Why three quarters of enterprises are struggling to meet enterprise data demands
The Artificial Intelligence (AI) era is here, and for many enterprises, its arrival will either push them ahead or leave them behind. AI has the power to cut through all existing information and unearth the insights needed to make critical data-driven decisions.
Whilst most organisations have taken significant steps to integrate AI into their operations, the reality is that some are unsure how to successfully harness rapidly evolving AI solutions. And, when AI works with what an organisation already has – already knows – aka, data, just having more of it is not enough to reap the rewards.
Data is driving our world – and by 2025, it's predicted there will be over 150 billion edge devices connected1, with over 50 percent of data created and processed outside of the traditional data centre or cloud2.
Its critical businesses evaluate how that data is managed, used, and analysed. Enter data maturity – the capacity with which an organisation can organise, analyse, and maximise the use of its data. In essence, the higher the level of maturity, the more capable an organisation is in leveraging data for decision-making purposes.
How mature do APJ businesses feel?
A recent global survey conducted by ESG examined enterprise data management readiness, what is impacting their readiness, and what successful enterprises do differently.
Surprisingly, while 83% of respondents from Asia Pacific stated they are very familiar with their organisation's technologies and processes related to data management3, 80% also said their current data management capabilities don't allow them to keep up with business demands or desired outcomes.
With traditional infrastructures, what worked before is now costing companies precious time and money, as they are not able to offer the scalability of modern solutions. While the way forward seems clear, 65% of respondents agree that the complexity of IT infrastructures slows operations and initiatives. This, combined with the cost and time of refactoring or re-platforming applications, also heightens the barriers to entry – adding complexity and risk to cloud migrations4.
Key to success: data maturity
The first step to improving a business' ability to maximise its data is first identifying the purpose and value of the data. There needs to be a clearer understanding of what harnessing valuable data can truly do for the organisation, including alignment on what exactly defines relevant or irrelevant data. Through the lens of relevant decision-makers, business cases can be made to support the creation of a new data framework and supporting foundations to actually drive value.
For example, a telco wanting to streamline customer service capabilities might first look at the data it has access to – even data as simple as customer mobile numbers and account names – and question its value in various facets of the business processes. From there, decision-makers can explore how those factors, when put together as part of a new data framework and supporting foundations, will drive value for the customer and, in turn, provide a return on investment for the telco.
Capturing value from data to thrive in the era of AI-enabled insight will require enterprises to rapidly mature their approach to cloud strategy and how they store, manage, and protect data across its lifecycle. And, while comprehensive data management and AI-ready infrastructure are requirements for success in the AI era, most organisations today struggle to keep pace with enterprise data needs.
How to get started
The majority of respondents (90%) agree that cloud operations optimisation is the answer, with on-premises IT infrastructure, data management services, and applications critical to minimising data management complexity. From the edge to the core to the public cloud, disparate siloes need to be united to ensure consistency and predictability of data management – delivering a single source of truth to monitor and analyse.
IT decision makers need to evaluate what is going to deliver their business the most value. Instead of being "hybrid by accident" through data management spread across thousands of edges and multiple data centres, businesses need to be "hybrid by design" – with strong foundations which ensure a consistent cloud experience everywhere.
With a modern hybrid cloud setup, enterprises should have the agility and flexibility to scale up and down as required, with interoperability between public and private clouds, while also providing improved visibility, control and compliance across the entire hybrid estate.
Ultimately, it should also enable businesses to optimise their environment to ensure workloads can be run at the right place and right time based on individual enterprise goals – whether for optimising performance, costs and even energy usage.
Looking ahead
It goes without saying maturity is linked to confidence. Confident, data mature businesses are more often reporting advances in their data and infrastructure management, dramatically reducing cost, and boosting efficiency. This, coupled with the internal benefits that come with the improved performance, organisations can yield greater stakeholder satisfaction – boosting overall business confidence and competitive edge.
Ultimately, more mature organisations launch more offerings, beat competitors to market and better meet the needs of enterprise data innovators. Not only are more mature organisations delivering more value to their businesses, but they are also reducing infrastructure costs and time spent managing data requirements for success in the AI era; most organisations today struggle to keep pace with enterprise data needs.
About the research
ESG Research surveyed 750 IT decision makers and data science/engineering individuals from a wide spectrum of industry and organisation sizes. ESG assessed maturity based on responses to questions related to the following topics: invisible, automated, AI-powered infrastructure management; comprehensive, automated, and effective data protection; a trend towards management simplification/unification; cloud provisioning experience for end users; workload placement intelligence and workload transformation prioritisation.
Sources:
1 Source: IDC FutureScape - Doc # US47845322
2 Source: Gartner Predicts 2022: The Distributed Enterprise Drives Computing to the Edge
3 4 Enterprise Strategy Group, Custom Research, Data and Infrastructure Management Maturity Survey, April 2023
