Posts Tagged ‘Netflix’
I’ve written before on this site about how companies like Netflix handle Big Data exceptionally well. To serve up customized video recommendations to over 30 million subscribers, many things need to happen. Recognition of the importance of data and massive investments in technology only get an organization so far. No company can succeed without the right type of people, and Netflix is no exception to this rule.
Check out the following Netflix job description for a Data Visualization Engineer – Operational Insight Team:
We’re looking for a passionate, experienced Data Visualization Engineer who will own and build new, high-impact visualizations in our insight tools to make data both understandable and actionable.
- Develop rich interactive graphics, data visualizations of “large” amount of structured data, preferably in-browser
- Work with an Information Architect/UX Designer and with multiple groups and members across reporting lines to design visual, analytical components within applications
A few things struck me about this job description. First, note here the emphasis on interactivity. Netflix understands that static dataviz only gets an organization so far. To really understand Big Data, it’s becoming important for employees to be ask better questions. In other words, the answer is not always obvious, much less linear. Building interactive tools allows users to find the signals in the noise.
Second, the job description demonstrates just how much Netflix appreciates the multidisciplinary nature of dataviz. This is not about creating tools for IT, HR, finance, or any one department. Increasingly, organizational lines are becoming blurred. Apple’s new corporate headquarters is circular for a reason, and Zappos’ CEO Tony Hsieh is following the same model with his company’s new building.)
Finally, Big Data and dataviz ideally help organizations realize valuable insights. It’s not about collecting as much data as possible. He with the most petabytes doesn’t win. Presenting data in a format that allows people to see what’s going on is paramount.
Despite its lead over its competition, Netflix understands the importance of keeping the foot on the pedal. By hiring new and talented folks, Netflix will put further distance between itself and others.
Silly is the organization that fails to recognize the cardinal importance of people. These days, data and technology alone are necessary but insufficient for long-term success.
What say you?
Everybody has a plan until they get punched in the face.
Who should do what on a Big Data project?
It seems like a logical and even necessary question, right? After all, Big Data is a big deal, and requires assistance from each line of business, the top brass, and IT, right?
Matt Ariker, Tim McGuire, and Jesko Perry recently wrote a HBR post attempting to answer this question. In Five Roles You Need on Your Big Data Team, the three advocate five “important roles to staff your advanced analytics bureau”:
- Data Hygienists
- Data Explorers
- Business Solution Architects
- Data Scientists
- Campaign Experts
To be sure, everyone can’t and shouldn’t do everything in an era of Big Data. I can’t tell you for certain that bifurcating roles like the authors recommend won’t work. Still, I just don’t buy the argument that Big Data lends itself to everything fitting neatly in to traditional roles.
Take data quality, for instance. As Jim Harris writes:
The quality of the data in the warehouse determines whether it’s considered a trusted source, but it faces a paradox similar to “which came first, the chicken or the egg?” Except for the data warehouse it’s “which comes first, delivery or quality?” However, since users can’t complain about the quality of data that hasn’t been delivered yet, delivery always comes first in data warehousing.
Agreed. Traditional data warehousing projects could be thought of in a more linear fashion. In most cases, organizations were attempting to aggregate–and report on–their data (read: data internal to the enterprise). Once that source was added, maintenance was fairly routine, at least compared to today’s datasets. These projects tended to be more predictable.
But what happens when much if not most relevant data stems from outside of the enterprise? What do we do when new data sources start popping up faster than ever? Mike Tyson’s quote at the top of this post has never been more apropos.
Simon Says: Big Data Is Not Predictable
My point is that IT projects have start and end dates. Amazon, Apple, Facebook, Twitter, Google, and other successful companies don’t view Big Data as “IT projects.” This is a potentially lethal mistake. For its part, Netflix views both Big Data and data visualization as ongoing processes; they are never finished. I make the same point in my last book.
When you starting thinking of Big Data as an initiative or project with traditionally defined roles, you’re on the road to failure. Don’t make “data hygenics” or “data exploring” the sole purview of a group, department, or individual. Encourage others to step out of the comfort zones, notice things, test hypotheses, and act upon them.
What say you?
I’ve written before on this site on how Netflix uses data in fascinating ways. The company’s knowledge of its customers–and their viewing habits–is beyond impressive. Moreover, it’s instructive for companies attempting to navigate the era of Big Data.
