by Svetlana Sicular | August 28, 2014 | 1 Comment
As a Gartner analyst, I am fortunate to frequently meet amazing people. Qaizar Hassonjee from Adidas is not only one of them, but one of the most memorable ones among the amazing people. He is at the heart of miCoach, including miCoach Elite, the system developed in partnerships with the top soccer players, coaches and teams of the world where soccer is known as football. For instance, German national team was practicing all last year with miCoach.
We invited Qaizar Hassonjee to talk at our Catalyst conference earlier this month, and he accepted our invitation! I was tweeting like crazy, “Everyone, drop everything, go to End-User Case Study: Smart Soccer With adidas miCoach Elite Team System!” This session is recorded by Gartner Events On Demand, which offers analyst and guest speaker presentations from all our conferences, woo-hoo!
Qaizar Hassonjee is a passionate leader who knows how to focus and what to focus on. He leads fantastic innovations, like creation of a sensor t-shirt to monitor an athlete’s heart rate and performance. And this sensor t-shirt is washable! I am writing this blog post, because Qaizar Hassonjee and his team got big data right. Here is the Gartner’s definition of big data (which I explained in the past):
Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.
This is how the big data definition plays out in digital sports.
Part 1. High-volume, high-velocity and high-variety information assets.
This is a screenshot of adidas VP of Innovation Qaizar Hassonjee’s talk at Catalyst
miCoach collects players’ heart rate, physiological parameters, geolocation and much more in real time, with a lot of unexpected uses of data. For example, a location heat map was important to people who maintained the field.
Part 2 of the definition. Information assets that demand cost-effective, innovative forms of information processing.
The miCoach team was focusing on serving the right analytics at the right time. They did not make typical mistakes of relying exclusively on their own expertise, but involved cardiologists, physiologists, equipment managers, and of course, coaches and players.
And finally, part 3 of the definition: Information processing for enhanced insight and decision making.
These are the main points that led to success of miCoach because of the big data insights:
- Don’t overload with data and information.
- Don’t sacrifice performance.
- Don’t over-engineer.
- Focus on integration of different components to bring a unique user experience and test, test, test.
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Category: analytics Big Data data data paprazzi Information Everywhere innovation Uncategorized Tags: analytics, big data, Catalyst, data paprazzi, experience, Information Everywhere, innovation
by Svetlana Sicular | July 29, 2014 | 7 Comments
Everybody talks about successes in big data. And everybody is curious about failures. Today, I want to illustrate some typical causes of big data project failures with real-life examples, no company logos to show, sorry. I’ll give not necessarily “fail fast” scenarios, but also the uneventful and painful “fail slow.” Let’s start with the amazing success story.
Management inertia. Our client, a household name among early internet travel companies, as well as the early adopter of big data technologies, ran click-stream analysis to find out how people navigate this travel site and how they make purchases. It turned out that the buying patterns were exactly opposite from the sales approach of the company’s upper management. This is the verbatim quote about this rare happy end:
“We’ve had great success with this technology. The insights we’ve had changed the business dramatically. To capitalize on these insights we brought in new management.“
How many companies are in a position to get rid of their upper management?
Selecting wrong use cases. Many companies start with advanced use cases that require a better understanding of technologies, which comes with experience. Other companies select the same use cases that they used to implementing on traditional technologies, and, consequently, they don’t see benefits. My blog post The Top Mistake in Evaluating Big Data Initiatives describes this situation.
Asking wrong questions. An automobile manufacturer with thousands of dealerships ran a sentiment analysis project to learn about its customers. Six months and $10M later the findings from big data were distributed to all thousands of dealerships, and all thousands of them were laughing out loud: every one of them knew all along what the big data project was digging out all this time.
Lacking the right skills. Every one of us considers him/herself an expert in human behavior, our native language or our own social life. So are people running big data analytics projects. A financial services company started a project to detect how people’s habits affect their propensity to buy retirement plans. Humans are creatures of habits, and of too many habits. People who ran the project decided (little by little, failing slowly) to narrow down all habits just to smoking / non-smoking. And failed again. It turned out (from my dialogs with a healthcare company, which coincided with this one) that healthcare professionals instead of a black-and-white “do you smoke?” would have asked, ” how many years did you smoke? How many times did you quit smoking? When was the last time you smoked?” The bottom line: look for professionals who know the field you analyze — healthcare experts, linguists, behavioral psychologists, social anthropologists and others who normally don’t belong to IT.
