by Jay Zaidi
Six recommendations that will enable firms to build an efficient Information Supply Chain (ISC) with robust data controls
This is the third in a series of posts about the ISC. In the previous two posts (see “Further Reading” at the end of the article for details), I explained what an ISC is and discussed its individual components. In this post, I’d like to give a real life example of an ISC, to put things in perspective, for those that wish to document their firm’s ISC or are curious to see what a real life ISC looks like. Since I am a visual person, I’d like to use a visual representation of an ISC to display it and describe the components, highlight the data-related challenges that this uncovers and discuss the data quality and information governance aspects.
There is a tendency for semantic mismatch and the quality of data to be modified as it flows through a firm’s ecosystem. Some of the data-related changes are required (e.g. decoding data values, standardizing data values, etc.), while others unintended (e.g. red explosions above depicting changes in data precision, data types, data completeness, data accuracy, etc.) – due to siloed data management practices and a lack of common understanding of the semantics and data quality requirements. The unintended changes expose themselves as operational incidents, audit findings, regulatory compliance issues and data corrections in production systems and certainly have a material impact on business operations and financial disclosures. They may result in significant legal, financial and reputational risk to the firm and its senior management (C-level and Board), if not identified and remediated proactively.
So, how do we bring order out of the “data chaos” described above? Below are six recommendations to enable an efficient ISC with robust data controls:
- Information Governance – Information Governance processes, procedures, ownership, accountability, and metrics
- Holistic Data Quality – Proactive and reactive quality controls across the ISC, akin to a data quality firewall with transparency into the quality of data as it flows from end-to-end
- Metadata or context data repository – Capture Metadata or context data related to the business processes, data stores, data dictionaries, glossaries, to facilitate transparency into business critical contextual information, impact analysis, root cause analysis, and other critical functions
- Data standards and policies – focus on logical and physical naming conventions, hand-offs, data integration and access, etc.
- Business intelligence related to data quality and governance – Measure quality and governance-related metrics to enable proactive identification and remediation of data-related issues and continuous improvement
- Straight through data processing – automate data management and data integration processes to reduce errors and enable faster time-to-market
Every firm must document its Physical Supply Chain (PSC) and Information Supply Chain (ISC), if it hasn’t already done so. It can use the recommendations provided above, to gain a better understanding of its data assets, proactively manage data quality, streamline operations, and reduce operational risk. The investment in people, process, technology and data will result in robust data controls, high quality data, faster time-to-market, and stronger governance. These capabilities translate into significant business benefits such as streamlining business operations, improving internal management reporting and decision making, improving the quality of externally distributed financial reports, enhancing compliance to regulations, supporting policy making, and improving risk management. The end result is a significant reduction in legal, financial and reputational risks and a positive impact to the bottom line.
Further Reading (other articles by the author)
Revolutionizing The Information Supply Chain – The Apple Story (Part One): http://www.dataversity.net/revolutionizing-the-information-supply-chain-the-apple-story-part-one/
Revolutionizing The Information Supply Chain – Components (Part Two): http://www.dataversity.net/revolutionizing-the-information-supply-chain-components-part-two/Data Governance Demystified – Lessons from the Trenches: http://www.dataversity.net/data-governance-demystified-lessons-from-the-trenches/ Holistic Data Quality – A New Paradigm in Data Quality Management: http://www.dataversity.net/holistic-data-quality-a-new-paradigm-in-enterprise-data-quality-management/ Proactive and Reactive Techniques To Address Information Quality Challenges Head On: http://www.tdan.com/view-articles/15835 Is Your Data Governance Program in Trouble? Use these best practices to resuscitate it: http://www.tdan.com/view-articles/15971 Ten Data Management Capabilities That Address Urgent Business Priorities: http://www.tdan.com/view-articles/15733