Posts Tagged ‘Google’
In late December of 2013, Google Chairman Eric Schmidt admitted that ignoring social networking had been a big mistake. ”I guess, in our defense, we were busy working on many other things, but we should have been in that area and I take responsibility for that,” he said.
Brass tacks: Google’s misstep and Facebook’s opportunistic land grab of social media have resulted in a striking data chasm between the two behemoths. As a result, Facebook can do something that Google just can’t.
To his credit, Mark Zuckerberg has not been complacent with this lead. This is an age of ephemera. He is building upon his company’s lead in social data. Case in point: the launch of Graph Search.
The rationale here is pretty straightforward: Why let Google catch up? With Graph Search, Facebook users can determine which of their friends have gone to a Mexican restaurant in the last six months in San Francisco. What about which friends like the Rolling Stones or The Beatles? (Need to resell a ticket? Why use StubHub here? Maybe Facebook gets a cut of the transaction?) These are questions and problems that Google can’t address but Facebook can.
All good for Zuck et. al, right? Not really. It turns out that delivering relevant social data in a timely manner is proving remarkably elusive, even for the smart cookies at Facebook.
The New News Feeds
As Wired reported in May of 2013, Facebook “redesigned its News Feed with bolder images and special sections for friends, photos, and music, saying the activity stream will become more like a ‘personalized newspaper’ that fits better with people’s mobile lifestyles.” Of course, many users didn’t like the move, but that’s par for the course these days. You’re never going to make 1.2 billion users happy.
But Facebook quickly realized that it didn’t get the relaunch of News Feed right. Not even close. Just a few weeks before Schimdt’s revealing quote, Business Insider reported that Facebook was at it again, making major tweaks to its feed and then halting its new launch. This problem has no simple solution.
Simon Says: Big Data Is a Full-Time Job
Big Data is no picnic. “Managing” it isn’t easy, even for billion-dollar companies such as Facebook. The days of “set it and forget it” have long passed. Organizations need to be constantly monitoring the effectiveness of their data-driven products and services, to say nothing of testing for security issues. (Can someone say Target?)
What say you?
I love Google and in a pretty unhealthy way. In my third book, The New Small, there are oodles references to Google products and services. I use Google on a daily basis for all sorts of different things, including e-mail, document sharing, phone calls, calendars, and Hangouts.
And one more little thing: search. I can’t imagine ever “Binging” something and, at least in the U.S., most people don’t either.
Yet, there are limitations to Google and, in this post, I am going to discuss one of the main ones.
A few years ago, I worked on a project doing some data migration. I supported one line of business (LOB) for my client while another consultant (let’s call him Mark) supported a separate LOB. Mark and I worked primarily with Microsoft Access. The organization ultimately wanted to move toward an enterprise-grade database, in all likelihood SQL Server.
Relying Too Much on Google
Mark was a nice guy. At the risk of being immodest, though, his Access and data chops weren’t quite on my level. He’d sometimes ask me questions about how to do some relatively basic things, such as removing duplicates. (Answer: SELECT DISTINCT.) When he had more difficult questions, I would look at his queries and see things that just didn’t make a whole lot of sense. For example, he’d try to write one massive query that did everything, rather than breaking them up into individual parts.
Now, I am very aware that development methodologies vary and there’s no “right” one. Potato/pot-ah-to, right? Also, I didn’t mind helping Mark–not at all. I’ll happily share knowledge, especially when I’m not pressed with something urgent.
Mark did worry me, though, when I asked him if he knew SQL Server better than MS Access. “No,” he replied. “I’ll just Google whatever I need.”
For doing research and looking up individual facts, Google rocks. Finding examples of formulas or SQL statements isn’t terribly difficult either. But one does not learn to use a robust tool like SQL Server or even Access by merely using a search engine. You don’t design an enterprise system via Google search results. You don’t build a data model, one search at a time. These things require a much more profound understanding of the process.
In other words, there’s just no replacement for reading books, playing with applications, taking courses, understanding higher-level concepts, rather than just workarounds, and overall experience.
You don’t figure out how to play golf while on the course. You go to the practice range. I’d hate to go to a foreign country without being able to speak the language–or accompanied by someone who can. Yes, I could order dinner with a dictionary, but what if a doctor asked me in Italian where the pain was coming from?
What say you?
