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Arpatech Website
Jan 10, 2025
Artificial Intelligence in Application Modernization: Key Benefits and Real-World Applications
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An Overview of Current Agile Trends & Practices
Of late, Agile has proved to be more than just a catchword in the IT industry.
At the start of another year of thrilling events, the arena of Agile Development continues to grow as organizations continue to discover more exciting ways of planning and implementing work. Technology and virtual communications approaches have grown phenomenally and are here to stay, while our capacity to maintain human connection continues to prove difficult even in the best of circumstances. Agile believes in communication and putting the whole team in once place to get the task done in order to avoid information silos.
The Covid-19 pandemic saw a major shift in the work ethic across all industries, including software. The concept of working from home, which was earlier not encouraged, was adopted, but it also suffered from serious communication issues.
At its heart, Agile suggests that the teams must be self-sufficient and be able to communicate to avoid information and communication gaps. Therefore, based on the pandemic and the need to increase output while maintaining the needed distance, the concept of Cross Functional Teams was introduced.
The cross functional teams include people from different functional domains of a project or a product life and put them together to conduct all phases of program from start to finish. In this structure each member of the team uses their expertise of respective domains to achieve a common goal.
The year 2020 gave us a chance to evaluate how many of these predictions of Agile trends were accurate. Here is our list of top 4 trends for PMOs in 2021:
Inevitably, that stress has been laid on digital transformation and alternate ways of working across traditional, remote, and hybrid models, just like much of 2020 was the year of digitization for PMOs. In an environment that is volatile and constantly changing, PMOs continue to encounter many challenges, one of which is adjusting to changes as it strives to support agile changes in their own exclusive ways. So, let us explore a little deeper into each of these trends and learn what they might offer in 2021.
Agile has swiftly sneaked out from the world of software development into virtually all product and service industries. And PMOs are trying to find ways in which they can retain monitoring and control of how projects are managed. Agile (uppercase A) works as a delivery framework while agile (lowercase a) refers to the executive culture framework and mindset. Regardless of whether (A)agile is a criterion for PMOs, upright behavior in process and attitude will enable positive continual change.
Many Agile transitioning organizations would contend that this is already the case for PMOs, whereby perceptibility, transparency, authority, and supervision are often left behind. To ensure good governance, several features of the business must be taken into account, such as adaptability and embracing frequent change.
“Agile makes you quick but to be agile you have to be fit. It’s just two letters away from “fragile”, so it’s best to get fit before you get nimble,” said Ms Mamoona, Agile & PMO expert at a global IT company.
Artificial intelligence has seen an enormous in the last few years; in fact, calling this technology our time’s industrial revolution won’t be off the mark. AI has revolutionized the way we think, design, work and produce. AI is already widespread and getting a tougher position by the day as we see the speedy rise and influence it has on many industries worldwide. Just look at your phone – it identifies your face to unlock your screen, search. More astonishingly, it is able to detect algorithms that help you to navigate online, with as simple as autocorrect allowing you to type emails quicker than ever.
When it comes to project management, the surge of AI reporting can simplify the delivery of performance indicators to empower decision-making processes. We have also observed that AI provides more targeted insights on how project managers are feeling through feedback provided in their status reports as an example. Artificial intelligence has turned to be a valuable tool to not only support PM but also in so many other exciting ways. For instance, using AI has already played such a vital part in the Financial Risk Management space for the Financial Services industry, which is mostly focused around payment services. Nevertheless, talking about the advantages of AI and its valuable place within the PMO is at a promising stage and yet to be wholly realized.
There are risks to executing or building systems that are keener than we are. Will AI replace the human component in PMO and project delivery? Although we do not believe it will, we do think that AI can help accelerate the automation within PMOs and allow for focus to be placed in fields of more significance.
Digital transformation is an old field. The standard hypothesis is that people tend to assume digital transformation is an IT transformation enterprise only. Whether that is a structural change from non-digital-driven to digital-driven or maybe the spur to change your digital experience with better and more resourceful solutions. One of the most significant things to comprehend is that it is fundamentally business transformation, reinforced by investments in new technology—not a new technology on the lookout for opportunities.
