Big Data is (still) a Big Deal
By Steve Holder, National Lead Analytics at SAS Canada
Big Data is a big deal. We’ve named it, so it’s gone mainstream. In the past, management and storage of data and its analysis was a black hole of arcane terminology and technology. It was exclusively the realm of technology professionals and statisticians. Now, Big Data is on our minds and is even in the daily news. It has leeched into our everyday vocabulary. What’s changed?
It wasn’t one change, but a variety of trends and innovations that led to the Big Data phenomenon: the democratization of technology, increased data volumes and access to technology at a lower cost; the impact of smart phones and tablets, with children interacting with and the technology from a very young age, and its ubiquity among adults and enterprise users; the sheer volume of data created and captured as we go about our everyday lives.
This democratized technology--mobile phones, social media, telemetry and more-- is generating billions, if not trillions, of data points every day. Hidden in the data are signals that, when captured, add value and make life better. The challenge in the past has been to store and analyze this data, quickly and cost-effectively. Rapid innovation and lower-cost technology have lowered this hurdle.
When we search and navigate the web, we access and are instantly presented with relevant information. This is Big Data. Behind the scenes, companies like Google, LinkedIn and Facebook are taking all these data points, storing them, and using them to empower, predict, provide recommendations, and interact with us in a contextually relevant way. As consumers, our lives are data-driven. As employees, that often is not the case.
Big Data isn’t making things worse but the divide between data driven decision making in our personal lives and corporate lives is widening. We have all the data organized the way we want at home, yet Monday to Friday it seems we struggle to make decisions on actionable information. To make matters worse most individuals in organizations focus on hindsight, what happened insights as opposed to leveraging data to make forward looking and optimized decisions. It’s not a bad news story though Big Data is on the minds of business leaders and organizations of all sizes are taking the first steps to getting on the Big Data bus.
In 2011, the McKinsey Global Institute (MGI) published Big Data: The Next Frontier for Innovation, Competition and Productivity. The study discussed the financial impact of Big Data on businesses. Four years later, in 2015, McKinsey found that “achieving the level of impact MGI foresaw has proved difficult.” It’s clear the opportunity is real. But, to date, organizations have struggled to become data-driven. Why is now the time for business leaders and marketers to unlock value from Big Data?
Over the years numerous business journals and white papers have been written on the state of analytics in business. What’s interesting is many of these show that the vast majority of business leaders still make decisions on gut feel as opposed to trusted information generated by their systems and people. So despite technology and a renewed focus on analytics we’re still operation blind. In recent years we’ve made progress on this but the race to an analytically driven culture has been more of a marathon pace as opposed to a sprint. In fact business intelligence and enterprise data warehouse technologies have existed since the 1990’s, promising to create the data-driven organization. Yet we still struggle to manage data, and, more importantly, turn that data into meaningful, actionable, information, It’s not a technology problem; it’s not a people problem; it’s not a process problem. It’s all of the above.
Market-leading enterprises are using innovations in Big Data and analytics to extract that information. Hadoop, data science and data visualization are powerful tools and concepts that allow enterprises of all sizes to become data-driven.
It’s not a bad news story though Big Data is on the minds of business leaders and organizations of all sizes are taking the first steps to getting on the Big Data bus.
In 2015, we see:
- Cost-effective storage and processing of huge volumes of data with technologies like Cloud, Hadoop and MapReduce
- User-friendly interfaces to search, navigate and analyze data
- In-memory computing, providing real-time access to the data to answer any question
- And, most importantly, powerful analytic minds that make data come alive
In 2015, agile access to vast amounts of data is no longer a dream, it’s a reality. How can your organization take advantage of it?
Find a champion. The world of Big Data changes quickly, and organizations must have executive alignment to ensure success and focus. At times, the landscape can be complex, technical and filled with jargon. This has led to the creation of the role of Chief Data Office (CDO). The CDO is usually a senior executive with a foot in both technology and business. A CDO helps educate the business about Big Data, while keeping IT aligned to business goals. A CDO creates and supports the vision and makes sure analytics deliver insights that are meaningful.
Start small. The very definition of agility is the ability to move quickly and easily. The Big Bang approach of the past is slow, cumbersome and costly. As you begin your journey of building a data-driven culture, it’s best to start with high-value, well-defined, quick wins that the business can rally behind. These small victories open the flood gates. Starting small allows organization to build the skills, the infrastructure and the process rigor required to move people to an analytic state of mind.
