Data and analytics capabilities have traditionally been considered distinct capabilities and managed accordingly. The shift to in-context data stories means that the most relevant insights will stream to each user based on their context, role or use. With the growing number of citizen data scientists, companies can enjoy access to more data sources as well as a broader range of analytics capabilities of a large audience of skilled information analysts within the organization. Every dataset is unique, and the identification of trends and patterns in the underlying the data is important. It combines the use of machine learning and AI techniques to transform how analytics content is developed, consumed and shared. This article has been updated from the June 9, 2020 original to reflect new events, conditions and research. How Machine Learning and AI will transform business…, Big Data: The Risks behind the Glamorous Opportunities, Heatmap – A Creative and Useful Way to Analyse Data, Tech trends that can impact business in 2020. Blockchain technologies address two challenges in data and analytics. To monetize data assets through data marketplaces, data and analytics leaders should establish a fair and transparent methodology by defining a data governance principle that ecosystems partners can rely on. Big Data Analytics trends from IoT, AI, ML, predictive analytics and more, we have covered the futuristic aspects and trends for Big Data technology in 2020. Here are the top 10 technology trends that data and analytics leaders should focus on as they look to make essential investments to prepare for a reset. Keeping on top of the latest developments in data analytics is essential to guiding your digital business transformation along the right path and achieving success in years to come. It combines the use of machine learning and AI techniques to transform how analytics content is developed, consumed and shared. of analytics. Data and analytics leaders need to prioritize workloads that can exploit cloud capabilities and focus on cost optimization and other benefits such as change and innovation acceleration when moving to cloud. However, many IMC solutions have a limitation, which is the high cost of storing all data in memory. Take a deep dive into each trend and uncover insights. Data and analytics should position blockchain technologies as supplementary to their existing data management infrastructure by highlighting the capabilities mismatch between data management infrastructure and blockchain technologies. But simply acknowledging the power of data analytics isn’t enough. The spectrum of roles will extend from traditional data and analytics roles in IT to information explorer, consumer and citizen developer as an example. When the power goes off, NVM retains its data instead of erasing them like what volatile memory such as DRAM does, so there’s no need for software-based fault-tolerance for IMC platforms. Data and analytics leaders must examine their business impacts and adjust their operating, business and strategy models accordingly. Using the existing usage and workload data, an augmented engine can tune operations and optimize configuration, security and performance. Eventually, this could lead to more scalable AI solutions that have higher business impact. Data and analytics leaders should look for augmented data management enabling active metadata to simplify and consolidate their architectures, and also increase automation in their redundant data management tasks. With more IoT sensors being connected to objects, an ever-expanding amount of data is generated. Read this report from Gartner to learn. Internet of Things merged with data analytics. All rights reserved. But these data can bring business value only when data analytics is involved to explore profound implications and point to possible solutions. Cloud-based AI will increase 5x between 2019 and 2023, making AI one of the top workload categories in the cloud. These dynamic insights leverage technologies such as augmented analytics, NLP, streaming anomaly detection and collaboration. Consider investigating how graph algorithms and technologies can improve your AI and ML initiatives. Over the next decade, more and more businesses will start using comprehensive in-memory computing platforms. By the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving 5X increase in streaming data and analytics infrastructures. Recommended Gartner client* reading: Top 10 Trends in Data and Analytics, 2020 by Rita Sallam et al. 12th floor, Viet A Tower, Duy Tan, Hanoi, Viet Nam. Data marketplaces and exchanges provide single platforms to consolidate third-party data offerings. According to a. report, the In-Memory Computing (IMC) market will reach the $15 billion mark by 2021, a significant increase from $6.8 billion. In the year 2020, up to 90% of large organizations are expected to be generating some sort of revenue from DaaS. The growing popularity of IoT in many organizations will entail the need for data analytics, making data scientists increasingly in demand within the next few years. Data analytics is the process by which data is deconstructed and examined for useful patterns and trends. A viable solution to this problem is memory-centric architecture which. AI and machine learning are critical realigning supply and the supply chain to new demand patterns. Modern technology trends in data analytics will combine with other future technologies like AI, IoT, Blockchain, Edge Computing, etc. Yet when working with business, several problems arise. AI techniques such as reinforcement learning and distributed learning are creating more adaptable and flexible systems to handle complex business situations; for example, agent-based systems can model and stimulate complex systems – particularly now when pre-COVID models based on historical data may no longer be valid. Data analytics software will allow businesses to analyze data efficiently, regardless of their volume and structure. of data-based tasks will be automated, which brings about higher productivity and more extensive use of data and analytics by citizen data scientists. The global augmented analytics market is expected to reap huge-scale revenues from a variety of industries in the next few years. Vendors offering end-to-end workflows enabled by augmented analytics blur the distinction between once separate markets. It helps data and analytics leaders find unknown relationships in data and review data not easily analyzed with traditional analytics. The following three trends in data and business