Big data, marketing and decision-making – what is it all about?

Sofie Sandell

Big data, marketing and decision-making – what is it all about?

Last week I got asked if I know about big data and how you use it in digital marketing. Yes, of course, I do. I’ve been using big data for years when analysing numbers from websites and social media.

I’ve also been fortunate to speak at many conferences where some of the speakers are fully trained ‘big data ninjas’, and I’m lucky to know some of them personally.

Big data is complex information, and it feels as overwhelming as a huge waterfall. It’s only if you present big data in a meaningful way it helps you to make better decisions.

Big data is inconveniently big. It’s hard to handle. Impossible to overview in its raw form.

On Wikipedia, you read: “Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying and information privacy.”

An acquaintance who is responsible for all digital marketing for a large hotel chain in the US told me about her struggle to start to look at numbers when making decisions. For years, they had been making most decisions based on prejudices, personal experiences, and their ‘gut feeling’.

Every hour their hotels have thousands of guests who are in touch with them, either online, over the phone or staying in one of their large hotels. The information they collect about their customers is big data.

The information they have about their guests comes in many forms. Some are internal data, and some are external. You have access to the data that you asked your guests for before their visit and during their stay, and then new random data that you collect from your guests.

Their challenge was to use all information they have about their customers in a meaningful way so they could make better marketing decisions. To kick this off, they spent several days in a large conference room trying to figure out every possible touch-point that their customers have with them. What they got was a big complex map that told stories about their customers. The map was not easy to overview, or understand. The next step was to set up data collection points that they could follow and then also improve everyone’s web analytics skills.

Analytics is a vital phase of the big data cycle. The most common tools marketers use is Google Analytics, and it tells you about your website visitors. With the help of this information, you can understand what was successful in a campaign and how many online leads it gave. You can analyse your conversion rate, and see how many visits lead to a sale or an inquiry.

It’s when the data shows you meaningful pattern that you can do something with it. To see those patterns in an excel spreadsheet can be hard. That’s why you use visualisation software to do this, there are amazing and beautiful tools that magically help you visualise data.

To start using web analytics in a meaningful way took a while for the hotel chain. It’s not a one-month projects, but more like an on-going continual improvement project where everyone has to be open for new learning and share their knowledge.

Five examples of big data in daily life: 
1) The Panama leak was a big and complex project with 11 million documents. And to understand them better, see the pattern the journalists used big data visualisations tool. They used Neo4j and Linkurious to follow a pattern and see where the money went.
2) Eye on the Reef program – people, are helping scientists to find out what’s going on with the reef by sending them updates.
3) The Airports of the Future Project
4) NASA earth image project
5) For the health sector, there is so much to be discovered. Last time when I visited my local GP, or ‘house doctor’ as you say in Swedish, the nurse told me that they keep track on patient’s blood pressure, ‘they give us a call and share their blood pressure weekly, and we add it into the patients journal.’ Right now they collect the information manually. In the future, it will be done over a digital application on your smartphone, and you may send it to your doctor if you wish to.

We will use new personal digital technology in the future. We already have the fitness bracelet and different health apps. We will track all kind of body functions, sleep, movement, pulse, blood pressure, periods, hormones, blood, saliva, and weight. Then we will connect this with our smartphone, and start to see graphics and other visualisation tools, and share this with our doctor. There is a lot of medical issues that you can keep track of and prevent this way.

With so many new digital devices we are collecting and storing more data than ever. One question we need to ponder is how we will use it, and how it can be helpful. More tracking tools will be developed, it will give us more data, and they may help us to make better decisions.

How many people who are working in digital marketing are big data ninjas? Not that many, unfortunately. Big data is complex, and by collaborating and sharing skills you can explore what it means to your organisation.

Big Data: The organizational challenge

Samsung uses it to power the content recommendation engine on its newest smart TVs. Progressive Insurance relies on it to capture driving behavior, determine customer risk profiles and decide on competitive pricing. LexisNexis Risk Solutions uses it to identify individuals, including family relationships, thus helping financial institutions and other clients reduce fraud. 

