The key business value generated with the data is not about having large amounts of data. It is about having the right data at the right time. Currently, enterprises are struggling to get into robust data-driven organizations.<\/p>\n
Research says by 2023, more than one-third of large organizations will have analysts practicing the discipline of decision intelligence, which includes decision modelling.<\/em><\/p>\n – Source: Gartner<\/em><\/p>\n Cigniti\u2019s experiences while partnering with a client in their digital transformation journey, established a data quality governance framework that enabled measuring business value. Before we get into data quality governance, let’s understand the business value story and build a business value model aligned with digital transformation services.<\/p>\n Business Value Model<\/strong><\/p>\n Gartner recommends having a “business value story” that translates metrics to value that are relevant, clear, and believable, and all of these are impacting the business. Each metric in the model is mathematically linked to the financial statements, providing CIOs with the ability to simulate the impact of IT investments and seize opportunities. The value pyramid by Gartner also helped to identify relevant metrics across different levels of the pyramid.<\/p>\n Source: Gartner for IT Leaders Toolkit<\/em><\/p>\n At Cigniti, we have internally developed a business value model aligned to our digital transformation services with the key feature of focusing on leading indicators rather than lagging indicators, as we know most performance management programs either focus on lagging metrics or make extensive use of accounting metrics. The business value model is positioned between enterprise governance and operational governance. <\/p>\n We leveraged the Goal Question Metrics (GQM) model and Value Pyramid to identify the key aggregates while building the business value model and its key contributors in measuring these aggregators. As an outcome of the exercise, we identified four key aggregates for one of the business use cases; the following are the key aggregates for the given use case. Governance of Data Quality<\/strong><\/p>\n As referred to, data quality governance was one of the keys to effective decision intelligence. We understood the impact of poor data quality, which negatively impacts the organization and its operations. Poor data quality can be due to many reasons, such as human error, calculation error, incomplete data sources, etc. As per the Experian benchmark report, 55% of business leaders say they lack trust in their data assets, and 32% say data quality has not improved. We realized this problem very early for this business case, which made us focus more on the data quality to enable effective decision intelligence, and the same report clearly calls out that improving data quality is a significant priority.<\/p>\n \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0Source: 2021 Global data management research, Experian Benchmark Report<\/em><\/p>\n We at Cigniti helped this client and played a pivotal role as data stewards. We built operational definitions for each metric identified to derive the business value and established a Data Quality Program (DQP) that enforced a systematic and frequent examination of the completeness and quality of the data sources in the tools. This further enabled teams to tie relevant data points together for ease of access, faster analysis, and to inform improvements towards the goal of improving the business value.<\/p>\n Conclusion<\/strong><\/p>\n With high-quality data or better data hygiene, teams can be more predictable and effectively connect the data points that help drive business value creation. The key learnings with effective data governance rely on high-quality data for improved decisions, focusing on investing more time in the data with the right processes, tools, etc., and measuring the business value delivered by the business. With data stewards and an effective process in place, the DQP can be leveraged.<\/p>\n![\"Business](\"https:\/\/www.cigniti.com\/blog\/wp-content\/uploads\/Slide6.png\")
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From the aggregates, we identified a list of metrics that are leading indicators to measure the business value and derived an overall metric, the Business Value Index (BVI), which is an aggregate to understand the business value. Further, to understand the next level of these metrics, it enabled us to drill down into the area of impact, thereby enabling quantitative decision making and building a continuous improvement plan. For an effective business value measurement, we need the right data at the right time, where data quality or hygiene becomes more vital to convert data into information or inferences.<\/p>\n![\"Priority](\"https:\/\/www.cigniti.com\/blog\/wp-content\/uploads\/Slide7.png\")
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As part of DQP, we started with real-time monitoring through customized Excel and Tableau dashboards that measured the DaHI against the key DQP attributes and measures and identified the opportunities for improving the data quality. Within a quarter, the data quality score improved by 40%, thereby improving the metrics to measure the business value of the program.<\/p>\n