So, you just landed a job as a Chief Data Officer at a large enterprise. Congratulations. You’ve got a lot of work to do.
After you’ve taken care of the preliminaries, like getting familiar with the vocabulary, the organizational structure, the key players, and the significant data stores and systems, you’ll want to begin adding value in support of your objectives as soon as possible. To that end, here is a simple to-do list template along with a few items filled in to get you started.
Objective #1: Ensure that planned business initiatives will have the data they need to be successful.
- Identify the top business initiatives of the company, and any important (funded) initiatives within departments and business units.
- Determine the data and analytics that are already planned within the initiatives, along with any data and analytics that probably should be a part of the initiatives (e.g., a predictable need to measure and monitor a planned business process change).
- Find commonalities in the data needs across initiatives, including the data itself and data management needs (quality, security, etc.) and develop a data and analytics roadmap to plan a rational deployment just-in-time and just-enough to support the initiatives.
- Establish an enterprise data governance function and other governing structures to oversee implementation; include identified business initiatives, near-term projects, and data needs on the data governance agenda to monitor progress and overcome obstacles.
Objective #2: Address data issues causing (or having the potential to cause) a negative business impact.
- Identify and prioritize operational data issues and their business impact, including data quality issues, data accessibility and understandability issues, and other known or potential issues.
- Identify technical issues affecting data access and analytics such as system performance issues, availability issues, and high maintenance and support costs of data resources and tools.
- Determine immediate and future regulatory compliance concerns and any other policy risks, including security and privacy.
- Include data issues and their resolution status on the data governance agenda.
Objective #3: Promote the value of data and analytics toward business goals.
- Identify use cases, culled from the organization and from external research, including industry-specific and cross-industry use cases, noting for each use case the potential value and whether it is proven, leading-edge, or hypothetical, and the degree to which it is implemented in the organization.
- Identify innovative data sources, such as commercial aggregators, partners, open data sources, and value chain data sources; link the data sources to cataloged use cases.
- Identify innovative analytic techniques, such as artificial intelligence and advanced visualization, while also identifying basic and widely applicable techniques; link the techniques to the cataloged use cases.
- Determine the value of monetizing proprietary data through partners, aggregators, or directly to customers, and consider how data could enhance the value of existing products and services.
- Communicate use cases, data sources, and analytic techniques to business leaders and practitioners to consider proposing as programs and projects or to enhance planned initiatives.
- Establish a “speed lane” for quick self-service data provisioning and analytics, with appropriately limited governance, including a mechanism to propose production projects inspired by hypothesis testing and prototyping.
Objective #4: Build the organizational machinery so that rational data and analytics deployment becomes routine.
- Participate in enterprise-wide strategic business planning and departmental and business unit planning, asserting the role of data and analytics to support the planning process itself and the resulting plans; revise the data and analytics roadmap accordingly.
- Establish enterprise data architecture representation within the enterprise architecture function, including linkage to IT governance functions; ensure proactive and coherent architecture planning to support the roadmap.
- Establish a review (via enterprise architecture if possible) of all project funding requests to advocate the role of data and analytics, ensuring alignment with the roadmap.
- Establish an analytic function to coordinate and support the activities of analysts throughout the organization. If the role does not already exist, the organization may consider establishing a Chief Analytic Officer role to be responsible for this function and to create a partnership with you for many of the other to-do’s.
- Work with IT to establish a coordinated organization for data acquisition, data management, and analytics, linked to various application teams that will provide and leverage the data.
- Embed data management practices (e.g., data quality, data modeling, the role of data stewardship) directly into the standard Solution Development Life Cycle (SDLC), especially for projects that deploy data to be shared across initiatives.
Objective #5: …
It may seem like a long list, but it’s practical. Many of these to-do’s simply re-shape, re-organize, and enhance work already being done and already planned, thus harnessing the energy of important activity so that every action leads towards a more coherent deployment of trusted data, rather than exacerbating data proliferation and other issues.
So, are you ready? Let’s get to work. BFA3CH-FTMFP
Kevin M Lewis is a Director of Data and Architecture Strategy with Teradata Corporation. Kevin shares best practices across all major industries, helping clients transform and modernize data and analytics programs including organization, process, and architecture. The practice advocates strategies that deliver value quickly while simultaneously contributing to a coherent ecosystem with every project.
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