Big Data: The Management Revolution

Public Health Data ShareBig Data

The authors write, is far more powerful than the analytics of the past. Executives can measure and therefore manage more precisely than ever before. They can make better predictions and smarter decisions. They can target more-effective interventions in areas that so far have been dominated by gut and intuition rather than by data and rigor. The differences between big data and analytics are a matter of volume, velocity, and variety: More data now cross the internet every second than were stored in the entire internet 20 years ago. Nearly real-time information makes it possible for a company to be much more agile than its competitors. And that information can come from social networks, images, sensors, the web, or other unstructured sources.

Artwork: Tamar Cohen, Happy Motoring

“You can’t manage what you don’t measure.”

There’s much wisdom in that saying, which has been attributed to both W. Edwards Deming and Peter Drucker, and it explains why the recent explosion of digital data is so important. Simply put, because of big data, managers can measure, and hence know, radically more about their businesses, and directly translate that knowledge into improved decision making and performance.

Consider retailing. Booksellers in physical stores could always track which books sold and which did not. If they had a loyalty program, they could tie some of those purchases to individual customers. And that was about it. Once shopping moved online, though, the understanding of customers increased dramatically. Online retailers could track not only what customers bought, but also what else they looked at; how they navigated through the site; how much they were influenced by promotions, reviews, and page layouts; and similarities across individuals and groups. Before long, they developed algorithms to predict what books individual customers would like to read next—algorithms that performed better every time the customer responded to or ignored a recommendation. Traditional retailers simply couldn’t access this kind of information, let alone act on it in a timely manner. It’s no wonder that Amazon has put so many brick-and-mortar bookstores out of business.

The familiarity of the Amazon story almost masks its power. We expect companies that were born digital to accomplish things that business executives could only dream of a generation ago. But in fact the use of big data has the potential to transform traditional businesses as well. It may offer them even greater opportunities for competitive advantage (online businesses have always known that they were competing on how well they understood their data). As we’ll discuss in more detail, the big data of this revolution is far more powerful than the analytics that were used in the past. We can measure and therefore manage more precisely than ever before. We can make better predictions and smarter decisions. We can target more-effective interventions, and can do so in areas that so far have been dominated by gut and intuition rather than by data and rigor.

As the tools and philosophies of big data spread, they will change long-standing ideas about the value of experience, the nature of expertise, and the practice of management. Smart leaders across industries will see using big data for what it is: a management revolution. But as with any other major change in business, the challenges of becoming a big data–enabled organization can be enormous and require hands-on—or in some cases hands-off—leadership. Nevertheless, it’s a transition that executives need to engage with today.

What’s New Here?

Business executives sometimes ask us, “Isn’t ‘big data’ just another way of saying ‘analytics’?” It’s true that they’re related: The big data movement, like analytics before it, seeks to glean intelligence from data and translate that into business advantage. However, there are three key differences:


As of 2012, about 2.5 exabytes of data are created each day, and that number is doubling every 40 months or so. More data cross the internet every second than were stored in the entire internet just 20 years ago. This gives companies an opportunity to work with many petabyes of data in a single data set—and not just from the internet. For instance, it is estimated that Walmart collects more than 2.5 petabytes of data every hour from its customer transactions. A petabyte is one quadrillion bytes, or the equivalent of about 20 million filing cabinets’ worth of text. An exabyte is 1,000 times that amount, or one billion gigabytes.


For many applications, the speed of data creation is even more important than the volume. Real-time or nearly real-time information makes it possible for a company to be much more agile than its competitors. For instance, our colleague Alex “Sandy” Pentland and his group at the MIT Media Lab used location data from mobile phones to infer how many people were in Macy’s parking lots on Black Friday—the start of the Christmas shopping season in the United States. This made it possible to estimate the retailer’s sales on that critical day even before Macy’s itself had recorded those sales. Rapid insights like that can provide an obvious competitive advantage to Wall Street analysts and Main Street managers.


