Key trends Driving Enterprise Data Modernization

Key trends Driving Enterprise Data Modernization-01

The modern business environment is rapidly changing, and organizations are increasingly adopting novel approaches to meet evolving customer expectations. Data modernization is one such technology accelerating change and reshaping how data is managed and accessed.

The digital world is exploding with new trends emerging each year and businesses bracing themselves to capitalize on opportunities through cutting-edge technologies. Data modernization creates limitless opportunities and benefits enterprises by increasing their ability to leverage Artificial Intelligence (AI), Machine Learning (ML), and analytics algorithms. In fact, modernization of data is no longer optional; it is critical to business success. It’s not about the amount of data enterprises have; it’s about how businesses can use that data to their advantage, and this is a significant step in that direction.

Here are a few trends that are driving enterprise-wide data modernization:

Increasing data volumes from a variety of sources

Data originates from a variety of sources, with non-database sources accounting for a sizable portion of it. Numerous organizations are experimenting with IoT and multi-channel marketing, which has resulted in an increase in the number of data sources available. Due to the diversity of data, there is an urgent need for data modernization in order to accelerate data processing and analysis. Businesses are being forced to re-examine their existing data processing systems as the number of data sources continues to grow. It is more important than ever to create optimal data experiences with actionable insights, and modernization of data efficiently addresses the complex processing requirements of large data sets.

Also Read: How to Boost Employee Engagement Through Technology

The Need for a Globally Shared Language  

Today, businesses face a number of challenges and opportunities as a result of explosion of data. Most enterprises rely heavily on it to make more informed decisions, and there has emerged a need for a universal, shared language for data access. Consistent, uniform access to data across the departments is necessary for improved decision-making. Its self-service analytics capabilities will allow non-technical members to effortlessly make sense of complex data and extract valuable information from a centralised location.

Advanced Analytics, Machine Learning, and Artificial Intelligence technologies highlighted

AI and Machine Learning are gaining traction as they enable faster process optimization. Adopting advanced analytics, AI, and machine learning systems necessitates data modernization, as legacy systems lack data automation capabilities. Any business can modernize their data using technologies like AI and Machine Learning systems. Strategies such as a data lake strategy, AI and Machine Learning applications can be extended to the realm of customer support. With personalization becoming a critical differentiator for businesses, modern database allows enterprises to perform natural language processing for chatbots and voice and text analysis.

Also Read: Three Key Strategies for Retaining IT Talent in Hybrid Environment

As hyper-personalization, enhanced security, and data democratization gain prominence, it should come as no surprise that businesses are making a paradigm shift toward new technologies for increased efficiency. Numerous organizations can assist businesses with data modernizations for mission-critical applications in technology partners and service providers. The best approach is to collaborate with such partners who can assist in planning and executing projects while also adding value to the business.

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Swapnil Mishra is a Business News Reporter with OnDot Media. She is a journalism graduate with 5+ years of experience in journalism and mass communication. Previously Swapnil has worked with media outlets like NewsX, MSN, and News24.