The future of enterprise IT will almost certainly be hybrid. After all, multi-cloud seems to be the preferred strategy among a majority of businesses. However, even within multi-cloud, a hybrid strategy that blends public and private clouds in orchestration seems to be gaining an edge over a strictly public- or private-only policy. This is according to the 2019 edition of RightScale’s annual State of the Cloud Survey, which revealed that companies leverage almost 5 clouds on average – a combination of 3.4 public and private clouds to run applications on, and 1.5 to experiment with (4.9 in total).
The distribution of enterprise workloads and applications across an integrated on-premise and multi-cloud environment comes with a lot of potential business value in terms of agility, scalability, innovation capability, and ROI. But this model of hybrid IT also introduces complexity. This complexity will drive enterprise IT organizations to new models of IT management in order to orchestrate hybrid deployments and maximize service availability and performance, ensure governance and compliance, minimize security risks, and optimize spend.
Hybrid IT management will be a challenge in a hybrid multi-cloud technology environment. But, in a data-driven world, one of the bigger challenges will be in terms of data orchestration and management. Companies need a new system of hybrid data management to synchronize and orchestrate real-time data – in different formats from different sources, and across on-premise and cloud-based systems – in order to unlock clear value from a hybrid multi-cloud IT model.
What Is Hybrid Data Management?
In a hybrid multi-cloud environment, enterprise data is distributed across multiple data lakes, multiple platforms and applications, and even multiple geographies because of cross-border restrictions defined by increasingly stringent regulations. Traditional data management practices are not adequately equipped to effectively manage the progression from data capture to insight in such a dynamic environment.
Hybrid data management is the simple idea that the distributed nature of hybrid data requires a centralized approach to manage it appropriately. A hybrid data management approach enables enterprises to create a centralized view of all data and blend it with a holistic perspective of business context and usage. In this way, enterprises are able to streamline and accelerate the journey from discovery to insight to enterprise value.
Key Principles of Hybrid Data Management
A rather concise hybrid data management (HDM) guide from IBM highlights seven key principles that will enable businesses to optimize this wealth of new data while remaining compatible with existing systems.
- Integrate technologies that enable the seamless access, sharing and analysis of all data regardless of source, storage or format.
- Integrate cloud and on-premises deployments so that users have access to data from across the ecosystem while the data itself is retained in the most appropriate deployment model.
- Prepare data management for machine learning so that all data can be used by ML models.
- Embrace open source technologies to encourage collaboration while improving security and reducing costs.
- Leverage the flexibility of HDM to constantly evolve data management strategies and practices with changing technology and business needs.
- Democratize data insights by empowering users across the enterprise with self-service access to relevant data.
- Address data fragmentation and sprawl by implementing an analytics engine to connect all data in the hybrid ecosystem and enhance analytics performance.
(Image source: ibm.com)
Data-related Challenges in a Hybrid Model
Data integration can present some serious challenges even in a monolithic IT architecture. But data integration challenges only get compounded as, to borrow Gartner’s phrase, islands of data explode. Even seasoned data integration veterans admit that hybrid environments make it quite challenging to do traditional data integration processes. Every aspect of data management – including aggregating, synchronizing and securing data, as well as implementing access control and data lifecycle management policies – becomes exponentially more complex in a hybrid multi-cloud environment.
Strict data governance and compliance regulations – like GDPR, for instance – that mandate what, how and where data can be stored, moved and analyzed, only serve to further complicate already exacting circumstances. The challenge for hybrid data management systems is to ensure compliance within each component of the hybrid model across the entire ecosystem.
However, the business value represented by a hybrid multi-cloud model significantly outweighs the challenges that the model presents. So, let’s look at some options that will enable companies to manage these challenges and realize the value.
Technological Solutions for Managing Hybrid Challenges
There are a number of technology solutions available that address the issues mentioned above. Take data integration for instance. Cloud-based Integration-Platform-as-a-Service (iPaaS) solutions can significantly simplify the integration of data, applications, security, and business compliance. These solutions are designed to be so simple as to be easy to use even for non-technical business users. iPaaS solutions can be deployed as a central pillar of a company’s integration strategy, or even as an extension of existing integration infrastructure.
Similarly, data-centric security solutions leverage the power of analytics, automation and artificial intelligence to ensure data protection, governance and compliance – even in the most complex hybrid IT environments.
A focused hybrid data management strategy will be a key driver of success and competitive advantage as hybrid becomes the predominant architecture for enterprise IT. It is true that the distribution of valuable enterprise data across multiple applications, platforms and vendors will create some challenges and complications for enterprise IT organizations. However, companies that can successfully address those challenges and transform the sprawl and fragmentation of data into actionable insights will stand to gain significantly in terms of unlocking new business value from their data assets.
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