Over the course of my career, I have written more reports than I can count. I’ve created myriad dashboards, databases, SQL queries, ETL tools, neat Microsoft Excel VBA, scripts, routines, and other ways to pull and massage data.
In a way, I am Big Data.
This doesn’t make me special. It just makes me a seasoned data-management professional. If you’re reading this post, odds are that the list above resonates with you.
Three Problems with Creating Excessive Reports
As an experienced report writer, it’s not terribly hard to pull data from databases table, external sources, and the web. There’s no shortage of forums, bulletin boards, wikis, websites, and communities devoted to the most esoteric of data- and report-related concerns. Google is a wonderful thing.
I’ve made a great deal of money in my career by doing as I was told. That is, a client would need me to create ten reports and I would dutifully create them. Sometimes, though, I would sense that ten weren’t really needed. I would then ask if any reports could be combined. What if I could build only six or eight reports to give that client the same information? What if I could write a single report with multiple output options?
There are three main problems with creating an excessive number of discrete reports. First, it encourages a rigid mode of thinking, as in: “I’ll only see it if it’s on the XYZ report.” For instance, Betty in Accounts Receivable runs an past due report to find vendors who are more than 60 days late with their payments. While this report may be helpful, it will fail to include any data that does not meet predefined criterion. Perhaps her employer is particularly concerned about invoices from particularly shady vendors only 30 days past due.
Second, there’s usually a great deal of overlap. Organizations with hundreds of standard reports typically use multiple versions of the same report. If you ran a “metareport”, I’d bet that some duplicates would appear. In and of itself, this isn’t a huge problem. But often database changes means effectively modifying the same multiple times.
Third, and most important these days, the reliance upon standard reports inhibits data discovery.
Look, standard reports aren’t going anywhere. Simple lists and financial statements are invaluable for millions of organizations.
At the same time, though, one massive report for everything is less than ideal. Ditto for a “master” set of reports. These days, true data discovery tools like Tableau increase the odds of finding needles in haystacks.
Why not add interactivity to basic reports to allows non-technical personnel to do more with the same tools?
What say you?
In my post from this past summer Through a PRISM, Darkly, I blogged about how ours is a world still struggling to come to terms with having more aspects of our everyday lives, both personal and professional, captured as data.
We rarely consider the data privacy implications of our brave new data world, which prompted me to ask why we are so concerned about the government accessing data that in many instances we voluntarily gave to companies like Google, which provides free services (not counting the money we do pay for our mobile phone plans and to our Internet service providers) that are not really free because we pay for them with our privacy.
“Google has sucked millions of people into its web by delivering a feature-packed email service that comes only at the price of our privacy,” David Braue recently blogged.
“We must face the unavoidable reality that we have sold our souls for free email. Think about it: We bleat and scream to the hills about the government’s invasions of our privacy, then turn around and mail our personal information using a service specifically designed to harvest that information.”
“Google has positioned Gmail as a gateway drug to a world where everything runs according to Google. Google wants to manage our photos, our social media, our email, our word-processing documents, our everyday tasks, even our general documents.”
“This is the brave new world of the Internet,” Braue argued, “where privacy is an historical footnote and we are tricked or simply bribed to give it up. By and large, we are quite happy to do so. We may not love the need to deliver our personal lives on a platter in exchange for a spam-free, easily-accessible and substantially awesome email experience — but we do so with a smile, over and over again.”
To Braue’s point, no one is forcing us to use Gmail. Many, myself included, use it for the convenience of managing multiple email accounts across multiple mobile devices.
And Google is certainly not our only enemy combatant in what I have previously dubbed the Data Cold War. However, when we trade convenience for privacy, we have to admit the inconvenient truth that Pogo taught us long ago: “We have met the enemy and he is us.”
We don’t give away those slips of paper in our wallets without realizing that’s a form of currency. And we don’t give away the digital currency that is our credit card numbers (e.g., via Twitter, you could use a single tweet to post seven of your credit card numbers, with one space after each 16-digit number, and hashtag it with #MyCreditCardNumbers — but I will assume you would not).
However, we do give away countless bytes of our personal data in exchange for Internet/mobile-based services that we consider to be free because, unlike the companies providing those services, we do not count personally identifiable information as a form of currency.
The reality is our privacy is currency — and we are giving it away.
Is contemporary dataviz really new?