Big data takes the form of messages, updates, and images posted to social networks; readings from sensors; GPS signals from cell phones, and more. Many of the most important sources of big data are relatively new. The huge amounts of information from social networks, for example, are only as old as the networks themselves; Facebook was launched in 2004, Twitter in 2006. The same holds for smartphones and the other mobile devices that now provide enormous streams of data tied to people, activities, and locations. Because these devices are ubiquitous, it’s easy to forget that the iPhone was unveiled only five years ago, and the iPad in 2010. Thus the structured databases that stored most corporate information until recently are ill suited to storing and processing big data. At the same time, the steadily declining costs of all the elements of computing—storage, memory, processing, bandwidth, and so on—mean that previously expensive data-intensive approaches are quickly becoming economical.

As more and more business activity is digitized, new sources of information and ever-cheaper equipment combine to bring us into a new era: one in which large amounts of digital information exist on virtually any topic of interest to a business. Mobile phones, online shopping, social networks, electronic communication, GPS, and instrumented machinery all produce torrents of data as a by-product of their ordinary operations. Each of us is now a walking data generator. The data available are often unstructured—not organized in a database—and unwieldy, but there’s a huge amount of signal in the noise, simply waiting to be released. Analytics brought rigorous techniques to decision making; big data is at once simpler and more powerful. As Google’s director of research, Peter Norvig, puts it: “We don’t have better algorithms. We just have more data.”

How Data-Driven Companies Perform

The second question skeptics might pose is this: “Where’s the evidence that using big data intelligently will improve business performance?” The business press is rife with anecdotes and case studies that supposedly demonstrate the value of being data-driven. But the truth, we realized recently, is that nobody was tackling that question rigorously. To address this embarrassing gap, we led a team at the MIT Center for Digital Business, working in partnership with McKinsey’s business technology office and with our colleague Lorin Hitt at Wharton and the MIT doctoral student Heekyung Kim. We set out to test the hypothesis that data-driven companies would be better performers. We conducted structured interviews with executives at 330 public North American companies about their organizational and technology management practices, and gathered performance data from their annual reports and independent sources.

Not everyone was embracing data-driven decision making. In fact, we found a broad spectrum of attitudes and approaches in every industry. But across all the analyses we conducted, one relationship stood out: The more companies characterized themselves as data-driven, the better they performed on objective measures of financial and operational results. In particular, companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors. This performance difference remained robust after accounting for the contributions of labor, capital, purchased services, and traditional IT investment. It was statistically significant and economically important and was reflected in measurable increases in stock market valuations.


So how are managers using big data? Let’s look in detail at two companies that are far from Silicon Valley upstarts. One uses big data to create new businesses, the other to drive more sales.

Improved Airline ETAs

Minutes matter in airports. So does accurate information about flight arrival times: If a plane lands before the ground staff is ready for it, the passengers and crew are effectively trapped, and if it shows up later than expected, the staff sits idle, driving up costs. So when a major U.S. airline learned from an internal study that about 10% of the flights into its major hub had at least a 10-minute gap between the estimated time of arrival and the actual arrival time—and 30% had a gap of at least five minutes—it decided to take action.

At the time, the airline was relying on the aviation industry’s long-standing practice of using the ETAs provided by pilots. The pilots made these estimates during their final approach to the airport, when they had many other demands on their time and attention. In search of a better solution, the airline turned to PASSUR Aerospace, a provider of decision-support technologies for the aviation industry. In 2001 PASSUR began offering its own arrival estimates as a service called RightETA. It calculated these times by combining publicly available data about weather, flight schedules, and other factors with proprietary data the company itself collected, including feeds from a network of passive radar stations it had installed near airports to gather data about every plane in the local sky.

PASSUR started with just a few of these installations, but by 2012 it had more than 155. Every 4.6 seconds it collects a wide range of information about every plane that it “sees.” This yields a huge and constant flood of digital data. What’s more, the company keeps all the data it has gathered over time, so it has an immense body of multidimensional information spanning more than a decade. This allows sophisticated analysis and pattern matching. RightETA essentially works by asking itself “What happened all the previous times a plane approached this airport under these conditions? When did it actually land?”

