepts of data science.
- Educating the Next Generation of Data Scientists – This is a roundtable discussion all about data science and data science education.
The very first issue of Big Data Journal is out. All the articles are freely available for download. The titles and authors of the articles look quite good. I will probably be posting more as I read through some of the articles.
New Street Communications is looking for authors. According to the call for proposals:
…especially interested to hear from professionals in the fields of IT, Data Science, Big Data and Cloud Computing.
If you have ever thought about writing a data science book, now might be a good time.
50 Top Open Source Tools for Big Data – Datamation.
The list is about 6 months old, but it still covers all the ones I would have listed and quite a few more.
I recently read, Big Data Education: 3 Steps Universities must take
Here are the 3 steps listed:
- Data Science cannot be an undergraduate degree
- A graduate degree should contain math, stats and computer science
Step 2 seems obvious. Math, stats, and computer science are some of the key areas for data science. I would add communication and presentation skills to the list because people with just math, stats, and CS skills are not known to be naturally good communicators. I agree with step 3. More research needs to be done, but most of the research will need to be interdisiplinary. Universities need to put more effort into interdisiplinary research.
Step 1 confused me a bit. The argument was data science has too many necessary skills and an applied focus area. Of course a person cannot learn everything about data science in an undergraduate degree. Earning a computer science degree does not mean you will know everything about computer science. It just means you know the fundamentals about algorithms, architecture, and operating systems. You know enough about computer science to understand the field and learn more as you go. I think 4 years should be enough time to do the same for data science.
What are your thoughts?
To start, here is a nice quote from the video. The quote is from Eric Schmidt of Google.
From the dawn of civilization until 2003, humankind generated 5 exabytes of data.
Now we produce 5 exabytes of data every two days.
…and the pace is accelerating
Rick Smolan provides a good talk. He is behind The Human Face of Big Data project. I don’t have a copy of the book, but it looks really intriguing. The talk briefly explains what the book/project is all about.