As we transition to a cloud computing architecture, data security and privacy must be given top priority. Data loss or data leakage can negatively affect an organization’s brand, reputation, and customer confidence. Data misuse is possible when multiple organizations share resources. Therefore, it is essential to protect data repositories as well as the data that is involved in processing, transport, or storage to reduce risk.
There has been a lot of buzz around decision intelligence (DI) recently. By 2030, 70% of firms, according to McKinsey, would employ decision intelligence in one way or another. Organizations are more likely to be data-driven as the popularity of big data and cloud computing continues to soar. Organizations who earlier desired to be data-first entities are now searching for more beyond merely being data-driven due to new difficulties and keeping competitive at the forefront.
Businesses need to reimagine the what and how of quality when they implement digital at scale. Quality today encompasses more than just functioning; it also includes customer experience and regulatory compliance. The path forward demands reimagining QA to ensure success in digital considering the increasing adoption of DevOps, Agile, and the influx of AI. Strong quality assurance is crucial as businesses streamline, upgrade, and secure their legacy environments for the digital era.
An agile maturity assessment is an exercise/tool to evaluate the current state of agile maturity and check on how successful the agile implementation is in an organization. The Agile maturity model clearly captures the areas for improvement and establishes goals to upscale the way your team works to adapt to business changes efficiently. Agile adoption statistics show the significant growth of 86% in Agile adoption within software development teams.
A token is a value that goes securely through the network to process payments without exposing actual card data. Tokenization aims to replace or represent specific sensitive information without compromising its security. The 16-digit payment card account number is replaced with a unique digital identifier, or the “token,” for mobile and online transactions. The tokens are randomly assigned, so it’s near impossible to reverse-engineer or compromise a token.
A decade ago, getting data was a critical task in every industry. Today, we see data flowing into the organization’s database from everywhere. Organizations can access massive amounts of data from sales, marketing, customer experience, finance, operations, human resources, and other departments. The requirement of today is how to use this data efficiently and effectively.
In the growing digital landscape, it’s a significant challenge to meet and cope with customer expectations, changing trends, use of technology, and expand the user base for digital applications. Many applications today fail to realize their full potential because they do not consider the complete end-user requirements during the design and development phase. It requires an exceptional effort to create and develop new technology accessible to all users.
In current times, no industry can survive without adopting relevant technologies, and the insurance industry is no exception. Technology plays a vital role in the insurance industry. Technology is redefining the way traditional insurance business is being done and driving smart reinvention of workflows. Technology is now part of all the stages of the insurance life cycle, right from the policy advice stage to claim settlement.
Like humans, Machine Learning (ML) models can recognize intricate patterns and anticipate the outcome of new data. On some natural language problems, ML models have even surpassed human performance. But much like people, ML models are susceptible to error. For every ML application in the real world, estimating how frequently a model will be inaccurate is essential. Intuitively presenting information and emphasizing the best ways to enhance a model are equally important.
