10 Qualities a Data-Friendly Business Culture Needs

[ New Perspective ] Tokyo Metropolitan Government Building, Shinjuku, Tokyo, JapanUsing and continuously optimizing data with the long-term goal of providing customer value and increasing conversion sounds easy; it can be difficult to execute. Getting the most from your data requires hard work and a willingness to adapt, to experiment, to learn from mistakes, to correct those mistakes as quickly as possible and to keep doing the process over and over without end. But, no matter the scale, crunching data to generate information and insight is valuable only if the organizational structures are in place to support the effort.

One of the most important problems corporations face is creating, at all levels of leadership a framework of support for and participation in new technologies that can help refine and expand how they do business. As pressure to adopt big data technologies grows stronger, many organizations have understandable fears concerning the integrity of their businesses. At the very least, leadership often perceives these technologies as threats to institutional knowledge and continuity.

Alex Miller of QVC, Anthony Bucci of RevZilla and Slava Sambu of Office Max have offered insight into the benefits of staying current with data processing technologies. They have discussed some of the stumbling blocks they have faced. But, as these three suggest, stumbling blocks are not dead ends. Organizations able to confront problems toward finding solutions soon begin to reap the rewards associated with using data effectively.

One of the goals—and it will become increasingly important—is corporate agility. With the ability to analyze and operate in real-time, a business can be far more responsive to its customers. It can quickly evaluate what is or is not working and correct the problem. It can identify problems and fix them immediately. For example, some data a business collects is going to be “dirty.” This is, and has always been, a common problem. Implementation of information derived from problematic data can potentially create a large problem with a large impact on the bottom line. A company needs to be agile enough to catch these problems, evaluate their nature and devise appropriate solutions as quickly as possible. As in right then; better still, an hour ago. Waiting while information makes its way slowly up the chain as each management level makes a decision—even waiting as long as it takes to organize a meeting of department heads—is not an option anymore.

Businesses are accomplishing this every day, and some have been employing these practices for years, decades. Amazon operates like this. Online newspapers operate like this. Alex Miller’s discussion of the need for immediacy in real-time data management of live broadcasting is particularly relevant. Real-time responsiveness is possible and advantageous with a sympathetic business culture.

What makes for a corporate culture that can successfully welcome and accommodate the emerging landscape of using data?

  1. An ability to understand a data-driven focus can out-perform many, if not all, previous business solutions
  2. An open-mindedness that supports the research, development and experimentation necessary to make best use of big data
  3. A perspective that supports the idea operating in real-time is an excellent way not only to enhance the customer experience but also monitor for problems and quickly correct them. However difficult it can be to negotiate at all levels, business agility is critical
  4. A marketing optimization framework that allows marketers to use the data effectively to make marketing decisions in real-time. Companies with higher conversion rates almost always have better marketing efficiency ratios (net contribution/marketing expenses). These companies understand it’s hard work to accomplish better marketing efficiency ratios, but it’s considerably more lucrative to do so.
  5. Higher standards of accountability throughout the organization, up to and including the CEO. Does the CEO know which factors of the customer experience impact sales, which projects or departments to favor, what truly needs to be done to optimize the marketing efficiency ratio? In a data-driven business climate, the CEO must know these things
  6. Leadership that promotes higher levels of communication, even collaboration, across all teams
  7. A willingness to grant certain decision making powers to smaller teams
  8. A commitment to employing conversion rate marketing principles
  9. An emphasis on making sure data as well as information and insight derived through analytics are flowing to individual teams across the organization so each can make clear decisions and execute in real- or near-real-time
  10. People and processes that foster a culture of risk-taking and ongoing testing

It took very little time to list (or read) these qualities. It will probably take much more time to internalize them so they become ingrained business practice. If you hope to stay competitive, however, just don’t let it take too long.

6 Steps to Begin Using the Data

Steps to Big Data SuccessThe possibilities in big data technology are exciting, but if you know very little about what these information technologies can do for your business, just thinking how to use them can feel like heading out with a plastic straw to fight a behemoth. Can you just walk away from the field? No. Big data technologies are here to stay, and businesses that wish to remain competitive need to embrace a big data mind set. These steps can get you started.

1. Forget the hype.

Every field has its own jargon. Insiders know it, outsiders don’t. It’s awkward to be the outsider still puzzling what data silos are while the jargon-laden conversation you’re listening to has moved on. Don’t let the jargon and all the technology-talk distract you, and definitely don’t let it dissuade you, from your goal.

Consider that ‘big data’ is merely a label people apply to an evolution in technology that provides much more granular information about your audience and allows you to refine—perhaps expand or automate—many of the ways you do business. Because providers of services use this label, it helps you find solutions that take advantage of new technologies. Beyond that, ‘big data’ is simply a slick term that garners attention. Don’t worry about it.

