Future Of Analytics http://futureofanalytics.com Useful Tips And Tricks Sat, 06 Aug 2016 23:54:37 +0000 en-US hourly 1 Analytics Professionals Hub! http://futureofanalytics.com/news/analytics-professionals-hub http://futureofanalytics.com/news/analytics-professionals-hub#respond Sun, 24 Jul 2016 02:52:49 +0000 http://futureofanalytics.com/?p=1178 AnalytiXHub is building a collaboration environment for professionals with analytical and problem-solving skills:

 

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Eight Data And Analytics Capabilities You’ll Need For The IoT http://futureofanalytics.com/analytics-articles/eight-data-and-analytics-capabilities-youll-need-for-the-iot http://futureofanalytics.com/analytics-articles/eight-data-and-analytics-capabilities-youll-need-for-the-iot#comments Sun, 08 May 2016 05:25:35 +0000 http://futureofanalytics.com/?p=1164 Source: Forbes

IoT-rt-graphic

There is widespread agreement that the Internet of Things will be a transformative factor in the business use of information. The prospect of billions of connected devices promises to transform home activities, transportation, industrial operations, and many other aspects of our lives.

The bad news about the IoT is that we have a lot of work to do before we are ready for it. We’ve got to up our games considerably with regard to data management and analytics if we’re going to capture, store, access and analyze all the IoT data that will be flowing around the Internet. The good news (in addition to its potential) is that most organizations have a few years to get better at these capabilities before the real onslaught hits. The sensor devices, IoT data standards, and data management platforms are still in their relatively early stages, and no customer, business partner, or CEO could reasonably expect that you could tame all that IoT data today.

Source: Forbes


But they will soon. So it’s time to think now about the data management and analytics capabilities you will need to have, say, over the next five years as the IoT matures and blooms. I’ll describe eight of them, but of course there are some other capabilities that underlie them (data security, for example). These are about running, but you will have already needed to walk first.

    1. Data quality tools on steroids—The IoT is going to generate a massive amount of data, and a good bit of it is going to be of problematic levels of quality. Sensors will send bad data, devices will go offline and create missing data, and integration platforms will fail to integrate. So companies need to improve their data quality capabilities massively and employ automated tools to a large degree. This includes identifying data quality problems, determining their seriousness, and fixing them both after data have been collected and at the source.
    2. Data curation on a grand scale—Similarly, companies will need to become much better and faster curators of multiple data sources. If you’re a car manufacturer, for example, your cars already have a couple of hundred sensors in place, and you’re probably planning a lot more. Data curation allows companies to keep track of their data sources, their formats, and their interrelationships. And the scale of the IoT is going to mean that companies will have to make widespread usage of tools like machine learning, which are already being applied to data curation in some companies and vendors. 
    3. Qualify your alerts—Alerts are one of the key ways to analyze IoT data, in that organizations will need to know what readings are in and out of normal bounds. But the vast amount of IoT data is going to make alert fatigue a common occurrence unless you have done a good job of qualifying alerts to ensure that they are real and important. You’ll also need to qualify the many security alerts that your IoT system will probably generate. All of this is going to require some high-quality diagnostic models, and I’m guessing that you don’t have them today
    4. Swim in a data lake—You’re not going to be able to undertake an extended ETL (extract, transform, and load) process to store your IoT data in a traditional data warehouse. Some data may eventually go there, but you need to store and refine it first. So you had better establish a data lake that lets you store the data in whatever format it comes in until you need to analyze it. By the time the IoT data arrives in force, you should be well-practiced in moving data into and out of your data lake.
    5. Predictive analytics—Most organizations thus far have only employed descriptive analytics with IoT data—bar charts, alerts, means and medians. These are useful but not nearly as useful as predictive analytics. We’ll want to know whether a machine is about to break down, whether your car is likely to arrive on time, and whether your good health will persist. That takes a solid competency at predictive analytics.
    6. Automated recommendations and actions—IoT data will flow into your organization at a fast and furious rate, and you’re not going to have enough humans to examine and decide upon it. That means you should be well-versed in building and using automated decision systems by the time the IoT is mature. This capability could take a variety of forms—simple rules, event-driven systems, or sophisticated cognitive capabilities (see the next two items). By the time the IoT is ready, you should be ready to employ the right automation technology for any situation.
    7. Machine learning to create analytical models—Automating IoT processes will require a large number of analytical models, and you won’t have the time or people to create them using traditional hypothesis-based methods. Each type of device and data is going to require its own set of models, and the analysis situations will change quickly. So machine learning is the ticket to developing models rapidly and with much greater analyst productivity. Start now to develop a facility with it, because machine learning is relevant to a wide variety of situations. Machine learning models can also be helpful in identifying unauthorized intruders into your systems, which is critical for IoT security.
    8. Deep learning models for image and sound data—Deep learning, which is based on neural network methods, is the best way to analyze large amounts of image and sound data. Want to know if the drone images you’re receiving detect an unauthorized intruder? Are your sonic sensors detecting squeaks and squeals from your car engine that indicate a lack of lubrication? Deep learning models are the way to make sense of this data. They can also be used to identify patterns in cybersecurity attacks.

