The world of data science and analytics is continuing to grow and adapt at an astronomical rate. Businesses are slowly able to piece together more information from different sources, meaning that they are able to make more sense of their data. Using data has become more and more important in creating new business opportunities and growth. Companies are still discovering the potential of utilizing data and the importance of monetizing that data in some form to benefit the business. Here’s what we can expect to see from the data science industry in 2017
At a glance: 2017 Data Science & Analytics Trends
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. The companies that have the new mathematical capabilities possess a huge advantage over those that don’t. Google, Facebook, and Amazon were created as mathematical corporations or ‘math houses’. This trend will accelerate. Superior Algorithms would lead to extended Competitive Advantage that involves not only better returns and lesser costs for a company by its implementation, but also opportunities in monetizing their proprietary algorithms by offering licensing to other non-competing organizations.
The heightened demands for faster and better decision making, teamed with Democratization of Data, and Data-as-a-Service (DaaS) going mainstream, will inevitably create entirely new markets to buy and sell advanced analytics algorithms. These algorithm marketplaces would have 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 based on customer data by using analytic algorithms, ostensibly human decisions converted into a set of equations, to drive competitive advantages. The future shall have algorithm economy driving the Internet-of-Things where inert machines can communicate autonomously to take actions without human intervention, powered by replaceable algorithms.
Detailed Analysis can be found here:
https://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 Science
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. For example, trying to identify a future technology that doesn’t exist today, but will have the most impact on an organization in the future. Predictive analytics in the area of causation, prescriptive analytics (predictive plus decision science), and machine learning are three primary means through which actionable insights will be found.
A Data Scientist (compared to a data analyst) should have a wide breadth of abilities: academic curiosity, storytelling, product sense, engineering experience and just a catch-all I call cleverness. But he or she should also have deep domain expertise in Statistical and Machine Learning Knowledge. A Data Scientist will have quite a bit of machine learning and engineering or programming skills and will be able to manipulate data to his or her own will. Armed with data and analytical results, a top-tier data scientist will then communicate informed conclusions and recommendations across an organization’s leadership structure. 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.
Detailed Analysis can be found here:
Artificial Intelligence (AI) & Cognitive capabilities permeating across all industries
Organizations are considering how they can leverage digital transformation to advance their competitive position and improve performance. Increasingly, it has become apparent that artificial intelligence (AI) may very well hold part of the answer to that question. If a bank can use AI to reduce the time it takes to approve a loan, it not only reduces its own costs, but also provides an improved customer experience. In the Retail domain, AI has enabled computers to curate product recommendations without needing human intervention. Also, it can foster Individual segmentation to create a super-personalized experience for their users, by maintaining hyper-granular behavioral profiles of individual customers’ shopping habits.
The number of sophisticated cognitive technologies that might be capable of cutting into the need for human labor is expanding rapidly. Innovative insurance business leaders are exploring the possibility of driving the next level of customer engagement with Cognitive Computing. Cognitive engines are being deployed in Insurance call centers as a tool to help their CSRs quickly glance through the enormous amounts of big data relevant to the customer and quickly come out with appropriate recommendations. In travel, cognitive apps eliminate the arduous process of surfing 20+ websites, travel aggregators and customer review sites when trying to research and plan your next trip. Cognitive computing companies that can deliver truly personalized customer experiences will become the next-generation market leaders in this rapidly emerging space.
Detailed Analysis can be found here:
https://sameerdhanrajani.wordpress.com/2016/11/18/sameer-dhanrajani-banking-evolution-using-ai/
Machine Learning (ML) , Deep Reinforcement learning & generative models usage getting proliferated
We witnessed a volley of Internet Companies open sourcing their ML / Deep Learning Frameworks. Starting with Google’s Tensor Flow, Microsoft open sourced CNTK, Baidu announced the release of PaddlePaddle, Amazon announced they will back MXNet in their new AWS ML platform and Facebook is supporting the development of not one, but two Deep Learning frameworks: Torch and Caffe. Also, Automated Machine Learning (AutoML) systems started becoming competitive with human machine learning experts. An MIT group created a Data Science Machine that beat hundreds of teams in the popular KDD Cup and IJCAI machine learning competitions. AutoML systems will start replacing human experts for standard machine learning analyses in 2017.