intelligence arm business analytics professionals to help companies leverage raw data to reach their goals. Graph analytics is a set of analytic techniques that allows for the exploration of relationships between entities of interest such as organizations, people and transactions. These trends can help data and analytics leaders navigate their COVID-19 response and recovery and prepare for a post-pandemic reset. Data analytics has also taken steps beyond better ways to visualize the "what." According to a Gartner report, the In-Memory Computing (IMC) market will reach the $15 billion mark by 2021, a significant increase from $6.8 billion. of IoT (Internet of Things) connected devices. As data and analytics moves to the cloud, data and analytics leaders still struggle to align the right services to the right use cases, which leads to unnecessary increased governance and integration overhead. The trials use a living database that compiles and curates data from trial registries and other sources. By 2023, more than 33% of large organizations will have analysts practicing decision intelligence, including decision modeling. According to Gartner, citizen data scientists are those without the expertise or technical skills that characterize data scientists, and those with the ability to “bridge the gap between mainstream self-service analytics by business users and the advanced analytics techniques of data scientists.” By focusing on simplification, data and analytics software platform vendors can help citizen data scientists carry out sophisticated analysis and create models that leverage predictive or prescriptive analytics. Discover the top 10 Data & Analytics trends to watch in 2020 and beyond. It is one of the current trends in big data analytics . Using raw data to answer specific questions or track trends isn’t a foreign concept to many. The top data and analytics trends highlighted in this report will further democratize and scale data and analytics to help organizations accelerate recovery, improve resiliency, and drive innovation over the next three to five years. Within the current pandemic context, AI techniques such as machine learning (ML), optimization and natural language processing (NLP) are providing vital insights and predictions about the spread of the virus and the effectiveness and impact of countermeasures. supports the use of other memory and storage types including spinning disks and storage technologies such as solid-state drives (SSDs), Flash memory and 3D Xpoint. Gartner Top 3 Priorities for HR Leaders in 2021, 7 Digital Disruptions You Might Not See Coming In the Next 5 Years, Manage Risks From the U.S. Election Today, Use Zero-Based Budgeting to Rightsize Tight Budgets, Top 10 Trends in Data and Analytics, 2020, Gartner Top 10 Strategic Technology Trends for 2018, Gartner’s Top 10 Strategic Technology Trends for 2017, Top Trends in the Gartner Hype Cycle for Emerging Technologies, 2017, Gartner Top 10 Strategic Technology Trends for 2019. In response to the COVID-19 emergency, over 500 clinical trials of potential COVID-19 treatments and interventions began worldwide. Along the same lines, Gartner has identified the top 10 data and analytics (D&A) technology trends for 2020 that can help data and analytics leaders navigate their COVID-19 response and recovery and prepare for a post-pandemic reset. Using machine learning and AI, augmented analytics is considered, by Gartner, as a disrupter in the data and analytics market because it will transform how analytics content in developed, consumed and shared. By the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures. The implementation of data analytics in security, customer service, recruitment, and optimizing the business's daily functions is essential. Trend 7: Data and analytics worlds collide. Memory-centric technologies are expected to become the key to cost-effective IMC adoption. Get best practices based on research and interactions with thousands of organizations. Decision intelligence brings together a number of disciplines, including decision management and decision support. If you have any questions or inquiries, please do not hesitate to call us at +84 24 3202 9222 or email us at contact@savvycomsoftware.com. The data analytics industry is transforming with the rise of AI, IOT, and other open-source platforms, hence becoming necessary for organizations to stay up to date with the changing trends. for software-based fault-tolerance for IMC platforms. Savvycom, founded by a team of four passionate software engineers, is a Vietnam's leading software outsourcing company. Connect us for various frontier technology services and IT excellence: Savvycom Blog would love to give you an insight into the Technology world and support you in making the decisions for your software projects. in 2023 at a Compound Annual Growth Rate (CAGR) of 39%. The first one is intelligence. In order to grab the thousands of data analytics career opportunities that will open up in 2020, you need more than just any education. All of them could become relevant to your business in 2020–21, not because they are the “latest thing” but because there are three major underlying forces pushing those trends forward. During the pandemic, AI has been critical in combing through thousands of research papers, news sources, social media posts and clinical trials data to help medical and public health experts predict disease spread, capacity-plan, find new treatments and identify vulnerable populations. Analytics requires a lot more memory than a traditional application – and faster networks to get to data that is not in memory – in order to make analytics work in real time. 10 hot data analytics trends — and 5 going cold Big data, machine learning, data science — the data analytics revolution is evolving rapidly. Here are the top 10 analytics and business intelligence trends we will talk about in 2021: Artificial Intelligence; Data Security; Data Discovery/Visualization; SaaS BI; Predictive And Prescriptive Analytics Tools; Real-time Data And Analytics; Collaborative Business Intelligence; Mobile BI; Data Automation; Embedded Analytics; Become Data-driven In 2021! The DaaS market is predicted to reach $12 billion in 2023 at a Compound Annual Growth Rate (CAGR) of 39%. What implications does this have for companies? Combining IoT and data analytics will positively impact the business. It is expected that business will work towards more analytics solutions for IoT devices to provide not only relevant data but also transparency. , the augmented analytics market is