Governing structured and unstructured content
- 62%of information that must be governed in an organization is unstructured data.
- 55%of organizations have serious concerns about private and sensitive data residing on servers and endpoints.
- 60%of digital businesses will suffer major service failure due to the inability of IT service teams to manage digital risk.
Enterprises need to move from a reactive to a proactive method of identifying, categorizing and remediating private or sensitive data. Organizations need deep levels of insights and control of data that spans all entry points, endpoints, file shares, servers, and cloud repositories to mitigate risk and boost business efficiency.
Our Step-by-Step Approach
Reliable Inc. will access your end-to-end data infrastructure, virtually integrate multi-source data sets, clean and synchronize. Our techniques will address both structured and unstructured data assets. Internal and external demands to manage risk, combined with competitive pressures that call for substantial increases in productivity and cost-efficiency, make it imperative to effectively employ data governance to achieve a single version of the truth across the enterprise. Yet the proliferation of data, applications, and technology can make data governance very difficult to achieve. By taking a practical and incremental approach using the seven steps outlined above, companies can unite business objectives, technology initiatives, and information policy—without embarking on a daunting, expensive data governance project. As a result, they can achieve immediate, measurable improvements by creating a consistent, highly correlated view of the truth for all activities in an enterprise.
Step 1: Prioritize areas for improvement
Although it may seem like a good idea to tackle all data issues at once, it’s far more effective to begin by targeting one or two specific assets. Companies must objectively assess where improved data governance can bring the most immediate benefit to the organization and establish a foothold there. This sets a firm foundation for taking data governance across other areas of the business.
Step 2: Maximize information availability
Data cannot be governed if it is not readily available and accessible. However, today’s information architectures are disparate and diverse. For example, information assets can exist in the form of EDI transactions, data warehouses, CRM and ERP applications, legacy file structures, partner systems, or other outside sources. Therefore, many companies need to leverage integration technologies and best practices, including pre-built integration components, to ensure that any and all data is easy to get to.
Step 3: Create roles, responsibilities, and rules
As a next step, the organization must determine who does what with data by creating formal roles, responsibilities, and rules for the processes people use when working with information. The best place to start is with business users, who can provide insight into the data itself: what problems exist, how data is used, what it should look like, and what the impact will be if quality issues continue or worsen. Business users can also help suggest rules and guidelines for maintaining information integrity. Those recommendations should then be shared with the company’s IT professionals, who can apply technology tools to cleanse the data. They must then create formalized plans for ongoing, proactive content-based or rule-based cleansing to keep the information intact.
Step 4: Ensure information integrity
One of the most crucial steps in any data governance initiative is to enhance and ensure the quality of enterprise data. We recommend using a four-phase process that includes:
- Profiling, to compare information to predefined quality metrics as a means of identifying “good” and “bad” data
- Parsing and standardization, to validate and correct industry-standard and organizational-standard attributes within the data, such as name formats or case standardization
- Enrichment, to extend and enhance existing data with new and complementary information, such as geocode data
- Monitoring, to uncover areas in need of process improvement and guarantee data quality on an ongoing basis
Step 5: Establish an accountability infrastructure
Processes alone do not ensure the integrity of information—people do. Thus, it is important to establish an accountability infrastructure that assigns “owners” to each information asset, and define policies and workflows that hold people responsible for the state of those assets. Additionally, these owners must be provided with the technology they need to keep asset integrity high, because manual processes—no matter how well intentioned—are likely to exacerbate the problem.
Step 6: Convert to a master data–based culture
Next, an organization must transform from a transaction data–based culture to one that is master data based. Master data is composed of the essential facts that define a business, including core entities such as bill of materials, products, employees, and chart of accounts that are of high value and used repeatedly in many mission-critical business processes. (Master data tends to be the information held in the dimension tables of a dimensional data warehouse.) Most organizations today are transaction data based in their perspectives, and it prevents them from leveraging the maximum potential of their data to support the business. By focusing on the effective management of master data, companies can foster better data governance through the facilitation of global identification, linking, and synchronization of information related to these key entities across all heterogeneous sources throughout a business. A single “system of record” is created, providing one unified, consistent, and accurate view of these entities to all stakeholders.
Step 7: Develop a feedback mechanism for process improvement
Finally, there must be a feedback mechanism built into the process that allows for continual assessment and improvement of data governance activities. Monitoring information assets over time will give a clear picture of how initiatives are performing and provide a way to identify both successes and failures in the process, so corrective action can swiftly be taken as needed. Graphical, real-time monitoring tools can be an effective way to enable this kind of feedback and enhancement cycle.