In this day and age, consumer needs are continually developing, and businesses are keeping up with variable degrees of success. Delivery systems are adapting and being modified to match accelerated development across many industries. Shareholders and board members uphold more traditional needs within these unsettling settings. ROI and practical decision-making around calculated synergy, risk exposure, and asset utilization still need to be established. The key focus should be how you support organizations on their digital transformation trip and leveraging technology to improve current business methods, elevate culture, and the general user experience — all with the objective to meet changing market and business requirements.
The Covid-19 pandemic has seen a large segment of the global workforce work from home. It hardly needs pointing out that the new work ethic has had a dramatic impact on team collaboration and output. There are no rules for how to do this effectively, and in true Agile fashion we are learning as we proceed. Remote working has rapidly begun to reshape the future of the workforce, combining changes and employee management that need transparent communication and engagement enterprises.
Being resilient to change is important to keep relevant and become resolute to thrive. 2020 has been the year of reconsidering internal processes to keep up with the quick pace expected for 2021. It is highly crucial that PMOs focus on how to drive value for their organizations and learn to adjust to procedures that will inspire internal progress from within the PMO itself.
Arpatech Website
Feb 19, 2021
Different Industries that are Using Data Science and An...
Now, these are some general trends in Data Science and Analytics that will be observed in 2021 and the coming years. However, there are many ways in which Data Science is changing the shape of different industries like marketing, finance, etc. Given enough time, Data Science might be a part of all the industries in the world, not just tech!
So let’s understand the role of this technology in different industries.
Data Science already plays a huge role in marketing and retail. The most basic thing that almost every company uses is marketing and retail dashboards that visualize the data to see the hidden patterns and trends. Data Science and Machine Learning are also very useful for customer analysis wherein customer data is collected to understand the demographic of customers, the products that are popular with different types of customers, their likes and dislikes, and how to market a particular product to a section of customers.
The internet and social media are a treasure trove of data! Google alone processes around 20 petabytes of data every day (That’s approximately 1 followed by 15 zeros) Companies can use the web and social media analytics to obtain data about their customers and feedback on their performance which can be used to improve their bottom line. Sentiment analysis is a great example of this where companies can obtain their customer reviews from the internet or social media and to understanding the sentiment of the customers towards the company.
Supply Chain and Logistics may sound boring but it is a critical aspect for companies. Can you imagine Amazon working if their system for transporting products from point A to point B crashed? No! Therefore, Data Science and Analytics is an extremely important part of the Supply Chain and Logistics that companies can use for Inventory Management, Procurement Analysis, Inventory Classification, etc. For example, data analytics algorithms can be used to understand the correlation between demand and supply for companies and create methods to increase sales by always ensuring in-demand items are available.
FinTech is a technology trend that is becoming more and more popular with time. It involved finance companies using cutting edge technology like Data Science and Artificial Intelligence to improve in areas like risk analytics, fraud detection, algorithmic trading, etc. Many big banks and finance companies use Data Science to analyze their large store of data to optimize their risk scoring models and decrease their risks. This data can include financial transactions, lending schemes, interest rates, customer interactions, customer trustworthiness, etc.
As you have seen, there are various new Data Science and Analytics trends emerging in 2021. You can leverage them to learn more about Data Science and improve your career using the data science courses offered by Great Learning in collaboration with The University of Texas. These programs will teach you right from the basics of Data Science such as Python, Business Statistical, and Data Visualization to various techniques of Machine Learning such as Supervised and Unsupervised algorithms.
You will also get direct domain exposure by doing projects relating to Data Science in different industries like Marketing and Retail, Web and Social Media Analytics, Supply Chain and Logistics, and Finance and Risk Analytics. Some of these projects include Facebook Comments Prediction, Retail Sales Prediction, Insurance Data Visualization, etc.
Arpatech Website
Jan 24, 2021
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