How 7 Data Analytics Trends are Facilitating Persistent Adaptation of Digital Transformation?
New Era of Data Analytics 4.0 Reinforcing “Nowcasting”
Insights using big data has been an enabler for long and post-pandemic it helped to optimize the costs for companies, helped them sustain in a competitive world, and most importantly drive innovation. The big data initiative became a data analytics trend when technology matured and started to have capabilities for data management.
In this era of analytics 4.0 organizations are pulling data from hundreds of sources and deploying highly automated decision-making tools using cloud and big data technologies, bringing new granularity and correlation between the near future (Nowcasting) and further events with the help of AI-enabled analytics. With this, companies try to deliver what digital analytics has promised in terms of information of potential clients and customers, helping companies to achieve the desired goal.
Post Crisis Learnings to Gain Momentum in Analytics 4.0
With evolving innovation in AI, ML, and effective XOps, companies looking for the adoption of new data analytics trends must see them through the lens of an AI-enabled analytics modeling that reflects digital transformation, business values, and enables better decision-making.
Align Analytics with Business Priorities: Adoption of appropriate data analytics trends must be aligned with data investments, better decision-making platforms and practices, security improvements, increase in productivity, and enhanced customer experience, to unlock operational efficiency and business growth.
Accepting Data Imperfection: Data acts as a muse for business leaders which inspires them to improve insights and decisions but can also produce heterogeneous results. Ironically what drives accuracy in forthcoming steps can sometimes be imperfect to produce the insights. Leaders need to recognize which set of data from prediction can be utilized.
However, companies must define their priority domain based on what is more important to their value chain in the new normal. Also providing analytical training in the organization would help to grow an individual’s understanding of data analytics which would drive easy adoption of data analytics trends.
7 Data Analytics Trends Facilitating Intelligent Decision Making
In a recent Gartner Data & Analytics Summit, May 2021, leaders learned skills & gained insights into data and analytics that highlighted the need, support, and metrics required for modern data and analytics trends.
The pandemic last year abated data analytics with unpredictable repercussions and decision-makers were pushed towards focusing more on small data rather than just big data analytics. The pinpoint details lost their essence with the emergence of big data analytics and companies were not capable of fully utilizing big data. Enthusiasts have started finding the essence of small and wide data which will dominate in the upcoming year.
Scalable AI & Analytics: A platform’s scalability plays a significant role in increasing the throughput like enhanced computational power, consistent performance even with increasing databases, and the improved end-user experience.
Scalable AI platforms built, must be able to operate with small and smart data like scoring machine learning (ML) for real-time information.
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Data Fabric: The disparate data and disconnected infrastructure were outworn when data fabric emerged as a new approach in analytics 4.0 that unlocks the best of cloud.
Data fabric is a long-stretched weave that connects multiple locations, types, and sources of data providing data visibility, data protection, and enhanced security.
According to Allied Market Research, the data fabric market size is projected to reach $4,546.9 million by 2026, growing at a CAGR of 23.8% from 2019 to 2026.
Decision Intelligence (DI): Organizations need to stay afloat in a sea of data and better decision-making with decision intelligence. It is a new discipline that uses the potential of AI and offers a framework to assist data to develop, align, implement, track and modify the present model and processes, related to business results and performances.
Edge Data and Analytics: The shift of data and analytics towards edge has moved data closer to physical assets. This reduces the latency of data-centric solutions and enables more real-time values. Edge analytics applies knowledge from the trained neural network and uses it to infer a result.
Technologically, data integration and sharing tools, data extraction, import, and discovery capabilities, are important from the start to bring data sources and external data together, helping in digital transformation and results in unimaginable efficiency.
For stepping into a successful digital transformation journey, adopt these 7 strategic steps.
AI-Powered Big Data To Reign Over Future With $ 103 Billion By 2027
Companies are using digital assistants based on cognitive computing and AI to extend their analytical capabilities and turn quality data into usable data.
Organizations looking to leverage Big Data and AI must define models that create a single source of truth for the business. According to NewVantage Survey 2019, 97.2% of organizations are investing in big data and AI, as data creation grows and is expected to reach 180 zettabytes by 2025. So leaders must take advantage of technologies such as cloud and machine learning to achieve scalability and speed to create strategic partnerships with cloud service providers to address internal gaps.
Migration towards the cloud must be done according to the needs of enterprises strategically.
Another concern that comes up while operationalizing smart data is security, which needs improvement and is a big hill to climb. So the right mix of technology and processes are required to protect data and integrate IT infrastructure.
To know more about the data analytics trends and post crisis learnings, read our blog.