What is a Big Data mindset? (and why should you care)


On his recent Forbes report, Greg Satell lays down 5 steps to get Big Data working in your business. The first four are very well captured, but it was the fifth that really caught my attention: “Adopt a Big Data Mindset“. This is exactly where I want to drill in with this blog post. Bear with me for some paragraphs.

Greg uses the example of Rob McEwan, CEO of Goldcorp, a gold mining company that was struggling. Rob basically challenged the rest of the world to use his proprietary business data in order to help the company find gold. Seems odd? Yes, but only if haven’t embraced the Big Data mindset! In the next few paragraphs I will try to shed light on what this mindset is, what it means for companies and their workers. What about the impact on Society? Well, we all know that the social impact of this mindset has taken other routes, but nevertheless important for companies.

Does this count as Big Data

Seems to me that people will only read blog posts or articles if they start with “Top 5 Tips for …”, “The 10 secrets you should master to …”, etc. I won’t number topics, nor impose you a recipe. Bear with me please as I share my vision which has been a result of my experience working in several countries, developing the Big Data business inside Oracle, giving keynotes throughout EMEA and learning a lot from other people that have embraced this mindset.

Four V’s? That train has passed.

After Gartner has introduced the Big Data definition that included 3 V’s, and some other IT vendors have added other V’s (could be other letter but they insisted in V), some people can now put their minds to rest, go back to their daily lives and have a definition they can throw into dinner parties and other social gatherings when asked about it. The example everyone would use was the impact of social media channels in organisations and how businesses could use that data and make money with it. The data monetization angle was very discussed but very few people were aware of how could that affect their daily lives.

The ones that actually went into doing some more digging found out that new technologies in the data management arena were triggering new use cases in every industry and some actually went a bit too far in imagining what could be achieved with a data driven solution. Examples were being discussed left and right regardless of their actual feasibility. My favourite was the ambulance that came to your rescue when your heart monitor showed a peak value in the context of a meaningful event (not jogging).

Then it came the Snowden effect and the general awe about what was really happening with our data, which lead to less enthusiasm and hopefully more maturity. That maturity unveiled new realistic ways of using data, and even new expressions.

Many people start using expressions like “Having a conversation with data” or “Dialogue with Data”. Let’s use that format!

What counts as Big Data? Disruptive uses of data! Examples: Promotions based on your location, RFID tags placed in newspapers or magazines to get footfall, or even churn models that include mood detection from your voice in a call center support call. I could be here all day, let’s just keep these (real) examples.

What Data should be used? All relevant data. Technology developments have raised the bar in terms of limits both at data capacity and processing power. Open source technology gave the last push making the investment in these solutions a fraction of what it could be using just proprietary technology.The key is to combine both so you don’t end up in Open Source dead-end or in Vendor Lock-In.

How do I know what is relevant? Good question. It’s easy: if you think you have all relevant data, you don’t. A bit like Socrates.

Socrates_Quote_by_Themis711

Embracing a Big Data Mindset

In a recent edition of the Kellog School of Management Magazine, an article of the exact same title was published: “Embracing a Big Data Mindset”. Florian Zettelmeyer, professor of Marketing at this school, brings a ray of light and clarity to the subject which I would like to share: “The big data mindset encompasses four elements: (1) DESIGN MARKETING PROCESSES WITH DATA IN MIND (2) ENGAGE IN RESEARCH AND DEVELOPMENT EVERYWHERE (3) USE PREDICTIVE ANALYTICS (4) CHALLENGE CONVENTIONAL WISDOM“. 

Ladies and gents: this quote comes from a professor of Marketing at a Business School. The cherry on top of the cake comes when Zettelmeyer addresses the volume and technology obfuscation some people live in: “Companies that seek to extract value from their data simply by investing in more computing power will be missing the opportunity“. So what should they be investing in? Seems clear now that a common culture of disruptive uses of data is the mindset you should get yourself in, and seek people that think alike. I’ve seen very talented people teaching themselves Artifitial Intelligence techniques so they could train a model with data, and they are not data scientists nor they aspire to be, but they have embraced a Big Data Mindset where techniques and technology are just a means to an end.