So would argue no. After all, many of the same reporting, business intelligence, and analytics applications also provide at least rudimentary levels of data visualization and have for some time now. Yes, there are “pure” dataviz tools like Tableau, but clear lines of demarcation among these terms just do not exist. In fact, lines between terms blur considerably.
But I would argue that modern-day dataviz really is new. This begs the natural question: How is contemporary dataviz fundamentally different than KPIs, dashboards, and other reporting tools?
In short, dataviz is about data exploration and discovery, not traditional reporting. To me, those trried and true terms always implied that that the organization, department, group, or individual employee knew exactly what to ask and measure. Example included:
- How many sales per square foot are we seeing?
- What’s employee turnover?
- What’s our return on assets?
These are still important questions, even in an era of Big Data. But contemporary dataviz is less, well, certain. There’s a genuine curiosity at play when you don’t know exactly when you don’t know what you’re looking for, much less what you’ll find.
In keeping with the data discovery theme of this post, why not try to answer my question about dataviz using dataviz? Still, while it’s only a proxy, I find Google Trends to be a very useful tool for answering questions about what’s popular/new, where, when, and how things are changing. For instance, consider the searches taking place on “data visualization” over the past four years throughout the world:
Since I live in the US, I was curious about how my home country broke down. In other words, is dataviz more popular in different parts of the country? With Google Trends, that’s not hard to see:
Note here that new and popular are not necessarily one and the same. Again, this was meant to serve as a proxy–and to illustrate the fact that dataviz doesn’t necessarily lead to a particular next step. I was exploring the data and, if I really wanted, I could keep going.
Data discovery doesn’t necessarily lead to a logical outcome–and that’s fine.
What say you?
“One man’s data is another man’s metadata”
As I pen these words, the PRISM scandal continues to unfold. The questions raised by the National Security Administration’s formerly furtive program strike at the very heart of a free society.
The fallout will continue for months, if not years. Maybe it will spark a deeper conversation about data ownership. Perhaps more people will echo the words of Jim Harris, who wrote recently on this site:
The Metadata Cop-Out?
I for one noticed something interesting buried in many of the non-denial denials, the carefully scripted and lawyer-approved statements from Microsoft, Apple, Yahoo!, Microsoft, Facebook, and others. Many press releases claimed (truthfully, for all I know) that these companies didn’t provide data per se to the NSA. Rather, they provided metadata. In other words, Yahoo! didn’t give up the actual contents of any single email, just things like:
- the sender’s email address
- the receiver’s email address
- the subject of the email
- the time and date that the email was sent
So, what is this distinction between data and metadata? And does it ultimately matter?
I discussed this very subject recently with my friend Melinda Thielbar, a real-life statistician and data scientist. She agreed with me that the distinction is becoming “essentially meaningless.” Equipped with enough of (the right) metadata, one can more or less figure out what’s going on–or at least identify potentially suspicious communications among persons of interest.
The quote at the beginning of this post is as true as its ever been. In a world of Big Data, metadata is increasingly important. It’s not just the video, picture, blog post, email, or customer record that matters. The data about or “behind” the data can be just as critical.
Is your organization paying attention to its metadata?
For the last few years, it’s become very difficult for IT to police who brings personal devices into the enterprise (never mind what people do with them). If you’re reading this site, you’ve probably heard of BYOD, a trend that will surely continue. Google Glass and its ilk are coming soon. These devices pose additional risks for IT departments determined to prevent data theft, security breaches, and industrial espionage.
But what about bringing your own software? Is this a nascent trend about which IT has to worry?
Yammer: A Case Study
True enterprise collaboration software has existed for a quite some time. More than a decade ago, we began to hear of corporate intranets and knowledge bases. One of the first proper collaboration applications: Microsoft SharePoint. Such promise!
Lamentably, employees did not consistently use these tools throughout the enterprise. For a host of reasons, many organizations continued to rely upon email as the killer collaboration app. Generally speaking, this was a mistake. Email doesn’t lend itself to true collaboration, and some CEOs even banned email.
Faced with the need to share information in a more efficient manner, many people began collaborating on the worst possible place: Facebook. From a recent study:
Facebook is a collaboration platform twice as popular as SharePoint — 74% to 39%. It’s also four times more popular than IBM Open Connections (17%) and six times more popular than Salesforce’s Chatter (12%).
