Choosing the right development platform can be pivotal for a developer's career trajectory, job satisfaction, and the quality of solutions they’re able to create. The latest data from the Appian Developer Sentiment Survey revealed that the Appian Platform not only advances developer careers but also redefines what they can achieve in the digital landscape. Developers are increasingly recognizing the value of Appian for enhancing job prospects and attaining overall job satisfaction.

Every digital interaction generates data. This data can provide invaluable insights and drive effective decision-making when managed effectively. . However, according to a survey, up to 68% of data within an enterprise remains unused, representing an untapped resource for driving business growth. One way of unlocking this potential lies in two critical concepts: data governance and information governance.

A data quality framework is a set of guidelines that enable you to measure, improve, and maintain the quality of data in your organization. The goal is to ensure that organizational data meets specific standards, i.e., it is accurate, complete, consistent, relevant, and reliable at all times—from acquisition and storage to subsequent analysis and interpretation. eBook: A Guide to Data Quality Management Download eBook.

Data democratization is an enterprise initiative to improve data-driven decision-making throughout an organization. Data democratization breaks down silos and promptly delivers data to the people who need it to make informed decisions. Data democratization has two core tenets: data access and data literacy. Both are simple in theory, difficult in practice. Data literacy is the ability to understand, analyze, interpret, and communicate with data.

Data completeness plays a pivotal role in the accuracy and reliability of insights derived from data, that ultimately guide strategic decision-making. This term encompasses having all the data, ensuring access to the right data in its entirety, to avoid biased or misinformed choices. Even a single missing or inaccurate data point can skew results, leading to misguided conclusions, potentially leading to losses or missed opportunities.

Working with large volumes of data requires effective data management practices and tools, and two of the frequently used processes are data ingestion and ETL. Given the similarities between these two processes, non-technical people seek to understand what makes them different, often using search queries like “data ingestion vs ETL”.

If you have a legacy system with customized capabilities and valuable features but nearing end of life, refactoring the system is a potential choice for modernization. Insurance platform modernization usually involves one-to-one code migration, which can often be more costly and time consuming than expected and typically tends to miss some of the integration and data architecture modernization that is foundational for getting the full value of digitization.

Today’s AI may be able to pass the Turing test, but I think we can all agree that doesn’t make it human. AI is a tool—a powerful one, but a tool nonetheless. And just like any tool, it can make our work easier, but it can’t do it for us.

What is hyperautomation for insurance? It might sound like just another buzzword, but let’s peel back the layers to see how hyperautomation works. Imagine this: you go on a deep dive to evaluate your insurance company’s processes and operational models. You discover inefficiencies, lag times, and that your team manages transactions with disparate, often manual processes. It’s clear you need a new solution. And the solution you need doesn’t currently exist.

With the explosion of artificial intelligence (AI), innovative companies are harnessing enterprise AI at scale for a variety of business use cases, including repetitive task automation, product and service development, supply chain management, customer service operations, decision-making systems, and more.