No doubt there will be other capabilities that IoT-centric organizations will need to develop, but this is a good start. And many of the ones I have mentioned have relevance to other types of data and analysis contexts. An IoT-capable data and analytics environment is basically one that is state of the art given the technologies and analytical methods that are available today. So it’s time to get busy and make sure you have an implementation trajectory that will ensure you are ready when the IoT data starts flowing in a big way.

Source: Forbes

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Analytics in NEWS: Google Cast gets an analytics tool for developers http://futureofanalytics.com/news/analytics-in-news-google-cast-gets-an-analytics-tool-for-developers http://futureofanalytics.com/news/analytics-in-news-google-cast-gets-an-analytics-tool-for-developers#respond Sun, 06 Mar 2016 04:53:42 +0000 http://futureofanalytics.com/?p=1152 Google today announced a significant enhancement to the developer tools for its Google Cast software, which lets people stream media from their PCs and mobile devices to TVs and speakers. The Google Cast software development kit (SDK) Developer Console now includes dedicated pages for analytics for apps that work with Google Cast.

The tool is a bit reminiscent of Google Analytics, which lets people see check website performance and usage. Developers can access it by clicking the View link under the Statistics column for a given app.

“The devices tab shows the number of Cast devices that have launched your application, the sessions tab shows the number of Cast sessions of your application, and the average playback tab shows the average length of media playback time per session for your application,” Google Cast software engineer Chris Dolan wrote in a blog post. Developers can break things down by geography and operating system and change the time range.

Read More:  http://venturebeat.com/2016/03/04/google-cast-gets-an-analytics-tool-for-developers/


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7 Great Blogs in Big Data, Analytics and IoT http://futureofanalytics.com/articles/7-great-blogs-in-big-data-analytics-and-iot http://futureofanalytics.com/articles/7-great-blogs-in-big-data-analytics-and-iot#respond Sun, 28 Feb 2016 07:00:02 +0000 http://futureofanalytics.com/?p=1058  

blogs

 

 

 

 

 

 

Smart Data Collective: A moderated business community for BI, predictive analytics, and data professionals.

Data Science 101: A blog for learning to be a data scientist.

Revolution Analytics Blog: A blog dedicated to news and information of interest to members of the R community.

Geeking with Greg:  A very informative weblog by Greg Linden

Shape of Data: A blog to explore the geometry behind machine learning, data mining, etc.

What’s the Big Data: A blog to explore big Data and business

FiveThirtyEight Blog:  A blog by Nate Silver for exploring analytics on politics, sports, and more


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Big Data, IoT and Analytics Events in March http://futureofanalytics.com/articles/big-data-iot-and-analytics-events-in-march http://futureofanalytics.com/articles/big-data-iot-and-analytics-events-in-march#respond Wed, 24 Feb 2016 20:48:45 +0000 http://futureofanalytics.com/?p=1045  

events

 

