Recognising the massive power of AI in the cloud
To be more responsive, customer-obsessed, and relevant, enterprises need real-time decision-making capabilities backed by AI/machine-learning-powered insights. Without end-to-end data visibility, free from infrastructure complexity, these will be difficult, if not impossible, to secure.
Enterprises still reliant on legacy systems find this particularly challenging. But to compete, they need to take action now.
Why? Because strategic investments in cloud and AI platforms and applications are growing rapidly. Research for the Accenture Technology Vision 2021 shows that the top two technology areas where enterprises are prioritising investment over the next year are cloud and AI.
Bottom line: as data strategies become increasingly integrated into enterprise growth objectives, the ability to extract maximum value from data assets – and harness the massive power of AI in the cloud – will be a crucial differentiator.
Extracting value from data: identifying the challenges
When enterprises move to cloud, they need to recognise the massive impact their decisions on data management will have. That’s why it’s best to take the key challenges into account early on in the journey;
- No unified data strategy
In their move from legacy to cloud, most enterprises adopt a ‘lift and shift’ approach without a unified data strategy. Hybrid, multi-cloud, and edge environments add to the complexity.
Problems often arise in the transitional phase. With data stored in multiple architectures, on-premise and on cloud, inconsistent user interfaces across different platforms make it challenging to extract relevant insights.
- Data can’t flow freely
In every industry, client data is shared across the business on a need-to-know basis. When that data resides on-premise and on cloud, problems arise because data on each of these platforms has a different governance structure.
The impact? Decentralised security protocols are hard to manage. This gets in the way of data democratisation and self-service analytics.
- Limited data visibility
To support multi-cloud strategies, most enterprises in Asia Pacific will soon be deploying new processes and tools, unified virtual machines and Kubernetes.
But without cross-cloud capabilities for secure data processing and management, however, they’ll be unable to realise the true value from their multi-cloud strategies.
- Unstructured data
Thanks to rapid growth in cloud deployments and escalating data volumes, enterprises need faster access to data across increasingly distributed landscapes. The challenge they face? Their legacy data management systems are unable to unlock value from unstructured data – which already accounts for almost 80% of all data.
Integration of AI/ML and natural language processing tools with legacy systems only adds to the complexity. This means data and analytics professionals spend most of their time on data cleansing/processing – and less time on data analysis.
Unlocking trapped value from data: taking action
To capture the full benefit from multi-cloud, enterprises must be able to process, analyse, and manage data across clouds. This can be achieved by taking three vital actions as part of the overall cloud journey:
- Develop a robust unified data strategy
Data complexity keeps widening the gap between business and IT goals. So when they implement their data management systems, enterprises must ensure their data strategies keep sight of business objectives.
This means integrating data strategy into business, culture, and processes. Collaboration with experienced strategic partners, critical to this process, will also provide support for assessing data maturity and mapping the journey to become truly data-driven.
We’ve developed a Data Maturity Model that helps enterprises navigate and accelerate this mission-critical journey, helping them shift from the idea of ‘single-use data’ and move rapidly towards optimised, insight-driven business decision-making.
- Invest in a cloud-based unified data management platform
By integrating data from multiple sources, platforms like Google Cloud Platform’s (GCP) BiqQuery provide a serverless, scalable, multi-cloud data warehouse for business agility.
Like this, enterprises can focus on data and analysis instead of worrying about upgrading, securing, or managing the infrastructure. Google does everything behind the scenes.
Whichever data management platform is selected, inbuilt AI/ML tools are a must-have. These enable advanced analytics at scale and help businesses learn, predict, respond and continuously optimise faster – as well as driving new efficiencies and revenue opportunities.
- Nurture a data-driven culture
Commitment to becoming data-driven must be enterprise-wide. But it starts at the top. It’s vital that the board, CEO and senior management all recognise how data can add value to the business – and model its importance every day.
Every enterprise also needs a data champion, with a seat at the leadership table. Whether that’s the Chief Data Officer or some other CXO, it’s essential that this champion understands the company’s business as well as technology.
Tools and platforms can help drive organisation-wide data literacy and adoption. Ultimately, however, becoming truly data-driven is all about building new habits, embedding them into how people work, and continuously measuring progress along the journey.
The road ahead
It’s all about data. The priority for enterprises from here on? Deploy new technologies, tools, and approaches to identify the connections between datasets that support complex data and AI modelling.
Harnessing the power of the cloud, these tools and techniques can help to turn massive and growing volumes of data into actionable insights, in real time – provided the enterprise follows a step-by-step approach, developing a holistic data and AI strategy that integrates technology goals with business objectives.