DATA SCIENCES & ANALYTICS TRENDS 2017SAMEER DHANRAJANIhttps://sameerdhanrajani.wordpress.com

8 KEY DATA SCIENCES AND ANALYTICS TRENDS FOR 2017

RISE OF ALGORITHM ECONOMY“ALGOCONOMY" The next competitive advantage in Analytics space will be

focused on how you do something with data, not just what you do with it. The biggest internet companies are not centered on data, but the company’s most precious resource – its algorithms. 

• Superior Algorithms would lead to extended Competitive Advantage turning companies into ‘Math Houses’ that involves monetizing their proprietary algorithms by offering licensing to other non-competing organizations

• These Algorithm Marketplaces would hSave millions of algorithms available, each one representing a piece of software code that solves a business problem or creates a new opportunity, operating in data-driven analytics space

• The CXOs must hone a business strategy leveraging algorithms, ostensibly human decisions converted into a set of equations, to drive competitive advantages

REFERENCE LINKShttps://sameerdhanrajani.wordpress.com/2016/02/12/mr-algorithms-the-new-member-in-the-board-room-to-discuss-algorithm-economy/https://sameerdhanrajani.wordpress.com/2016/10/26/sameer-dhanrajani-digital-era-algorithm-economy-emergence-of-new-age-business-models/

SOPHISTICATION OF ANALYTICS – DATA SCIENCES As a relatively new – but already highly sought after – position, it

can be hard to know where Data Analytics ends and Data Science begins. Data analytics seeks to provide operational observations into issues that we either know we know or know we don’t know. The goal of Data Science, on-the-other-hand, is to provide strategic actionable insights into the world were we don’t know what we don’t know.• A Data Scientist (compared to a data analyst) should have a

wide breadth of abilities: academic curiosity, storytelling, product sense & engineering experience

• He or she should also have deep domain expertise in Statistical, Coding and Machine Learning Knowledge

• As finding new revenue streams and gaining competitive advantage becomes primary, the role of Data Science offsetting Analytics will become more prominent in the coming years

REFERENCE LINKShttps://sameerdhanrajani.wordpress.com/2016/05/06/sameer-dhanrajani-sophistication-in-analytics-enter-data-science/https://sameerdhanrajani.wordpress.com/2016/08/29/sameer-dhanrajani-the-holy-grail-of-data-sciences-liquid-insights-amplified-intelligence-succinct-recommendations/

ARTIFICIAL INTELLIGENCE & COGNITIVE CAPABILITIES PERMEATING ACROSS ALL INDUSTRIES 

Artificial Intelligence will define how companies leverage digital transformation to advance their competitive position and improve performance. Also, cognitive computing companies that can deliver truly personalized customer experiences will become the next-generation market leaders. • AI will enable large scale curation of product

recommendations without needing human intervention and aid in creating a super-personalized experience for consumers

• Cognitive engines will become de-facto at Insurance call centers as a tool to help their CSRs quickly glance through the enormous customer data and quickly come out with appropriate recommendations

• In travel, cognitive apps will eliminate the arduous process of surfing 20+ websites, travel aggregators and customer review sites when trying to research and plan your next trip

REFERENCE LINKShttps://sameerdhanrajani.wordpress.com/2016/12/01/sameer-dhanrajani-how-machine-learning-and-ai-will-drive-digital-transformation/https://sameerdhanrajani.wordpress.com/2016/11/04/sameer-dhanrajani-the-ai-powered-retail-revolution/https://sameerdhanrajani.wordpress.com/2016/11/18/sameer-dhanrajani-banking-evolution-using-ai/

MACHINE LEARNING(ML), DEEP REINFORCEMENT LEARNING & GENERATIVE MODELS GETTING PROLIFERATED 

We witnessed a volley of Internet Companies open sourcing their ML / Deep Learning Frameworks like Google’s Tensor Flow, Microsoft open sourced CNTK, Baidu’s PaddlePaddle, Facebook’s Torch and Caffe etc.  Also deep reinforcement learning, generative models start becoming competitive with human machine learning experts (AlphaGo). We’ll witness increasing hybridization of deep learning with other ML/AI techniques, as is typical for a maturing technology.• AutoML systems will start replacing human experts for

standard machine learning analyses in 2017.• More advances in unsupervised learning and in the ability of

computers to understand and generate natural language, probably first with Chatbots and other dialogue systems.

