Technology is driven by ideas and innovation. The human society has come a long way with machine-based technology to software-based technology, and the result we are seeing the growing combination of both for business activities. Data and analytics have been one such technology vertical that has evolved over the current software development. From appointing, the Chief Data Officers to procure the latest software’s, business leaders are trying to utilize data but it hasn’t been an easy journey so far. The rising complexity in operation, size distribution, the type of data, speed of getting analytics, and the continuous intelligence required by the modern digital business has made the present data technology stretch. Businesses are based on ease of operations, and many of them are legacy based, so the rigid and centralized architectures and requirement of tools to break them down still depend on data.
Donald Feinberg, Vice President, and Distinguished research analyst at Gartner added that the continued survival of any business would depend upon developing a data-centric architecture and building an agile method that responds to various changes. While different business leaders have to respond in a different way to tackle digital distribution but by looking for the right offerings and technology will assist in streamlining the data process and make use of the unprecedented business opportunities will also give rise to more data solutions. The sheer amount of data makes the foremost technology experts seek stronger processing capabilities enabled with cloud technology, improving the algorithms for training and execution process that will help in getting reaching the complete potential for data. The algorithms will be the brain of your AI technology performing operational tasks at a faster rate.
According to Gartner, it’s critical for the business in understanding the following top 9 technology trends that will help in fueling the data technology story and prioritize them based on different business values to stay ahead.
1. Augmented analytics
Augmented analytics will be a major selling point when it comes to determining the analysis and Business Intelligence (BI) solutions. Using different machine learning and AI tools, the underlying augmented analytics will define the data and analytics to be more disruptive because it transforms how the analytics content is developed, consumed, and shared.
2. Augmented data management
Using the augmented data management sets the tone in utilizing the machine learning capabilities and AI technology to improve the data management categories. It includes different options such as data quality, master data management, data integration, metadata management, and self-tuning, and self-configuration for the data. Data management will mean that users will now have less area to focus on, and highly skilled resources will focus more on value-adding opportunities.
3. Operational Intelligence
Enterprises deal with live data, so it will be imperative for the machine learning algorithm to analyze and improve the business operation process. It’s about adding the real-time analytics that is combined with business operations, processing current and historical data to get an improved response for the events. Real-time data change could bring significant transformation in the job of the data and analytics team. By 2022, more than half of the new enterprises will add operational intelligence in their business decision making to improve the outcome.
4. Explainable AI
AI is already used by the businesses for data management and improve operational efficiency, but how can it be used by the enterprises to derive conclusions. Explainable AI in data science and machine learning platforms is about generating an explanation of data models that could be achieved with attributes, accuracy, model statistics, and features in natural language.
Graph analytics is a set of analytics technique that assists enterprises in exploring different relations between entities. Graphical methods are best utilized by the manufacturing and financial sector. By 2022, graph processing and graph database management systems will grow annually by 100 percent.
6. Natural Language Processing (NLP)
Could you have imagined an Artifical Intelligence (AI) tool conversing with humans just in a natural way? Natural Language Processing (NLP) or Voice are doing the same thing. They also play a vital role in search technology. The need to analyze a complex combination of data and to make analytics available to everyone will drive broader adoption of AI in industry. The tool will be easily adaptable to the requirements of the virtual assistant.
7. Data Fabric
Data fabric is all about getting a consistent data management framework, getting frictionless access, and data sharing in a distributed data environment as the soiled data storage can be deceptive. By 2022, data fabric configurations will be primarily be used as a static infrastructure and forcing organizations to completely adopt the re-design for more dynamic data mesh approaches.
Distributed ledger technologies are one of the major addition to the area of data analytics with added solutions impacting by providing the decentralized system for data storage that is secure and efficient. The ramification is all set to improve the analytics use cases, especially those leveraging participants relationships and interactions. Gartner added that it is still going to take several years for blockchain to take off in this area fully, meantime enterprises will partly integrate with blockchain technology and standards.
Data Analytics will be a big part of Artifical Intelligence and Machine Learning technology; it means that each component needs to be branded to work together more efficiently. Different AI and ML techniques can be used to provide commercial solutions rather than open source platforms. Commercial vendors have added the open source ecosystem to add innovation with the necessary understanding of their technology by AI and ML model management.
Though each industry is catering to different trends with available technology analytics and monitoring. What makes such technology as the foundational part is the rising data usage across the domain that sets them apart from all the other technologies. Many businesses today are looking to add a certain type of data programs and analytics solutions that can assist the users in determining and defining the business operations efficiency.
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