The year 2020 has been full of unforeseen curveballs. Having said that, it also provides rare opportunities on many fronts to harness technology. There has been visible, accelerated technological disruption in various sectors such as retail, eCommerce and others; from technology adoption for ensuring the safety of workers at home, to enhancing customer experiences. In order to bring about improvements to suit the changing market scenario, the implementation of data, analytics, AI, cybersecurity and other emerging technologies has witnessed exponential growth.
Looking at the previous year, 2021 looks like an opportune time tto explore new tech-trends and advancements. In the coming year, the highlights will be smart computers, hybrid cloud, increased NLP adoption, and an increased emphasis on data science and AI overall. Pragmatic AI, containerisation of analytics and AI, algorithmic separation, augmented data management, differential privacy, quantum analytics, among others, are some of the other developments that could see a rise in the coming year. Considering these patterns, it can be said that since the pandemic, knowledge and data is gradually becoming a vital part of organisations.
In 2021, because of the evolving technology, data-driven leaders will reassess their data management strategies. In order to efficiently protect, control, and analyse data across business functions through a single unified framework, organisations can prioritise investments in scalable data platforms. Regardless of where it resides, these systems can have greater control and allow seamless access to their data, eventually helping them gain useful insights and make informed business decisions.
In this blog, we will discuss the major data management trends that organizations should adopt in 2021 to make the most of evolving technology to grow their business.
The Hybrid And Multi-cloud Strategy
The emergence of hybrid and multi-cloud architecture and ongoing developments in AI and ML are pushing the demand for data management to continuously evolve with growing challenges, opportunities and strategies. The recent collaboration between two tech giants, IBM and SAP, explains the drive towards hybrid cloud adoption by organisations.
In recent years, cloud adoption has risen significantly, with the trend further accelerating by 2020, in the midst of the COVID-19 pandemic. With the pandemic serving as a catalyst, fueling online demand, the cloud computing services business growth soared in Q3. Companies have begun to transfer more and more of their workloads and information to the cloud, choosing several cloud environments over a single cloud provider at the same time.
They are gradually adopting a multi-cloud strategy as companies drive their migration to the cloud.
A multi-cloud approach helps businesses maintain a hybrid cloud environment that offers a mix of both security and advanced capabilities, such as integrated ML capabilities. Although standard data and applications can operate on cost-effective public cloud networks, the most security-focused workloads and data can be maintained in the private cloud. This form of architecture is proving to be a popular model for enterprises as it provides a rich range of cloud options that both help maximise cloud investment returns and lower lock-in for vendors.
Data is no longer in a single environment; it is distributed through on-site and cloud environments, suggesting that organisations are heading towards a hybrid world. Businesses are continually searching for ways to better leverage data assets that reside inside current on-site legacy structures with the rapid growth in data formats, sources, and deployments through organisations.
Data fabric can be considered as a weave spread over a wide space linking multiple data locations, forms, and sources with methods for accessing that information. Data fabric technology is designed to address the challenges of data disparity management in both on-site and cloud environments.
In the data technology industry, data fabric is an emerging concept. This concept enables organisations to concentrate on developing a data ecosystem that offers centralised access through a single, cohesive view of the data of an entity that inherits constraints on access and governance, regardless of the format or location of the data.
The Rise of ADM
A study published by G2 predicts that 80% of mundane data management activities will be automated by ADM by 2022, enabling data scientists to concentrate on developing models of growth to obtain advanced data insights. What is ADM, though?
Data scientists and data engineers spend much of their time accessing, planning, and managing information manually. Basically, ADM uses AI and ML technologies to automate manual tasks in various data management processes.
ADM can help organisations simplify, optimise and automate data quality, metadata management, master data management, database management systems, etc.-related operations, making them self-configured and self-tuned. An augmented AI/ML engine provides data professionals with insightful suggestions, allowing them to choose from several pre-learned solution models for a particular data mission. Automating manual data tasks within organisations would achieve higher efficiency among the data user group and increased democracy.
Databases for graphs are relatively old technology. Information graphs have been used by tech companies such as Google, Facebook, and Twitter to understand their clients, business decisions, and product lines for years. Information graphs are made up of an underlying database of graphs to store the data and a logic layer to search for and extract data insights.
It can be concluded that graph databases have proven to be a powerful method for modelling the spread of the coronavirus.
The effectiveness of data scientists has been improved by the ability of information graphs to uncover and evaluate complex heterogeneous data relationships to discover meaningful relationships. It also encourages the ability of users to learn and evolve organically on an ongoing basis through ontology.
Graph is one of the fastest ways of connecting data, especially when dealing with complex or large volumes of disparate information. In combination with AI and ML algorithms, implementing a knowledge graph can help instill meaning and reasoning into data.
5G Will Go Mainstream
In recent years, we have heard about the benefits of 5G, but it was not until remote jobs, video conferencing, and digital communication became the centerpieces of our lives last year that the desire for strong connectivity and more bandwidth turned into a true and necessary demand.
Our reliance on smartphones, tablets, and various devices, including an increasing number of IoT sensors on a regular basis, illustrates the multi-path expressway necessity that telecommunications organisations undoubtedly knew we would need. Organizations cannot bear to be disengaged today, and 5G deals have become a central part of the agreement. As we continue to operate from our homes, the importance of 5G will eventually become mainstream in 2021.
Organizations are gradually introducing multi-cloud approaches and shifting their workloads and knowledge to the cloud. Data will be stored both on-premises and in the cloud. A difficulty enterprises will recognise in 2021 is handling this fragmented data through various outlets, formats, and deployments. This will lead organisations to reimagine their data management strategy in order to follow a hybrid approach to data management in order to link and maintain data regardless of where it resides.
Read More Blogs
SGA Digital Marketing TeamMay 26, 2020
SGA Digital Marketing TeamMay 19, 2020
Aakarsh MukenamkateJanuary 18, 2019