“Any sufficiently advanced technology is indistinguishable from magic.”– said Arthur C. Clark. Big data technologies and practices are gaining traction and moving at a fast pace with novel innovations happening in this space. Big data companies are closely watching the latest trends in big data analytics to gain competitive advantage with the use of data. Businesses are wading into the big data trends as they do not want to take the risk of being left behind. This articles explores four latest trends in big data analytics that are driving implementation of cutting edge technologies like Hadoop and NoSQL.
Gartner Big Data Trends Predictions
Gartner predicts 85% of Fortune 500 companies will exploit big data for competitive advantage in 2015. IDC also forecasts that Big Data Analytics market will outpour from $3.2 billion in 2010 to $17 billion in 2015 with estimates that the Big Data Analytics services market is growing 6 times faster than the entire IT sector. IDC estimates that cloud based big data analytics is expected to grow 3 times faster than the on-premise solutions in 2015.
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A recent big data news on Forbes highlighted how “An Apple and IBM each day can keep you healthy”. Apple announced its partnership with IBM to use big data analytics in transforming digital health - to save lives by using its renowned supercomputer Watson, to crunch healthcare data collected through Apple’s gadgets.
Last year when Twitter and IBM announced their partnership it seemed an unlikely pairing, but the recent big data news on New York Times about this partnership took a leap forward with IBM’s Watson all set to mine Tweets for sentiments. The new analytics insight from IBM will harvest big data from millions of Tweets and make use of Watson supercomputer to analyse these Tweets for sentiment and behaviour.
These Big Data trends show how organizations are harnessing the power of big data and making technological advancements in big data analytics to get competitive advantage. Big data analytics is making waves in every industry sector with novel tools and technology trends. The ability to bind the intensifying amount of big data that is generated - has transformed almost every sector, such as decoding human DNA cells in minutes, pinpointing marketing efforts, controlling blood pressure levels, tracking calories consumed - are all big data applications in the healthcare industry; finding true love by predicting human behaviour, predicting a player’s performance level based on historical data, foiling terrorist attacks, provide personalized medicine to cancer patients, personalized shopping recommendations for users, etc.
Latest Trends in Big Data Analytics
Hadoop, NoSQL, MongoDB, and Apache Spark are the buzzwords with big data technologies - reverberating to leave a digital trace of data in everyone’s life which can be used for analysis. The big data analytics market in 2015 will revolve around the Internet of Things (IoT), Social media sentiment analysis, increase in sensor driven wearables, etc.
1) Big Data Analysis to drive Datafication
Eric Schmidt, Executive Chairman at Google says: “From the dawn of civilization until 2003, humankind generated five Exabyte’s of data. Now we produce five Exabyte’s every two days…and the pace is accelerating.”
The process that makes a business, data driven is by collecting huge data from various sources and storing them in centralized places to find new insights that lead to better opportunities - can be termed as Datafication. Datafication will take big data analysis to new heights - into real insights, future predictions and intelligent decisions.
A recent CivSource news article highlighted the creation of a big data transit team in Toronto routing path - for big data analytics in transportation sector. Tom Tom, global leader in Traffic, Navigation and Map products found that in Vancouver, Montreal and Toronto, commuters lose an average of 84 hours a year because of being delayed due to heavy traffic. As a solution to this problem, Toronto created a big data transit team for analysis of big data in the transportation services department. They partnered with McMaster University to analyse historical travel data. To establish Toronto as a truly smart city, it has requested vendors to showcase proven products for measuring and monitoring traffic and travel.
Datafication is not a new trend but the speed with which data is being generated in real time operational analytics systems is breath-taking. This is likely to bring about novel trends in big data analytics. Datafication of organizations will soon impact our lives in this fast changing world by formulating a data driven society.
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Datafication and IoT (Internet of Things) is the upcoming trend in 2015. The number of connected devices to the Internet is anticipated to be more than 25 billion by the year 2020, according to Gartner. Internet of Things connects conventional devices and products to the web for analysis. Organizations can think of several prospective aspects such as - how people move at their workplace (workforce analytics), how various products are used (product behaviour analytics) and how a driver behaves on the road (transportation analytics).
People expect that if they are hungry, they want their SmartBand to provide them suggestions and route map to the nearest Café. They would not mind if there is a voice enabled mobile application for pre-ordering drinks and meals while driving, which displays a notification when their order is ready. This might seem to have a futuristic ring to it but smart applications and gadgets today learn more about the wants and requirements of users. They generate real-time big data that will help businesses serve their customers better through intense analytic processes.
2015 will sense more sensor driven datafication that will make human lives more datafied in various fields such as datafication of cultures, datafication of relationships ,emotions and sentiments, datafication of back-office and offline processes, datafication of speech and much more to go. Many organizations are foraying into the business of sensor driven datafication that will focus on extensive research and development so that they can gain a gradual increase in the market share.