It, of course, is Big Data—the mining and processing of petabytes’ worth of information to gain insights into customer behavior, supply chain efficiency and many other aspects of business performance. We say of course, because Big Data is hard to miss these days. Industry analysts and media observers hype it as the next big thing for every enterprise, and many companies have been rushing to climb on board. But is building an advanced analytics capability really worth the investment? Until now, data to answer that question has been scarce.

A recent Bain & Company study, however, should put the question to rest. Early adopters of Big Data analytics have gained a significant lead over the rest of the corporate world. Examining more than 400 large companies, we found that those with the most advanced analytics capabilities are outperforming competitors by wide margins (see Figure 1). The leaders are:

• Twice as likely to be in the top quartile of financial performance within their industries
• Five times as likely to make decisions much faster than market peers
• Three times as likely to execute decisions as intended
• Twice as likely to use data very frequently when making decisions

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This helps to explain why so many companies are now asking where they stand on Big Data vis-à-vis their rivals— and whether they’re missing out on a new and essential competitive tool.

To get in the Big Data game, a company needs three kinds of table stakes. The first is the data itself: large quantities of information in a format allowing for easy access and analysis. Most large companies already have this—in fact, they generally have more than they can use. The second is advanced analytical tools, such as Hadoop and NoSQL. Both proprietary and open-source tools and platforms are widely available these days— all you need are people capable of putting them to work. That brings us to the third, and usually the most challenging, set of table stakes: expertise. Advanced analytics requires staff with state-of-the-art skills in everything from data science to worldwide privacy laws, along with an understanding of the business and the relevant sources of value.

But table stakes alone won’t help you win, because Big Data isn’t just one more technology initiative. In fact, it isn’t a technology initiative at all; it’s a business program that requires technical savvy. So you can’t just add more capacity and expertise, and expect your IT or marketing functions to begin generating data-based insights. Even if they did, the rest of the company would be unlikely to act on those insights.

As the analytics leaders have discovered, succeeding with Big Data requires a different approach: You need to embed Big Data deeply into your organization. It’s the only way to ensure that information and insights are shared across business units and functions. This also guarantees the entire company recognizes the synergies and scale benefits that a well-conceived analytics capability can provide.

Let’s look at what’s involved.

Ambition
Leading companies begin the embedding process by spelling out their ambition. We will embrace Big Data as a new way of doing business. We will incorporate advanced analytics and insights as key elements of all critical decisions. A declaration like this from the senior leadership team is an essential precondition for the kind of behavior change this article will discuss. But the senior team must also answer the question: To what end? How is Big Data going to improve our performance as a business? What will the company focus on?

There are four areas where analytics can be relevant: improving existing products and services, improving internal processes, building new product or service offerings, and transforming business models. These objectives often overlap. Progressive’s new “Snapshot” device, which monitors driving behavior, helps the company determine whether a given driver is the right customer for the company. Intuit’s acquisition of Mint.com has helped expand its business beyond purchased software to ad-supported software. Humana, the insurance provider, is using Big Data to transform its business: Using claims data, the company can determine who is likely to end up in a hospital for preventable reasons and then intervene early. Humana and other health insurance carriers are also mining data to help improve patient outcomes and to reward healthy behaviors.

Most companies are opportunity-rich when it comes to analytics, and large enterprises can pursue multiple avenues, either simultaneously or sequentially. Still, nearly every company can improve its trajectory by determining priorities and picking the right angle of entry.

Horizontal analytics capability
With ambition defined, Big Data leaders work on developing a horizontal analytics capability. They learn how to overcome internal resistance, and create both the will and the skill to use data throughout the organization.

This is a big job. Organizations don’t change easily and the value of analytics may not be apparent to everyone, so senior leaders may have to make the case for Big Data in one venue after another. They may need to help people change their everyday behaviors and then continue along the new path without backsliding. As with any major initiative, executives and managers have a variety of tools at their disposal. Leading companies typically define clear owners and sponsors for analytics initiatives. They provide incentives for analytics-driven behavior, thereby ensuring that data is incorporated into processes for making key decisions. They create targets for operational or financial improvements. They work hard to trace the causal impact of Big Data on the achievement of these targets.

