The modern business environment is evolving rapidly and organizations are increasingly adopting new approaches to keep pace with changing customer expectations. The digital realm is bursting with recent trends emerging every year and businesses bracing themselves to tap opportunities by leveraging the latest technologies. Data modernization is one such technology driving change and transforming the way data is managed and accessed.
To put it simply, data modernization refers to the process of moving siloed data from legacy databases to modern databases. It opens limitless possibilities and endows enterprises with cost-saving benefits along with greater scope to employ artificial intelligence (AI), machine learning (ML), and analytics algorithms. Data modernization is no longer optional but vital for business success. Businesses that were once averse to new technologies are now jumping on the bandwagon simply because everything revolves around data. It’s not about the quantity of data you possess, rather it’s how businesses can leverage this data to their advantage, and data modernization is a firm step in that direction.
There is no dearth of reasons for enterprises to adopt data modernization. However, we have narrowed it down to 4 key trends majorly driving data modernization across enterprises. Let’s dive in to explore them.
1. Cloud systems replace traditional data warehouses
Traditional on-premise data storage has proved ineffective in the context of large volumes of data that every enterprise deals with today. Traditional storage infrastructure such as massive data centers has not been able to cope with the evolving business needs. They are hard to manage with security concerns adding to the inconvenience. Enter cloud storage. Employing cloud-based data platforms is an important part of data modernization initiatives. Cloud storage offers better scalability at lower costs. Businesses are shifting towards public and hybrid cloud owing to its technical and economic advantages. With the cloud becoming a more feasible option for enterprises, data modernization is inevitable.
2. Rising data volumes from multiple sources
Technologies like IoT (internet of things) and smart devices have emerged as important data generators. Data is coming from everywhere with non-database sources claiming a large share of it. A large number of organizations are involved in IoT and multi-channel marketing, subsequently increasing the number of data sources in hand. This data diversity has led to an urgent need for data modernization to speed up data processing and analysis. Creating optimal data experiences with actionable insights is now more critical than ever and data modernization efficiently resolves the complex processing requirements of large data sets. Companies are compelled to reexamine their existing data processing systems as the number of data sources are growing at a rapid pace and unstructured and unprocessed data translates to lost opportunities and low profitability.
3. Need for ubiquitous, shared language to address business challenges
Today’s business challenges are vastly different from what it was a few years ago. The inexorable increase in data generation has presented businesses with as many challenges as opportunities. It is not just business intelligence analysts who utilize data today. The majority of the organization’s members rely heavily on data to make smarter decisions and there has emerged a need for a ubiquitous, shared language to access data easily. Data modernization is the way forward for enterprise wide decision support. Consistent, uniform access to data across departments within the organization is a prerequisite for improved decision-making and data modernization. Its self-service analytics capabilities will enable non-technical members to make sense of complex data and pull valuable information effortlessly from a single place.
4. Advanced analytics, ML and AI technologies come to the fore
AI and ML are gaining more prominence as they facilitate faster business processes optimization. Adopting advanced analytics and AI and ML systems mandates data modernization as traditional systems aren’t equipped with data automation capabilities. Any business wanting to leverage AI and ML systems to its advantage can do so with the aid of data modernization. When paired with data modernization such as a data lake strategy, the applications of AI and ML systems can extend to the customer support realm. With personalization becoming a key differentiating factor for businesses, predictive analytics that enables natural language processing for chatbots and voice and text analysis is one of the major benefits achievable with data modernization.
To summarise:
Data is at the forefront of every industry today. The key trends discussed here are paving the way for data modernization as businesses find ways to stay competitive and relevant. Data modernization is a critical digital transformation trend upending the way businesses will operate in the future. As hyper-personalization, improved security and democratization of data take center stage, it shouldn’t surprise anyone that businesses are making a paradigm shift towards data modernization for better efficiency. In terms of tech partners and service providers, many organizations can help in data modernizations for critical applications for businesses. The best way is to work with such partners who can help plan and execute data modernization of applications and add value to your business.
Also Read: Three ways AI can enhance Customer Service Operations
