How Data Management is Driving Digital Transformation Across Industries?

Although digital transformation is a buzzword nowadays, how many of us actually understand this concept? When we hear the word, the first thing that strikes our mind is ‘technology.’ Of course, digital transformation involves technology to carry out transformations digitally. However, what many of us fail to understand is that digital transformation goes beyond the boundaries of mere technological adoptions. It requires a shift in mindset and approach, not just an upgrade in equipment. 

Digital transformation reflects an organization’s approach to harnessing the power of data to drive value, optimize business processes, and enhance consumer experiences. Data propels digital transformation forward, encompassing opportunities and challenges for effective management of data. This data-driven approach considers information as an asset, integrates data from diverse sources, and generates actionable insights that align with organizational strategic digital transformational goals.

This article by The Silicon Journal unveils the undeniable role of data in driving digital transformation. The Silicon Journal is gradually making its mark as a reputable business media house in the USA, with its informative and highly engaging content. The magazine covers a wide range of industry-based topics to empower industry leaders, professionals, and common readers through articles, news, and blogs.

What is Data-Driven Digital Transformation?

Data-driven transformation is a strategy of leveraging data to fuel an organization’s strategic change. It radically changes a company’s business model, its processes, and customer services through advanced analytics, Machine Learning (ML), Big Data, and Artificial Intelligence (AI) to gain actionable insights. A data-driven digital transformation moves beyond conventional methods of placing data at the core of organizational decision-making. This enables an organization to increase operational efficiency, minimize risks, and improve customer experiences, identifying potential revenue streams for faster innovation and long-term business success. 

Principles Guiding Data-Driven Transformation

The development of a robust data strategy and governance structure to drive a data-driven culture can be guided through various principles. Let us have a look at some of the most dominant guiding principles of data-driven digital transformation.

• Technology and Infrastructure Modernization: Organizations must invest in adoptable and scalable data infrastructure and technology to address present and future needs. Data storage and processing must be optimized with cloud, edge, and hybrid settings. A substantial technological integration and interoperability should be ensured across an organization. AI, analytics, and ML tools must be adopted for informed decision-making.

• Data Advocacy and Strategy: Companies must align their strategic vision and data projects with company goals to guide the transformation. Through executive sponsorship, data literacy and data-driven initiatives must be facilitated. Organizations must encourage data-driven decision-making.

• Managing and Protecting Data: By enforcing a company-wide management standard for data, data accessibility, interoperability, and quality can be ensured. Data should be managed in compliance with ethical, regulatory, and legal guidelines. Regular data auditing and cleaning can also help manage data better. Sensitive data must be protected with robust security and recovery equipment.

• Data Governance: Establishing a data governing body could help maintain a company-wide compliance, consistency, and quality. Data quality and integrity policies must also be implemented, along with coordinated management of data to improve data usefulness. Operational efficiency dashboards can be developed for real-time feedback and continuous improvement.

• Data Lifecycle Management: Organizations must collect, clean, organize, and analyze data from multiple sources to generate high-quality databases. API centralization and gateways should be managed for external and internal customers. Data streaming and calculation are important for precise downstream numbers. With specified consistency standards, any data formats and types should be supported.

• Data Accessibility and Visualization: Unification of data without physical replication is a must. Companies must use effective data search methods to find information. Comprehensive data management techniques should be implemented to ensure great data visualization capabilities without IT interventions.

Businesses can curate a data-driven culture, robust data strategy, and efficient data governance and management with these principles.

Data Management for Digital Transformation: Why It Matters?

Data is the primary building block of digital transformation. Interactions in today’s digital realm generate valuable data, and businesses use it as a foundation for establishing baselines and benchmarks. If we trace back to the history of data usage, we can see that it was initially used for traditional stakeholder analytics and reporting. 

However, with the advent of digital transformation, the role of data has become more widespread. It not only helps predict but also prescribes solutions. Using data, companies can predict customer demands, automate their decision-making, streamline business operations, and optimize business outcomes.

Furthermore, data helps accelerate product, service, or business model innovations to attain differentiation and a competitive advantage. A robust data management framework is designed to handle the velocity, volume, value, and integrity of data in this digital world. To unlock digital transformation, businesses must have a clear data strategy that aligns with their business strategy, defining data objectives, vision, roles, priorities, responsibilities, standards, metrics, and policies. 

By treating data as a strategic asset, businesses can unify information, personalize customer experiences,  and unlock actionable insights, ultimately driving growth in this digital era. Moreover, data management fine-tunes existing business operations to better respond to ever-evolving consumer needs and react faster to market changes.

What Roles Does Data Management Play in Digitalization?

A McKinsey & Company study has shown that AI, deep learning, and ML are projected to cause a net savings of $200 billion to $360 billion in healthcare expenditure. 70% of respondents reported that their organizations prioritize digital and analytics transformation, but lack sufficient resources in this area.  A West Monroe survey reveals that in the era of generative AI, there is a necessity for data and digital transformation, and companies are integrating data into their digital strategies. The survey further reveals that 61% of business executives recognize digital transformation as a top priority. Below are the roles that database management plays in digital transformation:

Operational Efficiency

Through efficient data management, businesses can streamline processes by automating data flows across their applications and systems. Data can be used to monitor performance, optimize resources, and identify errors in operations. According to the Agency for Healthcare Research and Quality, researchers and policymakers in the USA use National Inpatient Sample (NIS) data to identify, track, and analyze trends in healthcare access, utilization, quality, charges, and outcomes.

Improved Customer Experience

Database management enables companies to understand their customers better by collecting and analyzing data from various touchpoints of the customer’s journey. By using this data, companies personalize their offerings, anticipate customer needs, tailor communications, and resolve issues faster.

Increased Innovation and Growth

It helps businesses discover new opportunities and insights by analyzing data from various domains. They can use this data to experiment with new solutions, new hypotheses, and scale up their initiatives.

Key Challenges of Driving Digital Transformation with Data Management

As businesses begin their transformation, they often find themselves navigating a maze of data challenges. These obstacles can stall progress. Here are some of the most pressing data management hurdles companies face as they embrace digital transformation.

1. Data Silos

Data silos across different organizational departments are one of the crucial hurdles in digital transformation. A fragmented data landscape often hinders data-driven decision-making.

2. Data Quality and Accuracy

Issues with data quality can suppress the success of digital transformation practices. Data governance practices (standardization, validation, and data cleansing) can ensure accuracy and reliability.

3. Compatibility Issues

Integrating new technology, applications, and data sources into existing systems is crucial for digital transformation. However, compatibility issues could occur between modern solutions and legacy systems. 

4. Data Privacy Concerns

Data privacy is one of the biggest concerns associated with digitalization. A business often relies on a data center to ensure safe and secure storage of data across networks. The development of a data-centered culture is essential for digital transitions of operations.