For example, Nordstrom elevated responsibility for analytics to a higher management level in its organization, pushed to make analytical tools and insights more widely available and embedded analytics-driven goals into its most important strategic initiatives. Another global consumer electronics company selected high-impact analytics projects for additional support, creating positive business results stories and additional demand for Big Data solutions. The company added incentives for senior executives to tap Big Data capabilities, and the firm’s leadership has reinforced this approach with a steady drumbeat of references to the importance of analytics in delivering business results.

An organizational home
The Big Data leaders then create an organizational home for their advanced analytics capability, often a Center of Excellence (CoE) overseen by a chief analytics officer.
Creating an organizational home involves several key design decisions. A company has to set its strategy for Big Data deployment. It has to assign collection and ownership of data across business functions, plan how to generate insights, and prioritize opportunities and allocation of data scientists’ time. It must host and maintain the technological infrastructure, set privacy policy and access rights, and determine accountability for compliance with local laws and data security. All of that is a tall order. To get it done, companies typically pursue one of four models:

• Business unit led. When business units have distinct data sets and scale isn’t an issue, each business unit can make its own Big Data decisions with limited coordination. AT&T and Zynga are among the companies that rely on this model.
• Business unit led with central support. Business units make their own decisions but collaborate on selected initiatives. Google and Progressive are examples of this approach.
• Center of Excellence. An independent center oversees the company’s Big Data. Units pursue initiatives under the CoE’s guidance and coordination. Amazon and LinkedIn rely on CoEs.
• Fully centralized. The corporate center takes direct responsibility for identifying and prioritizing initiatives. Netflix is an example of a company that pursues this route.

Note that in none of these models does IT own Big Data. While IT often plays a critical role in providing and maintaining the infrastructure and tools required to run Big Data analytics, most companies find that it’s a mistake to have IT own or manage the business adoption capability.

A company’s choice of model obviously depends on its ambition and its existing operating model. For example, companies with deep analytics capabilities and an emphasis on experimentation and innovation, such as Google and Progressive, can rely on a generally decentralized approach. But many analytics leaders have found that a CoE has the most advantages and the fewest limitations (see Figure 2). A well-functioning CoE enables cross-business-unit access and sharing of data. It takes responsibility for supporting and coordinating every initiative from a business unit, thus providing synergies and scale benefits. On the corporate level, the CoE serves as the go-to organization for analytics strategy and insight support. It sets the road map, and it establishes and maintains privacy policies. A leading European telecommunications company, for example, is in the process of deploying Big Data for a range of purposes, including analyzing customer data to provide better offers and services, and using network trsffic data to optimize network management and investments. It will house these capabilities in a variety of settings, but all will be coordinated by a CoE.

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Building a solid CoE from scratch can take time. The center needs experienced leadership and a clear plan for staying connected to the business. It should have a strategy designed to ensure continuous learning, so that it maintains state-of-the-art capabilities. Staffing can be a particular challenge. A CoE requires not only skilled PhD-level data scientists, but also analytics engineers, business managers to identify and prioritize opportunities, and legal talent for advice on standards for data privacy and security. Finding team leaders and identifying partners to fill out the center’s staffing may take between six and 12 months, with scaling up requiring another 12 to 18 months.

Getting started
Many companies are already dipping their toes into Big Data waters. But given the complexities we have discussed—in particular the need to anchor analytics capabilities in the organization—toe-dipping isn’t likely to produce significant insights. That’s why only a select few, so far, have made substantial progress. Right now, many of these leaders are pulling even farther ahead of competitors, so others are playing the necessary game of catch-up.

