The Enterprise Data Conundrum

Despite the undoubted transformational promise of data modernisation, enterprises today face fierce challenges to extract maximum business value from their cloud and data infrastructure investments.

A new data-driven AI era

The benefits of enterprise data modernisation are clear. It results in informed fact based decision making, and drives highly efficient operations and optimised customer experiences. It underpins the promise of AI and it is the catalyst for digital transformation. It gives you the ability to respond swiftly to market changes, safeguard sensitive data assets and ultimately protect and enhance customer trust.

Data modernisation goes beyond technology. It requires a data-driven culture and the ability to read, understand, interpret, and communicate with data effectively. Combined with predictive analytics, these attributes drive cross-functional collaboration, agility and the potential to create distinct competitive advantage.

The synergy with cloud migration

Both data modernisation and cloud migration are strategic imperatives for enterprises today. They form a powerful synergy: cloud migration provides scalability, real-time processing, and advanced analytics capabilities, while data modernisation ensures data is harmonised, secure, and accessible.

The vast majority of technologies that comprehend, interpret and consume data are hosted on the cloud. It’s where data can be gathered and distributed at scale with a lower total cost of ownership. Data can be ingested from multiple sources in real-time, combined and utilised for analytics, AI, and automation.

In short, data modernisation and cloud migration are complementary strategies that reinforce each other. Together, they empower you to harness the full potential of your data, and deliver innovation, efficiency, and competitiveness. Yet, enterprises are struggling to combine these two key imperatives and maximise their business value.

The conundrum

Adopting both a cloud-first and data driven strategy requires a shift in mindset.

Organisations must re-evaluate their perceptions of cloud security. There are still prevailing concerns regarding the safety of critical systems and data in the public cloud. It's a fundamental brake on cloud migration as evidenced by a recent study by McKinsey and IBM that revealed that enterprises have completed only about 20 percent of their cloud migration journeys. While basic workloads are in the process of migration, a staggering 80 percent of workloads remain on-premise.

Enterprises need to embrace a cloud-native approach when developing applications and services. Often, organisations approach the cloud by replicating on-premise data centres rather than harnessing the full spectrum of cloud capabilities, including Machine Learning (ML) and Artificial Intelligence (AI). This mindset hinders organisations from fully leveraging the cloud's potential for innovation and agility.

It's essential for enterprises to recognise that robust data integration is the cornerstone of effective data analytics. Often analytical initiatives are prioritised without a well-established data integration framework. Many enterprises concentrate on more visible aspects, such as data science and analytics, while neglecting the foundational data infrastructure.

A significant obstacle lies in the diversity of data sources, with data scattered across various systems and locations. The challenge of legacy systems holding data hostage makes migration to the cloud complex.

Unlocking the promise of data and the cloud requires siloed disparate data to be gathered and unified in an ultra-secure environment. Only then can you exploit AI, cloud services and business process optimisation. It requires mature foundations to be a real-time data-driven enterprise.

The characteristics of a mature data-driven enterprise

A clear data vision

Having a clear data vision is essential. It aligns data initiatives with business goals, prioritises efforts, and fosters buy-in. The data vision serves as a long-term strategy, guiding the adoption of technologies and data governance principles. With a clear data vision, you can develop a culture of data literacy, and set objectives and metrics to enable accountability and measurement. It promotes a data-driven mindset and reduces resistance to change.  

Proactive demand management

Mature data-driven enterprises anticipate demand. Resources are allocated efficiently by continuous monitoring of data usage patterns and data forecasting. By adopting a proactive stance, you can ensure a timely response to sudden changes in demand, promote efficient data processing and optimise performance. You can drive down costs and stay ahead of data challenges.

High data literacy

Fostering data literacy is crucial in the age of big data, AI and advanced analytics. A heightened understanding of data empowers your employees to excel in problem-solving, identify trends and seize opportunities. Risks are mitigated, collaboration is improved and real-time analytics facilitate fast decision-making. By embracing data literacy, you can unlock the full potential of your data and remain agile, competitive and innovative.

Modern infrastructure

A modern data foundation is ultra-secure to guarantee trust, scalable to accommodate data growth, flexible to handle different data formats, and capable of real-time processing for fast results. It also has a unified view of data enabled by its integration capabilities, and high quality tools for enhanced data accuracy and advanced analytics. A cloud based architecture delivers cost effectiveness and agility. It empowers you to capitalise on your data assets, and simultaneously drive innovation and operational productivity.

Automated data processes

Automated data processes are vital in enterprise data modernisation. They ensure data quality and consistency by automating data ingestion, transformation, integration, and validation. You can break down data silos and seamlessly migrate data between legacy and modern systems reducing downtime and data loss risks. Furthermore, data warehousing, governance, monitoring, data archiving and backup, and workflow orchestration can also be efficiently handled by automation. 

Accessible data

In a data-driven world, it is essential to make data easily available and understandable to authorised users. Data is centralised from various sources and catalogued for effortless search functions. Self-service analytics enable users to access and analyse data independently. APIs facilitate seamless data integration into applications and systems. The outcome? Improved decision-making, enhanced collaboration, increased productivity, and better customer insights.

Trusted data

Secure, trusted data is the cornerstone of a mature, data driven enterprise. This comprises ultra-strong data security, robust data quality and governance, reliable data auditing, and consistent and accurate integration. When you gain confidence in your data's accuracy, you can   reap the benefits of more effective decision-making and operational efficiency. It underpins your data modernisation efforts and empowers you to innovate, and build resilience and competitiveness.