Adaptive AI, edge analytics, and ecosystem strategies are helping businesses compete with new-age business models.
Data and analytics in 2022: key trends
While technologies like Artificial Intelligence and Machine Learning were in their infancy a decade ago, they have come a long way and are now as commonplace as cloud is in the post-2020 enterprise. If data is the new oil in the information age, analytics technologies are the new oil plants. And if a McKinsey forecast declaring AI as a run-of-the-mill technology of the enterprise by 2025 is anything to go by, the competitive heat these technologies will bring to the business landscape will be nothing short of disruptive. Here’s how the field of data and analytics (D&A) is evolving at the moment.
Digging deeper
- Adaptive Artificial Intelligence: Enabling AI models to continuously retrain themselves by learning from new data and user interaction will enable enterprises to operationalize more models. AI engineering practices can help businesses build this flexibility into their AI models.
- Ecosystem strategies for D&A: With the birth of data marketplaces and business-neutral exchanges, organizations participating in non-compete data-sharing ecosystems will unlock greater value from their own data sets.
- Context-driven data fabric: Metadata – i.e., the context of enterprise data is becoming the most sought-after commodity in the process of securing, democratizing, and operationalizing data. A metadata-driven data fabric is helping organizations do exactly that, by minimizing data management overheads.
- Connected data governance: In distributed global enterprises, connecting multiple D&A governance efforts is the key to minimizing redundancy and mitigating AI risks. Connected data governance can be enabled by digital platforms to improve trust between business and technology stakeholders.
- Edge-powered analytics: With a rising premium on response delays in both consumer and employee experience, powering AI decisioning at the edge helps deliver faster experiences. This is enabled by the rising number of edge offerings by cloud providers in the market, and connectivity technologies like 5G and Wifi 6.
- Flexible data stores: Innovative data technologies like graph databases and NoSQL are now enabling data engineers to cut data pre-processing times. This will help speed data discovery and development of new AI and ML capabilities in the enterprise.
So, how are these trends unfolding in the real-world business landscape? Take a look.
A cross-industry perspective
From elevating the employee experience to enabling differentiating customer experiences, D&A is now at the forefront of nearly every disruptive business strategy across sectors. Further amplified by rising consumer expectations from digital experiences across all industries, D&A technologies are helping businesses keep up with the trendsetters. Here’s a snapshot of this dynamic from a few industries.
Retail
CPG
Telecom
Manufacturing
Oil & Gas
Enabling business technology transformation with D&A
A lack of synergy between data goals of business and technology roadmaps is a key cause of slow and low-ROI digital transformations today. As a result, defining common data milestones is critical to unlock the complete potential of a D&A strategy today. The first step on this journey is to accelerate data maturity in an organization, which involves the familiar steps of eliminating dark data from the enterprise, documenting policies that guardrail storage and usage of data with compliance and governance requirements. In addition, identifying data assets that lend the enterprise a competitive edge must be identified and operationalized first with the right use cases. Building data platforms replete with a metadata layer will define the last stage of data maturity.
A well-orchestrated data and analytics-led transformation should prioritize development of use-cases that generate quick ROI along such a data maturity roadmap. Modern data analytics trends such as adaptive AI systems, edge analytics, and ecosystem strategies rely on modular architectures which consist of multiple microservices. This enables organizations to improve key components of a use case by integrating new technologies without rebuilding it from scratch. Finally, it is worth noting that a platform strategy will be critical in orchestrating an extensive D&A transformation strategy. So what is it worth?
Powering new-age business models with your D&A strategy
A D&A strategy carries the potential to build new revenue streams for businesses. For example, a heavy engineering organization can deliver AI-powered maintenance triggers to its customers as a subscription service, and additionally turn a one-time purchase into a lifelong relationship with the customer. Other ways in which the aforementioned D&A trends unlock new business models include monetization of data by sharing it in an ecosystem, commanding additional revenues by elevating user experiences with edge, and mitigating AI risks via connected governance while empowering experimentation and innovation at pace. In conjunction with other disruptive technologies like IoT, 5G, and edge computing, the possibilities of innovation are endless.
Foreword for the future
The reducing cost of data technologies and bullish forecasts for spending on AI by enterprises (expected to touch $120bn by 2025 in the US alone) point towards the strategic value of D&A in the enterprise of today, and tomorrow. While these technologies will add to the heat in the competition across industries, businesses should consider this an opportunity to lead the market. Moreover, the technology landscape is in favor of businesses: cloud providers are rolling out new innovative data and intelligence services, edge computing solutions, and 5G offerings for enterprises. While technology (and especially data) talent is short in supply, leveraging collaborations with technology leaders will be the strategic play of the decade in launching the business for success with an effective D&A strategy.
Written by Don Ward.
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