Unanticipated problems that are wider than just a big data technology. One large retailer ran a big data project in the cloud. The network congestion to stores was a problem that derailed the whole project. A team member summarized their learning from the failure:
“Supporting any new platforms on a remote site is more than a technology problem. It must factor in personnel, training, upgrades, maintenance and real estate.”
Disagreement on the enterprise strategy. There are many trains of thought in a large company. Here is an eloquent quote from a client, an information architect:
“We see information as the heart. Others believe cloud is the heart of our strategy.”
As a result, there is no enterprise-wide strategy, but a lot of unrelated initiatives, big data being rather small.
Siloed big data negates the whole idea of having it. This reason for failure relates to the previous one. A client who learned it on his own mistakes said:
“Prioritization of business projects is a bit more difficult because we are so siloed in business units. We do not do a good job justifying the platform as a whole. Whoever screams loudest gets it.”
Solution avoidance. The most typical example is pharmaceutical industry required to report any known adverse drug effects. This whole industry does not conduct sentiment analysis, because they have to report to FDA any event when, for example, a patient complains about a headache in the same paragraph where a particular drug is mentioned.
My list of big data failures can go on, and on, and on. I especially want to stress the need to understand the data, no matter if it’s big or not. There are tons of cases of not knowing data, and, as a result, inability to deliver anything new, or having so much data and no experience of how to manage, analyze or query it. I will talk about data, big data and greater data in two weeks from now, at our Catalyst conference in San Diego. Come over!
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Category: analytics Big Data data data paprazzi Information Everywhere Uncategorized Tags: big data, big data adoption, data paprazzi, Information Everywhere, pseudo-tweets
by Svetlana Sicular | June 17, 2014 | 6 Comments
People often ask me if there is a magic quadrant for big data. There isn’t. What we have is a Hype Cycle for Big Data with abundance of big data technologies, some of which are just nascent, some are on the plateau of productivity, and some, like Hadoop distributions, are in the unrightfully dreaded and largely misunderstood trough of disillusionment.
Gartner also has annual cool vendor reports where analysts write about up and coming companies with innovative ideas, services and technologies. Many reports cover awesome big data vendors, for example, Cool Vendors in Big Data, Cool Vendors in Data Science and Cool Vendors in Information Innovation.
At Gartner for Technical Professionals (where I am), we usually publish vendor-neutral research and do not write for cool vendor reports (to be fair, we submit our choices and peer-review these reports). Yet, our clients constantly ask me and my colleagues about vendors. Last week, Fortune magazine published my opinion on big data companies to watch. My opinion was not about the best or the most prominent, most hyped or most intriguing, most funded or most profitable companies, but about the companies to watch.
Katherine Noyes, the author of the Fortune Magazine article, asked me to name five big data companies to watch and to comment on some published big data vendors lists. Below is my full response, it explains my choices:
Well, selecting just five companies is a challenge since there are many more companies that do interesting things around big data. I have technical and non-technical considerations for giving my list of five. My top noteworthy big data companies would be:
- Neo Technology is a force behind an open source graph database Neo4j – I think graphs have a great future since they show data in its connections rather than as a traditional view of atomic elements. Graph technologies are mostly unexplored by the enterprises but they are the solution that can deliver truly new insights from data. I wrote about graphs some time ago in my blog post Think Graph.
- Splunk has an excellent technology, and it was among the first big data companies to go public. Now, Splunk also has a strong product called Hunk (Splunk on Hadoop) directly delivering big data solutions that are more mature than most products in the market. Hunk is easy to use compared to many big data products, and generally, most customers I spoke with expressed their love to Splunk without any soliciting on my side.