Many of the solutions you might find valuable help solve the same problems businesses have always faced. And, while the scope and scale of what new technologies can accomplish have grown dramatically, the reasons for the solutions have changed little. Trust you will learn the jargon along the way (just look at how familiar you are with the jargon in this simple article!). Trust there are individuals who can help you understand exactly what a big data solution can do for you.

2. Identify your problems.

All businesses have problems that lead to questions about improvement, whether improvement means increasing customer satisfaction or coordinating what you do online with what you do through other channels. You probably know what your problems and questions are. So itemize them, prioritize them. Do you need to find ways to undertake a more sophisticated approach to testing and optimization? Would it really help to automate your search engine strategies and PPC campaigns? Maybe you want to build a review system you can merge reviews with a variety of product-related applications. Perhaps you most want to build a comprehensive cross-selling platform based on relevance. Start with your problems, then …

3. Identify appropriate solutions.

You can employ a good data scientist(s) to help you develop applications in house. Many businesses find it more cost effective to contract with third-parties. Start learning what third-party solutions related to your problem are available. Many will focus on automated and ‘black box’ applications which are often easier to deploy. Talk to other businesses, attend conferences that associations and providers hold, share case stories from others who have successfully incorporated big data strategies. Be a good shopper, and keep in mind, a big data solution may not be the right answer to all of your questions.

4. Take stock of the data available to you.

This is usually an iterative process with identifying your big data solutions. How much in-house data do you have? From where does it come? How easy is it to access? Will you need to start collecting different data? Would it be valuable to start taking advantage of unstructured sources of data? Do you have or will you need to tap into outside data resources? Do you have data resources you never thought of as resources? Intelligence within your business and careful discussions with third-party providers can help you identify the value of what you have and suggest what you need.

5. Educate the people in your business.

Not so long ago, many organizations resisted incorporating analytics into operations. The big data phenomenon is to analytics as a skyscraper is to a house. One of the more difficult problems is getting everyone in your business to agree these new technologies have value and will work. Leadership often worries dramatic changes might fracture the cohesion of the company and change models that have always worked well; these are real and justifiable, if perhaps frustrating, concerns. You may become enthusiastically convinced a big data solution is going to revolutionize your organization’s effectiveness, but the history of institutional intelligence can be a tough nut to crack.

Help other departments understand how these solutions can actually make everyone’s work lives a little easier. Encourage interdepartmental communication and cooperation. Develop presentations and circulate articles so others can see the value of these changes. Reassurance that big data can change the efficiency and efficacy of how people accomplish their tasks without changing the business goals can go a long way to achieving support.

6. Keep an open mind.

An open mind is going to benefit everyone in your business today. It is even more important as you look to the future. In the upcoming years,  as this technology is refined and that technology is invented, the big data landscape is going to expand and change dramatically. Every business needs to pay attention to the evolution of these solutions and be willing to experiment toward optimizing the most effective ways to conduct business.

As you head out, plastic straw in hand, remind yourself that few things feel too big and threatening when you start breaking them down into manageable pieces.

You can do it.

How is Big Data Different From Previous Data?

Comparing apples to orangesThe three qualities that distinguish big data from all previous information-producing methods—big, unstructured and real-time —suggest a world of possibility. Businesses are creating and applying big data solutions in unprecedented ways that not only help them maximize profits, but also redefine their relationships with their customers.

Why get so excited? Is big data really so different from the kind of large-scale data processing that came before?

Sampling versus All the Data

Until recently, businesses analyzed massive amounts of data using statistical sampling techniques. This produced subsets of data the business could then analyze to infer information and make predictions based on those results. With today’s big data toolset, companies can analyze massive volumes of data all at once.

Big data not only puts businesses in the attractive position of being able to use massive quantities of data without sampling, it renders previous information structures impotent and requires new perspectives and technologies for turning data into information.

Agility

More often than not, companies have collected data and analyzed it later. Today, data analysis can take place in real-time. Businesses who can respond in real-time have the huge advantage of agility. An agile organization can act immediately on information to expand the business value of their data, no matter the speed at which it comes into the business.

There is a chance any dataset could be “dirty” (contains data that is not accurate). This is not new; it has long been one of the issues associated with data collection. As a result, businesses have always needed to understand the importance of listening to the data—not just the data providing good information, but also data generating more problems than it is solving. With big data technologies, an agile business can evaluate and correct potentially costly errors quickly, in real-time.

Data Sources

Prior to the big data era, businesses were constrained to using only structured data sources. These sources generate data suitable for relational databases. With big data technology, a business can start tapping into the huge pool of unstructured data now becoming available: video files; audio files; images; texts; tweets; Facebook posts and other yet to be created.

Costs

Data processing on a massive scale used to be cost prohibitive for most businesses. Big data can now become the great equalizer for businesses large and small. The cost structure and growing availability of big data solutions make them accessible to a greater number of businesses. As the technology continues to develop, big data can offer a Global 5 million, not just a Fortune 100, solution.

