What’s Big Data All About And Why Should I Care About It..?
The term ‘Big Data’ has received a lot of press lately and I feel it’s being significantly misunderstood. Does ‘Big Data’ mean ‘huge amounts’ of data…? This is where most peoples’ understanding of ‘Big Data’ goes off the tracks. If the main idea around it was just that there’s a ton of data, one would have a reasonable argument that large Data Warehouses already cover that concept, which would make ‘Big Data’ just another one of those buzzwords of the week.
The reality is, ‘Big Data’ is not an adjective, it’s a concept, it’s a method for running a business, and it’s a commitment that’s bought into at the highest levels of an organization and pushed downward. This is vastly different from companies with large-scale Data Warehouses that measure the success of their Data Warehouse by looking at user adoption rates. The teams responsible for Data Warehouses are consistently jockeying for end users’ attention to prove their value to them and the ‘data they supply’s value to the organization.
Companies who are on the ‘Big Data’ train and have implemented its philosophies are years, if not decades beyond customers with traditional large-scale Data Warehouses in that they don’t just see data as a strategic asset. They’ve made a commitment and placed a big bet that data WILL drive a radical transformation in their product development, research, innovation, and marketing processes. Now you may be asking yourself, “We’ve integrated our product, sales, and marketing data and even have near real-time access to that information and there’s a TON of data in it. Isn’t that ‘Big Data‘?” Let’s take a look at a few real world examples of companies that have doubled down on ‘Big Data‘ and show (1) what the data is (2) how that data is being used:
Amazon.com was an early adopter of the ‘Big Data‘ concept and early on, they captured data at a significantly low level of detail so they could test the following factors and the impact those factors had on sales (quantities and revenue amounts):
- Button location and placement on the page had a huge impact on Sales and customer button clicks.. Sure, today the buttons are fairly consistent, but imagine the data mining that had to be done to test which button location drove the most sales, highest revenue averages per sale, how active a user is with the different products shown on a page and where they’re located.
- The sequence of how content is displayed has a huge impact on cross-selling. Click on this link and see for yourself. Let’s say I decide to buy this Smartpen, and I’m also presented with accessories that I didn’t even know were available. I have a button right there to buy the Smartpen with accessories. I might have only come for the Smartpen and planned to buy the paper and ink refills elsewhere, or I may not have even known there were accessories for it, but it’s too easy not to buy it all together, especially when I get a discount when I bundle them. And, oh by the way, I actually did purchase this from Amazon with the accessories so they increased their overall revenue of this purchase an additional 50% by presenting me with these options. This is a great real world example of how their ‘Big Data‘ commitment is bringing in a lot more revenue by correlating products with like products that have sold together in the past.
- Using that same link above, take a look at the section “Customers Who Viewed This Item Also Bought” which is located towards the bottom of the page. You are shown 25 scrolling pages worth of products that other customers have bought when they purchased this Smartpen. If the above accessory section didn’t suck you into buying something else, this section most definitely will. It’s the peer pressure section that shows you what “everyone else is buying” so you better too if you want to ‘Be Like Mike’.
- It’s important to remain cognizant of the fact that data drove every scenario above. Not just line of business or sales data, but detailed metadata that’s captured during each button click on their site; and by button click that’s not only the purchase button, but rather an event history of how you scrolled the page, clicked through different products, the time you spent viewing each one, and the ability to completely simulate your browsing events, step by step. The impressive part about this isn’t the fact that they’re capturing such a massive amount of data. It’s the investment they’ve made in being able to do anything meaningful with that much data let alone incorporate the results back into their business processes and generate new revenue streams from it.
Social Media isn’t only for uploading pictures or to let everyone know you just woke up for the day. Companies such as Ford, Southwest Airlines, Pepsi, and others mine through huge amounts of worldwide data in real-time to get immediate impacts and reactions to their marketing campaigns. This insight helps evolve existing campaigns, foster new ones, and gives these companies an accurate and most important an immediate understanding of their consumers’ opinions regarding their bands.
- Keeping with the ‘Big Data‘ theme, let’s be sure to acknowledge how this fits into overall ‘Big Data‘. Like Amazon, these companies have so tightly integrated data into their business processes that they’re able to not only get a pulse of their customer satisfaction to a product or program, they’re able to immediately take action on that feedback.
- Imagine launching a ground breaking new product or program to your customer base only to find it’s not receiving the positive reaction you thought it would and has the potential to create devastating financial and/or brand impacts if nothing is done.
- Imagine the feeling in your stomach as you review these poor program results that were eventually compiled 6 months after the program launched, and after 50% or more of your customer base has gone to a competitor. If you still have a job, how do you reassure investors and customers that your business plan is viable? This is an unfortunate, yet extremely common scenario that companies will eventually get sick of being blindsided and decide to invest in their ability to be proactive to the reactive. Many companies avoid taking risks such as introducing possible ground breaking changes to their product line out of fear of what could happen.
- Now imagine you’re one of the above listed companies and you’ve built a closed loop process that ties in real-time social media results with program management. You have the extremely unique yet priceless amenity of testing new programs with immediate visibility into how they’re performing and the consumer outlook of the program so you can react accordingly.
- If the program is negatively received by your consumers or initial results are well below expectations, the program can be immediately suspended, avoiding any further negative impacts to financials and/or your brand.
- However, proactive insight isn’t just for dealing with negative scenarios. What if your new program is a smashing success? Non ‘Big Data‘ companies would say, “Great, we nailed that one” a few months into the program’s launch when the data finally rolls in. ‘Big Data‘ companies will have the early results, see it’s a smashing success, triple their production output and run their manufacturing plants 18 hours a day instead of 12 hours so they can capitalize on the positive market outlook.
The reality is, ‘Big Data‘ enables companies to experiment safely yet aggressively. Consumers have more options than ever when buying products. The days of people buying your product simply because it’s a certain brand are long gone. Consumers are smart and will sniff out the best value, even if it’s an off brand that can provide similar value in return. Along the same lines, your competitors are doing everything they can to steal your customers, one purchase at a time. The integration of Social Media with ‘Big Data‘ adds context to your data that’s just not captured in line of business data.
Implementing ‘Big Data’ as essential components of management decision making will require a very firm commitment to the following:
New Capabilities (Companies need to master the technologies used to capture, integrate, and analyze the valuable information they can already access today, internally. These capabilities may be software related, hardware related, and even analytical related)
- Organizational (Most companies are nowhere near capable of accessing all their data which may require organizational changes to better align data ownership/stewardship. Changes could also be required when external data is brought into the mix)
- Culture Changes (The ‘Big Data‘ commitment must start at the top with the message then driven downward. Experimenting in business today often goes against the grain so it’s critical that the different management tiers in the organization view senior leadership as role models so they can show how experimentation can be a positive experience when combined with the overall value achieved with ‘Big Data‘ and real-time visibility into the experiment results)
- Right Talent and Processes (Having the right talent can make or break any successful initiative, and ‘Big Data’ is not immune to that. This doesn’t only refer to technical capabilities in your talent, but also grasps the approach of ‘Big Data’, can help drive it throughout the organization, and can design experiments that allow ‘Big Data’ to provide business value)
‘Big Data‘ is a topic I’ll be writing many posts about going forward so stay connected to my blog for future ‘Big Data‘ updates.
Thanks for reading,
Jason Volpe



Transactions

Check your 
