- MemSQL – an in-memory relational database that would be effective for mixed workloads and for analytics. While SAP draws so much attention to in-memory databases by marketing their Hana database, MemSQL seems to be a less expensive and more agile solution in this space.
- Pivotal – while it might not be the most perfect big data solution, Pivotal is solving a much larger problem – the convergence of cloud, mobile, social and big data forces (which Gartner calls the Nexus of Forces). Eventually, big data is not a standalone technology but it should deliver actionable insights about the rapidly changing modern world with its social interactions, mobility, Internet of Things etc. That’s why GE is one of the major investors in Pivotal with the purpose of building the Industrial Internet.
- Teradata – it might be a surprising choice for many big data aficionados who chose Teradata as a target for religious wars of new big data technologies against the data warehouse, where Teradata is an easy prey because it’s a pure play in the data warehousing (as opposed to Oracle, IBM or Microsoft who have many more products). Meanwhile, Teradata delivers a unified data architecture that combines best of both worlds, and enterprises need both.
As you may have noticed, I am covering various segments of big data technologies. If I had more than five companies to choose from, I’d also add companies in other segments:
- Big data analytics: Actian and Datameer
- Predictive analytics: Revolution Analytics and Ayasdi
- Data integration: Pentaho and Denodo (particularly for data virtualization)
- Big data cloud providers: Qubole and Altiscale
- Hadoop: Cloudera – “first in space” for big data, huge recent investments from an interesting set of investors, most notably, Intel.
- Development framework: Concurrent with the open source product called Cascading, now included in some Hadoop distributions. Given that applications are about to explode on Hadoop, Concurrent should do very well.
Please note, this is not a comprehensive research and there are more very good companies. The companies I listed are “to watch” rather than best overall.
Now, to comment on the list of 100 big data companies you pointed me to. Out of this list, the following companies look appealing to me: Dataguise, MapR, MatterSight, Manhattan Software (an excellent player in real estate!) and Data Tamer (I would prefer Paxata though) – see my blog post Big Data Quantity vs. Quality.
I’d like to especially stop on The Hive. This VC company specializing in data has an unusual approach that I personally greatly appreciate. It conducts weekly live meetups, which cover diverse subjects and draw diverse people who are interested in big data. The Hive became one of the most well-known gatherings that attracts the brightest minds in big data as speakers (and as attendees). It became a social “big data hub” in Silicon Valley, and I believe, in India too. Being in the center of the big data life, the Hive has a great opportunity to make successful investments on early stages of data companies.
Finally, I’d like to remind you again: Gartner cool vendor reports are a much more comprehensive and pointed reading than my casual overview.
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Category: "Data Scientist" Big Data big data market data paprazzi Gartner hype cycle Hadoop Information Everywhere innovation The Era of Data Trough of Disillusionment Uncategorized Tags: big data, cloudera, data paprazzi, data scientist, Information Everywhere, innovation, MapR, Silicon Valley, vendors
by Svetlana Sicular | June 3, 2014 | Comments Off
In 2009, a CRM icon Tom Siebel was attacked by a charging elephant during an African safari. Ominously, this was exactly the time of changing epochs signified by another elephant, Hadoop. It was not obvious back in 2009 that the era of CRM came to the end and the era of data began. This very year of 2009, Cloudera announced the availability of the Cloudera Distribution of Hadoop, and MapReduce and HDFS became separate subprojects of Apache Hadoop. This was the year when people started talking about beautiful data.
The era of data is about the process of data commoditization, where data is becoming an independently valuable asset that is freely available on the market. A “commodity” is defined as:
- Something useful that can be turned to commercial or other advantage
- An article of trade or commerce
- An advantage or benefit
Looks familiar? That’s what we want data to become. And we are getting there, not very fast but steadily. Information patterns derived from data are already changing status quo; they disrupt industries and affect lives. Sometimes, data is useful, yet not turned to commercial advantage. For sure, data is increasingly becoming an article of trade or commerce. Notice, the number of new available Web APIs that give public access to data started growing explosively around the beginning of the era of data.
