Xebia Articles

Almost everything we see around us has advanced technology embedded into it. One of such breakthrough technologies is artificial intelligence. Any machine that is "intelligent" can learn how to perform tasks and improve at them, an avenue explored by enterprises to their fullest. Global sending on AI and other cognitive systems will reach 57.6 million dollars in 2021 and 190 billion dollar industry by 2025. An AI platform is a space where you can train and host your machine learning models and make predictions about new data at your convenience. Google's Machine Learning Platform features a convenient location where you can do hassle-free development, with a faster production time, availability of no-code-based tools, and robust governance having interpretable models. 

The birth of big data occurred simultaneously with the web and internet when there was a creation of multiple avenues to model unique data collection methods, which analyze and store the massive amounts of structured and unstructured data and extract useful information from it. However, as time passed, data from web-based content was dwarfed by data generated by mobile devices and most recently, the Internet of Things. A record of 40 zettabytes of data was created by 2020 and the market is growing at a rate of 45% annually.

What does RPA mean?

Let's start with a hard and inconvenient truth: transforming your business is a highly complex undertaking with social, organizational, and technological factors at play. Many (research McKinsey 70%) organizations fail to deliver on their transformation's promises.

How to Be Ready for the Future? Invest in Sustainable Growth. Invest in the Workforce of the Future.

Ask CHROs how much time and money they spent on finding the right person for a job - they will admit it's quite a lot. A large part of HR's budget will go towards recruiting and onboarding people to introduce new skills and competencies. But what about developing the capabilities of your current team? Why is that option so frequently overlooked?

Data is the new gold, and becoming a data-driven company is at the top of many companies' change calendars. However, transforming your business proves to be challenging. It requires great alignment across many layers and effective collaboration between Business, IT, and Data Science.

Shifting paradigms unlocks new possibilities. The COVID-19 pandemic has been a great case in point, showing that being Digital and Agile is what differentiates between those that adapt, and those that fail and become irrelevant as part of a natural evolution process. Still, most companies are not able to take advantage of this shift. Often, a clear shared vision to build a strategy and set concrete goals and expectations is missing, diluting the value delivery down in the organizational trenches. Furthermore, the organization often gets stuck in politics, individual motives, and human biases, also known as a ‘social strategy’, disabling, and debilitating the ability for change to be manifested in the desired behaviors. In many cases, the leadership shies away from making big moves and instead sticks to the status quo. This results in semi-cooked or no digital and agile change. This is understandable, since managing change amidst complexity is risky and requires bold moves and significant time investment from the leadership.

Azure Cognitive Services is a segment of Microsoft's cloud-based services. It has REST APIs and client library SDKs. This helps you build cognitive intelligence into your applications. The biggest advantage is that you need not have skills in AI or data science to pull that off. This is a significant contribution in the world of IT, where businesses without knowledge of machine learning and the like can delve into the realm of intelligent applications. The library of cognitive features enables you to simply add pre-made templates to your application. With the help of Azure's Cognitive Services, various cognitive solutions that can see, speak, hear, and understand are built. It can even make its own decisions. 

DigiBankASIA and Xebia Forge a Strategic Technology Partnership for the Launch of New digital bank UNO

Data science teams frequently struggle to deliver business value because the results of their work are not embedded in the organization. Often data science teams work somewhat isolated and try to push their output into the organization. As a Product Owner (PO) you are uniquely positioned to pull this work into the organization, thereby increasing the odds of creating business value. To do this effectively, you may need to better understand data science and how you can leverage it to build better products, regardless of the product you own. Let’s explore.