It’s not an evolution, it’s a revolution.
4 suggestions for better understanding Big Data.
Big Data. We deal with it in our businesses every single day. It drives our processes and nestles its way into our companies’ hearts.
By definition, Big Data is: “high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation” (Gartner IT).
When I see terms such as “enhanced insight, decision making and process automation” I think, “pathways to successful business.”
No matter how much we wrestle with taking Big Data, and finding methods for using it to the benefit of our businesses, no one company has really found the perfect solution. I could try and write a manifesto, solving the complexities of Big Data, backing it up with theorized and/or factual data and we could part ways; I would be the man that has slain the mighty dragon and you would be dazzled and dumbfounded. I’d probably win a Nobel Prize, meet world leaders and sell the solution to Google for billions.
That solution could be found one-day, and that person would be deemed ruler of the digital world. I, sadly, am not that person. I am, however, fascinated and floored by Big Data as I continue to work with it every day. I respect Big Data’s enormity and importance in the business world.
With that being said, I think the best way to work towards conquering the mighty dragon comes within building an understanding of the mighty dragon. Through that understanding, we can formulate better practices of humanizing and communicating its complexities and significance within our own businesses on a daily basis.
So, how can we begin to better understand Big Data?
- Take it from those who know best.
After extensive interviews, Forbes magazine wrote an article commenting on the digital transformation in the business world, especially in regards to the importance of understanding big data for the supply chain. The article was compiled from interviews of some of the world’s most forward thinking supply chain experts.
Bill McDermott of SAP talks of BDA (Big Data Analytics) and Big Data as landscapes not to just better the quality of our businesses, but the quality of the relationships within our businesses and our customers. The stepping-stones for building and improving those relationships starts with mutual understanding for the customer’s wants and needs; Big Data makes this process feasible.
Jean Pascal of Schneider Electric speaks of Big Data, and the analytics received from studying that data as an outlet for differentiation and visibility in brand and value of their supply chain. Her commentary illustrates how taking big data head on can help set your company apart from competitors. (Forbes)
2. Tackling the V’s
Any article you read about Big Data, I assure, you’ll find a section eluding to the V’s; volume, velocity and variety.
Big Data is information, so large, moving so fast, and derived from such a wide range of encompassment that drawing true analytics from it becomes difficult for many businesses. The V’s are a guideline to the understanding the structural makeup of Big Data, and they allude to the most important information you can begin to analyze from that data.
Volume: This concerns information beyond the storage capacity and capabilities of traditional data management. “Intel considers that organizations creating approximately 300 TB of data weekly are in the group of Big Data volume generators”. Volume of Big Data creates a demand for new and dynamic means of data management for business success.
Velocity: This refers to the high speeds that data is created and consumed. According to IBM; “every day 2.5 quintillion bytes of data are created, so much that 90% of the data in the world today has been created in the last two years alone.” This is from an article written in 2014, but those statistics are staggering and continuously growing (Rozados 2014).
Variety: This refers to the amount of different formats one can receive, and achieve, Big Data gathering from.
3. It’s right in front of us; we’re just too close to see it.
After it was all said and done the 2016 Presidential Election was, at the core, one of the largest displays of Big Data’s reach there has been in the past years.
Pundits compared and contrasted, polled and reported, gripped and grappled with predictive Big Data and analytics that pointed to one thing and one thing only; Donald Trump didn’t have a chance.
Donald Trump, now president-elect Donald Trump, did in fact have a chance; as we all now know.
Pundit’s predictions were wrong, but they might not have been totally off base. Quite possibly, the predictions made for the 2016 presidential election analyzed Big Data, and did it thoroughly with in-depth analysis of the traditional indicators of election predictions.
However, this presidential election was the furthest thing from traditional. This year, the real data lied within untraditional and social media Big Data; twitter, memes, underground blogging and fake news (The Economist).
Facebook and Google have taken measures to eradicate their sites of any fake news to the best of their abilities. However, the impact and following this sort of Big Data caused for candidates such as Trump, has served its purpose.
Simply put, Big Data can’t be ignored, real or fake, social or professional. These same lapses in judgment, when gauging which parts of Big Data is important data for your business, can too be costly. Simple solution; all Big Data is important.
4. Jump aboard or you risk sinking
The information compiled in this post demonstrates that Big Data has been a revolution, not an evolution. It’s confines and constructs are still, very much, a mystery. Understanding it’s importance to our businesses and our world happens on a daily basis. New functionalities for shrinking its size, making the data readable and analyzable, and using it’s possibilities for dynamic information to our advantage to minimize risk, time and resources is an on-going bout.
So this begs the question, do you want have what it takes to begin understanding Big Data?
Big Data certainly understands you.