• Progress in computer vision will continue as we see more applications, including of course self-driving cars

REFERENCE LINKShttps://sameerdhanrajani.wordpress.com/2016/12/01/sameer-dhanrajani-how-machine-learning-and-ai-will-drive-digital-transformation/https://sameerdhanrajani.wordpress.com/2016/07/18/sameer-dhanrajani-chatbots-the-protege-of-ai-data-sciences/

EMERGENCE OF CITIZEN DATA SCIENTIST & SELF-SERVICE ANALYTICS 

The idea that business users without statistical training will conduct data science, will foster a major trend in Self-Service Analytics. Data and analytics leaders must shift from content authors to insight enablers. Self-service data preparation will simplifies how users assess, catalog, clean, audit, share and collaborate on reusable components, which can be of tremendous value to companies needing to rapidly deploy agile analytics in a trusted way across the enterprise to remain competitive.• Smart Data Discovery will address a key skills shortage

highlighted by Gartner that most business users do not have the training necessary to accurately conduct or interpret analysis.

• Expert analysts should encourage business users to adopt analytic techniques as opposed to simplistic visualizations as they will become more informed consumers of the insights expert analysts deliver.

• Later on, the multiple styles of data discovery, including smart, governed, Hadoop-based, search-based and visual-based, will converge as their unique features and benefits become standard requirements for modern analytics platforms

REFERENCE LINKShttps://sameerdhanrajani.wordpress.com/2016/07/04/sameer-dhanrajani-enterprise-data-sciences-how-organizations-can-embrace-to-gain-better-insights-intelligence-recommendations/https://sameerdhanrajani.wordpress.com/2016/08/22/sameer-dhanrajani-how-data-sciences-accelerated-adoption-is-creating-more-shift-of-budgets-for-business-impact-generation/

PERVASIVE, INVISIBLE, EMBEDDED ANALYTICS – ANALYTICS ANYTIME, EVERYWHERE 

One of the hottest trends in Data Science and Analytics world today is embedding analytics capabilities into transactional applications. Formerly, only software vendors embedded analytical tools into applications, but now organizations in every industry are doing so. It is only when analytics is invisible to the end user that analytics will become pervasive.• Future Recommendation Systems - Shoppers will get

recommendations on their mobile devices, based on their historical shopping behavior, their preferences and their location

• In Fintech Services, embedding predictive analytics into a variety of operational business processes such as financial transaction systems will become mainstream.

• With Real-time fraud prevention systems, analytics will be applied to every single credit card transaction to automatically detect anomalies and flag transactions that should be investigated

REFERENCE LINKShttps://sameerdhanrajani.wordpress.com/2014/06/24/industrialization-of-analytics/https://sameerdhanrajani.wordpress.com/2014/07/16/making-sense-of-the-iot-phenomenon-extracting-value-transforming-business/

IOT ANALYTICS – INTELLIGENT SYSTEMS, DEVICES, PRODUCTS AND SOLUTIONS 

For IoT, it is not like once an analytics model is built, it will give the results with same accuracy till the end of time. Data pattern changes over the time which makes it absolutely important to learn from new data and improve/recalibrate the models to get correct result. Because this factor is indispensable, Continuous improvement and Continuous learning in IoT systems will be on the rise in the coming year. • The coming years also bring the evolution of IoT Edge

Analytics, where data can be processed near the source and not all data is sent back to the Cloud

• Edge Analytics induced Remote, distributed analytics deployment will reduce the data management and storage overhead by looking for just the actionable data. As a result, only the necessary data is analyzed or sent on for further analysis.

REFERENCE LINKShttps://sameerdhanrajani.wordpress.com/2015/09/25/sameer-dhanrajani-real-time-streaming-analytics/https://sameerdhanrajani.wordpress.com/2014/07/16/making-sense-of-the-iot-phenomenon-extracting-value-transforming-business/

ACCELERATED DIGITAL TRANSFORMATION, BUSINESS MODELS DISRUPTION ENABLED BY DATA SCIENCES 

With the amount of information in the world nearly doubling each year, it is no surprise that data complexity is the top challenge standing in the way of digital transformation. The question being asked by chief executives around the world is not if digital disruption will occur, but what it means for their business. More importantly, organizations are considering how they can leverage Data Sciences to morph from ‘Doing Digital’ to ‘Being Digital’. • Although part of digital transformation involves leverages a

portfolio of specific data science tools, it will really about the overall experience

• Future holds a scenario of being able to shop for the right data as easily as you shop for your next mobile phone and use new open source technologies, such as Spark, MongoDB and Redis, that provide speed and agility with just the right mix of flexibility and security

REFERENCE LINKShttps://sameerdhanrajani.wordpress.com/2016/09/09/sameer-dhanrajani-digital-industry-solutions-underpinned-by-data-sciences/https://sameerdhanrajani.wordpress.com/2016/12/23/sameer-dhanrajani-how-data-sciences-will-redefine-consulting-to-drive-digital-transformation/

THANK YOU- SAMEER DHANRAJANI