Most organisations in Malaysia have a multi-cloud strategy, but typically one cloud provider will be given the heavier load with mission-critical operations. Especially in the public sector, there is limited capacity to work from home due to unstructured and scattered cloud service adoption to access and cross reference data.
According to Gartner, it is predicted that by 2025, 75% of the data generated by enterprises will be created and processed outside traditional, centralised data centres or cloud environments. Over recent years, some of the mature enterprises have grown increasingly interested in data ops. These enterprises are embracing data for better insights and business resilience.
During the pandemic-induced lockdown, significant growth can be observed in web traffic, driven by work- from-home arrangements, digital learning, e-commerce, telemedicine and so on. Data point references have increased, giving businesses more insights into user patterns and for opening up new markets.
Enterprises need data management simplicity and agility to maximise the benefits they can get from their data. They could shift resources away from mundane data management tasks like data entry to focus on using data to innovate and add business value. Instead of focusing on data for operative purposes, enterprises need to pay attention to the dynamic nature of data and to identify where it needs to be leveraged.
3 Phases in data management evolution
Phase 1: Most enterprise data is located on-premises, used across many applications and stored in data warehouses. This volume, arising from both structured and unstructured data, adds complexity and leads to the popularity of data lakes.
Phase 2: Enterprises started migrating their data lakes to the cloud. Amazon Web Services (AWS) was first to market with its cloud platform. Soon, other vendors such as Microsoft and Google, brought their own strengths to the cloud.
Third phase: This was launched as organisations began storing their data in multiple clouds. Today, this multicloud environment promises both cost reduction and operational efficiencies. Yet, it also adds another layer of complexity to data management. Moving multiple siloed data lakes to the cloud does not solve all data management problems.
Despite the constant evolution, enterprises struggle with governing and managing their data close to the edge. While moving data to the data centre or cloud for analytics, companies will face issues like time investment, limited budget, lack of data infrastructure to conduct different types of data processing tasks at the same time.
With the convergence of transactional applications and analytics application, every application is rapidly becoming a smart application, embedded with analytic and artificial intelligence. Enterprises must effectively bridge the smart application consuming and processing multiple data. This convergence is the key data management challenge today.
Answering the challenge
Enterprises need to gain business agility and reduce complexity from edge to core to cloud. Our leading solutions have a few critical features for enterprises to look into and surpass the evolutionary phase in preparation for business agility.
1) Flexible data infrastructure: Your data needs to be in the right place at the right time, and you
must have a cost-effective way to store it.
2) Automated data governance: AI-driven governance capabilities work to clean, move and fix your data. Automated data management should implement policies such as those that cover your local data privacy and industry compliance.
3) Intelligent data placement: Intelligent data placement allows you to place your data in close proximity to the applications and analytics that need it. It is also cost efficient and compliant.
Moti Uttam Managing Director, Malaysia Hitachi Vantara
Moti has vast experience in the global IT industry and has witnessed the quantum leaps in technology over the last two decades. He currently leads Hitachi Vantara’s operations in Malaysia and views the current state of digitalisation as the beginning of a much-awaited shift.
