Building a Competitive Advantage Through Data Maturity
Data Maturity considers where data lives, how it is managed, data quality and the type of questions being answered with data. A comprehensive analytics environment can be achieved, as organizations advance through the stages of Data Maturity, to effectively analyze information to make decisions about future products, markets, and customers.
A comprehensive analytics environment often includes an integrated, accurate, consistent, consolidated and enriched view of core data assets across the entire enterprise. This data environment can be provided by automating effective data integration, data cleaning, data enrichment, consolidation/entity resolution, and Master Data Management as well, as descriptive, predictive and prescriptive data analysis.
In this paper, we describe six stages of Data Maturity and what organizations can achieve at each stage to enable effective decision making and gain a competitive advantage through a true single view of the business.
Data is evolving into a powerful resource for making insightful, forward-looking predictions and recommendations. This evolution is happening as compute and storage technology is improving, enabling more powerful analytical tools that are being used as a competitive advantage by more skilled data management professionals. The size, variety and update rate of data is growing fast, and quality is often an issue, as it comes from databases, web application logs, industry-specific transaction data and location-aware devices like mobile phones and many kinds of sensors. Today, full-time data management teams, including data scientists, analysts, and engineers are responsible for creating and maintaining a single source of truth for the company. These teams are finding and fixing data quality issues, performing exploratory, predictive and prescriptive analyses to answer the tough questions and enabling other analysts in individual business units.
- Stage 1: Manual data collection and management
- Stage 2: Automated data collection, integration and management
- Stage 3: Data quality
- Stage 4: Master data consolidation
- Stage 5: Single view of the enterprise
- Stage 6: Getting results: data analytics to improve decision making