In the areas of deep reinforcement learning, generative models, and neural machine translation, we have seen instances where machines were able to par humans in intelligence. AlphaGo, DeepMind’s network beat the Go world champion using deep RL. 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 but I have the impression that in general the community is under-estimating the challenges ahead before reaching true autonomy. We’ll also see increasing hybridization of deep learning with other ML/AI techniques, as is typical for a maturing technology.
Detailed Analysis can be found here:
Emergence of citizen data scientist & self-service analytics
Businesses have been talking about the ‘data driven enterprise’ for a while and now they are excited about the Citizen Data Scientist. The idea that business users without statistical training will conduct data science, will foster a major trend in Self-Service Analytics. Business users will continue to visualize data themselves and analyze it accurately and efficiently. 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.
The new approach is called Smart Data Discovery ensures users are warned about potential hidden factors that might better explain a visually exciting pattern and protects users from taking statistically unsound decisions. This functionality addresses 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 business users start adopting true analytical techniques, they will become more informed consumers of the insights expert analysts deliver. Hence the demand for analytics and expert analysts will only increase. 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 BI platforms
Detailed Analysis can be found here:
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. These capabilities can actually reside outside the application, reusing the analytic infrastructure built by many enterprises. However, to be considered embedded they must be easily accessible from within the application itself. Embedded analytics is not new, but the technology for integrating charts, reports, dashboards, and self-service tools has evolved considerably in the past 30 years. Formerly, only software vendors embedded analytical tools into applications, but now organizations in every industry are doing so. They have applications in Future Recommendation Systems – Shoppers will get recommendations on their mobile devices, based on their historical shopping behavior, their preferences and their location. Also, in Fintech Services, where embedding predictive analytics into a variety of operational business processes such as financial transaction systems.
Organizations are now starting to embed predictive analytics into a variety of operational business processes such as financial transaction systems. With real-time fraud prevention systems, analytics are applied to every single credit card transaction to automatically detect anomalies and flag transactions that should be investigated. This saves financial organizations millions of dollars. In a manufacturing company, too, analytics might be embedded into process monitoring for preventive maintenance in order to predict risk of process failure or even events that might have a negative impact on product quality. This approach can be extended to intelligent monitoring of all kinds of processes in extreme environments. It is only when analytics is invisible to the end user that analytics will become pervasive. As embedded Analytics becomes more pervasive, vendors will move upward on the scale to add richer functionality.
Detailed Analysis can be found here:
https://sameerdhanrajani.wordpress.com/2014/06/24/industrialization-of-analytics/
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. Models could be built and deployed across devices to capture learning at various points. This learning is then consolidated through a central model. The new data is continuously monitored and when data patterns change, the model is re-calibrated to accommodate the latest knowledge.
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. For example, applications such as leak detection in utilities and oil and gas require response times in seconds. When that data is sent to a central location for analysis, it loses its value. For large-scale IoT deployments, this functionality is critical because of the sheer volumes of Data being generated. Remote, distributed analytics deployment using Edge Analytics 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.
Detailed Analysis can be found here:
https://sameerdhanrajani.wordpress.com/2015/09/25/sameer-dhanrajani-real-time-streaming-analytics/
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. Machine learning models can surpass human capability in coping with significant volumes of data, finding high-order interactions and patterns within the data and dealing with highly complex business problems. Digital businesses will adopt machine learning in more and more use cases, driven by the availability of sensor data, expanding bandwidth and sinking storage costs. The question being asked by chief executives around the world is not if digital disruption will occur, but what it means for their business. Perhaps more importantly, organizations are considering how they can leverage digital transformation to advance their competitive position and improve performance.
Increasingly, it has become apparent that Data Science may very well hold part of the answer to those questions. Although part of digital transformation involves leverages a portfolio of specific data science tools, it is really about the overall experience. Imagine 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. And making these capabilities and the data that powers them available through tools of choice across personas to make data science enabled digital transformation a true team sport.
Detailed Analysis can be found here:
Demystifying Data Analytics, Decision Science & Digital 
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