After switching to RightETA, the airline virtually eliminated gaps between estimated and actual arrival times. PASSUR believes that enabling an airline to know when its planes are going to land and plan accordingly is worth several million dollars a year at each airport. It’s a simple formula: Using big data leads to better predictions, and better predictions yield better decisions.

Speedier, More Personalized Promotions

A couple of years ago, Sears Holdings came to the conclusion that it needed to generate greater value from the huge amounts of customer, product, and promotion data it collected from its Sears, Craftsman, and Lands’ End brands. Obviously, it would be valuable to combine and make use of all these data to tailor promotions and other offerings to customers, and to personalize the offers to take advantage of local conditions. Valuable, but difficult: Sears required about eight weeks to generate personalized promotions, at which point many of them were no longer optimal for the company. It took so long mainly because the data required for these large-scale analyses were both voluminous and highly fragmented—housed in many databases and “data warehouses” maintained by the various brands.

In search of a faster, cheaper way to do its analytic work, Sears Holdings turned to the technologies and practices of big data. As one of its first steps, it set up a Hadoop cluster. This is simply a group of inexpensive commodity servers whose activities are coordinated by an emerging software framework called Hadoop (named after a toy elephant in the household of Doug Cutting, one of its developers).

Sears started using the cluster to store incoming data from all its brands and to hold data from existing data warehouses. It then conducted analyses on the cluster directly, avoiding the time-consuming complexities of pulling data from various sources and combining them so that they can be analyzed. This change allowed the company to be much faster and more precise with its promotions. According to the company’s CTO, Phil Shelley, the time needed to generate a comprehensive set of promotions dropped from eight weeks to one, and is still dropping. And these promotions are of higher quality, because they’re more timely, more granular, and more personalized. Sears’s Hadoop cluster stores and processes several petabytes of data at a fraction of the cost of a comparable standard data warehouse.

Shelley says he’s surprised at how easy it has been to transition from old to new approaches to data management and high-performance analytics. Because skills and knowledge related to new data technologies were so rare in 2010, when Sears started the transition, it contracted some of the work to a company called Cloudera. But over time its old guard of IT and analytics professionals have become comfortable with the new tools and approaches.

The PASSUR and Sears Holding examples illustrate the power of big data, which allows more-accurate predictions, better decisions, and precise interventions, and can enable these things at seemingly limitless scale. We’ve seen big data used in supply chain management to understand why a carmaker’s defect rates in the field suddenly increased, in customer service to continually scan and intervene in the health care practices of millions of people, in planning and forecasting to better anticipate online sales on the basis of a data set of product characteristics, and so on. We’ve seen similar payoffs in many other industries and functions, from finance to marketing to hotels and gaming, and from human resource management to machine repair.

Our statistical analysis tells us that what we’re seeing is not just a few flashy examples but a more fundamental transformation of the economy. We’ve become convinced that almost no sphere of business activity will remain untouched by this movement.

A New Culture of Decision Making

The technical challenges of using big data are very real. But the managerial challenges are even greater—starting with the role of the senior executive team.

Muting the HiPPOs.

One of the most critical aspects of big data is its impact on how decisions are made and who gets to make them. When data are scarce, expensive to obtain, or not available in digital form, it makes sense to let well-placed people make decisions, which they do on the basis of experience they’ve built up and patterns and relationships they’ve observed and internalized. “Intuition” is the label given to this style of inference and decision making. People state their opinions about what the future holds—what’s going to happen, how well something will work, and so on—and then plan accordingly. (See “The True Measures of Success,” by Michael J. Mauboussin, in this issue.)

Big data’s power does not erase the need for vision or human insight.

For particularly important decisions, these people are typically high up in the organization, or they’re expensive outsiders brought in because of their expertise and track records. Many in the big data community maintain that companies often make most of their important decisions by relying on “HiPPO”—the highest-paid person’s opinion.

To be sure, a number of senior executives are genuinely data-driven and willing to override their own intuition when the data don’t agree with it. But we believe that throughout the business world today, people rely too much on experience and intuition and not enough on data. For our research we constructed a 5-point composite scale that captured the overall extent to which a company was data-driven. Fully 32% of our respondents rated their companies at or below 3 on this scale.

