Two Big Data Observations

I may be suffering from selective bias but two articles came across my email recently and thought they were valid in my own exploration of Big Data. I came away from these articles with two main conclusions:

1. A good Business Intelligence strategy could put you in an excellent position. You must have the right technology, people and purpose to find value in your company's Big Data exploration. A team's Data Warehouse experience will enhance how they are able to leverage new data technologies. 
2. The human element involved in business purpose, interpretation, and molding good data tend to be sacrificed in the Big Data Marketing hoopla. The old adage I learned in my first programming class, “garbage in, garbage out,” still applies. Big Data is not a magic solution that just solves problems on its own.

Here are the articles and my own observations:

What Makes Big Data Projects Succeed 

1. Technology
   “I found that companies can program big data applications with existing languages like SQL. I also learned that companies with existing data warehouse environments tend to create value faster with big data projects than those without them.”
2. People
   “The large companies I interviewed about big data projects said they were not hiring Ph.D. level data scientists on a large scale. Instead they were forming teams of people with quantitative, computational, or business expertise backgrounds.”
3. Good Change Management
4. A clear business objective
   “it will be an unproductive fishing expedition unless a company has a business problem in mind.”
5. Good project management

Google Flu Trends’ Failure Shows Good Data > Big Data
“The core challenge is that most big data that have received popular attention are not the output of instruments designed to produce valid and reliable data amenable for scientific analysis.”

“more data in itself does not lead to better analysis, as amply demonstrated with Flu Trends. Large datasets don’t guarantee valid datasets. That’s a bad assumption, but one that’s used all the time to justify the use of and results from big data projects.”

"Progress will come when the companies involved in generating and crunching OCCAM datasets restrain themselves from overstating their capabilities without properly measuring their results."