- Source: ProgrammableWeb
I am not the first one pointing to the commoditization of data. Bob Grossman, who epitomizes a data scientist to me, gave a detailed account of commoditization in his outstanding book The Structure of Digital Computing: From Mainframes to Big Data. In particular, the commoditization of time took most of the 17th century. We take our watches, clocks and phone timers for granted — think in a perspective: in the future, someone will take for granted access to all kinds of data. The last chapter of the Grossman’s book is entitled “The Era of Data.”
Open data is the strong manifestation of this new era. The first government open-data websites — data.gov and data,gov.uk — were launched in 2009. The government mandates and open data policies from multiple countries and public entities continue to contribute to the process of data commoditization. Openness has the benefit of increasing the size of the market. The greater the size of the market and the demand for a resource, the greater the competitive pressure on price and, hence, the increase in commoditization of the resource.
When data gets free or inexpensive (as a result of commoditization), the opportunity exists to unite people over data sets to make new discoveries and build new business models. Many companies choose Hadoop because it is a cheap data storage. This entry point is the first step on the journey to the data operating system, a term that I heard three times during past five days, notably from Doug Cutting who brought to the world Hadoop the elephant and the data operating system. This year’s Hadoop Summit starts today. It brought together 3,000 people from 1,000 organizations.
The last part of the “commodity” definition is “an advantage or benefit.” Gartner analysts Mark Beyer and Donald Feinberg predicted several years ago:
By 2014, organizations which have deployed analytics to support new complex data types and large volumes of data in analytics will outperform their market peers by more than 20% in revenue, margins, penetration and retention.
According to my observations, it’s true. If this is true for you? If not, be patient —an elephant’s pregnancy is almost two years long.
P.S. Tom Siebel survived the elephant attack. He is running a big data company C3 Energy now.
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Category: Big Data big data market data paprazzi Hadoop open data The Era of Data Uncategorized Tags: big data, cloudera, data paprazzi, Gartner predicts, hadoop, Information Everywhere, market analysis, Silicon Valley, The Era of Data
by Svetlana Sicular | May 22, 2014 | Comments Off
Increasing adoption of big data technologies brings about the big data dilemmas:
- Quality vs. quantity
- Truth vs. trust
- Correction vs. curation
- Ontology vs. anthology
Data profiling, cleansing or matching in Hadoop or elsewhere are all good but they don’t resolve these dilemmas. My favorite semantic site Twinword pictures what a dilemma is.
You get the picture. New technologies promote sloppiness. People do stupid things because now they can.
Why store all data? — Because we can.
What’s in this data? — Who knows?
Remember the current state of big data analytics? — “It’s not just about finding the needle, but getting the hay in the stack.”
Big data technologies are developing fast. Silicon Valley is excited about new capabilities (which very few are using). In my mind, the best thing to do right now is to enable vast and vague data sources that are commingling in the new and immature data stores, and are confined in mature data stores. Companies store more data than they can process or even fathom. My imagination fails at a quintillion rows (ask Cloudera). Instead, it paints a continuous loop: data enables analysis, analytics boosts the value of data. How to do this? It starts dawning on the market — through information quality and information governance!
My subject today is just the information quality piece. It continues my previous blog post BYO Big Data Quality. (I explained the whole information loop on this picture in Big Data Analytics Will Drive the Visible Impact of the Nexus of Forces.)
Data liberation means more people accessing and changing data. Innovative information quality approaches — visualization, exception handling, data enrichment — are needed to transform raw data into a trusted source suitable for analysis. Some companies use crowdsourcing for data enrichment and validation. Social platforms provide a crowdsourced approach to cleaning up data and facilitate finding armies of workers with diverse backgrounds. Consequently, the quality of crowdsourcing is another new task.
Big data is a way to preserve context that is missing in the refined structured data stores — this means a balance between intentionally “dirty” data and data cleaned from unnecessary digital exhaust, sampling or no sampling. A capability to combine multiple data sources creates new expectations for consistent quality; for example, to accurately account for differences in granularity, velocity of changes, life span, perishability and dependencies of participating datasets. Convergence of social. mobile, cloud and big data technologies presents new requirements — getting the right information to the consumer quickly, ensuring reliability of external data you don’t have control over, validating the relationships among data elements, looking for data synergies and gaps, creating provenance of the data you provide to others, spotting skewed and biased data.
