Here are 10 big data and analytics trends to look out for in the year ahead.
Data has become the world’s most valuable asset. Advancements in the accessibility and capacity of tools for collecting, transmitting, storing, analyzing and acting upon data is making it easier to gather information and turn it into knowledge.
Driven by the rise of IoT – billions of devices collecting data around the world, every second – and the continued digitization of businesses, the huge data inflow known as big data is set to become a US$274 billion industry by 2022. That value is in large part driven by the rise of artificial intelligence (AI), which requires vast amounts of data in order to train systems – anything from predictive analytics solutions to hyper-personalized virtual assistants.
Big data, and the means to analyze it to derive actionable insights, has become a crucial resource for public and private enterprises, and the evolution of cloud software making is making advanced big data and analytics more accessible than ever. Here are 10 big data and analytics trends to look out for in the year ahead.
Data cleaning takes up as high as 80% of data scientist’s valuable time, according to IBM. Augmented analytics aims to resolve that by automating the time-consuming task of data preparation to create analytics-ready data pipelines.
Augmented analytics is touted as the future of business intelligence employs statistical and linguistic technologies to improve data management process. As data freeways become even more crowded, the augmented analytics market is projected to hit US$18.4 billion by 2023.
COLD STORAGE AND CLOUD OPTIMIZATION
Data never goes out of fashion, but enterprises are grappling with what to do with the data which is trapped in data silos and legacy systems. Cold storage delivered as a cloud service might be the answer. Optimizing the cloud services for viable data solutions is changing the way organizations store and deliver information on the edge. As the amount of big data generated touches more than quintillion bytes, cold storage solutions could save as high as 50% of the overall data storage costs.
Continuous Intelligence (CI) integrates data pipelines with automated decision analysis, which makes big data insights accessible to all in the business besides supporting the decision-making process, and encouraging automation endeavors.
Though an emerging trend, continuous intelligence aims to deliver tailored intelligent solutions that surround big data to match the customer’s need and expectations. A big data trend expected to gain momentum is supported by predictions by Gartner that forecasts 50% of new business systems to deploy CI by the end of 2022.
The most verbal debate that surrounds big data lies in its veracity and data privacy. Blockchain can assure big data security besides making it accessible only to its stakeholders.
Though an emerging trend, it will be interesting to see how AI/machine learning models are devised which operate on the top of distributed, transparent and immutable blockchain-generated data layers. The front runners who imbibe this trend will roll in investments and generate enormous profits.
Though our digital footprints generate mammoth quantities of big data, organizations end up using only a fraction of it. The remaining data gets trapped in dark data silos.
Defined as a web of unstructured data, dark data is not just a small chunk but is the fastest-growing component of the big data pie. It holds considerable information potential on customer behaviors, competitor analyses, and target markets for those who can access and devise strategies to mine.
The pandemic will usher a new era into business continuity, instigating more businesses to move their operations into the cloud. Subsequently, supplementing the rise of the Internet of Things (IoT) devices over the internet.
That need has brought about a trendy model of cloud computing – edge computing which takes the most critical data, processes, and stores it in a location that is readily accessible to the IoT sensors, before transferring to the cloud for future access.
Big Data-as-a-Service (BDaaS) software brings together data warehousing, infrastructure and platform service models under a unified platform to deliver advanced big data analysis efficiently for intelligent business insights. BDaaS lets enterprises enjoy multiple benefits of Data ETL delivered through Data Warehouse- and Data Lake-as-a-Service.
BDaaS will drive the future of global economies. The ability to tap into omnipresent data resources securely without investing hugely in building a data community will be critical to enterprise success.
Live big data analytics from data pipelines generate actionable business intelligence on-the-fly. This can help detect cybersecurity threats, and measure the performance of critical applications and services deployed over the cloud. Real-time big data analytics finds its way to a real-time dashboard and looks a very promising trend, for many businesses.
SELF-SERVICE BUSINESS INTELLIGENCE
Citizen data scientists and self-service Business Intelligence (BI) will propel the seamless movement of big data across data warehouses over the entire value chains. Self-service BI will let businesses integrate into a data-driven framework through ERPs, financial programming, CRMs, and marketing automation.
This would give more freedom to developers to create more personalized experiences enriching big data-based actions over the BI loop.
BIG DATA STRATEGIES
Advancements in Natural Language Process (NLP) systems will enable big data stakeholders to engage users over customized data conversations. Big data strategies would be paving their way across multiple industries, including telecommunications, transportation, retail, BFSI, insurance, and dynamic e-commerce.
Their ability to drive a disruptive change will bring an in-depth insight into digital transformation spearheaded by cutting-edge analytics. - (Source) Tech HQ