The Four Stages of Data Modernization for Digital Transformation

Digital Transformation (DX) is crucial to every business, from small-scale enterprises to medium-sized businesses to multinational corporations. Our world is becoming increasingly digital, and how a company carries out its DX initiatives determines its competitiveness and relevance today. 

Since the term ‘digital transformation’ means different things to different businesses, it is hard to define it. However, in general terms, digital transformation is the integration of digital technology into every key area of a business that impacts the fundamentals of its operations and its value delivery to customers. 

Beyond that, DX is a cultural reform in which enterprises continually challenge the status quo and get comfortable with trials and errors. This also means that the business organizations should walk away from legacy business operations on which the company was built and embrace new business practices. 

What Is Data Modernization? 

In today’s digital Information Age, unimaginable volumes of data get generated every second from countless devices and sources in structured and unstructured formats. The mixture of structured data (such as documents and spreadsheets) and unstructured data (blog posts, videos, and social media comments) brings storage and data processing challenges. 

Unfortunately, most organizations struggle to put this enormous data into effective use without realizing two essential things. First, their legacy data architectures stand in the way of generating deep insights for effective decision-making. Second, data modernization is the key to unlocking the limitless potential of data processing. 

Data modernization helps enterprises move siloed data from legacy databases to modernized cloud-based databases or data lakes. Legacy systems have several inefficiencies, complexities, and bottlenecks. An enterprise that embraces data modernization eliminates those obstacles and turns into an agile one. So, data modernization is the foundation of digital transformation in an absolute sense. 

The Four Stages of Data Modernization 

Let’s explore the four stages of an efficient data modernization process toward successful digital transformation. 

1. Data Migration

Data migration is the first step of data modernization and most DX projects. Unfortunately, in most cases, the professionals complicate the process or tend to prematurely transform the data before data migration. Instead, they could have performed a lift and shift migration: a process that allows quicker data migrations and lets organizations leave legacy systems faster. 

2. Modernization of Data and Application 

After the data has been migrated to the cloud, the data and application modernization phase commences. As it is carried out on the cloud, this phase enables a wide range of capabilities that are difficult to attain from on-premises (on-prem) systems. Some examples are real-time collaboration, easily accessible data sharing, and a more straightforward yet more informative Business Intelligence (BI) dashboard. 

3. Implementation of Modern Analytics

Data modernization empowers an enterprise to obtain more meaningful insights from data. And with modern analytics, an organization can learn more about their customers, identify customer behavior patterns, and make more informed decisions. 

In addition, connecting multiple data sources to cloud-based modern analytics is simpler than connecting on-prem databases to similar data sources. Cloud-based data pipelines are easier to build and smoothly navigate through problems such as data gravity that on-prem databases fail to deal with. 

4. Apply Artificial Intelligence and Machine Learning for Innovation

Businesses have been using Artificial Intelligence (AI) and Machine Learning (ML) to overcome several business challenges in recent years. The last stage of data modernization is to unlock the innovation potential of AI and ML. 

Some AI/ML use cases include manufacturing companies using AI/ML solutions to reduce waste by AI-based predictive maintenance. In addition, AI/ML solutions have been used by organizations to create customer profiles, learn more about customer behavior patterns and devise marketing strategies based on these profiles and forecasts. 

Sailing Smoothly Through Data Modernization 

According to a study conducted by Statista, 34% of respondents confirmed that their organization had fully implemented data modernization technology, while 50% stated that their organization is currently undergoing the implementation process. 

Data modernization may still be a daunting, time-consuming process for some, even after breaking it down into four stages. It is a fact that if conducted in a standalone manner, the data modernization process wastes tons of your time and resources. The lack of continuity is one of the crucial challenges most enterprises face when implementing data modernization. 

The best way to overcome this challenge is to shake hands with an Information Technology (IT) consulting firm like GoDgtl. With a focus on helping clients transform their off-line organizational activities and legacy business processes, we can help you modernize your data stack and enjoy modern and rich data experiences.