Like every technology, data management is evolving. Successful companies strive to manage their data efficiently, and much of the focus today remains on digital transformation, which may include incorporating cloud-native technologies.
However, as the Harvard Business Review explains, “The rate of digital transformations failing to meet their original objectives ranges from 70% to 95%, with an average at 87.5%.” While many believe that adopting these technologies allows organizations to adapt and scale, legacy systems still exist in many organizations’ tech stacks, and it doesn’t make financial or practical sense to rip and replace them. We also can’t forget about the data that exists within some of these legacy systems.
A balance must be struck when working with legacy systems and incorporating digital technology solutions. Additionally, companies must follow the proper process when transitioning from traditional legacy data management systems to modernized approaches.
A Balanced Approach
According to Statista, about 328.77 million terabytes of data are created daily. In addition, the amount of digital information generated of late has increased dramatically thanks to new technologies like AI and the proliferation of IoT. Billions of devices gathering and transmitting data daily utilize AI algorithms, so this abundance of data is essential for building precise and efficient AI models.
Because of the global shift throughout the past 22 years from the analog to the digital era, enterprises have become increasingly focused on digital transformation. While necessary for survival and essential for saving money and resources, this shift adds another layer of complexity for organizations as they try to balance the old with the new.
To focus solely on new technologies and digital transformation and, in turn, neglect legacy systems would be forgetting an important piece of the equation, the journey from legacy to modern data management. It is imperative that, as we make this digital transformation, we give thought to what is left behind.
Integrating legacy systems with modern digital technologies should not be overlooked or rushed but instead requires a thoughtful approach to legacy (systems/application/data sources) to set the stage for a truly transformative journey toward innovation and efficiency. Observability tools, for example, can help organizations seamlessly transition while keeping track of data in legacy systems.
Overcoming Challenges: Risk, Cost and Growth
Change can always present challenges, and transitioning from legacy to modern data management is no exception. The three main challenges enterprises face along this journey tend to include risk, cost and growth:
Risk Management
Overlooking legacy systems can put businesses at risk for a host of threats and hazards, such as security vulnerabilities, compliance problems, restricted functionality, higher expenses, decreased productivity, data silos, interoperability problems and lost opportunities for innovation.
Legacy systems must continue to remain operational while new technologies are integrated to avoid the above risks.
Cost Concerns
The idea of a quick “rip and replace” approach to modern data management might seem appealing, but it is ineffective and cost-prohibitive.
Multiple factors need to be considered when developing a budget and cost-benefit analysis to ensure a successful transition. Cost concerns can include the initial investment in new tools as well as the migration costs associated with transferring data from legacy systems to the new platforms.
Additionally, integrating data into existing infrastructures can require time and financial resources. Some of the most important things to anticipate throughout this transition are the unexpected costs that could happen.
Scalability And Growth
When shopping for new data management tools, it’s important to think proactively.
While impossible to predict the future, selecting modern technologies that can accommodate an organization’s growth and changing needs over time is vital. As mentioned, companies are seeing massive amounts of growth in data, and modern solutions must be able to handle the influx of data.
There also needs to be seamless integration and interoperability among the various data sources. Addressing these scalability and growth challenges throughout the transition from legacy systems to modern application performance monitoring, observability and data management is a “must.” Only then will organizations be able to effectively utilize the data to support analytics, reporting and decision-making.
The Role Of AI
Discussing this transition from legacy to modern data management would be incomplete without mentioning AI. Like every other technology area, AI also plays a role in data management.
While AI has the power to be transformative in most areas of our lives, it has also created more data, further exacerbating the data management problem. We must recognize that integrating AI into modern data management systems can also have advantageous outcomes.
AI can potentially improve data quality by using machine learning to clean and standardize datasets and also help with enhanced data analysis, utilizing AI algorithms to sort and identify trends within data.
However, the question remains: What do we do with all the added storage requirements that AI creates plus all of the other digital data? How can we manage all of this data affordably and efficiently? We do not yet have all of these answers, but it’s something we are giving great thought to and will continue to work toward a solution.
Smoothly Transitioning To The Future
Every new idea experiences a hype cycle. When the buzz finally gives way to reality, the key to unlocking the future lies in a collaboration of old and new.
While the latest and greatest technology has us understandably excited, we must not forget about pre-existing legacy data and systems. Organizations need a balanced approach while acknowledging potential challenges along the way.
Navigating the path from legacy to modern data management doesn’t have to be stressful if done right. By using observability, prioritizing integrating legacy systems with modern technologies and considering the above challenges, organizations will drive both innovation and efficiency, ensuring no data is lost along the way.
In the context of the Industrial Revolution, applying technology to operating mechanisms in each organization is extremely necessary, and is also an inevitable trend to minimize workload while still ensuring efficiency and enhance its competitive position in the market. Furthermore, applying management software into a business will also help build an organization with a clear system, promoting consistency, transparency and accuracy. Tasken eOffice, researched and built by Opus Solution – a business consultant in Vietnam – is an internal work management system as well as the management of automated, online, user-friendly approval processes, allowing businesses to operate more effectively on the path of digital transformation.
Forbes