We’ve all seen this one before: 90% of the world’s data has been generated over last two years. We know this is bringing about change. For many in strategy, it’s raised new questions and left most looking for new ways to answer. According to a KPMG study from earlier this year, 85% of surveyed CFOs and CIOs say they don’t know how to analyse their collected data. The unprecedented availability of information was never meant to overwhelm, but instead to elevate business intelligence, shooting our industry into a new space of zero gravity, vast and boundless where curiosity is the only limit…
We can still get there, we just need a change of trajectory.
Back on earth
Little has changed in addressing ‘big’ data’s biggest problem – fitting the new into the world of the ‘old’, the world of just data. An example; while we can create metrics that represent a consumer’s sentiments for brands and products, we can hardly extend this back beyond Twitter’s tipping point.
Consumers held sentiments long before 2010 and managers had some understanding of how these linked to a business and its key drivers – albeit based on different methods than today. The point is not about picking between current and past data approaches, but to stress the importance of the continuity of knowledge. The most dangerous decision a business can make is to ignore the past.
So how do we build knowledge? First, we must admit that big data is a cultural shift and then use an objective lense to see wider than just the last few years. We first have to ask ourselves, why do we have all of this data to begin with? Where does it come from? How was it collected? When we dig deeper and see big data as the extension of technological change in our daily life, we understand that data isn’t as much new as it is newly revealed.
For example, more than ever we now know how people fidget, browse and choose what to buy on a mass and continuous scale. While the setting isn’t a store in the way we knew it years ago, an online store is a store nonetheless – complete with shopping cart, check out, etc. We have similarly advanced our ability to quantify our communities, our relationships, our jobs, and our hobbies. Casting a cultural lens over these developments allows us to identify common sources of meaning between now and the past, and connect them.
This exercise is as much about continuing knowledge as it is about understanding the limits of big data. While one or two of us have found a way to upload ourselves into the Matrix, most of us live with one foot in and one foot out; still buying in a store as much as we shop online, still meeting friends for coffee while simultaneously sending them YouTube videos on our phones and still doing business in person despite being able to video conference from anywhere, anytime. Having statistics about the size of data today can sometimes mislead us that it is big enough to capture all aspects of our society – it isn’t. Understanding the limitations are imperative for understanding data’s value.
Creating nets out of webs
Big data solutions are usually defined as facilitating more data, arriving faster and in different formats, but good data solutions will not stop there. IBM is the world leader in big data revenue – with over a billion dollars from its big data solutions in 2013. Why? IBM’s Data Magazine published an article earlier this year titled “10 Mistakes Enterprises make in Big Data Projects” and it’s no surprise to us at Truth that the most common pitfall is ignoring the context of data and simply automating data storage. We extend IBM’s successful approach to marketing by:
1. Reflecting on your existing webs of data to create networks that can hold true meaning about your customers, your products, and your business
2. Casting your existing information into a wider understanding of the various aspects of groups, markets and societies, thus identifying how data fits into a bigger picture for your brand and business goals
3. Using the above organisation to identify blind spots, perspectives of your business which have not yet been addressed and collecting information that does this best
Good data-driven strategy has always been about creating a setting where we can find meaningful insights and today, part of this is about bringing together new perspectives to create a complete picture. Complete stories reveal truth.
Cutting through the noise takes a measured approach
Searching through highly dense data streams to find meaning is a completely different challenge. For one, it’s not the lack of observations which is problematic, but instead the vast amount of possible interactions.
Take, for example, the challenge of capturing engagement in the digital space. Is it the time we spend on the content that captures our engagement? Is it the number of clicks? Is it the number of shares? Shares to whom? Are some more meaningful to us than others and therefore represent higher engagement? Do we need to monitor how the mouse moves around the screen? Our eyes? Is it that we absorb content on one device and then react on another, say the phone, meaning that we have to examine all of the above but on another device?
All of this information is available and can quite easily be integrated, however – finding meaning across dense data sources which overlap is a process that requires a measured subtraction. Here, an agency with extensive experiences across various research approaches and sectors can build those initial hypotheses which are fundamental to finding the signal in the noise.
Change by Truth: Connect
Change is a Truth initiative based on our clients need to make good strategic decisions about where and how to focus resources and plan for the future. This initiative consists of six key streams that make use of digital and data driven innovations, and this post focuses on the first of these, ‘Connect’. At the heart of this offer is data driven strategy, which bridges your existing data and integrates it with the potential of vastly accumulating and interesting world. It continuously feeds information through data pipelines, empowering some of Change’s continuous insight offers. But what brings this all together is an on-going process of reflection that seeks order, context, and focus – all as essential in the pursuit of Truth as much as the data itself.