Top 10 Technologies Transforming the Future of Data Analytics

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Adversity drives innovation. Pandemic has spurred many organizations worldwide to trust data to drive transformation within their businesses. There has never been a better time to create impact with your own business data. The question is ‘how’? From plugging in cloud computing to the Internet of Things (IOT), AI, to smart cities and supply chain optimization – data driven breakthroughs are facilitating giant leaps forward for the businesses.

One classic story of a brand that leveraged data analytics to maximize customer retention is Coca-Cola. Coca-Cola sells hundreds of different products across various countries. Thus, customer behavior and perceptions can differ from market to market. That posed a compelling challenge for Coca-Cola in designing analytics solutions as per the region requirements. The objective was to increase market share and brand loyalty among its consumers worldwide.

Therefore, with data powered analytics, they could easily recognize the differences of choice and perception across geographies, and this helped Coca-Cola frame customized branding messages for different markets rather than a common approach. Coca-Cola laid their big bets on data analytics to produce and market more desirable options for customers around the world according to their interests and preferences, and increased its market growth by multifold.

With the expansion in data sources recorded every day, businesses are rigorously looking towards AI, ML, and natural language processing. It will benefit them to identify and act on insights hidden in distinct datasets. Companies need to improve their perception of implementing data analytics solutions to access data and build their business strategies more adequately. Businesses must identify that they can apply these modern technologies in their companies to sustain automation. Here, we will explore the technology reforming the future of data analytics.

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Top Technologies Reforming the Future of Data Analytics

About 95% of surveyed organizations have deployed big data initiatives on a department or enterprise level. IT and security teams need to work together to identify threats and vulnerabilities proactively across their data ecosystems. Here we will discuss various trends shaping the future of data analytics:

Hyper-personalization: Hyper personalization is an advanced process for personalized marketing where it uses artificial intelligence and real-time data to provide more relevant product and service information to every user.

Companies do not require to transfer one product through a selected set of marketing strategies. With data analysis, they get detailed and precise knowledge about customer personas, behavior, preferences, etc. They understand customer needs much better, thereby tailoring products and marketing strategies to suit the user. More brands are adopting this for their progress.

Artificial Intelligence and Machine Learning: AI and ML are being maintained by companies extensively to evaluate big data about multiple aspects of their working and design, respectively, for better results. This is particularly true in the case of improving and providing a seamless customer experience.

Organizations of all sizes use AI to enhance their business processes. Machine learning allows them to recognize designs and detect irregularities in large data sets to provide advanced data analysis capabilities more efficiently. This comprises recognition systems for image, video, and text data. Also, it can provide automated information classification, natural language processing abilities for chatbots and practices that can obtain optimal solutions among the abundance of data.

Augmented Analytics: Organizations are adopting the power of machine learning to automate data preparation and presentation and produce rapid outcomes in data-driven domains. Augmented analytics also automatically provides insights that users may not have considered by analyzing user behavior. And with the help of augmented analytics, users can instantly gain insights by receiving answers to their data questions in natural language.

Predictive Analytics: It is widely embraced by organizations to solve problems in an insightful and structured manner. Organizations use this tool to forecast future behaviours for greater profitability, minimize risks, improve business operations, etc.

This strategy is extremely efficient in correcting analyzed assembled data to predict customer response. This enables organizations to define the steps they must practice by identifying a customer’s next move before they even do it.

Cloud services: 34% of enterprises are now using cloud-based services, whereas 71% of IT leaders say they find a cloud-based deployment model when evaluating new analytics tools. Various providers and platforms offer cloud services that have eased business concerns about handling and storing today’s big data. This technology is here to stay.

Edge Computing: It has determined connectivity and delay threats combined with data travel and has transformed technology with IoT-enabled smart devices. Edge computing will build up its position with more significant drones, wearable technology, and autonomous vehicles.

It provides an increase to Data Streaming, including real-time data Streaming and processing without containing latency. It enables the devices to respond immediately. Edge computing is an efficient way to process massive data by consuming less bandwidth usage. It can reduce the development cost for an organization and help the software run in remote locations.

XOps: XOps allow data and analytics professionals to operationalize their processes to obtain defined goals that align with business priorities. The purpose of XOps is to gain effectiveness and markets of scale. XOps is accomplished by implementing DevOps best practices.

Therefore, assuring performance, reusability, and repeatability while conquering technology, process replication and allowing automation. These innovations would allow prototypes to be balanced, with adjustable design and agile orchestration of supervised systems.

Behavioural Analytics: Organizations currently use it extensively for personalization, customer intelligence, and marketing. However, efforts are to explore more ways of user behaviour analytics, especially in innovative city projects, traffic pattern identification, tracking medical shipments and cold storage for breaches, etc.

Internet of Things (IoT): The IoT market was growing fast and expected to expand four times its size by 2022, owing to further advancements in data processing and advanced analytics. IoT data analytics is the analysis of huge data volumes generated by connected devices.

Organizations can derive several benefits from it such as optimize operations, control processes automatically, engage more customers, and empower employees. The combination of IoT and data analytics has already proven beneficial in retail, healthcare, telematics, manufacturing, and smart cities. However, its true value for organizations has yet to be fully realized.

Graph Analytics: This technology is used to organize relationships in big data and find the strength and direction of such relationships. There is a solid case to apply it to areas such as detecting financial crimes, conducting research in bioinformatics, logistics optimization, etc.

Blockchain Technology:
According to reports, the global blockchain market size is predicted to grow from USD 3.0 billion in 2020 to USD 39.7 billion by 2025, with an annual growth rate (CAGR) of 67.3% during 2020–2025. With the success of cryptocurrencies that use blockchain technology, data scientists and business organizations are analyzing blending big data with blockchain technology to advance processes and build better fraud detection mechanisms.

The End Note

Over the years, modern technologies in Big Data Analytics have been changing continuously. However, companies need to implement the right trends to stay ahead of their competitors. Embee helps you achieve your analytics concerns with a wide range of advisory, stand-alone, and trending data analytics services. It provides fully managed data analytics solutions that rigorously perform testing and verification. Our data analytics management team helps you improve critical data and workloads instantly to ensure business succession. We aim to keep your team and customers connected to essential data and applications.

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