Increasingly, companies are embarking on a comprehensive enterprise information management program to address the growing demands on data coming from regulators, lawmakers and internal business executives. The goals of these programs include higher data quality, more transparency and control, faster access to information, and better insight into internal operations and customers. Compounding the task is the growing volume of data and the increasing knowledge required to handle the sophisticated data technologies.
While these demands can be handled separately, the best companies are realizing the same customer, product and financial data will likely be involved in multiple projects with conflicting schedules and different priorities. Individual data management programs with unique tools, processes and people are also expensive. Each project can require the involvement of the same key subject matter experts, busy people with specialized knowledge of the data and the processes used to create it.
Hence, the business value of an EIM program is the coordination, prioritization and implementation of a broad set of business and IT initiatives that plan and manage critical data holistically and efficiently across the company. A sound EIM program has the following components:
Data strategy: The company’s vision and goals for the data environment are best represented by a comprehensive data strategy. The strategy includes the technical and business direction for the critical data of the company. Because it is aligned with the company’s business goals, every change to corporate strategy requires that EIM be re-evaluated.
Enterprise governance: Governing data requires data definitions, standards, policies and controls. Included in governance are the various forums for decision-making as well as the responsible roles and the people accountable for the data programs.
Metrics/controls: Agreed-upon goals to be achieved by the EIM program are measured to ensure success.
Data quality: Programs that continuously measure and improve data quality dimensions, such as accuracy, validity, completeness, timeliness and consistency, demonstrate the value of the EIM program.
Skills: Hiring and training skilled information management professionals, in both IT and the business, to carry out the data initiatives is foundational to enterprise governance.
Enterprise data services: Enterprise data services are common tools and methodologies available to business and IT users of data and encapsulate best practices, facilitate reuse and contain costs. Examples of these services include metadata services, search/create/delete processes, ad hoc reports and data mart development. An often-overlooked set of services is the internal communication forums necessary to keep employees informed on the EIM program.
Trusted data sources: High quality, certified, common data sources are to be used across the company, including master data and the enterprise data warehouse.
An EIM program is broad by its very nature. EIM is a collection of multiphase, multiyear initiatives where responsibilities, processes and technology help create change. Core funding and a dedicated team are necessary to implement the components of the program and manage its progress.
An EIM Scorecard
How should progress be measured and communicated to senior management and to those who are funding the data programs? An EIM scorecard is one solution.
Similar to a balanced scorecard with key performance indicators, an EIM scorecard is implemented annually to measure progress against the EIM strategy and its various components. Once an overall EIM scorecard is established, individual scorecards can be developed outlining the contribution of various groups to the year-end metrics. A scorecard can be created for a local department, function or project that ties to the overall year-end enterprise metrics. If your organization has business data stewards, then develop scorecards at a business data steward level. A scorecard is also an effective technique for measuring the business data steward’s effectiveness.
As in the balanced scorecard KPI project, selecting the right metrics is critical. Determining the appropriate targets requires collaboration across the various owners of the metrics and the EIM program owner. Metrics should be easy to understand and reasonably easy to track. EIM scorecard metrics can be defined in the following categories.
1. Data infrastructure metrics measure the progress toward the technical data strategy vision. Because most companies have legacy duplicate data that drives data quality issues, data integration issues and costs, reducing these data stores and using corporate-approved trusted data is an indicator of progress. Additionally, target to reduce the total cost of the hardware, software and resources of the data infrastructure of the company to improve utilization and efficiency.
2. Data control metrics define new data standards, policies and processes that are necessary to manage data effectively. When new controls are defined, affected departments must comply in a certain time frame with supporting plans. Data control metrics measure compliance plans as well as any compliance testing results. The completeness of the enterprise metadata repository can also be measured in this category. All important data stores should have an entry in the repository with minimum information established by the EIM governance program.
3. The organization maturity metric measures the progression in training, skill development and role staffing. Any EIM strategy necessitates new skills and responsibilities in data management. Consider using one of the industry information management maturity tools to baseline the current data capabilities of the company and establish an annual improvement plan. The maturity tool can be administered internally or through a consulting company and includes a survey of internal stakeholders.
4. Issue management, such as logging data issues consistently across the company and addressing the high impact issues, is a critical barometer for management. Data issues that arise from internal audits, security testing or operational incidents are of special concern to the company and should be monitored via the scorecard. Logging issues in a consistent fashion also allows visibility into trends and the ability to identify hot spots to be improved on an annual basis.
5. Data quality improvement is a fundamental component of an EIM strategy. During the first year, the company would most likely baseline the dimensions of quality that need improvement. In subsequent years, projects are funded and more dimensions of data quality can be slated for improvement. This metric measures the year-end improvement plans against a set target. If possible, create an aggregate score of data quality for the firm, because too many metrics are often confusing.
6. Financial/cost improvements are at the heart of a scorecard. Clearly, no scorecard is complete without a set of financial or cost metrics that validate a solid ROI. Tracking individual project costs as well the overall business case for EIM would be included in this metric.
Each metric category has an assigned owner. The owner is the person or organization in the best position to affect change. The owner is accountable for establishing the baseline metrics as well as the year-end improvement targets and plans. The EIM program manager drives and owns the scorecard planning and reporting process. Creating quarterly or monthly interim metrics, where possible, provides management with early warning signs if the metrics go off track and provides an opportunity for remediation. It is highly recommended to automate the collection of the metrics, especially if interim metrics are necessary.
Communicating the progress of data initiatives to business leaders and executives is challenging and requires a clear and conciseformat. The EIM scorecard is emerging as an effective tracking formatthatprovide aconsistent mechanism to show yearly progress and communicate results.
This article was originally written by Mariah C. Villar.
Maria C. Villar is managing partner at Business Data Leadership. Maria Villar is an IT professional with more than 25 years of experience in IT, technology re-engineering and enterprise data management. She has held senior executive positions in both the technology and financial sector that included responsibilities for data quality, governance, architecture and database technology solutions. She built the first company-wide Enterprise Business Information Center of Excellence at IBM. The COE was recognized externally by for best practices in data governance and business intelligence applications. Villar has been recognized in Hispanic Business Magazine as one of the Top 100 Influential Hispanics and received the Distinguished Hispanic IT Executive award from Hispanic Engineer National Achievement Awards Conference.