Okay, Here's the Plan
Organizations should rethink what they are doing and make some key, fundamental changes across their organizations. For example, organizations should recognize data management as a business function versus an IT function. The organization needs to charge the CDO with developing the organization’s data strategy and ensuring that this plan directly supports the organization’s strategic goals and objectives. As part of this change, data management requires executive leadership that reports to the organization’s most senior leader (e.g., the CEO) just like other C-level officers.
Organizations should recruit a qualified CDO who reports within the C-suite at the same level as other resource managers: HR, Finance, IT, and the like. Also, the two data teams (reporting to distinct VPs) should be combined and report to a single CDO who reports outside of the CIO organization.
Now more than ever, the ability to manage torrents of data is critical to an organization’s success. But even with the emergence of data management functions and CDOs, most organizations remain behind the curve. Cross-industry studies show that, on average, less than half of all organizations use structured data to make decisions — and less than 1% of its unstructured data is analyzed or used at all. More than 70% of employees have access to information they should not, and 80% of analysts’ time is spent simply discovering and preparing data. Data breaches are common, rogue data sets propagate in silos, and organizations’ data technology often misses the mark.
Having a CDO and a data management function is a start, but neither can be fully competent in the absence of a coherent strategy for organizing, governing, analyzing and deploying an organization’s information assets. A data strategy typically includes a vision statement, goals and objectives, priorities, scope, defined business benefits, a data management framework, high level roles and responsibilities and governance needs. It frequently includes a description of the approach used to develop the data management program, the high-level compliance approach and measures and a high-level sequence plan (roadmap). A data strategy usually encompasses, at a minimum, the following facets.
Version. Whole number changes represent iterations with iteration improvements and clarifications indicated to the right of the iteration cycle number, (ie, 43 represents third version of the fourth iteration)
Vision.Vision is concerned with what the organization aspires to be Its purpose is to set out a view of the future to enthuse, gain commitment from and improve the performances of its workers.
Mission. A mission provides employees and stakeholders with clarity about the overall purpose and raison d’êtreof the organization.
Goals. Goals are open-ended statements of what one wishes to accomplish with no quantification of what is to be achieved and no timeframe for completion.
Objectives. Objectives are the end results of planned activity. They state what is to be accomplished by when, and they should be quantified if possible
Plan. A plan is a statement that prescribes specific actions to be taken to implement established policies
Priorities. Facts or conditions that are given or merit attention before competing alternatives constitute priorities
Scope. The scope is the sum of the products, services and results to be attained as a project.
Data strategy needs to reinforce use of standards and outline the overall governance framework the organization will employ to make decisions about implementation. It should also take into account initial implementation considerations, such as architectural initiatives and technology transformation initiatives that are underway or planned, and it needs to define a sequence plan to guide implementation.
The organization’s data strategy must evolve as the needs of the organization change, and, as a result, organizations will be able to affect change through close collaboration of different organizational components. Cooperation is essential to building and maintaining an effective data management program. One example of improved collaboration is broader, executive led responsibility for data quality reflected throughout the data lifecycle. The most useful data management strategies are those that are visibly and actively endorsed by executive management and supported by mandatory organizational policy. In effect, these strategies are institutionalized. The ensuing collaborative project — developing the data management strategy — is a powerful mechanism for clarifying executive decisions and directives, as well as fast-tracking the data management program. In the ideal instance, all key players have had a voice in the process. They reach agreement on objectives, priorities and measures. They secure administrative approval for capabilities to be improved, and all relevant stakeholders understand the impacts of the plan.