Information governance, the orchestration of people, process and technology that enables an organization to leverage data as an enterprise asset, or innovation? It’s a question that presents itself with considerable frequency for Chief Data Officers. Lately, I’ve been considering this binary outlook as it relates to the “data integrator” role that CDOs strive to fulfill for their organizations, and I believe the question itself misses the mark.
Data integration is an important consideration for all CDOs, whether you are in your first 90 days or are a veteran of the profession. Instead of governance and innovation being a diametrically opposed “either-or” proposition, good data integration and governance facilitates and promotes innovation within the organization.
Starting with an awareness of your company’s monetization strategy and using that to create your data strategy is essential. It is also important to recognize that the level of integration and governance provided must be tailored to suit the needs of various stakeholders inside and outside of the organization. Some need the ability to explore raw data. Others wish to take analytical discovery into their own hands. Still others will benefit from packaged, value-added data. The desired level of integration and governance activities will vary considerably.
Therefore, the question becomes not, “which do I want—governance or innovation?” but rather “how much integration and governance is ideal for each group of stakeholders so that innovation may be achieved?” In the following sections, I aim to address that question more specifically for three groups of stakeholders.
Data science teams may balk the most at heavy handed governance. They want the opportunity to dig into raw data and build it into applications without a lot of interference. In addition, they prize solutions that are open and agile. As CDO, this necessitates an approach to integration and governance that is balanced. Seek opportunities to supply the right level of integration and governance without blocking their exploratory, agile nature by making sure that the tools they use allow for proper data management and preparation.
Consider the data repositories being used. Are they flexible? Do they provide uninterrupted data access? Are they fully managed? If not, there may be room to more fully embrace the open and agile approach the data science team desires. A number of fully managed database options are available including a NoSQL database service and a cloud hosted platform of open source database technologies.
Providing your teams with the ability to clean, reconcile, and match data in a single environment tailored for data science can also be beneficial. In this way, data preparation simply becomes part of the overall process—a step along the path to valuable insight.
Increasingly, business users are seeking to take analytics into their own hands and turn insights into innovative competitive advantage. But as we all know, insights are only as good as the data on which they are based.