In reality, a data scientist job is 80% of a data quality engineer, and just 20% of a researcher, dreamer and scientist. Data scientist spends enormous amount of time on data curation and exploration to determine whether s/he can get value out of it. The immediate practical answer — work with dark data confined in relational data stores. Well, it’s structured, therefore, it is not really new. But at least, you get enough untapped sources of reasonable quality, and you can extract enough value right away, while new technologies are being developed. Are they?
While Silicon Valley is excited about Drill, Spark and Shark, I am watching a nascent trend — big data quality and data-enabling. Coincidentally, I got two briefings last week, Peaxy (that I liked a lot for its strength in the Industrial Internet) and Viking-FS in Europe with the product called K-Pax, mainly for the financial industry. A briefing with Paxata is on Friday, and a briefing with Trifacta is scheduled too. Earlier this month, I got a briefing from Waterline Data Science, a stealth startup with lots of cool ideas on enabling big data. Earlier, I had encounters with Data Tamer, Ataccama and Cambridge Semantics among others. Finally, have you heard about G2 and sensemaking? Take a look at this intriguing video. All these solutions are very different. The only quality they have in common is immaturity. For now, you are on your own, but hold on — help is coming!
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Category: "Data Scientist" analytics Big Data big data quality Crossing the Chasm data governance data paprazzi Humans Information Everywhere innovation Inquire Within Uncategorized Tags: big data, crossing the chasm, data, data janitor, data paprazzi, data scientist, data spy, Information Everywhere
by Svetlana Sicular | May 16, 2014 | 2 Comments
In the absence of best practices for big data quality, individual companies are coming up with their own solutions. Of course, these organizations first have problems. Let’s look at the example from Paytronix, a cool company providing loyalty management for restaurant chains including my favorite Panera Bread. Paytronix is converging social, mobile, cloud and big data for its business (aha! The Nexus of Forces!). And — by the way — cutting edge technologies help a lot to attract top talent to the company. But first things first, Paytronix had a big data quality problem, here is the description:
- Over a quarter of their clients, restaurants, do not ask for age
- Of those who ask age, 18% leave it blank
- Of those who answer, approximately 10% are blatant liars
All of the above means that identifying families with kids is a huge challenge (spoiler: Paytronix successfully met the challenge). People with kids are younger. They tend to fill the restaurants earlier in the evening. Check average is higher when orders include a kids meal (I confirm for our orders in Panera). That’s why restaurants often want to market to people with children: when they offer a kids meal coupon they get 25% more redemptions. But! What customers say is different than what they do. (Aren’t we all customers?) In other words, here is the picture, instructive for parents:
Big data quality is new and different: Traditional models do not work, familiar standards do not apply, typical metrics miss the mark. Most important, people’s mentality has to change when they assure quality of big data. My colleague Martin Reynolds likes to cite, “most people are woefully muddled information processors who often stumble along ill-chosen shortcuts to reach bad conclusions.” This quote appeared in Newsweek in 1987, BC (before Cloudera, the first commercial Hadoop distribution vendor). E.g. the problem is eternal although it wasn’t so widespread in data management because there was not much data management in 1987. That Newsweek with the quote still advertised typewriters, best in the world. Wikipedia gives a daunting list of cognitive biases —each bias is a big data quality factor because quality applies to the resulting analysis, and to intermediate results, and to iterative data science. In case of Paytronix, segmentation was biased. Biases also apply to data mashups: to evaluating granularity, trustworthiness and dependencies of participating data sets. And sometimes, biases matter even to the absence or presence of particular data sources. Martin Reynolds shared with me the most astonishing example of cognitive bias.
Paytronix solved its big data quality problem by deciding not to change how people think. They validated data by giving it in cubes in a familiar BI tool to good old people. By the way, crowdsourcing is another excellent big data quality method that relies on people. But this is a subject of my next post — I will tell what vendors are doing about big data quality, and even maybe about big data governance. As DBAs like to say, stay tuned.
