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    For a decade, Data Science has now been a hot topic, but most of its concept was present theoretically. The practical  application of Data Science became only possible after the existence of large data sets to work upon, effective machine learning algorithms, and systems to operate these algorithms.

    Data Analytics is a lifeline for the IT industry right now. Technologies and techniques like Big data, Data science, Machine learning, and Deep learning, which are used in analyzing vast volumes of data are expanding rapidly. To refine data analytics strategy and to be a successful data scientist, gaining deep insights of customer behavior, and system performance is a must. So be on the apex with knowledge of latest data analytics trends for 2018.

    data analytics trends

    1. Internet of Things (IoT)

    IoT will become the backbone of future customer value. Adoption of intelligent agents like Amazon Alexa or Google Assistant is on the rise. This has open marketers’ eyes to new ways of interacting with customers and they’ll wake up to IoT opportunity. The Internet of Things market is expected to grow from USD 170.57 Billion in 2017 to USD 561.04 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 26.9%, states Markets and Markets Research.

    2. Hyper Personalisation

    Now that everybody is tech-savvy and uses variety of devices and platforms to suit their needs, businesses are evaluating and evolving their modes of interactions to build a more sophisticated, intuitive and personalised relationship with their customers. Hyper-personalization is the action of creating extremely targeted messages that resonate and connect with a specific subset of the overall audience. It’s more like companies willfully abandoning broad reach marketing messages and creating multiple different campaigns for multiple different groups of people. This concept revolves around “what people want” and it’s predicted that 2018 will see ecommerce companies connecting their brand with their customers through hyper personalisation.

    3. Artificial Intelligence (AI)

    One out of five enterprises will use AI to make decisions, offer customers, recommend terms to give suppliers, provide real-time instructions to employees on what to say and do — in real time. Older generation text analytics platforms were very complex. Very few companies were successful in analyzing the text data. With deep learning in artificial intelligence, it will be possible to successfully analyze both structured and unstructured text data.

    The insights on AI during a Gartner ITxpo 2017 symposium, suggests that AI will become a positive net job motivator, creating 2.3M jobs in 2020 and there will be 2 million net-new AI jobs by 2025.

    4. Machine Intelligence (MI)

    As the name suggests, machine intelligence is a combination of computer systems and human intelligence. The simplest machine intelligence example is face recognition, which is widely used in gadgets like smartphones or laptops for unlocking the device and also in social platforms, like Facebook, in photo tagging. The importance of MI is explained by the fact that it allow devices to act independently and to collect accurate and efficient best user experience. This technology is used for real time product targeting, visual search, sizing & styling, conversational commerce, location based marketing & analytics, integrated online & in-store analytics, and predictive merchandising. Machine intelligence is going to be more prominently used in healthcare, financial, and e-commerce sectors.

    5. Augmented Reality

    Augmented reality is an enhanced version of reality where live direct or indirect views of physical real-world environments are augmented with superimposed computer-generated images over a user’s view of the real-world, thus enhancing one’s current perception of reality. It may not be as exciting as visual reality but it is a powerful and useful tool. Developing AR apps in bulk has became possible with the launch of Apple ARkit. Development and growth of Google’s Tango will further boost it.

    6. Behavioural Analytics

    Behavioural analytics is about analysing consumer behaviour, the understanding of what they do and how they act. This analysis helps enterprises to detect about what their customers want and how they might react in future. But behavioural analytics is more than just tracking people. Analysing the interactions and dynamics between processes, machines and equipment, even macroeconomic trends, yields new conceptions of operational risks and opportunities, which makes this field a bit more complicated.

    7. Graph Analytics

    Graph analytics is an analytical tool that leverages graphs to analyze, codify, and visualize links that exist between databases or devices in a network. Graph analytics will not replace the classical relational database technology but will rather be an addition to it. Enterprises are considering migrating towards graphs analytics as they are facing difficulties in their current data analysis set up. These difficulties are often termed a ‘Forbidden Queries’ as they are difficult to solve or answer back. This analytics is used for detecting crimes, applying  influencer analysis in social network communities, or while conducting medical research and bioinformatics.

    8. Journey Sciences

    Journey Sciences is termed as the new frontier in big data analysis. A journey or a story is a series of related events of a customer or patient, or employee or a machine. Journey Science is the intelligent art of deriving purpose through connected set of series of events or connected data.  Data for the billions of events occurring every minute are being captured and analysed. This includes business journeys, customer journeys, IoT journeys and even individual personal journeys.

    9. Agile Data Science

    A self-organizing, cross-functional team sprint towards results in fast, iterative, incremental, and adaptive steps is all about Agile methodology and this is taking root in data science. Agile in data science boosts productivity of complex collaborations among teams as well as between data scientists and other developers. Almost 25-50% of data science teams are employing Agile methodologies, states Wikibon community research.

    10. The Experience Economy

    Harvard Business Review states experience economy is where “a company intentionally uses services as the stage, and goods as props, to engage individual customers in a way that creates a memorable event.” The rise of the experience economy is one of the most important global trends in marketing.

    With the world revolving around gadgets and technology, consumers desire spontaneous scope of entertainment. They want multi sensory experiences, beyond sight and sound. Also they don’t want to be restricted by criteria like venue or time for their entertainment, and crave experiences that say something unique about them, which they can share with their friends and followers.

    Source:Top 10 Data Analytics Trends 2018 | SpringPeople Blog