Modernization of legacy applications: is this a new phenomenon or has it been around for a while? We've heard the old adage 'Necessity is the mother of invention'. This certainly rings true as far as Modernization is concerned. Modernization initiatives have always been around but in the recent past, customer needs have become a priority. They are driving technology advancement and modernization at a fast pace. Earlier, it was the other way round - product owners were driving modernization. In the recent past, we have also witnessed the concept of beta testing, where in the customers are directly involved in testing the product and providing feedback. So the process is getting more customer-centric and their requirements are getting heard.
We also see a change in the development period of a product. Earlier, deployment cycles of products or software used to be lengthy and convoluted. They literally took years. However, the present day picture is in stark contrast. This duration has now come down drastically from years to months to weeks. With the increase in competition, Time to Market and overall cost involved in meeting customer demands are becoming priorities in all management and solution discussions.
In the software space, this has led to an opening of the floodgates, where solutions have begun exploring:
Open Source platforms
Much more flexible options
Developing BOTs to achieve automation and increase productivity
Continuous integration, deployment and operations leading to the DevOps concept
We're also witnessing another paradigm shift of sorts. Years ago, it was traditional user applications that generated data. We now usher in a brand new era where data is driving the development of applications. These new applications being developed are more data-driven and data-centric than ever. What has triggered this sudden change? Or more specifically, what has changed in the recent past that has brought about the development of this new era of applications? Let's delve a little deeper.
Earlier, sources from where data was being generated were few. We did not have the modern technology that could tap into other non-traditional sources of data, for e.g. sensor-based data, unstructured data, etc. With advancement in technology, new channels have been added through which data is being generated, and at a frantic pace to boot. Over the years, a repository of historical data has also been built. Social media, mobile, Internet of Things (IOT) etc., are some of the new sources which are generating data. All this has led to an explosion of data in the last few years. So now that we have an abundance of data at our disposal, what do we do really do with it? Can it help us extract vital information? Does it contain valuable information which needs to be tapped to help us make more informed decisions?
New-age data comes in different formats - structured, semi-structured and unstructured. Another paradigm shift has occurred in this regard. Previously, data was generated, then analysed and processed to take action, which was more reactive in nature. Currently, the immediate need is to process data upfront and be proactive rather than reactive. Instream processing of data using modern analytics platforms is the need of the hour, like in the areas of fraud detection, health care services, weather departments, etc.
To meet new modernization requirements, future applications should be able to process both structured and unstructured data from various sources and should be able to address the four V's of data - Volume, Velocity, Variety and Veracity.
We are now experiencing a new era of legacy modernization, where in the Time to Market has reduced. With the four V's of data requirements coming into play, the cloud readiness, AI and ML needs are gaining importance. Keeping all these in mind, the current Legacy Modernization initiatives and solutions should cater to new demands and be customer focused, addressing all their needs comprehensively. To achieve this, enterprises must focus on using DevOps methodologies in building their solutions which includes, continuous integration and delivery, microservices architecture, Infrastructure as a code and monitoring & logging. Apart from these, legacy modernization applications should also be cloud ready and be able to cater to the analytical needs of the organisation.addressing all their needs comprehensively. To achieve this, enterprises must focus on using DevOps methodologies in building their solutions which includes, continuous integration and delivery, microservices architecture, Infrastructure as a code and monitoring & logging. Apart from these, legacy modernization applications should also be cloud ready and be able to cater to the analytical needs of the organisation.