For many organizations, data has become the most important corporate asset. Better data means better productivity, ameliorated risk exposure, heightened customer service/satisfaction, robust business intelligence, and increased regulatory compliance. As data proliferates throughout the enterprise at increasingly exponential rates, CIOs are finding it more difficult to fully understand the breadth of their data assets and assure the timely delivery of data to internal business users and external customers with acceptable levels of quality and security. Although data is a commodity, the stewardship of data is not. The cutting-edge management of data—data stewardship and data governance—and its supporting infrastructure has become a leading mechanism in sustaining competitive advantage and growth. Increasing numbers of large to medium-size organizations are discovering the value of dashboard-enabled quality management of data such as:
- Data Distribution Control
- Data Redundancy Control
- Data Quality Control
- Metadata Persistence
- Data Semantics
- Data Milestoning and Archiving
Using a dashboard driven approach to data stewardship offers a return on investment that far outweighs the costs of construction, deployment, and maintenance of the dashboard. Having quality data means being able to seamlessly share and distribute “one version of the truth” throughout the enterprise. One Trusted Version of the Truth means tireless attention to many factors:
- Integrity
- Consistency
- Timeliness
- Minutiae
- Intelligibility and Definition
- Visualization
- Virtualization
- Accountability
- Documentation and Education
- Rules and Transformations
- Distribution
- Ontology: Supply Chains, Dependencies and Origins
- Business Processes (Mapping to)
Much of my winter of 2008 was spent in Japan where I met with numerous technology and business executives to discuss their data strategies and related challenges in business intelligence (BI). A startling realization was that many organizations were still in the beginning stages of implementing formal data governance and stewardship programs. Moreover, the more data savvy Japanese-based businesses—ones that had at least conducted data audits—were still at a loss on how to better monitor, measure, and improve data quality and consistency. Data audits will invariably show that large portions of enterprise data is:
- Siloed
- Inaccurate and Inconsistent
- Undocumented
- Irreconcilable
- Not Delivered in a Timely Fashion
- Not Easily Accessible
- Semantically Confusing
- Not Secure or Confidential
Thus, data audits are a great catalyst for change; however, companies need to confidently take the right steps to fix all the problems that are exposed in the audit. Jason de Luca, the President of Smart Partners Japan (a Tokyo based consultancy) elaborates: “Our clients know they are in trouble with their data, but they seldom have a decent roadmap that will lead to an essential solution.” It is a matter of fact that organizational political quagmires usually dictate that they look to an outsider, i.e. a management consulting company, to ask the tough questions that will ultimately produce pivotal and lasting results in data quality:
- What are the current trusted data sources of record for Market data, Reference Data, Transactional Data?
- Which data stores are the most critical to the business? …to operational continuity?
- What kinds of business processes are involved in the creation of mission critical data? Who owns these processes?
- What documentation exists now—such as data models, system mappings, rules, etc.?
- Is there a current enterprise data integration strategy
The key is to relentlessly remind executives of the consequences of poor data governance and stewardship, the perils and hazards of which can be far reaching. For example:
- Obtaining an overall picture of corporate performance will be impossible
- Customer service be a perpetual laggard
- Support for regulatory and audit activities will be minimal
- It will be impossible to strategically align and govern enterprise business segments
- Impossible to identify risk in a cross-functional manner
- Cross-functional identification of risk is onerous
Inversely thinking, the positive consequences of vigorous data governance offer tremendous value and return on investment (ROI) to corporations. David Uze, a Managing Partner of Anderson International K.K, expounds further: “It is vital to show executives upfront ROI--in real dollar terms—of any and all projects that portend to clean up and enhance their organization’s data assets”. Uze should know: As the former President of Advanced Micro Devices Japan and General Manager at Dell Japan, Uze relied on winning CRM and BI strategies to successfully identify opportunities in the Asia Pacific markets and grow his company’s businesses accordingly. Getting executive buy-in is much easier when it can be empirically demonstrated that data management best practices result in a flexible and dynamic organization that is able to produce sustained value innovation and is crisis ready. All companies in existence need improved quantifiable knowledge of their enterprise’s performance.
Case Study Brief
For many companies based in Japan (including foreign conglomerates) customer data quality rigors constitute some of the most important and troublesome challenges facing executives from both technology and operations—companies have an abundance of customer data, but they are still knowledge poor. A primary cause for confusion in customer data there stems from the fact that there is no government issued or sponsored number that uniquely identifies Japanese citizens. Because of this, historically many data models and point-of-service business processes were implemented without a robust enough mechanism to capture differentiation in customer identity and relationships/roles of a customer to a company’s products and policies. The obvious outcome of this scenario has been poor and segmented customer service and business processes that are less controllable and repeatable from year to year. In such cases customer service policies will forever be poorly aligned with business strategy and best practices, not to mention reactive, unproductive, and immune to required governance and audit controls. A number of Japanese companies have had to resort to very expensive direct mailing and personalized door-to-door solicitation campaigns in order to better capture ambiguous and missing customer data and data relationships. Aside from being expensive, these types of campaigns have a heavy reliance on 3rd parties, and have created an added layer of unstructured data needing to be properly transposed and keyed into corporate databases. To add insult to injury, these types of direct mailings have a 20% customer response rate.
Nevertheless, one Tokyo-centered financial company has been cleverly solving the problem of data harvesting and capture by installing electronic kiosks at their branch offices and, in parallel, giving customers incentives (free gifts) for coming into the branch to verify their information at the kiosk. Customers can directly update and correct their data via the kiosk interface; this data is then saved in electronic form (XML-based format) and validated as close to the source of entry as possible. Since many of their customers are non internet-savvy, the kiosk idea has gotten a much better response than an internet-based data entry solution would have. The burden of customer data cleansing/harvesting has been shifted (somewhat) to the customers, but in a way that is appealing and fun—and most importantly rewarding for the customer. After customer data has been kiosk-entered it is sent to through an additional ETL data matching process which is managed by a dashboard/portal which is (co-)managed by both IT and Customer Service.
Copyright 2008 - Dashboard Insight - All rights reserved.