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Results tagged “Analytics”

New Generation of Data Analytics and New Generation of Opportunities

 

As someone once said "Intelligence is nothing but amount of meaningful information harvested from the data available".  So it's the data and the skill/talent/pattern to extract the meaningful information that defines the intelligence, knowledge and statistical predictions.

Traditionally analytics were driven by deriving trends and patterns from the historically accumulated data, these data normally included conventional data that were logged or recorded as they occurred and were done for this specific purpose. With the advancement of technology and its penetration into different turf things have changed, today application of technology in different space is in a way digitizing the offerings where the actual primary outcome of the technology inadvertently serves as a data for breakthrough analytics that could be derived by connecting different dots. The dots that are meaningless independently but when connected gives you the "Big Picture". Here in this blog I will cite some example where information is extracted from an unconventional and innovative way that otherwise were not obvious.

There is a saying that "A picture is worth a thousand words" and for Data Analytics what this means is that every picture has a treasure of information waiting to be harvested. Traditionally it was difficult to deal with images and extract meaningful information out of it as you would have done with character data's but the advancement of technology is breaking this barrier. Google Goggles was among one of the earlier efforts that ventured into this field of unlocking this data that hid within images and today their latest mobile app Google Photos leverages this capability of searching image for their contents, through Image processing and geotagging information.  This pattern of extracting information from image has today paved way to cutting edge analytics where Satellite images of parking lots of big retailers like Walmart, Home Depot and others are used to predict their quarterly earnings thus giving research analyst actual raw data that could be trusted. Similar pattern on a bigger scale is helping predict global economy where satellite images of Oil Storage, Movement of Trucks in Mines, Agriculture fields and Night lights are reflecting the current status of world economy as they shape, this all has been possible by having real times images transformed into precious data.

Showing real time traffic has been one of the common features of GPS devices and apps used today, initially this information were sourced through traffic sensors placed by government authorities and other concerned departments as a result only few roads had this privileges for their traffic data to be analyzed, all this changed with company like Google going the crowdsourcing way, with crowdsourcing google was able to improve the reach and accuracy of its traffic information and predictions, when users use google maps for navigation on their phone, the phone sends back the data anonymously which help the company determine how fast the cars are moving in any specific roads, with acquisition of Waze Google was able to add human touch to this algorithm as drivers could provide real-time feedback of their driving experience on specific routes. So does this stop here? Nope, with more and more users using google maps for navigation, Google has access to vast amount data pertaining to end users driving habits - How much the user drives, where does he drive, what time he drives, the speed, the critical points the user navigates and much more, with this immense amount of data company like Google is well positioned to provide Auto insurance based on unique data analytics that could rival traditional actuarial methodology.

To end this post I would like to cite a personal example and opportunity that I have been lucky to come across. Being tech freak, last year, I bought a car adapter called Automatic it comes with a companion mobile app and helps your car to be connected, it tracks your driving behavior, miles driven fuel consumed and routes taken, basically a handy device if you are interested in analyzing your driving habits at leisure. As the new year 2016 arrived, I got a report from Automatic that had some key aspects based on my driving habits for the gone year like, average fuel consumed, States travelled e.t.c, but one of the fact reported was interesting, the report mentioned that I left my home to work 16 minutes earlier than an average Automatic user did and I arrived home 44 minutes later than the average Automatic user did (huh.. me working hard J), Automatic had enough data and pattern to determine my office commute, now when I think about this, there is a huge potential of connecting the other dots and harvesting some incredible facts and opportunities. For example based on my parking location my place or organization where I work could be determined or I could be given an incentive to let reveal where I work and in turn would get insights to how my fellow workers (of course anonymous details) fared with their working hours. This collective data can give out quite some interesting facts about the work place culture like, average hours an employee works,  average data of employee's commuting hours and timings in a day can determine how flexible the organization working hours were, average regular data of employee commuting to work place can determines organizations policies and support for Work From Home, thus the data which are logged for certain aspect can be traversed to different context and augmented with other details to gain wealth of information, which in turn can turned into tremendous opportunities. Imagine company like Glassdoor taping into such data by forming an alliance, it could be a goldmine of actual raw data to augment their current analytics.

This is just some of the examples how today data no longer come from traditional intended sources but are harvested from wide ranges of technology offerings and then augmented and enriched with other factors to give some incredible depths and insights. Today with analytics, anymore it's not the sky but the Imagination that's your limit, hence it's time to get Datatized!!


The brave new world of Big Data Analytics

We all have heard about Big Data and much more about the hype surrounding it. Is it worth the excitement? Can Big Data live-up to its expectations or is it just another technology fad?

Every day 2.5 exabytes (2.5 billion GBs) of data is generated, and 90% of current data has been generated as recently as in last two years. [1] Data is not just growing, but it's exploding exponentially. We have witnessed data explosion trend over the past few years. The trend is certain to continue as the world embraces digitalization further. Digitalization has allowed organizations to capture/store more data and details, which was previously either infeasible or expensive.