The study, of 4,000 users and 1,000 business and IT decision-makers in 22 countries, also said 77% of managers and 68% of users say they now use some form of enterprise social networking technology. IT decision makers said such social technologies make their jobs more enjoyable (66%), more productive (62%) and “help them get work done faster” (57%). All in all, said Avanade, of those businesses currently using social collaboration tools, 82% want to use more of them in the future.
The governance, security, and privacy issues posed by using the world’s largest social network as an enterprise collaboration tool are hard to overstate. Yet, many experienced IT professionals became fed up with clunky top-down collaboration tools like SharePoint.
Consider the recent success of Yammer, a Freemium-based and organic alternative to top-down tools like SharePoint. Yammer became so popular precisely because it was organic. That is, employees could download the application and kick its tires. IT did not need to deploy or bless it. Organizations could date before they got married. If they wanted to expand its use–or unlock key functionality, they could pay for Yammer. And that’s exactly what many organizations did. To its credit, about a year ago, Microsoft purchased Yammer for more than $1 billion in cash.
CXOs should think very carefully about whether their current applications enable their organizations and employees to be productive. These days, it doesn’t take a computer scientist to circumvent restrictive corporate IT policies. Yammer is not unique. In other words, if it can go viral within an organization, other applications can.
What say you?
Few companies do data management better than Amazon–and I’m not just talking about their internal practices. Regardless of how well the company’s analytic systems generate über-accurate recommendations, it’s not perfect. Nor, for that matter, is it a substitute for human intuition.
To the Amazon’s credit, it recognizes the inherent limitations of relying exclusively upon its sophisticated algorithms and machines. Why not let customers refine, customize, and even remove their own recommendations, à la Netflix? In fact, Amazon does just that. Just look at the image below:
When perusing books, Amazon lets each customer override its own algorithm-generated recommendations. In this case, I can tell Amazon that I have no desire to read Guy Kawasaki’s book. (This was random. I have no bone to pick with Apple’s former chief evangelizer.)
It’s evident to me that machines can spawn remarkable recommendations. Collaborative filtering is nothing short of amazing–and I’m more than willing to consider Netflix gentle suggestions. But organizations adept at Big Data realize the inherent limitations of a computer- or data-only method to data management. In fact, there are typically legitimate reasons to ignore the results of even very accurate algorithms. Brass tacks: they’re not always right.
Even mighty Google–another Big Data stalwart–isn’t batting 1.000 vis-à-vis algorithm accuracy. Consider the recent Nature.com story on Google Flu that “drastically overestimated peak flu levels.”
The Limits of Democratized Data
No one is saying that all data should be democratic. I can’t imagine allowing employees to update their own pay rates or companies letting vendors tweak their own invoices. (In fact, ERP self-service tools have been with us for more than a decade, although many organizations refuse to use them for a cauldron of reasons).
Still, it’s hard to see the downside of Amazon’s move here. After all, don’t customers know what they like better than some machine? What’s the real harm in allowing them to remove items from their search or browsing history that they have no intention of buying? I’d argue that the benefits of this type of move far exceed their costs.
Organizations ought to learn from the examples set by Big Data leaders such as Amazon, Apple, Facebook, and Google. No CTO, CIO, or individual employee should be so enamored with his or her algorithm or technology that common sense is ignored. As a starting point, yes, emerging technologies and fancy algorithms can do amazing things and tap into heretofore unknown insights. By the same token, though, a user-override can often improve a good but imperfect result.
What say you?
Few companies manage information better than Google. If there were an annual award for corporate information management (IM), I’m sure that Google would have won it–or had been in the top three–over the past decade.
Why take IM so seriously? Because, quite frankly, without it, Google becomes much, much less valuable. Sans information, how does Google really help us? It helps us find what we want; Google doesn’t directly give us what we want. In other words, we don’t spend much time on google.com. We use it to go to the sites that let us buy things.
It turns out that we ain’t seen nothing yet. Google has been fine-tuning Google Now (Google Alerts on steroids.) From a recent TechCrunch article:
Google Now is a standard feature of Android Jelly Bean and up. It’s an easily accessible screen that shows you information about your daily commute (because it learns where you go every day and makes an educated guess as to where ‘home’ and ‘work’ are for you), appointments, local weather, upcoming flight and hotel reservations (assuming you give it access to scan your Gmail account) and how your favorite team did last night (it learns that from your search behavior). It also notices when you are not at home and shows you how long it’ll take you to get back to your house, or, if you are travelling, presents you with a list of nearby attractions you may be interested in, the value of the local currency, the time back home and easy access to Google Translate.
