In previous articles, we talked about three of the five capabilities needed to turn data into insight. The fourth key capability is to have “data-centric processes.” What we mean by this is twofold:
Many articles have been written about data-management-specific processes, including the two previous installments in our CIO.com series — "Ensuring the Quality of 'Fit for Purpose' Data" and "Mastering and Managing Data Understanding." In this installment, we cover processes that already exist within an organization. We look at the role of data and discuss how to make the related processes more data-centric. We also break down the ways data-centric processes can have the most impact on an organization in:
As data management projects or initiatives are identified and launched, there is an opportunity to leverage data understanding to improve the Project planning process. According to the Project Management Institute's Project Management Book of Knowledge, the typical Project Management life cycle has these five phases:
In the Initiate and Plan steps, you should define the project’s vision and scope as well as identify stakeholders. Data is critical to the definition of scope, because there will be data used in the project and there may be data created as part of the project. The way data is used, created or even acquired will impact the cost of the project — potentially the risks identified in it, of course, the impact of the project on other people, processes and technology across your organization. Without a clear understanding of your data (as we discussed in "Master and Managing Data Understanding"), the scope may not be properly defined and this could result in significant budget and resource overruns.
The data that is used in a project may also be used by others within the organization, so a full understanding of how data flows across the organization will help to ensure the correct stakeholders are identified. In the previously mentioned article, we talked about the creation of a data inventory or data landscape. This artifact is critical to the ability to know who else in the organization touches the data, and, therefore, should inform the project initiation and stakeholder identification phase.
Stakeholders should represent different functions and processes that are critical to the success of the project, so knowing who “owns” or uses the data ensures you have the appropriate decision-makers as part of the team. Of course, if you have an established data governance organization, they would be central to this process to triage the impact of the project on the rest of the organization.
In this way, putting a data-centric lens on the project planning process through design and definition will ensure you have a full picture of the costs, risks, resource implications and benefits of the project before a significant amount of money is invested.
During the project’s execution phase, there are additional ways to optimize data as you implement new technologies or processes in an organization. A data-centric implementation approach focuses on addressing the data requirements, ensuring data consistency across existing models and metadata, and extending the data understanding as part of the project process.
Data-centric development projects, such as master data management and business intelligence and analytics projects, have risks that are different from traditional process-centric development processes.
Unfortunately, process-centric methodologies, mainly systems development life cycle (SDLC, a.k.a. waterfall) and agile, are used in these projects and largely miss the data-centric needs. For instance, the starting point of a data-centric project is existing production data, as opposed to a process that needs automation, which is usual in process-centric projects. It is highly unlikely the business users will understand this data sufficiently to provide detailed requirements.