But it isn’t too late. A good first step is to benchmark your industry and determine your company’s current position in Big Data analytics and capabilities, compared with that of your chief rivals. This exercise will help you determine the investment necessary to improve your relative position. If you are significantly behind the competition, you will have the kind of burning platform that is often required to create and sustain change. You can then begin experimenting, testing hypotheses to learn where and how advanced analytics is most likely to help your business. This type of review will help you determine your Big Data ambition, embed a culture of analytics and decide where Big Data’s organizational home should be.

Travis Pearson is a partner with Bain & Company and based in the firm’s San Francisco office. Rasmus Wegener is a Bain partner based in Atlanta.

The Future Of Digital: 10 CEO Predictions At D11

Electric cars, beer-proof tablets and wearable computers were just a few of the ideas (or developing products) dancing across the minds of the D11 guests last week. While perspectives ranged from the foundational to the unbelievable, all shared a vision that is extraordinary in scope and virtue.

When it comes to the future of digital, here’s what the CEOs on stage had to say: 

Big Data ROI
“There is a massive business opportunity in using software to anticipate industrial equipment maintenance needs,” said Jeffrey Immelt, CEO of GE. “Take the jet engine. It has about 20 sensors that capture real-time continuous data—temperature, engine performance, etc. If I can take that data and use it to model a consumer outcome—say, more time on the wing or less fuel burn—that’s worth an awful lot of money to my customers. A one percent change in fuel burn for an airline is worth hundreds of millions of dollars.” 

Connected Stadiums
Sony CEO Kaz Hirai and San Francisco 49ers chief Jed York are teaming up to bring “beer-proof tablets” to the stadium experience. Come 2014, their smart stadium will connect fans in more ways than just replays. The tablets will be capable of showing the best places to park, the best routes to stadium destinations and even ordering food from your seats.

“The camaraderie of being at the game—there’s nothing like that,” York said. “We want to take that great home-entertainment experience and bring it to the stadium.” 

Electric Cars and Trips To Mars
“I think it’s important that we transition to sustainable transport,” said Elon Musk, founder of Tesla, SpaceX and SolarCity. “Eventually we’ll face extremely high gasoline costs and the economy will grind to a halt if we don’t.”

Musk says the ultimate goal, though, is to get technology to the point where it can take us to Mars.

“Either we spread Earth to other planets, or we risk going extinct,” he said. “An extinction event is inevitable and we’re increasingly doing ourselves in.” 

Fertility Apps
PayPal co-founder Max Levchin’s latest project aims to help women get pregnant. His new fertility company, Glow, uses analytics to track ovulation cycles and advise best times to conceive.

“My wife and I were lucky. We had our children without any issues,” said Levchin. ““But we have people close to us that have gone through multiple IVF trials, and we’ve heard them say, ‘We’re not going to put my wife’s body through this anymore.’”

Beyond pregnancy, Levchin hopes to use this model to give people more data on other areas of their health that will ultimately decrease health care costs overall. 

Internet Of Things
Pinterest CEO Ben Silbermann thinks his company is well positioned for the future of the Internet.

“Many things were once very text-based and very popular,” Silbermann said. “But instead of being time-based, we made it visual . . . I think the web and media are becoming more visual in general.”

Silbermann also freely admitted that Pinterest isn’t making any money yet, but that it takes “more of a long-term perspective” to build a company that will stick around. 

Mobile Data
“Transport will become free,” said Cisco CEO John Chambers, predicting that cellular data charges will fall like voice cell service. “Architectures will change. With intelligence throughout the network, the network will become the platform of the future.” 

Smarter Phones
This fall, Motorola will release a “hero device” called the Moto X. The new phone will have a variety of always-on sensors that makes it more contextually aware—like knowing when you take it out of your pocket.

“We’re going to play a different game than Motorola has played in the recent past,” said Motorola CEO Dennis Woodside. “It’s not going to radically change the world in the first launch, but we do think that the products will find their markets.” 

TV Disruption
“We’ve recognized that Twitter is the second screen for TV, and TV is more fun with Twitter,” said Twitter CEO Dick Costolo when asked about the next stage of the company. “There are a bunch of ways that we can be complementary to broadcasters. Traditionally, many in our area have viewed broadcasters as competitors—we think of it as complementary. 

