What this blog covers:
- Learn about data intelligence that involves the comprehensive management of data, from collection to consumption.
- Understand how data intelligence ensures that data used by BI tools is accurate, trust-worthy, accessible and aligned with business goals.
- Know how data intelligence transforms raw data into reliable, actionable insights that drive smarter, more informed business decisions by adding context, quality and lineage.
Business leaders often question or find it extremely difficult to trust the data that is used to discover insights and create reports for decision support. An exponential rise in the volume of data collected and the growing number of sources further compound their misgivings about the lineage of the data used. Data sprawl introduces challenges dealing with its handling and inherent quality, slows processing speeds and multiplies computing costs and resources required. Overwhelmed by the complexities, the data-to-decision intelligence pipeline is often ignored, surpassed or at best used for justifying the choices already made.
What Is Data Intelligence?
When data that is used by the business team is transparent, granular, consistent, timely and comprehensible, leaders develop trust in the reports and dashboards. The process, tools and methods used to understand, analyze and add context to raw data, transforming it into critical information that guides business strategy and decisions, is what is referred to as data intelligence.
According to IDC’s definition, “Data intelligence leverages business, technical, relational and operational metadata to provide transparency of data profiles, classification, quality, location, lineage and context; Enabling people, processes and technology with trustworthy and reliable data.” In effect, data intelligence empowers leaders with actionable insights garnered from data they trust to make smarter decisions and achieve better outcomes.
Data intelligence is knowing the myriad relations between what data is being collected, where is it stored, when is it updated or accessed, and facilitating its consumption— who uses the data, how and for what purpose (why).
Data Intelligence, Analytics and Business Intelligence
Data Intelligence is being intelligent about enterprise data from collection, storage and management to consumption. Analytics is about applying statistical and mathematical modeling to this data to infer trends and patterns, find hidden insights, understand root causes, track performance metrics and use models to extrapolate and predict future outcomes and situations. It is about deriving intelligent information from the data itself.
Though closely related, both serve distinct roles within the data ecosystem. Data intelligence sets the stage by establishing a robust data foundation, while analytics leverages this foundation to drive data-driven decision-making. The former provides a holistic view of data management and utilization, ensuring that data is accurate, accessible and aligned with organizational goals; the latter dives deeper to extract actionable insights.
Business intelligence (BI) is closely associated with analytics and often used interchangeably. The focus of BI tools is on organizing information and presenting it to business users in an understandable, contextual and actionable manner. They focus on descriptive insights that drive operational decisions and strategy. BI is usually backward-looking, directed at explaining what transpired and why.
Both BI and analytics tools rely on the underlying data foundation to execute and perform.
Role in Building a Smart Data Foundation
A smart data foundation incorporates several features that make it data intelligent, efficient, scalable and business semantic aware.
Traditional practices of data management fail to scale as data volumes explode. Neither are they capable of handling multiple data sources and types nor of delivering the processing speed required for enabling agile decision making. A modern data foundation is designed to meet these challenges, composing a seamless, intelligent and integrated data environment.
A comprehensive architecture ingests, integrates and orchestrates multiple data sources into a common layer that is accessible to all users and tools across the enterprise. The design promotes data democratization, enforcing governance, control and compliance policies across the enterprise data platform. To handle the complex and high volume of data that needs managing today, smart data layer design is usually automated using AI and ML.
A smart data foundation thus ensures that the enterprise data made available for analysis is comprehensive, high quality, trusted and readily available.
Universal Semantic Layer and Kyvos
The data foundation meshes all enterprise data into an integrated, unified and seamless data view; however, it is in a physical data model. Analytics tools need to work with a logical data model that is aligned with the business using standardized terminology like “revenue” or “cost” that is easily understood by users.
A semantic layer over the foundational data layer provides this critical translation while adding several features like processing acceleration, high-speed querying, security and cost-effective, self-service capabilities. A universal semantic layer serves as the last-mile connectivity between the data foundation and the consumption layer, such as BI tools, applications or AI/ML engines.
A Gen AI-powered semantic layer platform like the one provided by Kyvos is thus a critical component of the data flow, powering the raw data to data intelligence and further onto insights seamlessly and efficiently. Kyvos employs an AI-powered smart aggregation technology that leverages machine learning and advanced algorithms to create massively scalable data models. It delivers unmatched performance on large and complex datasets while enabling multidimensional analysis for thousands of concurrent users.
The platform is also designed to proficiently manage vast data volumes and query loads. The query engines are optimized for OLAP queries on multidimensional data models, ensuring interactive response times even on petabyte-scale data. Kyvos uses a price-performant querying model that scales analytics without cost constraints using smart aggregates.
Kyvos augments and accelerates the consumption of data insights by business leaders. It complements the data fabric that provides data integration, management and governance while also adding the much-required data intelligence to the mix.
For more details on how we do this, please talk to our experts.