The Five I’s of Big Data: Where True Value is Found
As described in an earlier post, “innovative, sustainable Big Data Business Models are as pervasive and sought after as they are elusive (i.e. “data is the new oil”)”. Part of the reason for this is that many companies have a hard time seeing big data as only one ingredient – out of perhaps many other ingredients – of an otherwise more nuanced value proposition.
A good big data value proposition is based on the tenet that the data a company collects while perhaps selling some other good and/or service can be turned into something of value on its own. One of the simplest (aforementioned) examples of this is Amazon’s seemingly magical ability to offer you additional products based on its knowledge of previous sales and customer reviews of the products you’re looking at and/or purchasing. For Amazon the value of big data in this case is obvious: better recommendations equals more product sales. Amazon’s value proposition to its customers (i.e. us) in this case is that we will always find exactly what we need as well as things we “forgot” we needed, but want to buy now. Pretty simple.
So, what’s behind this and every other successful big data business model and value proposition? It’s what I like to call the “Five I’s of Big Data”: Information, Insights, Inspiration, Integration, and Innovation.
It goes without saying there is vast (x1,000) amount of data available to any organization that wants to create some new value propositions from it. However, data alone is meaningless at best. To make data useful, it must be turned into information. So what’s the difference between data and information? Data is often raw, unorganized facts. Data is often made up of too many or too few actual data points to be able to make any sense out of it. For instance, the results of a single question on a single survey without any other context are data. On the other hand, information is structured, organized and has context. The average results of that same question on the same survey with a link to the context of the survey are information. To create information from data, you need tools and people skilled in creating information from data.
Insights go hand in hand with information. Insights are the answers to questions you asked the data, which resulted in information, that then led to some correlations or other meaningful results. If your original query of the survey in the example above was, “what percentage of our customers likely use X software?” and you received something like, “36% of the respondents say they use X software”, what you’ve got is a little bit information and little bit insight. However, if you went further and made some correlations between other survey information, which gave you a better overall understanding about “which kind of customer from where used X software when as well as how many customers like these answered in a similar way” you’ve got insights.
What do information and insights bring you on their own? Nothing. Information and insights without a story, hook, or call to action are nothing but cleaned up data with some charts and graphs. To create something that has real value for your customers, whether they are internal or external customers, you must inspire them with something that is timely, meaningful, and causes them to act. My favorite example of this – one that I use often – is Mint’s use of its customers personal credit card data which it uses to present credit card offers (from advertising partners) that might save customers money, time, etc. By showing me credit card offers which may save me money or gain me some travel points, Mint is inspiring me to follow a path that it has built for me outside of what I probably went to Mint for in the first place.
Just as you want to use the information and insights created from data to inspire your customers to take some action presented to them, you must also consider where that inspiration and action take place. In the case of Mint, it chooses to show me some “ways to save”, which has been integrated into the rest of the service in a way that makes it seem useful, meaningful, and timely, without being obtuse or in my face. Mint has chosen in this case to take the approach and voice of a helpful, consultative financial partner, rather than an advertiser looking to make money. In fact, as you can see from the image below, it’s more than just credit cards: Mint is speaking to me about any number of “savings” I might find in various areas of fiscal life. And, Mint has integrated this into its site/service in an authoritative and instructive (non-cheesy) way.
Alas, what would a big data post be if we didn’t at least touch on innovation? Innovation, at the end of the day, is what most companies are looking for when they put out the call to use big data for something. However, in this case innovation is being used to define not simply the innovative use of big data, but the use of big data as part of an innovative (and sustainable) business model. Sure, you can create information and insights that inspire customers to do the thing you want them to do. However, what’s the return? And, how do you sustain the key activities needed to create the inspirational value proposition and so forth? You do this by first designing an innovative business model built to sustain your big data value proposition, and hopefully grow your company. In the case of Mint, its revenue streams come from advertising, which it then uses to continually enhance its big data value proposition, which in turn is integrated into its service.
At the end of the day, creating big data value propositions that resonate with your customers takes quite a bit of forethought, planning, and work. However, I think most successful big data practitioners would agree that the planning and work you put into designing and executing your own big data value proposition (and long term strategy) is totally worth it. Like, renewable energy, big data should be seen as a raw resource, that with the right technology and processes in place, can be turned into a real money maker and/or change agent. Just remember, there’s more to big data value propositions than just data. To be successful, you need to turn that data into information and insights used to inspire customers to take action through a set of integrated services you’ve designed which is all underpinned by an innovative (and sustainable) business model.
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