Big Data is not just about storing all social media activities, clickstreams, public web, transaction and application data, machine logs, etc., but it has more to do with insights generation using collated data.

Big Data Analytics at work

We will discuss three examples which will help to understand the potential of this field.

During FIFA 2014 World Cup, Microsoft's Bing prediction engine predicted winner of each match even before the match started! The prediction engine was so successful that it was able to point out winner for each elimination match right till the finale, that's an impeccable record of 15 matches in a row! The prediction algorithm used various types of data fields like winning/losing margins, offensive and defensive stats, team composition, player position, game timing, playing condition like weather, and venue distance from playing country. If these exclusive insights would have been made available to one particular team, their FIFA journey would have been far more focused and successful. In near future, don't be surprised to see brands partnering up with Bing (or any other for that matter) to predict the winning team in order to make their sponsorship decisions.

Let's take a look at another example, this one is from the media and entertainment industry. Netflix started off as a content distribution company, soon they realized the value of their existing dataset in terms of customer taste. Netflix went on investing $100m into production of a TV show 'House of Cards' without even considering the pilot episode (Pilot episode is used to evaluate the performance of a new TV show in order to get go ahead with investment for full production). Netflix knew the show will be a hit even before the production started! In line with Netflix expectations, the show was well received by subscribers. About 10% subscribers began streaming the show on Day 1, and many of them ended up binge-watching. Thanks to Big Data analytics capabilities of Netflix that analysed billion hours of viewing patterns along with reviews and feedback of its 50 million subscriber base. The same technique is now used to invest in other TV shows/documentary productions. These shows being exclusive to Netflix, are fuelling up Netflix subscriptions, and also, they are making their presence felt at various award functions such as Emmy/Golden Globe Award/Oscar.

Let's take insurance sector now. If it's one of those days you think you need your car insurance more than ever may be due to bad weather or just trusting your intuitions, big data analytics can come to your rescue. Understanding the need of a customer segment, the vehicle insurance companies have come up exciting innovation of urge based insurance policies like 'Pay as you drive' and 'Pay how you drive'. The insurer will quote a premium considering various factors like driving location, traffic and weather condition, time of drive and data from on-board diagnostics. Such products are sometimes more economical than traditional products. These products are sure to gain further traction, once the cars become digitally connected.

The way forward

In above examples, we have seen organizations making bold move in order to leverage their big data capabilities. These are early days for big data, especially for big data analytics (BDA), and we have seen promise of what technology can help us achieve.  BDA in near future is sure to have a significant impact on business model of many companies. Also, we are certain to see innovative products and new revenue streams solely powered by analytics.

For any business or organization to invest in big data technology, they need to understand that BDA is not a crystal ball, at times it has its own limitation. Further, for any successful implementation of BDA project requires right blend of technology, machine learning expertise along with strong business acumen.

[1] http://hbr.org/2012/10/big-data-the-management-revolution/ar

Criticality of Predicting Customer Churn

My personal experience of various chrun prediction solutions, and the way the problem is being looked at across various industries is what i am making an attempt to narrate in this blog post. One may actually read it as scratch notes gathered in the process of understanding Churn Prediction process and lifecycle. This is by no means a comprehensive and exhaustive list, however can be good check points if you are embarking on this road or not able to reap benefits with your investments in predicting churn.

Big Data Enabled Enterprise

Larger enterprises have extremely high volumes of data, coming in at a rapid pace from a wide variety of sources. Without a proper Big Data solution, finding relevant relationships is like fishing in the dark. For chief information officers, priority is to enable their businesses to make better decisions faster.

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Big Data: a retailer's currency for competitive advantage

I loved it when a Infosys Vice President , Sandeep Dadlani opened his statement at a panel discussion at TUCON 2012, Las Vegas with a 'Big Data is the B-word we don't want to speak about'. He could not have said it better. There has been much spotlight on Big Data and it is important to realize what it can do for a business especially retailers than stand in awe and reverence to all the statistics being thrown around in the name of Big Data.

Cutting the chase to where it matters - in a gloomy economy, retailers are struggling more than ever. Consumer spending seems to dip at a steady pace; Pressure on margins is high and competition is forcing prices down. And digital consumers are forcing retailers to re-think customer service and fulfillment experience. 

Taming the elephant: 10 Big Data trends for 2013

Devices. Processes. Customers. Today, these are sources of elephantine amounts of data that is hard to store, and harder to process. While enterprises were still trying to wrap their heads around the Big Data phenomenon in 2012, many of them will finally start taming it in 2013 with strategies and technology solutions. But what are the capabilities they desire? How will they leverage Big Data for greater business value? The trends in this infographic will give you some answers. If you think these trends will interest your peers or colleagues, share it with them via email or social media.

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