Data trends that will define 2021 – and beyond
Last year, the COVID-19 pandemic became much more than a health crisis resulting in a heavy economic toll on nations, communities, and individuals. As the world continues to grapple to contain the virus, technologies are playing a crucial role in keeping nations and societies’ functional.
The global outbreak enabled some nations to tap into big data, machine learning, and other digital tools as the virus spread in their countries in order to track and contain the outbreak.
As big data is becoming mature, businesses and institutions across the world are working towards increasing their digital footprint and becoming more data-driven in the process.
We have selected our top five data trends (from different sources) that will define the year ahead.
1. Faster Innovation With Data Is The New Norm (Source: Forbes)
2021 will see the continued acceleration of innovation. 2020 showed us that we have to be able to move fast to get remote workforces up and running in days, move the nation’s classrooms online overnight and move our economy from in-store visits to touchless experiences. These timelines were unthinkable even 18 months ago, but they are the norm today. In the coming year, companies will be looking at ways to predict and adjust to market dynamics and social shifts overnight. This, again, will be predicated on access to and understanding of data.
2. Improved credit scoring systems (Source: Leads Squared)
The credit scoring systems of traditional banks are not suitable for SMEs. There is a lot of overhead with conventional scoring systems. SMEs often do not get access to credit because of this system. Some issues include extensive documentation, high-interest rates, and long decision-making times. Now, Fintechs are providing an alternative scoring system. They use data-driven models along with bank details.
These data points include:
· Personal data, such as age, name, contact details, bank credit score
· Businesses data like cash flows, bank account statements, financial statements, and POS transactions
· Behavioral data, such as psychometric tests and spending patterns.
Fintech also uses other data points, such as education level, degrees, and occupation. These alternative methods improve the accuracy of data-driven automated models. It eventually reduces the time and cost of servicing a loan. Thus, end customers can get funds with minimal effort.
3. Increased collaboration and partnerships (Source: Lead Squared)
Lending and Fintech partnerships will grow in the coming years. Lenders are willing to pair up with data providers, third party processors, and technology companies. The tie-ups will enable organizations to digitize their operations.
Moreover, Financial organizations will rely on the products and services from tech companies. The offerings include digital signing, fraud checking, credit scoring, ID verification, and video KYC. It will reduce manual labor to a great extent.
Technology will also replace numerous roles in the lending business. Organizations will have to restructure. Employees will have to upskill or reskill as lending will become more algorithmic.
For example, beverage managers have to look at the entire beverage ecosystem to understand larger trends. Perhaps keto is impacting beverage categories and a manufacturer can take an ingredient from a food product and introduce it to a new beverage that will cater to keto consumers. Brands will look beyond what customers think of a product or brand to the topic in general and the associations they are making.
5. Data Marketplaces and Exchanges (Source: Business2Community)
Gartner predicts online marketplaces will entice 35% of large organizations to become either buyers or sellers of data by 2022. This trend should accelerate cloud, data science, AI, machine learning, and even deep learning, claims Gartner. Companies like White Pages, ZoomInfo, or Acxiom have been selling data for decades, but these new data exchanges provide platforms to consolidate third-party data offerings.
Singularitynet, a blockchain-backed decentralized network that calls itself ‘The Global AI Marketplace’, is just the latest exchange to offer developers and/or companies the chance to create, share, and sell AI services and/or models to customers.