The last few years have shone a spotlight on the role of technology in our lives. Both public and private sector companies in India have depended on the cloud and other emerging technologies like artificial intelligence (AI), machine learning (ML) and Internet of Things (IoT) to drive business continuity, agility, and scalability across their organizations. From manufacturing to retail, financial institutions to telecoms, we are witnessing firms use these technologies in creative ways that are revolutionizing the industries. The continued evolution of AI and machine learning algorithms has also driven the further adoption of machine learning and processing.
As more industries mature digitally and widely adopt these technologies, 2023 will be a pivotal year for organizations looking to deploy emerging tech solutions across business functions. Here are three key trends that will likely dominate business priorities in the coming year.
Trend 1: Treating data as a strategic business asset
Recent years have seen organizations generating unprecedented volumes of data as a by-product of their digitalization activities and increasing digital customer touch points. This is especially so in industries like telecom, retail, healthcare, manufacturing, insurance, and financial services industries. And with the deployment of 5G networks in India, this volume of data will increase significantly.
In India, we have observed that organizations are doing, or aiming to do, more with their data, and reduce the time to value. Data contains valuable insights for critical business decision-making and the most innovative and successful organizations recognize data as a strategic resource that demands its own strategy. How this strategy looks depends on the organization’s unique business needs as one affects the other. There is no one size fits all approach; the strategy must continue evolving with the business’s priorities.
What is certain is that having an enterprise data strategy aligned to the organization’s cloud strategy and business priorities will help the organization drive greater business value by improving operational efficiencies and unlocking new revenue streams. According to findings from the Enterprise Data Maturity research, senior decision makers at Indian enterprises with mature data strategies in place report achieving higher profit growth at an average of 8%.
With the right tools in place, distilling actionable insights from data to achieve business objectives or unlock new revenue streams is easily achievable for organizations of all sizes across industries, especially with the availability of self-serve functionalities that does not require specialized ops or cloud expertise.
Trend 2: Operationalizing adaptive AI systems for quicker business decision-making
With the increase in demand for real-time data processing, streaming, and sharing to power organizations transform into data-driven organizations, we anticipate more businesses investing in building adaptive AI systems that can ingest large amounts of data at frequent intervals and adapt to changes and variances quickly.
What will determine the winners from the laggards will hinge on the speed at which predictive analytics can be executed, and the cost-benefit ratio related to these algorithmic paradigms. An organization’s ability to create trust with usable and explainable AI for faster and flexible decisions will separate the leaders from the pack.
We foresee organizations pivoting focus beyond the algorithm to things like business-ready predictive dashboards, visualizations, and applications that simplify the use of AI systems to reach conclusions. These will help business leads quickly understand the impact to their business and act with confidence.
We have been working with Indian organizations to operationalize data analytics and AI solutions to unlock data-driven decision-making and operational efficiency, with them quickly seeing distinct business benefits. For example, business teams across Reliance Industries Limited previously relied on disparate applications to process and analyze information in silos. The company lacked a single, common view of the data and needed an enterprise-wide data modeling in place. Realizing the need for an organization-wide data strategy and repository, RIL created a centralized data lake to bring together and integrate data across various oil and chemical businesses and make available curated real-time business data. This enabled the roll out of several real-time business operation centers across RIL, significantly bettering business performance and lowering operational risk through increased process transparency and timely decision making.
Trend 3: Continued move to the public cloud and hybrid cloud, optimizing deployments
Public cloud spend and workload volumes continue to accelerate for organizations of all sizes as cloud-first policies and cloud migration remain top of the agenda for senior IT leaders. However, a significant amount of this spend is wasted as organizations struggle to optimize costs effectively.
According to Flexera’s 2022 State of the Cloud Report, respondents self-estimated that their organizations waste 32% of cloud spend in 2021, up from 30% the previous year. As cost optimization remains the top cloud initiative for organizations for the sixth year running, we will likely see organizations opt for more cost-effective strategies to deliver results quickly and efficiently, including:
- Migrating more workloads to the cloud to free up resources while driving agility
- Implementing data and analytics solutions that can manage the end-to-end data lifecycle – from ingesting data from multiple sources to storing, processing, serving, analyzing and modeling it to drive actionable insights
- Repatriating some machine learning workflows back on-premise, where complex processes are more cost effective, to optimize cloud spend for compliance, governance and security
This is where leveraging modern data architectures like data lakehouse, data fabric and data mesh is essential to driving business efficiencies across diverse operations. In addition to managing data on-premises and in public or private clouds, these modern data architectures are also intrinsically designed to handle complexities such as security and governance related issues. They also address the concerns of IT teams in allowing access to organizational data.
Organizations can consider moving to hybrid, unified data platforms that can manage the entire life cycle of data analytics and machine learning. The platforms must have features of openness and interoperability that allows ease of sharing and enables self-serve functionality, such as the Cloudera Data Platform (CDP) which has a built-in shared data experience feature. These features provide businesses with a common metadata, security, and governance model across all their data.
Overall, organizations must take the time to evaluate their business processes before embracing cloud, edge, and data capabilities. It is crucial to determine the approach and strategies that best fit the unique needs of their business, where these capabilities are a means of benefitting the entire organization and not just to solve specific problems.
(The author is Mr. Remus Lim, Cloudera’s Vice President for Asia Pacific and Japan and the views expressed in this article are his own)