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Category: Big Data big data market Crossing the Chasm crowdsourcing data data governance data paprazzi Hadoop Information Everywhere innovation Inquire Within skills Uncategorized Tags: big data, big data adoption, cloudera, data paprazzi, data spy, end users, Hadoop distribution, Information Everywhere, innovation, pseudo-tweets
by Svetlana Sicular | April 28, 2014 | 1 Comment
Big data finally reached Wall Street. Not for small science experiments, but seriously, in grand style, with exorbitant salaries, for production and DevOps. A couple of years ago, Silicon Valley companies were bragging to each other about hiring Wall Street quants for data scientists. Now Wall Street is happy to grab — whom? No, not data scientists (New York has plenty of its own quants) — Data Architects, the breed who can come up with new architecture to combine structured and unstructured data where architecture for one use case usually (still) does not apply to another.
Dice.com returned just 34 data scientist jobs vs. 645 data architects in the New York area tonight. Even my search of “data architect + Hadoop” returned twice as many data architecture jobs compared to data scientists. Sorry, data scientists: data architects are sexy! My client inquiries shifted lately to no-nonsense big data architecture, management and real-time use cases. Big data vendors hinting left and right about “Wall Street customers in alpha,” e.g. newly signed contracts. My friend, a Wall Street recruiter, had to cut his vacation short – Wall Street is in a hurry to get data architects right now. And also, performance engineers, and developers, and administrators. And did I say, DevOps? Yes, for agility, my friends.
This hiring means that Wall Street is ready to use big data strategically. The picture below shows typical stages of big data adoption described in my research note The Road Map for Successful Big Data Adoption. The red dot on this picture — a stabilized infrastructure — is the most prominent milestone. After the infrastructure has been built, a capability to derive value from big data technologies leaps to a new level. Nonbelievers turn into believers.
At the red dot, big data becomes the new normal. It eventually gets related to other information sources. (When using the term “big data” analytics and information management professionals (across the Globe!) first say that they don’t like it.) At the red dot, companies substantially expand the number of nodes or totally rebuild the earlier small layouts.
A widespread myth is that Hadoop is inexpensive to implement. Really? With Wall Street salaries? An initial implementation is usually more expensive than expected. It involves a lot of unanticipated technical and nontechnical difficulties. By the way, another myth is that big data infrastructure usually takes advantage of commodity hardware. Maybe, but not on Wall Street. Enterprises buy high-end hardware.
I gave my friend a Wall Street recruiter a t-shirt stating “My data is bigger than yours”— he wears it to work (on Fridays). I should make one for myself, with the text Data is what used to be big data. Wall Street is getting there.
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Category: "Data Scientist" Big Data big data market Crossing the Chasm data Hadoop market analysis Uncategorized Tags: big data, big data adoption, crossing the chasm, data, data paprazzi, end users, hadoop, hiring, market analysis
by Svetlana Sicular | March 18, 2014 | 3 Comments
I’ve been watching the CRM space since the term CRM was coined. The view of the customer remained at invariable 360° while new ideas, methods and companies kept adding degree by degree to the full view. Back in 2009, a CRM icon Tom Siebel was attacked by a charging elephant during an African safari. Ominously, this was exactly the time of changing epochs: another elephant, Hadoop, signified a new era in the 360° view of the customer. This very year of 2009, Cloudera announced the availability of Cloudera Distribution Including Apache Hadoop. This very year MapReduce and HDFS became separate subprojects of Apache Hadoop. The era of data has begun.
Massive amounts of data about the interactions of people open the door to observing and understanding human behavior at an unprecedented scale. Big data technology capabilities lead to new data-driven and customer-centric business models and revenues. Organizations change because of new insights about customers. Depending on a use case, “customer” could mean consumer, employee, voter, patient, criminal, student or all of the above. Last Sunday, I became a “skier.” That’s how they call customers at Mt Rose Ski Tahoe. One more degree. The most successful innovators are primarily guided by a focus on meeting the needs of the end users whom their solutions serve — the customer, the client, the employee. Our recent research note Focus on the Customer or Employee to Innovate With Cloud, Mobile, Social and Big Data speaks about it in great depth.