Fail Early. Failure can be a bad thing. In the past the cost to wrangle technology and business requirements into a meaningful solution caused organizations to shy away from failure. With this new Big Data world we should adopt an iterative approach to projects which allows an organization to weed out early approaches that don’t work. In other words save precise time and resources by failing early and often.
Define success – Once you have your champion and your charter, it’s key to determine what success is and align that to a business goal. The lower cost of entry to Big Data analytics and an agile approach to development leads to quick wins. But teams have to ask themselves so what and who cares? Defining success helps the quick wins become the project that shows value as opposed to a science experiment. Over the years I’ve seen situations where analytics has been implemented and the teams declare success. Yet it never went anywhere beyond a proof point. In nearly all the cases these proof of concepts lacked a goal aligned to a problem. In 100% of the cases these proofs had really smart people who did cool things only to find the business leaders responsible found what they did novel at best or worse a distraction from more meaningful projects. This may sound obvious to most people. However as organizations embark on a Big Data journey they need to double down on what defines success, because the use case and value is there but you need to know where you’re going.
Take care of your brains. Data scientists deliver on Big Data and analytics. They’re scarce. They’re in demand. They’re creative. Recognize their value; give them the tools to succeed. Don’t saddle them with mundane tasks like data preparation or be protective of their process and insights. The new world makes them partners with the business through deep analytics. Organizations that focus on attracting and developing data scientists will create a data-driven culture.
While Big Data is important to every line of business and industry, marketing is a key passenger on the Big Data bus. In some ways, marketing was doing Big Data before Big Data was cool. Concepts like segmentation, offer management and propensity to turn over have always required data and analytics. Big Data doesn’t change the marketer’s role. It makes it evolve. It allows marketers to use ALL data, not a just sampling, to do what they already know how to do while giving them the ability to do things they only dreamed about. Take Vail Resorts. By embedding radio frequency (RF) technology in every ski lift pass, the company captures, stores and analyzes real-time data, starting with the very first lift ride. The data is accessible in real time at the company’s digital customer experience and loyalty platform, EpicMix.com, and through the resort’s mobile applications, and used to enhance customer experience in real-time.
Throughout the day, EpicMix tracks what guests are doing – such as how many vertical feet they’ve skied – and it delivers statistics guests can use for “bragging rights”. They have delivered on Big Data for both the consumer by giving them bragging rights and for their marketing group by being able to see how the consumer interacts with the mountain and the associated services. Ultimately they are able to use the data generated by guests to deliver better services and bottom line to the business.
It’s a win win. In its first season alone, nearly 100,000 guests activated their EpicMix accounts. Forty percent downloaded the mobile apps and nearly 6 million digital ski pins were given out. Plus, 45 percent of the users shared their accomplishments on Facebook and Twitter—resulting in more than 35 million social impressions.
Canada’s very own Alberta Tourism, Parks and Recreation is another great example. Responsible for 250 campgrounds and 14,000 campsites that receive more than 1.8 million overnight visitors every year. They implemented analytics to tease insights from its customer surveys in an effort to improve satisfaction ratings of visitors to the park.
In the past, they would spend weeks at the end of the season inputting the text data, manually assigning a code to each comment. Now Alberta Parks is using SAS® Text Miner, which applies information retrieval and data mining techniques across a variety of feedback channels – phone calls, email, surveys and social media – with both structured and unstructured data. Instead of waiting for an end-of-season slide show, Alberta Parks’ regional and district management get weekly feedback based on those text comments.
That feedback leads to midstream operational changes with an immediate impact on the customer experience. Alberta Parks can respond throughout the season to customer feedback about everything from the cost of firewood to the timing of caretaking operations. And the results from the analytics also assist park managers in prioritizing their capital spending.
These are just two examples of how technology and data have delivered new and unique outcomes. With Analytics and Big Data we need to reevaluate how we measure success and in some cases what we measure. The possibilities are endless and the impact of Big Data is real. It’s here to stay, and it’s a big deal. Don’t get left behind.
As National Lead Analytics for SAS Canada Steve is responsible for driving the software go to market plan for SAS Canada. Providing customers with thought leadership around Analytics, BI and Big Data; defining creative opportunities to apply technology to drive tangible business benefits.