Coffee Vending Machine that can interact with a person’s mattress to sense when she or he is going to wake up , post a notification to the users Smartphone asking which flavour would they prefer as they wake up and automatically order those coffee beans from Amazon when the user is running low on supplies is the wave of future analytics.
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2) Big Data Analytics to gain power of novel Security tools
Technology information source week recently published an update about the new funding investment plans of security analytics firm Niara. Niara is building its big data security analytics platform to detect many sophisticated threats that existing security tools cannot detect. With a funding budget of $20 million, the security platform is anticipated to be available by second half of 2015.
Big data security advances is another important emerging trend for 2015 to protect the public as a whole. Target data breach is recorded as one of the largest data breaches in history of US. It was reported that close to 40 million debit and credit card numbers have been stolen during the busiest shopping time of the year (Nov 27 –Dec 15).Big data breaches have been quite common in US, every week there is some announcement in media from the US government or the businesses directly that they have been impacted by big data breach.
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Big data breaches make a fuss over the reputation of the organizations leading to various legal, regulatory and financial consequences. Businesses are targeting to invest in advanced methods of encryption to prevent big data breaches by adopting novelties in security technologies and training staff on security aspects.
The capabilities of traditional security software systems are anticipated to increase in 2015 with novel techniques employed for data collection, storage and analysis. Data assets of organizations are increasingly maturing and stakeholders plan to adopt various predictive analysis techniques to bridge the security gap in big data by predicting probable data threats. Industry experts have started looking at big data analysis as a robust tool for protecting data security by identifying signals of tenacious security threats. In 2015, big data security has the potential to make more noise in the market as an emerging trend.
Neil Cook, CTO of Cloudmark says- “In identifying the source of a current spam attack by tracking where the attacker has sourced target email addresses, it is possible to identify other address lists that attacker has ‘downloaded and use that information to predict, and prevent, the next attack.
2015 will welcome the dawn of big data analytics security tools to combine text mining, ontology modelling and machine learning to provide comprehensive and integrated security threat detection, prediction and prevention programs.”
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3) Deep Learning soon to become the buzz word in Big Data Analysis
With deep learning, there could be a day when big data analysis would be used to identify different kinds of data such as colors, objects or shapes in a video. The world will experience a great pull from big data vendors in cognitive engagement and advanced analytics. Let’s hope for some innovative hit in deep learning to real time business situations by end of 2015.
Google’s latest deep learning system built on recurrent neural networks aims to identify motion in videos and interpret various objects present in the video by feature pooling networks.
Deep Learning is a machine learning technique based on artificial neural networks. Deep learning involves ingesting big data to neural networks to receive predictions in response. Deep Learning is still an evolving technology but has great potential to solve business problems.IT giants are making researches in deep learning to strive hard to build up customer choice and expectations.
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Deep learning helps systems to find out items of interest from huge amount of binary and unstructured big data without the need of specific programming instructions or models. Deep learning employs artificial neural networks to find patterns in large unstructured data sets without having to program specific functions manually.
For instance, there was a deep learning algorithm which identified from the data in Wikipedia - that Texas and California are popular states in US. The biggest difference here, from the previous machine learning algorithm and deep learning algorithm in the example is that - the algorithm need not be modelled to understand the fundamental concepts of state and country.
It is just the beginning- big data has lots to be explored with diverse and unstructured text by using advanced analytic techniques. Deep learning is a machine learning derivation in the making which is yet to be tested with real time applications.
4) NoSQL Matures- Increased demand for more, better NoSQL
According to Allied Market Research “Global NoSQL Market- Size, Industry Analysis, Trends, Opportunities, Growth and Forecast, 2013 - 2020", the global NoSQL market, is projected to reach$4.2 billionby 2020, recording a CAGR of 35.1% during 2014 - 2020. The matured usage of NoSQL in big data analysis will drive the NoSQL market as it gains momentum.
There are approximately 20 NoSQL databases each one having its own gaining expertise. For instance, a NoSQL graph database helps analyse network of relationships between sales staff and customers more quickly than a RDBMS. In a survey conducted in 2014, 24% of the people said that they would prefer a NoSQL database as it is faster and render more flexible development than RDBMS whereas 21% of the people said they would prefer a NoSQL database because of it lower software and deployment cost.
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Among the different types of NoSQL databases, the key-value pair database stores are anticipated to gain more traction because of their extensive usage in e-commerce, social network management and web session management. The key-value stores are driving the NoSQL market, though NoSQL databases have been there around quite some time but they are gaining momentum because of excessive businesses need of big data analysis.
For instance, if a PwC client installs sensors on store shelves to monitor what are the available products, how much time the customers take to handle them and for how long do the customers stand in front of these shelves-undoubtedly, the sensors will generate tons of data that will grow exponentially over time. Under such circumstances Key-Value pair of NoSQL database comes to rescue because of its high performance and light weight features.
2014 was a fantastic year for Big Data analytics but the emerging big data trends show that 2015 looks even better with many more technological innovations. If there are any other significantly emerging trends in big data analysis that will make noise in the market, please leave a comment below.