Consider this Wired article explaining how Netflix operates and utilizes its data:
But Netflix doesn’t only know what its audience likes to watch; it also knows how viewers like to watch it—beyond taste, the company understands habits. It doesn’t just know that we like to watch Breaking Bad; it knows that we like to watch four episodes of Breaking Bad in a row instead of going to sleep. It knows, in other words, that we like to binge.
Think about the astonishing level of knowledge that Netflix has developed on its customers. Not just the what, but the how, the when and (increasingly) the why. Talk about the Holy Grail. This should be the goal of every for-profit enterprise. Period.
Equipped with information like this, Netflix can make more informed business decisions. Case in point: Its decision to resurrect the very popular cult classic Arrested Development. While by no means an inexpensive or risk-free venture, Netflix management is reasonably confident that its bet will pay off for one simple reason: its data supports the decision.
Note that each move Netflix makes is hardly guaranteed. No business decision even resembles complete certainty. But, by virtue of its exceptional data management, Netflix has moved the needle considerably. Its odds of success are without question much higher because it has minimized risk.
Think about the way in which far too many organizations operate these days. Forget Big Data and effectively harnessing its power. I’ve personally seen many enterprises manage their data so poorly that a comprehensive and accurate list of customers could not be produced in a reasonable period of time. Without this information, questions like how, when, and why could not be answered. The downstream effect: Decisions were based upon conjecture, rank, culture, and policy. This type of scenario is hardly ideal.
Rome, as they say, was not built in a day–and neither was Netflix. Rather than fret over the state of your data, take the steps now to improve your ability to analyze data in a few years.
What say you?
A few years ago and while its stock was still sky-high, Netflix ran an innovative contest with the intent of improving its movie recommendation algorithm. Ultimately, a small team figured out a way for the company to significantly increase the accuracy with which it gently suggests movies to its customers.
It turns out that these types of data analysis and improvement contests are starting to catch on. Indeed, with the rise of Big Data, cloud computing, open source software, and collaborative commerce, it has never been easier to outsource these “data science projects.”
From a recent BusinessWeek article:
In April 2010, Anthony Goldbloom, an Australian economist, [f]ounded a company called Kaggle to help businesses of any size run Netflix-style competitions. The customer supplies a data set, tells Kaggle the question it wants answered, and decides how much prize money it’s willing to put up. Kaggle shapes these inputs into a contest for the data-crunching hordes. To date, about 25,000 people—including thousands of PhDs—have flocked to Kaggle to compete in dozens of contests backed by Ford (F), Deloitte, Microsoft (MSFT), and other companies. The interest convinced investors, including PayPal co-founder Max Levchin, Google Chief Economist Hal Varian, and Web 2.0 kingpin Yuri Milner, to put $11 million into the company in November.
The potential for these types of projects is hard to overstate. Ditto the benefits.
Think about it. Organizations can publish even extremely large data sets online for the world at large. Interested groups, companies, and even individuals can use powerful tools such as Hadoop to analyze the information and provide recommendations. In the process, these insights can lead to developing new products and services and dramatic enhancements in existing businesses process (see Netflix).
Of course, these organizations will have to offer some type of prize or incentive. Building a better mousetrap may be exciting, but don’t expect too many people to volunteer their time without the expectation of significant reward. Remember that, of the millions of people who visit Wikipedia every day, only a very small percentage of them actually does any editing. If Wikipedia (a non-profit) offered actual remuneration, that number would be significantly higher (although the quality of its edits would probably suffer).
Consider the following examples:
- A pharmaceutical company has a raft of data on a new and potentially promising new drug.
- A manufacturing company has years of historical data on its defects.
- A retailer is trying to understand its customer churn but can’t seem to get its arms around its data.
I could go on, but you get my drift.
While there will always be the need for proprietary data and attendant analysis, we may be entering an era of data democratization. Open Data is here to stay and I can certainly see the growth of marketplaces and companies like Kaggle that match data analysis firms with companies in need of that very type of expertise.
Of course, this need has always existed, but unprecedented power of contemporary tools, technologies, methodologies, and data mean that outsourced analysis and contests have never been easier. No longer do you have to look down the hall, call IT, or call in a Big Four consulting firm to understand your data–and learn from it.
What say you?
TODAY: Mon, April 24, 2017April2017