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Andreas Weigend, former Chief Scientist at Amazon, sheds some light into this subject by saying that companies shouldn’t be worried about how to connect with people, but instead understand how do people connect with each other. In his upcoming book co-written with Steve Baker, there will be more clues, but he has touched the most important point in the Big Data Mindset: customer engagement is a two way street. “Give to get” or in other words: will the disruptive uses of data implemented by companies start what they were expecting or will the cause become a consequence? Very interesting, specially taking in consideration that marketing strategists have been aching all these years for an interactive way to connect with their audiences, so they can better adapt their strategies.

 

Big Data Mindset in the Workplace: The Data Strategist

The Stockholm based learning institution “Hyper Island” will kick start in the summer of 2014 a new executive programme called “Digital Data Strategist: Data is changing the world. You will be part of a new breed of creative problem-solvers armed with controversial competence: a cocktail of data-driven business development, design and storytelling in the Digital Data Strategist program“.

Is the Data Strategist a new profession? Yes! So what’s the difference between Data Strategist and a Data Scientist? Here’s a table to help you spot the differences:

Screen Shot 2013-09-19 at 4.55.26 PM

In Data Governance world the role associated with the operational aspects around data were commonly referred as Data Custodians. To quote the Wikipedia definition: “Simply put, Data Stewards are responsible for what is stored in a data field, while Data Custodians are responsible for the technical environment and database structure. Common job titles for data custodians are Database Administrator (DBA), Data Modeler, and ETL Developer“.

On the other hand (still in Data Governance world) the Data Steward is the person responsible for the data itself, its quality, “reuse ability” and integrity. These are the experts in Master Data Management (MDM), Data Quality, and Data Integration. Because it sounds so boring, lots of Data Custodians raised the flag of stewardship taking over these tasks. The result can sometimes be so catastrophic, that people lose confidence in their data.

The Data Strategist oversees all data governance roles and activities and makes sure the organisation is not only maximising its data, but also guaranteeing that external data gets incorporated and used efficiently and according to security, compliance, and other internal and external regulations.

The “maximising its data” piece of the Data Strategist tasks is the area of Data Science. Hence the reason why there are so many overlapping tasks between Data Strategist and the Scientist: the first lays the data strategy that maps the to the business and innovation needs and the former executes that strategy, side by side with the stewards and custodians. Sounds like a boring bunch, but they will be the Big Data rockstars.  A bit like Cooking Chefs. Who would have thought they would be today’s rockstars?

 

Big Data Mindset in the Street: The Data Addicts (Rise of the Gazers)

At this point in time we’ve all became Gazers when in the comes to the consumption of data. Some coffee shop owners complain that people don’t talk to each other anymore, they gaze. In family restaurants it’s common to see all elements from adult to kids, simply gazing. Trains, museums, streets, bus stops, you find them everywhere. Gazers. If gazers would be holding up a book, they would be called readers, if not, they could be just gazing at the bottom of coffee cups. But just because they stare at digital screens of all sizes, we’ll call them Gazers. They are either consuming/producing information, playing some game or communicating (or a combination of the 3 – sometimes all at the same time!).

sergei-yolkin-internet-addiction-2011

Data has become an addiction. People just can’t get enough of more and more information. But this information needs to be easily consumable, inspiring and interconnected. This is were Big Data Mindset in the Workplace clashes with Big Data Mindset in society (street). Information in the workplace is rarely easily consumable nor it seems to be interconnected. The digital age might have turned us all into information addicts, but it surely opens up much more possibilities and doors than never before. Quick example: the other day Google Now just reminded me that I should be leaving the office and start my journey towards a customer meeting otherwise I wouldn’t be there on time according to Google Maps estimations and current traffic situation. This is Google today, the company that once was referred by Doug Cutting as “having the map of Big Data”.

 

Final Thought (not plural)

In the spirit of “Do.Make.Say.Think” it’s clear that without the skilled people able to Do, companies won’t be able to Make solutions to have people Talk (Say) about hence inspiring the whole world to Think.  This is why you should care.

dmst

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