New roles.

Executives interested in leading a big data transition can start with two simple techniques. First, they can get in the habit of asking “What do the data say?” when faced with an important decision and following up with more-specific questions such as “Where did the data come from?,” “What kinds of analyses were conducted?,” and “How confident are we in the results?” (People will get the message quickly if executives develop this discipline.) Second, they can allow themselves to be overruled by the data; few things are more powerful for changing a decision-making culture than seeing a senior executive concede when data have disproved a hunch.

When it comes to knowing which problems to tackle, of course, domain expertise remains critical. Traditional domain experts—those deeply familiar with an area—are the ones who know where the biggest opportunities and challenges lie. PASSUR, for one, is trying to hire as many people as possible who have extensive knowledge of operations at America’s major airports. They will be invaluable in helping the company figure out what offerings and markets it should go after next.

As the big data movement advances, the role of domain experts will shift. They’ll be valued not for their HiPPO-style answers but because they know what questions to ask. Pablo Picasso might have been thinking of domain experts when he said, “Computers are useless. They can only give you answers.”

Five Management Challenges

Companies won’t reap the full benefits of a transition to using big data unless they’re able to manage change effectively. Five areas are particularly important in that process.


Companies succeed in the big data era not simply because they have more or better data, but because they have leadership teams that set clear goals, define what success looks like, and ask the right questions. Big data’s power does not erase the need for vision or human insight. On the contrary, we still must have business leaders who can spot a great opportunity, understand how a market is developing, think creatively and propose truly novel offerings, articulate a compelling vision, persuade people to embrace it and work hard to realize it, and deal effectively with customers, employees, stockholders, and other stakeholders. The successful companies of the next decade will be the ones whose leaders can do all that while changing the way their organizations make many decisions.

Talent management.

As data become cheaper, the complements to data become more valuable. Some of the most crucial of these are data scientists and other professionals skilled at working with large quantities of information. Statistics are important, but many of the key techniques for using big data are rarely taught in traditional statistics courses. Perhaps even more important are skills in cleaning and organizing large data sets; the new kinds of data rarely come in structured formats. Visualization tools and techniques are also increasing in value. Along with the data scientists, a new generation of computer scientists are bringing to bear techniques for working with very large data sets. Expertise in the design of experiments can help cross the gap between correlation and causation. The best data scientists are also comfortable speaking the language of business and helping leaders reformulate their challenges in ways that big data can tackle. Not surprisingly, people with these skills are hard to find and in great demand. (See “Data Scientist: The Sexiest Job of the 21st Century,” by Thomas H. Davenport and D.J. Patil, in this issue.)


The tools available to handle the volume, velocity, and variety of big data have improved greatly in recent years. In general, these technologies are not prohibitively expensive, and much of the software is open source. Hadoop, the most commonly used framework, combines commodity hardware with open-source software. It takes incoming streams of data and distributes them onto cheap disks; it also provides tools for analyzing the data. However, these technologies do require a skill set that is new to most IT departments, which will need to work hard to integrate all the relevant internal and external sources of data. Although attention to technology isn’t sufficient, it is always a necessary component of a big data strategy.

Decision making.

An effective organization puts information and the relevant decision rights in the same location. In the big data era, information is created and transferred, and expertise is often not where it used to be. The artful leader will create an organization flexible enough to minimize the “not invented here” syndrome and maximize cross-functional cooperation. People who understand the problems need to be brought together with the right data, but also with the people who have problem-solving techniques that can effectively exploit them.

Company culture.

The first question a data-driven organization asks itself is not “What do we think?” but “What do we know?” This requires a move away from acting solely on hunches and instinct. It also requires breaking a bad habit we’ve noticed in many organizations: pretending to be more data-driven than they actually are. Too often, we saw executives who spiced up their reports with lots of data that supported decisions they had already made using the traditional HiPPO approach. Only afterward were underlings dispatched to find the numbers that would justify the decision.Without question, many barriers to success remain. There are too few data scientists to go around. The technologies are new and in some cases exotic. It’s too easy to mistake correlation for causation and to find misleading patterns in the data. The cultural challenges are enormous, and, of course, privacy concerns are only going to become more significant. But the underlying trends, both in the technology and in the business payoff, are unmistakable.