User experience that supports people’s personal goals and lifestyles, whether they are customers or employees, is key to success more than ever. Personal analytics is a noteworthy and totally new type of analytics, quite distinct from the well-known business analytics. Personal analytics empowers individuals to make better decisions in their private lives, within their personal circumstances, anytime, anywhere. How many more degrees does that add to the 360° view of the customer?
Siebel Analytics was the first customer analytics solution. It ended up as OBIEE. (By the way, Oracle just acquired BlueKai — degrees and degrees of “audience” data!) Siebel Analytics nourished many analytics leaders, off the top of my head — Birst, Facebook Analytics, Splice Machine and even Cognos. ”I was very fortunate to have survived something you might not think was survivable,” said Tom Siebel about the elephant attack. Tom Siebel is now running a big data company called C3. Data is pouring from more and more sources. Beacon devices for in-door positioning are gaining more attention. This means imminent customer tracking in retail stores and ball parks.
The bottom line: When declaring a 360° view of the customer, count carefully. It could be 315°, or it could be 370°. Any angle greater than 360° means that the customer view is not expanding.
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Category: analytics Big Data big data market data data paprazzi Hadoop Humans Uncategorized Tags: big data, cloudera, CRM, data paprazzi, hadoop, Hadoop distribution, Information Everywhere
by Svetlana Sicular | March 7, 2014 | 3 Comments
The rocket ship of big data analytics is launched and on its way to orbit. Data and analytics are gaining importance with a cosmic speed. The rocket ship is fueled by cloud, mobile and social forces. Information is a single force that gets to the foreground over time while cloud and mobility, once implemented, become less visible. Then big data and analytics turn into a long-lasting focus of enterprises. Information architects and analytics gurus, get ready for a much greater demand for your expertise within next several years!
Last fall, my fellow analysts (covering social, mobile and cloud) and I (big data coverage) interviewed 33 people from truly innovative companies that have implemented social, mobile, cloud and information together (a.k.a. the Nexus of Forces). These were the brilliant innovators who were not just thinking about it, but those who have already done it. They were not implementing each force individually, but were taking advantage of technologies in combination. One visionary told us,
The secret sauce is optimization and trade-off to achieve the best whole, bringing it all together for a unique user experience.
Fascinating things are happening: companies in different industries think of themselves as data companies, information quality is ripe for disruption, everybody is craving for information governance, personal analytics is born and growing quickly (my colleague Angela McIntyre predicts, By 2016, wearable smart electronics in shoes, tattoos and accessories will emerge as a $10 billion industry). Convergence of forces surfaces my favorite subjects: big data, open data, crowdsourcing, and the human factor in technology.
We will talk about Lessons Learned From Real-World Nexus Innovators in a webinar on 11 March.
Three research notes describe our findings, in this order:
- Exploit Cloud, Mobile, Data and Social Convergence for Disruptive Innovation — analyzes how the Nexus of Forces is a platform for disruptive innovation and provides Key Insights for the entire Field Research project.
- Focus on the Customer or Employee to Innovate with Cloud, Mobile, Social and Big Data — analyzes how enterprises focus on the individual to capitalize on the Nexus opportunities.
- Big Data Analytics Will Drive the Visible Impact of Nexus of Forces — analyzes how big data analytics will be key to enabling transformative business model disruption.
And here is a quote from one of the interviews about the state of big data analytics:
“It’s not just about finding the needle, but getting the hay in the stack.”
The rocket ship is launched. Get ready for orbit.
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Category: analytics Big Data cloud crowdsourcing data governance Information Everywhere innovation open data Uncategorized Tags: analytics, big data, data paprazzi, Information Everywhere, innovation
by Svetlana Sicular | February 5, 2014 | 1 Comment
If Russian is Greek to you, use translation tools such as Google Translate or Translate.com — they will express the gist of my text. But if you want nuances, crowdsourced translation could be a better solution. Learn more about crowdsourcing in my webinar Crowd Sorcery for Turning Data into Information on Thursday, 6 February.