In a post-PC world, it’s not hard to understand the vast potential value of a personalized technological companion who can help you navigate an increasingly busy and complex world. With what Google knows about you via email, Web-surfing habits, social connections via Plus, and the like, Google Now may in fact be a game-changer.
Implications for Big Data
But you can only do so much on your Android device. What if you could see things? What if wearable technology and augmented reality could make your life even easier (or creepy, depending on your point of view)? Enter Google’s Project Glass, a pet project of Sergey Brin.
If you think that data is big now, get ready for Really Big Data. What if you could just think about recording a walk in Paris and publish it to YouTube in the process? What if you could review a product on Yelp by talking to yourself at the store? Perhaps speech-to-text technology would then publish that review automatically? Maybe your car will drive you to your next appointment on your Google calendar.
Does this sound Kurzweilian? It should. Google just hired the legendary futurist.
The implications are nearly limitless. As technology continues to evolve into heretofore “protected” areas, more and more data will be generated. Companies like Amazon, Apple, Facebook, Twitter, and Google have the compute power and storage capacity to actually do something with this data. Machine learning, text analytics, natural language processing, and other
The enabling technologies behind Big Data are getting better every day. We’re just getting started. Your organization ought to be preparing for a data-driven world right now. If not, it may very well fall and not get up.
What say you?
Last week, I discussed sloth. I examined the inherent tendency of some people to postpone working–often to the detriment of the organization. This week, I’ll cover lust defined by Wikipedia as:
an emotion or feeling of intense desire in the body. The lust can take any form such as the lust for knowledge, the lust for sex or the lust for power. It can take such mundane forms as the lust for food as distinct from the need for food. Lust is a powerful psychological force producing intense wanting for an object, or circumstance fulfilling the emotion.
Alright, but what does that have to do with IM projects? Moreover, is lust inherently bad?
Lust and IM
Many organizations want to do more than they are currently doing–and, just as important, are currently capable of doing. Think about the new CIO or CEO who comes in to an organization, astonished to see that it isn’t doing anything with respect to Big Data, semantic technologies, business intelligence, or other “Enterprise 2.0″ things. Or perhaps that new CXO finally wants the organization to embrace a more data-oriented mind-set.
New leaders want to put their stamps on their organizations. After all, they were recruited because they had a specific vision of where to take the company. If promoted from within the organization, the same principle applies. Succession plans exist for a reason, and few people are supposed to just maintain the status quo. (Yes, this even applies to Tim Cook.)
Is lust inherently bad?
I’d argue no. In an IM context, lust is a fundamentally function of wanting to do something better, whether it’s selling more widgets, increasing profits, doing more with technology, and the like. So, what’s the problem with lust? Isn’t it tantamount to self-improvement?
In short, lust can get leaders, departments, and organizations in trouble when it involves reaching way beyond the capabilities of the enterprise, individual personnel, and/or departments. For instance, it’s sure easy to envy Apple, Facebook, and Google. However, there might be a very good reason (or ten) that a mature organization cannot interpret data as well as those companies.
When listening to the oft-compelling pitches from enterprise software vendors and consulting firms, one starts to dream, “What if we could do exactly what Google does? Wouldn’t that be great?”
Yes, it would, but here’s the rub: Your organization isn’t Google. Specifically, Google doesn’t struggle with basic data management. Its employees don’t cling to antiquated ways of doing things. The company doesn’t rely upon a cauldron of legacy systems to run its enterprise. Google employees don’t have to manually cobble together user statistics over a period of months. Google doesn’t struggle to provide real-time statistics to advertisers, users, and partners.
If your organization does these things, then it’s unlikely that it will be able to reap the benefits of Google-type technologies.
Use lust sparingly. Yes, it can serve as motivation to get better, but don’t let it cloud your judgment. Realize the limitations of your organization. Take steps now to improve data management, organizational culture and agility, and the like now. Only then can you satisfy your lustful IM desires.
What say you?
Next week: pride.
TODAY: Mon, April 24, 2017April2017