Predictive Analytics Innovation Summit, March 2-3, Melbourne, Australia

Big Data and Analytics Summit, March 2-3, Singapore

Gaming Analytics Summit, March 3-4, London, UK

European Smart Grid Cyber Security, March 7-8, London, UK

Big Data Paris, March 7-8, Paris, France

2nd Global Data Science Conference, March 7-9, Santa Clara, CA

Structure Data, March 9-10, San Francisco, CA

Internet of Things Forum, March 9-10, Cambridge, UK

10th Sloan MIT Sports Analytics Conference, March 11-12, Boston, MA

IEEE International Conference on Big Data Analysis (ICBDA 2016), March 12-14, Hangzhou, China

Gartner Business Intelligence & Analytics Summit, March 14-16, Grapevine, TX

Discovery Summit Europe, March  14-17, Amsterdam, The Netherlands

The Internet of Things Conference, March 14-17, Munich, Germany

Re:Work Connected City Summit, March 16-17, London, UK

HR and Workforce Analytics Summit, March 16-17, London, UK

Data Innovation Summit, March 22, Stockholm, Sweden

Industrial IoT, March 23-24, London, UK

2nd IEEE Conference on Big Data Computing Serivce and Applications, March 29-April 1, Oxford, UK


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Great Job Boards for Data Scientists http://futureofanalytics.com/articles/great-job-boards-to-for-data-scientists http://futureofanalytics.com/articles/great-job-boards-to-for-data-scientists#respond Sun, 21 Feb 2016 03:53:18 +0000 http://futureofanalytics.com/?p=1018

Jobs-Graphic

 

https://datajobs.com/

https://www.kaggle.com/jobs

http://www.datasciencecentral.com/page/job-board

http://analytictalent.com/

http://www.kdnuggets.com/jobs/index.html

http://datasciencedojo.com/community/job-board/

 


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Poll: Data Ownership and Wearable/ IoT Devices! http://futureofanalytics.com/polls/995 http://futureofanalytics.com/polls/995#respond Sun, 14 Feb 2016 22:55:16 +0000 http://futureofanalytics.com/?p=995 /* Main Container --------------------------------------------------------------------------- */ #yop-poll-other-answer-1_yp58b32749726fe-other { color:#000000; } .yop-poll-customfield-1_yp58b32749726fe { color:#000000; } div#yop-poll-container-1_yp58b32749726fe { background:#B70004; box-sizing: border-box; display: inline-block; font-size: 14px; color:white; padding: 14px; width: 350px; zoom: 1; } /* A nice little reset */ div.yop-poll-container * { } form#yop-poll-form-1_yp58b32749726fe { } /* Error message .................................. */ div#yop-poll-container-error-1_yp58b32749726fe { font-size:1em; font-style:italic; color:red; text-transform:lowercase; text-align:center; } /* Success message .................................. */ div#yop-poll-container-success-1_yp58b32749726fe { font-size:1em; 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Who should have the data ownership for wearable/IoT devices?

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We are Recruiting! http://futureofanalytics.com/articles/join-us-and-be-published-today http://futureofanalytics.com/articles/join-us-and-be-published-today#respond Wed, 10 Feb 2016 06:14:16 +0000 http://futureofanalytics.com/?p=964 Looking for geeks to join our great analytics team!

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You will make 25 $ /post!

Refer us a great candidate and make 25$ !

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Can advanced analytics have a significant impact in the NFL? http://futureofanalytics.com/news/great-article-article-by-ben-volin-can-advanced-analytics-have-a-significant-impact-in-the-nfl http://futureofanalytics.com/news/great-article-article-by-ben-volin-can-advanced-analytics-have-a-significant-impact-in-the-nfl#respond Sun, 10 Jan 2016 10:37:22 +0000 http://futureofanalytics.com/?p=948 5278cdbdf2c6478799799cbeca07a09b-5278cdbdf2c6478799799cbeca07a09b-0

Source: https://www.bostonglobe.com/sports/2016/01/09/can-advanced-analytics-have-significant-impact-nfl/UJYTVqUVXLRpCSz7AxKdfN/story.html

The Cleveland Browns went way outside the box last week, hiring Paul DePodesta away from the New York Mets to be their new “chief strategy officer.”