expected to grow from USD 4.8 billion in 2018 to USD 18.4 billion by 2023, at a CAGR of 30.6% during the forecast period. Data and analytics leaders need to evaluate opportunities to incorporate graph analytics into their analytics portfolios and applications to uncover hidden patterns and relationships. Second, blockchain provides transparency for complex networks of participants. It is projected that by 2020, over 40 per cent of data-based tasks will be automated, which brings about higher productivity and more extensive use of data and analytics by citizen data scientists. First, blockchain provides the full lineage of assets and transactions. The growth of the Internet of Things (IoT)is having a big impact on lots of areas within many IT companies, one of which being data analytics. The drivers behind this impressive growth are increased adoption of big data analytics across different industry verticals and cloud-based services in enterprises as well as rising demand for real-time data analytics. Analysing data trends is an age-old and powerful tactic that is used to measure the performance of marketing campaigns over time and to predict future outcomes. In in-memory computing (IMC), storage of data occurs in RAM across multiple computers instead of in a centralized database, resulting in fast performance and scaling of data in real-time. The possibilities of data analytics are endless. Naturally, data analysts will continue to grow as they take on more projects and build experience in the industry. Fast Growing IoT Networks. Trend #2: Augmented data management Secondly, data scientists often work with data without having proper business context, leading to the resulting analysis failing to fulfil business purposes. These trends will continue growing well into 2020 and beyond. 1. defines Data as a service as “a cloud strategy used to facilitate the accessibility of business-critical data in a well-timed, protected and affordable manner.”. © 2020 Gartner, Inc. and/or its affiliates. By 2023, graph technologies will facilitate rapid contextualization for decision making in 30% of organizations worldwide. This trend started well before the pandemic, but COVID-19's impact on … 4. These trends fit into three major themes. For starters, the data generated by IoT comes in a huge volume and varying sets in terms of structure. For example, as the world scrambles to respond to current and future pandemics, graph technologies can relate entities across everything from geospatial data on people’s phones to facial-recognition systems that can analyze photos to determine who might have come into contact with individuals who later tested positive for the coronavirus. Memory-centric technologies are expected to become the key to cost-effective IMC adoption. However, Gartner projects that through 2020, “a lack of data science specialists will inhibit 75% of organizations from achieving the full potential of IoT” and as staff with the necessary skills will be scarce or expensive, organizations will “seek ways to use them more effectively or will find alternatives to human involvement, perhaps using machine learning rather than human data analysis.”. Data and analytics leaders use X analytics to solve society’s toughest challenges, including climate change, disease prevention and wildlife protection. Data as a Service is like Software as a help, Infrastructure as an assistance, Platform as a help. Gartner has identified the top 10 data and analytics technology trends that will have significant disruptive potential over the next three to five years. Explore using decision management and modeling technology when decisions need multiple logical and mathematical techniques, must be automated or semi-automated, or must be documented and audited. This impacts not only the technologies and capabilities provided, but also the people and processes that support and use them. It also converts metadata from being used in auditing, lineage and reporting to powering dynamic systems. We share in this article, the current Data & Analytics trends to help your business thrive: #1 – The use of new AI techniques. How to Use Facial Recognition Technology Responsibly and Ethically, Data Sharing Is a Business Necessity to Accelerate Digital Business, Future of Sales 2025: Data-Driven B2B Selling to Drive Digital Commerce. Public cloud services will be essential for 90% of data and analytics innovation by 2022. With fast-paced technological advancement, it’s a smart choice to stake at top data analytics trends 2020 to keep your business ahead of the game towards a more competitive future. The collision of data and analytics will increase interaction and collaboration between historically separate data and analytics roles. Leverage data and analytics ecosystems enabled by an augmented approach that have the potential to deliver coherent stacks. Gartner's research helps you cut through the complexity and deliver the knowledge you need to make the right decisions quickly, and with confidence. During nearly 10 years of development, Savvycom has been constantly striving to complete the mission of delivering powerful products whether they're mobile application or web-based application development, enterprise management solutions or Cloud & DevOps services. E-commerce Case In Point: Break Down Etsy's Success, Creating A Robust P2P Payment App For Banking Industry, Dissecting 5 Trendiest Technology In Banking, Prep for iOS 14: A Guide For App Publishers, The New Normal of Digital Learning Post COVID-19, Keys to Successful Digital Transformation, Tik Tok and The Implications For Future Social Media Platforms. It reflects business performance, provides actionable market and customer insights and helps business leaders make informed decisions. Augmented data management uses ML and AI techniques to optimize and improve operations. Data science teams consist of experts in analyzing and interpreting complex digital data to assist the decision-making process of business. Data and analytics capabilities have traditionally been considered distinct capabilities  and managed accordingly. The DaaS approach brings enterprises lots of benefits such as the ability to move data easily from one platform to another, ease of administration, compatibility among diverse platforms and global accessibility. Gartner coined the term “X analytics” to be an umbrella term, where X is the data variable for a range of different structured and unstructured content such as text analytics, video analytics, audio analytics, etc.
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