The evidence is clear: Data-driven decisions tend to be better decisions. Leaders will either embrace this fact or be replaced by others who do. In sector after sector, companies that figure out how to combine domain expertise with data science will pull away from their rivals. We can’t say that all the winners will be harnessing big data to transform decision making. But the data tell us that’s the surest bet.

, ,
392 comments to “Big Data: The Management Revolution”
  1. Pingback: buy propecia online from canada

  2. Pingback: Leandro Farland

  3. Pingback: Arie Baisch

  4. Pingback: Madelyn Monroe MILF Porn

  5. Pingback: best-domains

  6. Pingback: Essay writer

  7. Pingback: Do My Assignment For Me

  8. Pingback: Click Here

  9. Pingback: Click Here

  10. Pingback: Click Here

  11. Pingback: Click Here

  12. Pingback: Click Here

  13. Pingback: Click Here

  14. Pingback: Click Here

  15. Pingback: Click Here

  16. Pingback: Click Here

  17. Pingback: Click Here

  18. Pingback: Click Here

  19. Pingback: Click Here

  20. Pingback: Click Here

  21. Pingback: Click Here

  22. Pingback: Click Here

  23. Pingback: Click Here

  24. Pingback: Click Here

  25. Pingback: Click Here

  26. Pingback: Click Here

  27. Pingback: Click Here

  28. Pingback: fundamentals of robotics

  29. Pingback: robotics case study

  30. Pingback: Click Here

  31. Pingback: Click Here

  32. Pingback: Click Here

  33. Pingback: Click Here

  34. Pingback: Click Here

  35. Pingback: Click Here

  36. Pingback: Click Here

  37. Pingback: Click Here

  38. Pingback: Click Here

  39. Pingback: Click Here

  40. Pingback: Click Here

  41. Pingback: Click Here

  42. Pingback: Click Here

  43. Pingback: Click Here

  44. Pingback: Click Here

  45. Pingback: grand rapids same day crowns

  46. Pingback: Click Here

  47. Pingback: grand rapids teeth whitening

  48. Pingback:

  49. Pingback: Click Here

  50. Pingback: Click Here

  51. Pingback: Click Here

  52. Pingback: Click Here

  53. Pingback: Click Here

  54. Pingback: Click Here

  55. Pingback: Click Here

  56. Pingback: Click Here

  57. Pingback: Click Here

  58. Pingback: invite and earn

  59. Pingback: Click Here

  60. Pingback: Click Here

  61. Pingback: Click Here

  62. Pingback: Click Here

  63. Pingback: Click Here

  64. Pingback: Click Here

  65. Pingback: Click Here

  66. Pingback: Click Here

  67. Pingback: Click Here

  68. Pingback: Click Here

  69. Pingback: 카지노 게임 온라인

  70. Pingback: 라이브 딜러 카지노

  71. Pingback: 카지노 리뷰 및 평가

  72. Pingback: best-domains

  73. Pingback: premium-domains-list

  74. Pingback: yoga pants

  75. Pingback: Google reviews

  76. Pingback: reputation defenders

  77. Pingback: 2023 Books

  78. Pingback: obituary

  79. Pingback: memorial

  80. Pingback: search dececased

  81. Pingback: rip

  82. Pingback: how to bet on football for beginners

  83. Pingback: Chirurgie esthétique Tunisie

  84. Pingback: Chirurgie esthétique Tunisie

  85. Pingback: National Chi Nan University

  86. Pingback: Faculty of Computers & Information Technology future university in egypt

  87. Pingback: Research opportunities

  88. Pingback: Finance courses

  89. Pingback: الممارسات الأخلاقية

  90. Pingback: fue

  91. Pingback: and international arenas

  92. Pingback: الصيادلة

  93. Pingback: Large Lecture Halls

  94. Pingback: ماجيستير علاج الجذور

  95. Pingback: أفضل كلية هندسة فى مصر

