It's time to understand how the various data are going to help support your various marketing objectives.
If you've been around the analytics space long enough, then you'll know that there's always something new that comes along to grab our attention. From the original inception of web analytics itself to video analytics to mobile analytics and social analytics. There's always something that we need to get our heads around. As we know, the next big thing is "big data" and, a bit like social data a couple of years ago, if you stick that in the title of an article these days you're usually bound to capture people's attention! And that's my point, in a way - what's the story behind big data and how should we react to it? Do we need it, can we avoid it, and what should we do about it?
Well, I don't think we can avoid it. That's a bit like trying to push back the tide. Big data is here to stay and is only going to get bigger. Various estimates project the growth in data is going to be exponential and data volumes in a few years' time will dwarf those that we are experiencing today. It isn't going to go away, so how do we deal with it?
Recognition of any problem is half way to solving it.
Those of you who are not deep in the weeds of your data probably need to be increasingly aware of what this means for you and your organization. Data is often a double-edged sword. Understood and used well, it can be a tremendous asset in an organization and can even be a source of competitive advantage. We don't have to look far to see some of the companies that leverage data to reduce risk and improve decision making, and thus business performance. Misunderstood or used badly, it can cause confusion and paralysis. It can consume vast amounts of time as organizations and the people in them wrestle with it and try to tame it. From a marketing perspective, it's time to understand how the various data are going to help support your various marketing objectives.
To do this, you need the right skills.
The term "data scientist" is out there with big data. Whatever you mean by data scientist, the reality is that organizations will increasingly need to think about how they grow, nurture, and retain analytical talent. At most conferences or networking events that I go to these days on both sides of the Atlantic, people are looking for people. It's widely acknowledged that demand for analysts and data scientists is going to outstrip supply. So organizations will need to have strategies in place for managing and developing analytical talent within their companies.
One of the challenges I have with the notion of the data scientist is that the position is portrayed as some kind of analytical superhero. Someone who can write code to extract data from humongous databases, use complex statistical and algorithmic processes to create amazing insights from that data, and then present them to the business in a way that they understand using the very best in data visualization. Undoubtedly these people do exist, but there aren't going to be enough of them to go around. Organizations are going to have to figure out how they get all those things done, and it's more likely than not to be a multi-disciplinary approach. People who manage the data integrity of the business, people who are skilled in the art and science of analytics, and people who can consult with the business to create the insights.
Delivering actionable insights is the point, and that leads me back to the title. One of the things I worry a bit about with big data is that it feels a bit like a solution looking for a problem. There is the danger that organizations become obsessed with gearing up for big data and leave behind the opportunities that already exist on the table. Most organizations already have enough data that they're not leveraging, let alone needing more. What's needed is more focus on what insights are needed to solve the problems that the companies face. As Rufus Pollock, founder and co-director of the Open Knowledge Foundation in the U.K. puts it, "The real opportunity is not big data, but small data. Size in itself doesn't matter - what matters is having the data, of whatever size, that helps us solve a problem or address the question we have."
Big data is an inevitability and we have to get ready for it, but I'm all up for small data and big insights first.
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Neil Mason is SVP, Customer Engagement at iJento. He is responsible for providing iJento clients with the most valuable customer insights and business benefits from iJento's digital and multichannel customer intelligence solutions.
Neil has been at the forefront of marketing analytics for over 25 years. Prior to joining iJento, Neil was Consultancy Director at Foviance, the UK's leading user experience and analytics consultancy, heading up the user experience design, research, and digital analytics practices. For the last 12 years Neil has worked predominantly in digital channels both as a marketer and as a consultant, combining a strong blend of commercial and technical understanding in the application of consumer insight to help major brands improve digital marketing performance. During this time he also served as a Director of the Web Analytics Association (DAA) for two years and currently serves as a Director Emeritus of the DAA. Neil is also a frequent speaker at conferences and events.
Neil's expertise ranges from advanced analytical techniques such as segmentation, predictive analytics, and modelling through to quantitative and qualitative customer research. Neil has a BA in Engineering from Cambridge University and an MBA and a postgraduate diploma in business and economic forecasting.
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This Magic Quadrant examines leading digital commerce platforms that enable organizations to build digital commerce sites. These commerce platforms facilitate purchasing transactions over the Web, and support the creation and continuing development of an online relationship with a consumer.
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