9 Big Data Challenges of 2021
Rodrigo Inostroza, 2020-12-23
From better machine learning technology to development of tools for qualitative analysis to revolutionizing the health industry and climate change research, big data will play a key role in 2021. Continue reading to learn more about the big data challenges of 2021.
1. Instant Data Stream Handling: One of largest big data challenges for 2021 is going to be focusing efforts on data that actually serves business decisions in real time. Many companies are generating data by the second, which requires better analysis solutions that are able to ingest and process data continuously rather than in batches. This continuity ensures that no data is lost, unlike when data is broken up into batches. An application of instant data stream handling in our everyday life is, for example, self driving cars.
2. Improving Data Culture in Companies: Companies will have to become more comfortable with interweaving data use throughout all aspects of their production chain. From individual contributors and employers to business leadership, having a strong data culture will be paramount to an efficient cycle of data analytics inside the company. Using modern practices such as agile processes, scrum teams, and open communications make it possible for organizations to take advantage of true digital transformation through data driven decisions and advanced analytics.
3. Smart homes? Think Smart Cities: The IoT has seen revolutionary advancements with the use of Big Data. The smart home experience for example, has reached groundbreaking advancements, like Honeywell's smart thermostats that use machine learning to know the home owner's habits. This trend will slowly but surely start being applied to making our cities smarter. The big data challenges in this scenario, according to researchers in the University of Pennsylvania, would be to "improve the performance of health, transportation, energy, education, and water services leading to higher levels of comfort of their citizens."
4. Natural Language Processing: Natural language processing is a subfield of linguistics and artificial intelligence that uses computational methods to analyze human nuanced language. The idea is to allow better interactions between ourselves and computer intelligence, where the AI is able to detect different unstructured qualities of our spoken language. One of the big data challenges of 2021 for every business is to provide better customer service, which is why many are implementing NLP techniques to integrate virtual customer assistants or chatbot technologies to their different contact channels.
5. Augmented Analytics will Dominate: As Gartner has defined, Augmented Analytics is an approach to analytics that automates insights using machine learning, AI, and NLP. It relieves companies' dependency on data scientists by automating insight generation by using advanced machine learning algorithms and artificial intelligence. These big data tools are basically analyzing themselves to figure out a better, easier and more straightforward way to create insight for the company's different interests.
6. Incorporating Accessible Data Management as a Service: Cloud based services are already staples of data storage that can be used by anyone or anywhere. Data lakes and flexible storage environments should be more accessible than ever before, not only in accessing the data, but allowing new forms of visualization, analysis and predictive modelling. In other words, cloud based services will need to up their game to offer new tools for processing the data they are storing.
7. Data Security: With a rapidly advancing and shifting data landscape, there are few data scientists that have the cybersecurity skills that are currently needed, making privacy and security one of the main big data challenges of 2021 (according to Cybercrime Magazine, unfilled cybersecurity positions will reach 3.5 million by 2021). Another interesting opportunity in the cybersecurity space is the use of blockchain technology in offering security due to its network architecture that is difficult to counterfeit or change.
8. Big Data Uses for the Health Industry: With the need for a rapid response during the Covid-19 pandemic, there has been a monumental change in the way vaccines are created. With the use of big data analytics, scientists are able to predict protein structures which further develops their understanding of how viruses operate and react to different medicines. This was the case with Google’s Deepmind, which was able to determine 3D shapes of proteins decades before scientists expected to solve the problem.
9. Big Data Uses for Climate Change: If not the biggest big data challenge of 2021, it is surely one of the most urgent. Big data technology will be key in reducing the carbon emissions of the world, where the combination of meteorological data, earth sciences, ocean research, and even nuclear facilities, will shape the future for better urban planning, transportation and energy efficiency.
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