Virtual Assistants
“I think we will see virtual assistants within two years that are quite robust,” said Nuance CEO Paul Ricci. “I also believe that within two years we will see that virtual assistants will work across platforms.” 

Wearable Computers
Wearables were a hot topic at D11 this year, especially with the buzz surrounding Google Glass. Here’s D11’s compilation of prominent speakers (including Hirai, York, Costolo and Tim Cook) sharing their thoughts and feelings about wearable computing devices, the future of that industry and whether they plan to get involved. 

And of course . . . Apple isn’t about to give anything away.
“We release products when they are ready,” said Apple CEO Tim Cook. “We believe very much in the element of surprise. We think customers love surprises. I have no plan on changing that . . . We have several more game changers in us.”

Author: CEO.com Staff

Big Data ROI Still Tough To Measure

Business leaders believe in big data's potential but are frustrated by lengthy projects and complicated tools, according to IDG/Kapow study.

By Jeff Bertolucci 

5 Big Wishes For Big Data Deployments

(click image for larger view and for slideshow)

Big data proponents are a vocal bunch, promoting sundry tools and technologies that enable enterprises to mine a steady stream of unstructured data for hard-to-reach insights. But how easy are big data projects to set up? And once implemented, are they worth the cost?

A recent survey by IDG Research Services and Kapow Software shows a fair amount of disillusionment among big data pioneers. But despite the negativity, businesses still see big data projects as a potential boon that's worth pursuing.

The survey of more than 200 IT and business leaders at large organizations shows mixed feelings toward big data. For instance, more than 85% of respondents agree that big data can help businesses make "more informed" data-driven decisions. However, just 23% of these leaders see big data projects as a "success" thus far, while 52% of respondents call the projects "somewhat successful."

So why the chasm between hope and reality? A Kapow Software white paper that examines the survey results offers this explanation: "Big Data projects are taking far too long, costing too much and not delivering on anticipated ROI because it's really difficult to pinpoint and surgically extract critical insights without hiring expensive consultants or data scientists in short demand. The broader issue at hand is a growing mass of data that's difficult to collect from a wide variety of sources."

Obviously, the complexities of big data projects play a major role in this dissatisfaction among business leaders. One of the respondents' thorniest big data problems is the inability to quickly and effectively automate structured and unstructured data. In addition, 60% of respondents say that big data projects typically take a long time -- 18 months or more -- to finish.

Another problem is the dearth of easy-to-use big data tools. Respondents report that business employees who lack special training in big data analysis -- meaning most of them -- must rely on IT departments to glean information from big data sets.

"The lack of simple tools that make it easy to consume and put critical data into the hands of business users is a perceived barrier that keeps Big Data an IT endeavor rather than a business-driven initiative to support decision-making across the organization," the white paper asserts.

Given the implementation challenges and lack of end-user tools, it's no surprise that just 1 in 10 survey respondents say that the big data solutions available today are effective at getting important information to their workers in a timely fashion. (In other words, 90% disagree with that statement.)

All isn't bleak, however. While just 32% percent of companies polled have set up big data initiatives to date, the number of big data projects should double in the next 12 months, the survey says.

There are other glimmers of hope as well. Numerous tech firms are starting to market big data tools for regular business folks, a development that might help alleviate the skills gap, reduce pressure on IT departments, and make big data platforms more accessible to everyone. 

BigML, for instance, is a Corvallis, Oregon-based startup with a cloud- based machine learning platform that lets business users create predictive models quickly. And companies like Talend, SnapLogic, which calls its SnapReduce technology "Hadoop for humans," and Tableau Software, maker of the Tableau Desktop drag-and-drop analytics tool, offer big data tools for non-techies as well.

It's little surprise that 85% of survey respondents believe that big data strategies should be "user-centric." Without that focus, the data-driven future we've been hearing so much about will never fulfill come to pass.

What do you think? Are big data projects delivering the goods? Or is their ROI, well, MIA?