Another superb post, if you are thinking seriously about data, big or small, read this: 4 Principles of a Successful Data Strategy
Mo Data stashed this in Data Strategy
1. Think about where the value to your business can be created
2. Exactly what are your data assets
3. Think of all the places where data is created or consumed
4. If data is your asset, your raw material, put in place some quality assurance
Large companies struggle to access, manage, and leverage the information that they create in their day-to-day processes. The rapid growth in the number of IT systems has resulted in a complex and fragmented landscape, where potentially valuable data lays trapped in fragmented inconsistent silos of applications, databases, and organizations.
IT's not your faultExperience has shown that this is not a technology problem, it is a business problem. Creating an effective data environment requires change and coordination across the board, with business and IT joined at the hip. To ensure success, they must create a practical data strategy that guides process changes as well as ongoing investments in their data assets.
Ques. No. 1 - How does data generate business value?
Improving the quality or accessibility of enterprise data is not an end in itself it is merely an enabler for creating business value. The data strategy must be driven by an understanding of how information can enable or improve a business process. For example, increasing cross-channel sales (a business value) requires data about your current customers and the products they own (the data); or reducing the cost of manual reconciliation for financial reporting (the business value) requires standardizing and consolidating redundant and inconsistent data across business applications (the data).
Ques. No. 2: What are our critical data assets?
Not all data in the business is critical. In fact, most data is specific to an application, business function, or transaction. Data that is critical typically has two characteristics:
- It is associated with something of long-term value to the firm, (e.g., product, customer, financial information); and
- It is used across multiple systems and business processes.
ROIIn our experience, identifying and improving critical data assets in large companies can yield tens of millions of dollars in benefit, and justify millions of dollars of investment in implementing a data strategy.
However, we believe it is just as important to keep the set of critical data assets as small as possible. Note that there are very few attributes listed above; the most critical data asset for these subject areas is a common identifier. Maintaining the unique identity of customers, products, interactions, and contracts is what links information across the enterprise. Once that is tackled, attributes can be added to the enterprise record incrementally over time.
Ques. No. 3: What is our data ecosystem?
For most businesses, data is an active asset that is captured, created, enhanced, and used in many business processes and applications. To manage this dynamic environment, the flows of data across systems and processes need to be organized in a coherent way.
We use a business architecture (not a technology architecture) to define core data capabilities that business and IT must create together. These capabilities organize technology platforms and business processes based on their function in the ecosystem: capturing and creating data, cleansing and organizing it, mining business insights from it, and using those insights to drive intelligent actions in the business.
By capturing data that measure the outcomes of our actions, we create a closed loop that allows companies to use their data to test, learn, and improve their processes.
Ques. No. 4: How do we govern data?
Ultimately, the implementation of a data strategy is not a project, it is an ongoing function of the company that must be governed. Because data is so ubiquitous, the governance structure must be federated, with a central governing body addressing the most important, common data, and most of the data managed locally in the lines of business.
We have found several elements of this model critical to successful governance.
First, the stewardship community is business heavy, with executive business data owners supported by business data stewards who report to them. IT custodians ensure that the systems incorporate and monitor the requirements of the business.
Second, companies should incorporate data governance as a part of other standard governance procedures as much as possible, including architectural review boards, audit and risk review processes, system development methodology, and security processes. Over time, a distinct governance body for data may disappear as it is fully embedded in other business governance activities.
Third, it is important to launch data governance with a small facilitation team and some data governance related infrastructure, such as data quality, metadata, and lineage tools to provide visibility and measures to the data governance board.