The team is 14-34 and about to hire its third head coach in three full seasons under owner Jimmy Haslam, but DePodesta’s hiring shows that Haslam is at least trying to commit to a more mathematical and stats-based approach to try to turn around the Browns’ fortunes. Despite firing the head coach and general manager last week, Haslam retained analytics expert Ken Kovash.

Everyone knows DePodesta’s prominence in “Moneyball” and how he is going to bring analytics to the Browns. But what exactly does that mean in a football context? Unlike baseball, football has several players who don’t produce statistics (offensive linemen), success is more team-based than individual, and the sample size of statistics is much smaller.

How can DePodesta’s background in identifying statistical inefficiencies help the Browns on the field? And how new is this approach, anyway?

“It’s really just an extension of what quality control coaches have been doing for years,” said Joe Banner, a former executive vice president with the Eagles (1995-2012) and CEO of the Browns (2012-14). “‘What’s the probability they’re going to blitz on third and 6?’ Every coach has probabilities for virtually every scenario you could come up with, and that’s sort of analytics.”

The most important aspect to remember is that unlike in baseball, where the scouting and stats-based communities tend to disagree on how to evaluate players, few in football believe that advanced stats are as important as good, old-fashioned scouting — game film, interviews, body type, motivation, medical checks, and more.

Banner says if you let analytics “be the engine that drives the machine, you’re in trouble.” Aaron Schatz, creator of the advanced stats website Football Outsiders, calls analytics “just a tool in the toolbox.” And no matter what the stats say, there will always be outliers.

The stats said that the Seahawks’ decision to throw the ball on the 1-yard line only had a 3.1 percent chance of being intercepted.

DePodesta, though portrayed as a math nerd in “Moneyball,” played football at Harvard in the early 1990s, and his first job out of school was an internship with the Baltimore Stallions of the Canadian Football League.

“The best analytics involves the scouting,” Schatz said. “The goal is to look for inefficiencies to find ways to improve your team, and get a better handle on how good players are and what strategy you should use without the bias of memory.”

Bill Belichick made a good point about overreliance on advanced stats last week.

“If you can’t see an 80 percent tendency, then, like, what are you looking at?” he said. “Now, is it 51-49? What are you going to do with that? Do you want to bet on 51, do you want to bet on 49? At that point, what’s the difference? You’ve got to play it straight.”

But any opportunity to eliminate some of the chance involved in player personnel and on-field decisions is welcome in the NFL.

And this is not a new concept. Gil Brandt was making computer databases of NFL Draft prospects for the 1970s Dallas Cowboys, Dick Vermeil and Andy Reid were long proponents of advanced statistical analysis, Chip Kelly took it to another level with the Eagles with his focus on wellness and nutrition, and of course Belichick has Wall Street veteran Ernie Adams in his ear during games.

Belichick is believed to be one of the first coaches to talk with a stats consultant during games. Broncos coach Gary Kubiak had analytics director Mitch Tanney on the sideline with him this season, and don’t be surprised if the Browns have a similar setup next year.

Every NFL team does basic to mid-level statistical analysis, and most are highly protective of their information. Despite Belichick claiming last week that analytics are “not really a big thing with me,” his team is deeply invested in advanced statistics for player personnel, draft strategy, on-field strategy, salary cap management, free agent valuation, and off-field business strategy.

“Any notion that Bill Belichick isn’t using or doesn’t believe in analytics is the silliest thing I’ve heard in my life,” Banner said. “He has been an NFL leader, although most of us kept our analytics departments secret for a long time. Who really knows? But he is a big, big practitioner of analytics.”

Advanced stats are essential for game strategy. It is well known at this point that advanced stats say that teams should punt less and go for it on fourth down more often, that the corner fade is a terribly low-percentage play to run inside the red zone, and that “establishing the run” doesn’t correlate to success.

Teams also produce their own studies to determine, for example, which combination of four defensive linemen is most effective on third and long, or the most efficient combination of offensive skill players inside the red zone.

DePodesta’s challenge will be getting the next Browns coach to actually implement the strategies on the field. Not many coaches outside of Belichick have the credibility or job security to go for it consistently on fourth down, or kick off to start overtime.