Business Structures

Big data does not change how we need to apply information from new data sources to long-standing business issues, but it does give companies a far more finely-tuned edge for honing business intelligence, and this is the arena in which business efforts to apply the information make all the difference.

Because big data is new, because it requires business cultures to rethink how big data will change the way their organizations operate, businesses that want to benefit from big data solutions must have an organized way to integrate all areas of data usage and information application (e.g., conversion rate marketing, across channels and silos). To accomplish this through experimentation, creative development and application, businesses must have buy-in from higher level superiors and C-level officers.

New Possibilities

Big data creates new possibilities of how we process data to generate useful information. Previously, analytics was a way of discovering information that already fit into the framework of how companies processed what they collected. Big data changes everything.

Explaining how it views big data, IBM writes, “Big data is more than simply a matter of size; it is an opportunity to find insights in new and emerging types of data and content, to make your business more agile, and to answer questions that were previously considered beyond your reach. Until now, there was no practical way to harvest this opportunity.”

What is Big Data?

data in cloud comicThe nature of big data suggests a world of possibility, and businesses are applying information derived from big data in ways that are redefining their relationships with their customers and helping them maximize profits. But businesses are lacking a big-data language they can understand. A technology-based language doesn’t help most ‘normal,’ non-technical, business folks ‘get it.’

All businesses can or do generate huge amounts of data. Most do not have a real plan for what to do with that data. If you don’t understand what big data means, how can you apply big-data-generated information to your business? How will you know which big data tools will make a difference?

That’s where we hope to be of service. We wrap a language and a business perspective around the world of big data to help you develop big-data business strategies.

We start with some simple labels to define big data.

Big, Unstructured and Real-time

People usually define big data by three qualities: it’s big; it can come from unstructured sources; you can use it in real-time.

3-vs

Big. Big data is … big. The-mind-can’t-comprehend-it big. We now generate every two days an amount of data equivalent to all the data in the Library of Congress before 2003. In just one minute, 639,800GB of global IP data are transferred:

What-happens-Every-60-Seconds-On-The-Internet

135 botnet infections
6 new Wikipedia articles are published
20 new victims of identity theft
204 million emails sent
1300 new mobile users are added
47,000 applications are downloaded
$83,000 in sales take place on Amazon
61,141 hours of music is streamed on Pandora
100 new accounts are created on LinkedIn
3000 images are uploaded to Flickr (20 million are viewed)
320 new Twitter accounts are created (100,000 tweets are sent)
277,000 people log in to Facebook (6 million view a Facebook page)
2 million search queries are entered into Google
30 hours of video is uploaded to YouTube (1.3 million videos are viewed)

As soon as you read these numbers, they are out of date; the staggering volume of digital data we are creating is growing at a phenomenal rate.

When you have massive amounts of data coming from multiple channels and across silos, how do you use it? This is an important business question: what you need to know influences the data sets you relate to each other and the best formats for presenting information in a way you can quickly understand it so you can act on it.

Unstructured. The variety of data available to us is impressive. About ten percent of it comes from structured data sources and can be processed by conventional means. Ninety percent comes from unstructured sources, and processing it requires new technologies. As digital technologies for data-mining and text-analyzing develop, we increasingly use these data sources that aren’t well-suited to relational database formats.

Structured data can come from sources inside or outside a company, from utilities, government agencies and GPS-enabled devices to radio-frequency identification chips (RFIDs), site search and website clicks. These are primarily structured data sources.

Unstructured data comes from from videos, audio files (for example, telephone conversations, audio recordings of presentations), images, texts, tweets, Facebook posts and more.

One of the most powerful aspects of big data is its ability to accomplish the equivalent of comparing apples and oranges without having to normalize them as fruit across datasets.

Real-time. Ideally, some online transfers should take place almost instantaneously. If someone just used your credit card illegally, you hope your bank discovers that immediately, not at the end of the month. The speed at which you can distill useful information from your data and execute a decision confers agility. An agile business has the advantage of being able to respond immediately, in real-time, as opportunities present themselves. Big data technology makes this possible.

big data is big unstructured realtime

IBM, a leader in creating and providing big data technology, defines big as Volume, unstructured as Varied and Velocity as the speed at which the technology can stream data into the business so the business can use the data immediately. These are great definitions; they are just aimed at a more technologically-literate audience. Business people do not use this language, so we substitute these with more intuitive terms.

IBM includes Veracity—the accuracy of the data—as an important big data quality. While this is a critical concern, from our point of view, accuracy is an inherent problem associated with all data, not a distinguishing feature of big data.

“Big Data is really about new uses and new insights, not so much the data itself,” says Rod A. Smith, an IBM technical fellow and VP for emerging internet technologies.

Businesses require the technical expertise of data scientists to extract, manage and refine information from data sources. Technical considerations usually fall outside our purview.

We care about how business people make sense of the information big data generates and how you can apply big data information to your business decisions.