  96. Pingback: Software Engineering

  97. Pingback: computer science courses

  98. Pingback: Professional Development

  99. Pingback: QS World University Rankings

  100. Pingback: برامج الإقامة الخاصة بتقويم الأسنان

  101. Pingback: Online MBA program in Egypt

  102. Pingback: الفاعلية التعليمية

  103. Pingback: Admission requirements for future university

  104. Pingback: Admission requirements for future university

  105. Pingback: Academic Year

  106. Pingback: Seattle University

  107. Pingback: كم عدد سنوات كلية الصيدلة

  108. Pingback: وظائف خريجي ماجستير إدارة الأعمال في مصر

  109. Pingback: طب الاسنان المعاصر

  110. Pingback: best university in egypt

  111. Pingback: Pharmacognos

  112. Pingback:

  113. Pingback:

  114. Pingback: دراسة ادارة الاعمال بجامعة المستقبل

  115. Pingback: Future University Egypt business programs

  116. Pingback: Research projects

  117. Pingback: political mass media

  118. Pingback: Awareness Campaigns for pharmacy students at future university

  119. Pingback: Pharmacy Practice and Clinical Pharmacy

  120. Pingback: Dental specialties

  121. Pingback: Summer Courses

  122. Pingback: Electrical Engineering

  123. Pingback: نظام الامتحانات

  124. Pingback: Computer Science Programs

  125. Pingback: Changing World

  126. Pingback: developing the educational process

  127. Pingback: top university in egypt

  128. Pingback: Periodontal Continuing Education

  129. Pingback: ماجستير طب الأسنان

  130. Pingback: Transfer students admissions to future university

  131. Pingback: Maillot de football

  132. Pingback: Maillot de football

  133. Pingback: Maillot de football

  134. Pingback: Maillot de football

  135. Pingback: Maillot de football

  136. Pingback: Maillot de football

  137. Pingback: Maillot de football

  138. Pingback: Maillot de football

  139. Pingback: Maillot de football

  140. Pingback: Maillot de football

  141. Pingback: Maillot de football

  142. Pingback: Maillot de football

  143. Pingback: Maillot de football

  144. Pingback: SEOSolutionVIP Fiverr

  145. Pingback: SEOSolutionVIP Fiverr

  146. Pingback: SEOSolutionVIP Fiverr

  147. Pingback: striscia led corridoio

  148. Pingback: led luci camera

  149. Pingback: illuminazione a binario

  150. Pingback: striscia led letto

  151. Pingback: gymnase extérieur

  152. Pingback: mur ninja warrior

  153. Pingback: parcours d obstacle militaire

  154. Pingback: Fiverr Earn

  155. Pingback: Fiverr Earn

  156. Pingback: Fiverr Earn

  157. Pingback: Fiverr Earn

  158. Pingback: Fiverr Earn

  159. Pingback: Fiverr Earn

  160. Pingback: Fiverr Earn

  161. Pingback: Fiverr Earn

  162. Pingback: Fiverr Earn

  163. Pingback: Visualizza la striscia led soffitto

  164. Pingback: Hooled controsoffitto led

  165. Pingback:

  166. Pingback:

  167. Pingback:

  168. Pingback:

  169. Pingback:

  170. Pingback:

  171. Pingback: Advance-Esthetic LLC

  172. Pingback:

  173. Pingback: red boost mediprime

  174. Pingback: TMS System

  175. Pingback: blue frenchie houston

  176. Pingback: clothing manufacturer

  177. Pingback: clothes manufacturer

  178. Pingback: clima para mañana

  179. Pingback: weather

  180. Pingback:

  181. Pingback: french bulldog

  182. Pingback:

  183. Pingback: blue merle frenchies for sale

  184. Pingback: what can frenchies not eat

  185. Pingback: french bulldog puppies texas

  186. Pingback: bernedoodle