Getting the entire organization to buy in to analytics is not always easy in football. When Banner was Browns CEO, he commissioned a $100,000 report to determine which quarterback prospect in the 2014 draft had the likeliest chance to succeed in the NFL based on historical success.

After several months and dozens of data points, the report concluded that Teddy Bridgewater, Derek Carr, and Blake Bortles (in that order) had the highest probability of success, and that Johnny Manziel would fail. Three months before the draft Haslam fired Banner and drafted Manziel, anyway.

“I would probably put Teddy second on that list of how they’d done so far, and I’d put Carr first. That’s why I say [analytics is] just an element. But the reality is it predicted Manziel would fail,” Banner said. “Everything every coach is doing, every personnel guy, is just an attempt to increase your chances to get it right. If you smartly and proportionally use analytics, it can absolutely be a positive vehicle in that mission.”

Source: https://www.bostonglobe.com/sports/2016/01/09/can-advanced-analytics-have-significant-impact-nfl/UJYTVqUVXLRpCSz7AxKdfN/story.html

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Top 5 Reasons Why Analytics Projects Fail http://futureofanalytics.com/news/top-5-reasons-why-analytics-projects-fail http://futureofanalytics.com/news/top-5-reasons-why-analytics-projects-fail#respond Sun, 13 Dec 2015 18:40:55 +0000 http://futureofanalytics.com/?p=943 Whether you are a seasoned analyst or a business executive with a significant investment in analytics, chances are you’ve seen the powerful impact of analytics as well as the failures. So what drives an analytics project’s failure or success?

Analytics projects fail when they produce no actionable insights. Even a seasoned analyst’s efforts to coax insights from the data can be futile unless a number of key factors come together to increase the odds of success.

Let’s talk about the top 5 reasons analytics fail.

  1. Starting with data instead of the question. The most common misunderstanding about analytics is that if you look at data hard enough, you will find insights. Staring at daily dashboards in the hope that insights will miraculously reveal themselves is often overwhelming, confusing and unsuccessful.Successful analytics start by identifying the question you’re trying to answer from the data. For example, if site conversion is an issue, instead of studying your website data hoping to find reasons for low conversion, narrow down your efforts to a specific question. In this case, it might be “How can we increase conversion from 23% to 26%?” This approach allows you to focus on finding actionable drivers of conversion that can have impact.
    1. An exploratory approach to analytics. Once you have identified the question you are trying to answer, do you explore all the data at hand in the hopes of finding insights or do you identify which data to study by using hypotheses as guard rails?The exploratory approach often fails to find any insights and if it does, is a lengthy process. On the other hand, using hypotheses to narrow down both the scope of the project and the data set needed, leads to the answers quickly. This process also generates secondary questions to ask data to further refine the insights.In our example, the hypotheses might involve certain pages or experiences thought to be driving lower conversion. These hypotheses are then used to identify the data needed to find segments of low conversion, and, once proven, address them.
Source: http://www.forbes.com/sites/piyankajain/2015/12/12/5-reasons-why-analytics-projects-fail/

  1. Weak hypotheses. An analytics project can still fail even when it begins with a business question and a structured approach for analysis if the hypotheses used to narrow down the scope of the problem are weak. Weak hypotheses result from failure to follow due process with the right stakeholder.
    1. Unengaged or absent stakeholders. Successful analytics projects deliver actionable insights that are then used to make changes in the product or customer experience and ultimately drive business results. Any successful analytics project therefore requires active engagement of the right stakeholders—the decision makers and owners of the business processes involved in the problem being studied. Working with the wrong or absent stakeholders leads to weak hypotheses, long cycles to analysis and wasted or no insights.In the conversion example, the product manager responsible for conversion, the product dev team making site changes, as well as the analyst need to be involved to make sure any actionable insights are acted upon promptly.
Recommended by Forbes
  1. Inaccessible or bad data. Lastly, analytics can fail even after following a hypothesis-driven structured approach with involved stakeholders if the organization doesn’t have easy access to clean and reliable data. The data needn’t be perfect for successful analytics, just cleaner and with fewer data issues. Data maturity is thus a prerequisite for analytics maturity.
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