Любимая когда-то мною (а я – ею) «Красная бурда» выдала в прошлом тысячелетии смешную фразу — обработка полей солдатами. Эта фраза оказалась пророческой, хоть и не в своем отечестве. Вот, к примеру, почти классический манускрипт Crowdsourced Databases: Query Processing with People (обработка запросов людьми). Как следует из заголовка, массовые усилия разрозненных людей называются crowdsourcing, а сами разрозненные люди называются толпой. Толпа эта, однако, не дружная: на улицу ее метлой не выгонишь, сидит себе дома и толпится. И вместо того, чтобы смотреть по вечерам телевизор, работает. Часто не корысти ради, а чтобы не скучать. Или даже заниматься любимым делом, если на работе не всегда удается.
Люди посвящают досуг защите нашей планеты от астероидов или предсказанию сколько народу в этом году попадет в больницу. Кто предпочитает научную или интеллектуальную работу, а кто и механическую. Навалятся гурьбой — и все быстренько сделают, да еще и дешево. Один оксфордский ученый гласит, что некоторые легионеры толпы даже продолжают работать несмотря на страдания от изоляции. Т.е. толпа уже настолько продвинулась, что испытывает не только подъем, но и всю остальную гамму чувств, присущую обыкновенному работнику. Так что если кто хочет использовать толпу – уже можно. Толпа помогает не потому, что
у вас все глупые, а она – умная, а потому, что она отстраненная, не погрязшая в вашей рутине. Чем дольше работаешь на одном месте, тем сложнее придумать новые решения или прийти к неожиданным выводам.
Когда пешеходы идут по мосту в ногу, мост начинает шататься и даже может разрушиться из-за резонанса. Поэтому солдатам, если те идут через мост, приказывают маршировать вразнобой. Так и с нашей толпой работников: в одних случаях эффект резонанса приносит поразительные плоды, в других случаях— нарушение резонанса приводит к не менее чудотворным результатам.
Вот пример резонанса: замечательная организация Coursera уже второй год предоставляет всем желающим разные бесплатные курсы, которые читают профессора ведущих университетов мира. На первый же курс к основателю организации, Стэнфордскому профессору Эндрю Нг, записались больше ста тысяч человек. Ну как один профессор, или даже с помощниками, может поставить оценки такому количеству народа? Оказалось, что с помощью кое-каких методов, народ может отличнейшее оценивать однокашников сам (±10% по сравнению с профессором). Довольный профессор Эндрю Нг подытожил, что за первый год Coursera собрала больше сведений о том, как люди учатся, чем все университеты за всю предыдущую историю высшего образования. Например, в первом же курсе, 2000 человек ответили неправильно на один и тот же вопрос, и все две тысячи – одинаково! Значит, надо что-то в университете подправить.
Отсутствие резонанса особенно удобно иллюстрировать на примере карты мира: каждый вносит свою уникальную информацию о месте, в котором живет и которое хорошо знает. И каждая тоже. Кстати, есть всякие узкоспециализированные толпы: женщины, дизайнеры, отставные военные. Последние – не только для обработки полей, но и для решения задач, для которых нужен допуск.
Высказать свою, не похожую ни на какую другую, точку зрения в наше просвещенное время почти невозможно – поди разбери, чья это точка зрения, если она черпается из немногих, очень популярных, средств массовой информации. А толпа – это массы. Но тем толпа и хороша, что в ней всегда найдутся исключения, которые выскажут что-то новое и глубокое. Это называется «мудрость толпы», по-нашему, народная мудрость, каковая часто ставит вопросы с ног на голову. Например, покупать (продавать конечно же) духи не за то, как они пахнут, а за то, как долго длится запах. Я вот тоже, хоть и не толпа, поступлю наоборот, и окончу эпиграфом:
И Шуберт на воде, и Моцарт в птичьем гаме,
И Гёте, свищущий на вьющейся тропе,
И Гамлет, мысливший пугливыми шагами,
Считали пульс толпы и верили толпе.
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Category: crowdsourcing Humans Information Everywhere innovation skills translation Uncategorized Tags: crowdsourcing