  187. Pingback: exotic bullies

  188. Pingback: mini french bulldog

  189. Pingback: isla mujeres golf cart

  190. Pingback: jute rugs

  191. Pingback: seo in Qatar

  192. Pingback: Piano Disposal and Recycling

  193. Pingback: Long-term Piano Storage

  194. Pingback: Piano Storage Solutions

  195. Pingback: Best university in Egypt

  196. Pingback: Private universities in Egypt

  197. Pingback: Top university in Egypt

  198. Pingback: Best university in Egypt

  199. Pingback: Best university in Egypt

  200. Pingback: Top university in Egypt

  201. Pingback: Private universities in Egypt

  202. Pingback: Best university in Egypt

  203. Pingback: golf cart isla mujeres

  204. Pingback: isla paddle board

  205. Pingback: isla mujeres golf cart rental

  206. Pingback: french bulldog adoption

  207. Pingback: french bulldog vs pug

  208. Pingback: crypto news

  209. Pingback: vietravel tour

  210. Pingback: sorority jewelry

  211. Pingback: teacup frenchies for sale

  212. Pingback: Google Rezensionen löschen lassen

  213. Pingback: clima fresno ca

  214. Pingback: mini frenchie for sale

  215. Pingback: iPhone repair Orange County

  216. Pingback: french bulldogs for sale tx

  217. Pingback: Personalised jewellery for him

  218. Pingback: best Samsung

  219. Pingback: best deals

  220. Pingback: future university

  221. Pingback: future university

  222. Pingback: future university

  223. Pingback: future university

  224. Pingback: future university

  225. Pingback: future university

  226. Pingback: french bulldog houston texas

  227. Pingback: bandeau set

  228. Pingback: multisbo

  229. Pingback: seo services vancouver

  230. Pingback: bulldogs puppy

  231. Pingback: Fiverr

  232. Pingback: Fiverr

  233. Pingback: Fiverr

  234. Pingback: Fiverr

  235. Pingback: french bulldog for sale dallas

  236. Pingback: french bulldog in austin

  237. Pingback: merle french bulldog

  238. Pingback: future university

  239. Pingback: renting golf cart isla mujeres

  240. Pingback: bulldog frenchie puppies

  241. Pingback: Lean

  242. Pingback: Warranty

  243. Pingback: FUE

  244. Pingback: FUE

  245. Pingback: FUE

  246. Pingback: FUE

  247. Pingback: FUE

  248. Pingback: Furniture protection

  249. Pingback: Interstate moving

  250. Pingback: Furniture transport

  251. Pingback: pcfinancial ca activate

  252. Pingback:

  253. Pingback: برنامج MBA بمصر

  254. Pingback: FiverrEarn

  255. Pingback: FiverrEarn

  256. Pingback: FiverrEarn

  257. Pingback: Fiverr.Com

  258. Pingback: Fiverr

  259. Pingback: FiverrEarn

  260. Pingback: FiverrEarn

  261. Pingback: FiverrEarn

  262. Pingback: Free Local Classified Ads

  263. Pingback: Free Local Classified Ads

  264. Pingback: FiverrEarn

  265. Pingback: Training Philippines

  266. Pingback: FiverrEarn

  267. Pingback: FiverrEarn

  268. Pingback: FiverrEarn

  269. Pingback: FiverrEarn

  270. Pingback: FiverrEarn

  271. Pingback: Pornography Australia

  272. Pingback: pupuk anorganik dan pupuk organik

  273. Pingback: pupuk cair terbaik adalah di

  274. Pingback: pupuk organik terbaik

  275. Pingback: partners

  276. Pingback: skin care products

  277. Pingback: revive daily

  278. Pingback: prostadine

  279. Pingback: java burn

  280. Pingback: skincare supplement

  281. Pingback: illuderma

  282. Pingback: FiverrEarn

  283. Pingback: FiverrEarn

  284. Pingback: FiverrEarn

