The need to stay competitive in modern-day business has forced organizations to restructure their workspace. In a way, enterprises must step forward and pay heed to the bytes of data they have within their business.
Enterprise data has, for long, been disposed. The increasing popularity of data analytics has paved way for gathering unattended data, analyze, assess, and process them to make better and smarter decisions. Hence, the need for data modernization.
If you are new to this segment, this article will be of help. Talking about data modernization, we help you understand what exactly it is and how can you achieve the same without any hassle.
What do you mean by Data Modernization?
To begin with, we first need to understand what Data Modernization is.
To define, Data Modernization is moving data from a legacy system to a new/modern platform. That is to say, data modernization is the transfer of all the applications as well as the enterprise data from the siloed infrastructure to one that is digitally-driven. The modern-day infrastructure is backed by emerging technologies and can be accessed from anywhere across the globe. The extent of flexibility offered by the present-day system is one of the reasons why it is a must for every organization to opt for data modernization.
Another good example could be company acquisition. To be honest, one company acquiring another is pretty common. But the deal doesn’t end with signing the papers. There’s a lot to be done. Primarily, merging systems and the database is a crucial task. It could so happen that one of these has a legacy system. It is impossible and inconvenient to synchronize data when the two have different systems, hence the need for Data Modernization.
Also Read: Data Modernization and Mobile BI
Challenges with Legacy System
Learning what data modernization is might help you clear the clutter. The next thing to talk about is why should you opt for data modernization. Is your existing system not good enough to run business operations?
That, if said, would be wrong. However, it isn’t wrong to say that the legacy system is slower than the modern-day infrastructure and disrupts the business performance, on a global scale. Besides, there are few more challenges that link themselves with the legacy system. The common ones include:
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Lack of IT Professionals:
Since legacy systems are outdated and no longer in use, you might find it hard to hire professionals that will help you with the management needs. There might occur a situation when you are stuck running a query, and seek the support of an expert. Finding one in this case is extremely tough and will only make things worse for you. -
Older Data Model/Architecture:
Needless to state that the legacy systems or the traditional infrastructure encompass older architecture of the data. The model is no longer used by enterprises and working on it only adds to the problems experienced while serving a customer. -
Infrastructure and Maintenance Cost (Including Data Capturing, Storing and Processing) is high:
Since the system is age-old, it is possible that the cost of storing, capturing, and processing data is high. As stated, it takes a lot of time to process one unit of data, thereby adding time delays in the entire process.
What you need to do for Data Modernization
Wondering how to proceed with the data modernization process? To move your legacy system to a modern infrastructure/system is not a tough task. If you are aware of what needs to be done, you can take the matter into your hand, and move ahead with the modernization process.
- Identify the Areas which require you to migrate to the new system: The first thing to do is highlight the areas or the systems that you need to lift and shift to the newer infrastructure. Being clear on what needs to be changed makes it easy for you to proceed.
- Planning (includes Data scaling, Identify the tool to use for Data Modernize) and Data Modeling in the new system: The next thing is to sketch out a plan. Starting without painting out the entire journey will cost you huge. It is always recommended to outline each and every step, the data and the tool to be used for data modernization.
- Setting up Security and Data Protection Layer: Irrespective of whether you are doing it all by yourself or hiring an external to do the job for you, make sure you have paid immense care to the security protocols. Under no circumstances should you compromise on the privacy and protection of the data.
Advantage of Data Modernization
- Real-Time Data Analytics: An exciting feature is assessing the data to analyze them and use the same to make data-backed decisions.
- Data Availability on Cloud: Power up your business operations with cloud exposure. Data modernization specifically aims at taking your data to the cloud and storing it for seamless access. Whether you are at your desk or on the run, you will always have access to your enterprise data.
- Integration with New Technologies (Mobile, Social Media, etc.): Empower your customers with best-in-industry solutions integrating leading technologies. This way you win customers’ trust and place yourself as a changemaker.
- Faster Process: One of the best things about data modernization or the modern system is that it has a huge processing speed. Irrespective of whether you have fewer data or bytes of the same, data modernization will accelerate the pace.
Challenges in Data Modernization Implementation
- Lake of process documentation that is implemented in the legacy system
- User training to adopt a new tool/system
Conclusion
It has become a priority of any organization to build a flexible, scalable environment, one that enables their user to use actual data analytics at their fingertips. This is probably because the infrastructure is cost-effective and efficient enough to provide proper ROI. Data modernization does the same.
We are happy to help you!
If your primary need is to modernize data, you can better do it with cloud migration. We will be happy to advise and work together on your digital transformation journey. Get in touch with us and our experts to gain assistance.