About 90% of efforts to successfully execute AADA (analytics, automation, data platforms, and AI) Quadfecta engagements involve retrieval, ingestion, processing, and analysis of unstructured and fragmented data scattered across an enterprise. Unless you have sorted your data, either you would be unable to leverage the full potential of the AADA technology spectrum, or the success would be confined to a few narrow spaces within the organization. Insights from the recently published HFS Horizons AADA Quadfecta Services 2024 report underscore the critical need for enterprises to focus on data.
Here are three key insights that surfaced during our interaction with business leaders while discussing the impediments identified in Exhibit 1:
- Data readiness—the foundation for enterprise transformation: A staggering 78% of respondents cite lack of data readiness, driven by incomplete and fragmented data, as the top barrier to AADA adoption. Beyond modernization and governance, enterprises must adopt a ‘data-as-a-product’ approach, treating datasets as reusable assets tailored to outcomes. Platforms such as Snowflake and IBM Watsonx offer scalability and governance but should be complemented by dynamic, event-driven architectures and decentralized data ecosystems to enhance agility and enable real-time context-aware utilization.
- Unclear ROI highlights the existing data value gap: Unclear ROI, cited by 52% of respondents, often stems from overly narrow metrics tied to traditional KPIs. To truly bridge the data value gap, enterprises must redefine success metrics to include customer experience enhancements, risk mitigation, and ecosystem-wide value creation. Providers such as Accenture and Infosys embed analytics into pipelines to deliver predictive insights, but enterprises should explore AI-driven scenario simulations that quantify long-term business impact. Adopting models such as pay-for-performance partnerships can also help align investments with outcomes, ensuring ROI is tied to measurable improvements rather than upfront costs.
- Closing the data talent divide with strategic roadmaps: The lack of an adoption roadmap and the talent gap, cited by 43% of respondents, highlight the need for a data-driven workforce. Traditional training must shift to AI-powered upskilling platforms, and data fluency should extend to all roles—not just technical teams. Companies such as TCS and Red Hat OpenShift show how scalable frameworks drive transformation, but adaptive roadmaps aligned with priorities and market dynamics are key for long-term success.
Adopting the modern data ecosystem to alleviate your data challenges
In addition to these strategies, our work with enterprise clients indicates the immediate need to adopt modern data and infrastructure tools catering to various stages of data lifecycle management. The following, if applied right, can help enterprises overcome their long-standing data challenges:
- Data storage and management: Tools such as Snowflake and Databricks handle storing, organizing, and managing structured, semi-structured, and unstructured data.
- Data integration and ETL/ELT tools: Tools such as Fivetran and dbt (data build tool) extract, transform, and load data from various sources into target systems such as data warehouses.
- Data processing and analytics: Tools such as Apache Spark process large datasets for actionable insights.
- Infrastructure automation and management: Tools such as Terraform and Kubernetes automate cloud and on-premise deployments.
- Machine learning and data science: Tools such as H2O.ai build, train, and deploy ML models.
- Data governance and security: Tools such as Collibra ensure data quality, governance, and compliance.
- Real-time and stream processing: Tools such as Apache Flink process data as it streams in.
The Bottom Line: Enterprises should invest in modern data tooling, business-driven KPIs, and upskilling the workforce to take big strides in their AADA Quadfecta projects.
To maximize the AADA Quadfecta potential, enterprises must embrace some approaches—adopting decentralizing data ecosystems, constantly measuring it against outcome-driven ROI metrics, and continuously training its workforce while also equipping them with best-of-breed data tools.