  285. Pingback: FiverrEarn

  286. Pingback: FiverrEarn

  287. Pingback: live sex cams

  288. Pingback: live sex cams

  289. Pingback: live sex cams

  290. Pingback: FiverrEarn

  291. Pingback: FiverrEarn

  292. Pingback: FiverrEarn

  293. Pingback: FiverrEarn

  294. Pingback: FiverrEarn

  295. Pingback: FiverrEarn

  296. Pingback: FiverrEarn

  297. Pingback: FiverrEarn

  298. Pingback: FiverrEarn

  299. Pingback: french bulldog austin

  300. Pingback: FiverrEarn

  301. Pingback: FiverrEarn

  302. Pingback: FiverrEarn

  303. Pingback: FiverrEarn

  304. Pingback: FiverrEarn

  305. Pingback: FiverrEarn

  306. Pingback: FiverrEarn

  307. Pingback: FiverrEarn

  308. Pingback: FiverrEarn

  309. Pingback: FiverrEarn

  310. Pingback: FiverrEarn

  311. Pingback: FiverrEarn

  312. Pingback: FiverrEarn

  313. Pingback: FiverrEarn

  314. Pingback: FiverrEarn

  315. Pingback: FiverrEarn

  316. Pingback: FiverrEarn

  317. Pingback: FiverrEarn

  318. Pingback: ფილმები ქართულად

  319. Pingback: Best Lightroom Presets

  320. Pingback: seo company texas

  321. Pingback: seo company vancouver

  322. Pingback: anniversary

  323. Pingback: shopping cart

  324. Pingback: Best University in Yemen

  325. Pingback: Situs Slot Online

  326. Pingback: Scientific Research

  327. Pingback: Kampus Islam Terbaik

  328. Pingback: FiverrEarn

  329. Pingback: FiverrEarn

  330. Pingback: FiverrEarn

  331. Pingback: FiverrEarn

  332. Pingback: FiverrEarn

  333. Pingback: FiverrEarn

  334. Pingback: Generator Repair near me Sheffield

  335. Pingback: slimcrystal legit

  336. Pingback: cheap sex cams

  337. Pingback: french bulldog buy

  338. Pingback: live sex cams

  339. Pingback: freeze dried candy

  340. Pingback: rare breed-trigger

  341. Pingback: Litigio fiscal

  342. Pingback: Alienlabs Zkittlez

  343. Pingback: laundry service in bangalore

  344. Pingback: 늑대닷컴

  345. Pingback: Bandar judi online

  346. Pingback: One Peace AMV

  347. Pingback: nangs near me

  348. Pingback: superslot

  349. Pingback: freelance web designer

  350. Pingback: allgame

  351. Pingback: 918kiss

  352. Pingback: หวย24

  353. Pingback: Skincare for healthy skin

  354. Pingback: bulldog in clothes

  355. Pingback: pg slot

  356. Pingback: regles 421

  357. Pingback: cybersécurité

  358. Pingback: Raahe Guide

  359. Pingback: aplikasi slot online free spin

  360. Pingback: upstate hotels

  361. Pingback: resort lake placid

  362. Pingback: megagame

  363. Pingback: electronic visa

  364. Pingback: 300 win mag ammo

  365. Pingback: duromine

  366. Pingback: 6.5 grendel ammo

  367. Pingback: 220 swift

  368. Pingback: sicarios en españa

  369. Pingback: itsMasum.Com

  370. Pingback: itsMasum.Com

  371. Pingback: itsMasum.Com

  372. Pingback: itsMasum.Com

  373. Pingback: itsMasum.Com

  374. Pingback: la bonne formation pôle emploi

  375. Pingback: soc cybersécurité

  376. Pingback: nang tanks

  377. Pingback: Nangs delivery

  378. Pingback: nangs sydney

  379. Pingback: read more

  380. Pingback:

  381. Pingback:

  382. Pingback:

  383. Pingback: emeraldchat

  384. Pingback: teen chat

  385. Pingback: strangerchat

  386. Pingback: talkwithstrangee

  387. Pingback:

  388. Pingback: Film institutionnel Nantes

  389. Pingback: nairobi jobs

  390. Pingback: karachi jobs

  391. Pingback: ny jobs career

  392. Pingback: vienna jobs

Comments are closed.