When someone thinks of an office manager, what sorts of tasks come to mind? Interpersonal communication, managing personalities and pressures, hiring, firing, and maintaining a professional relationship are all industry staples. In an era of big data, there exists a new type of manager – a data manager, or more specifically, a data steward. For an organization, simply possessing vast volumes of data means little if said data is not readily available, usable and secured. For data to be best leveraged, stakeholders have to know what their data says, where to use it, if it is safe from prying eyes, and if it is in compliance with existing laws. So much for the good old days of simply managing office gossip.
Key to any data governance strategy is a data framework. The framework is the how a company manages data when it is actually in your possession. This can mean policies related to what data is available to whom, how the data is cleaned and managed, who is responsible for securing the data, or how your collection practices comply with local laws. This last point in particular is increasingly becoming a hot topic, especially in the wake of the Cambridge Analytica scandal. Indeed, the European Union (EU) has already enacted a sweeping new law, the General Data Protection Regulation (GDPR). If you’re interested in national efforts at data regulation, check our blog post.
Of course, regulatory compliance is only one element of data governance. Stakeholders also need to establish SOPs and frameworks within their own companies. Interestingly, some of the same elements of a successful personnel management strategy also go into a successful management strategy. For example, there is the “who.” Just as a traditional manager is required to weigh in on whose talents are best suited where, a data manager needs to decide who gets access to what data. Clearance issues aside, if the right analyst(s) are not placed in front of the correct dataset(s), then the final deliverable will likely not satisfy expectations.
Another key consideration is how the company as a whole is going to develop a data management strategy. This is often done in the form of a Data Governance Council (DGC), which IBM defines as “A master data governance council is a cross-functional, multi-layer team that collectively owns master data, and steers master data management initiatives at the program level.”
What does the DGC look like, operationally? While it may be tempting to have the DGC consist of just executive-level and data personnel, it is important to remember that data management is a company wide concern. Ideally, the council should be made up of company management, IT specialists, data personal, business analysts and subject matter experts. IT personnel are best suited to see how the existing data fits within the company’s infrastructure, business analysts can incorporate the data management strategy into the business plan, while subject matter experts place that data in its proper context. In short, a successful DGC requires a team effort that goes far beyond just the relationship between management and the analysts.
Finally, data management requires flexibility and continuous reassessment. The sheer pace and volume of big data means that trends shift day-to-day, rather than quarter-to-quarter, or month-to-month. As a result, the DGC membership should always be willing to add, remove or change policies, procedures and outlooks, as the data environment warrants. Implicit in this is the willingness to embrace new technologies on the front-end, thus staying ahead of the curve on the back-end. DGC members should be open-minded and willing to embrace a new platform or technique if it better suits the needs of the company, even if that means parting with older technologies.
Data management is becoming as vital to industry players as personnel management. Because data is a 24/7 flow, in some ways it needs to be handled in a different way than the traditional 9-to-5. However, there is a lot of crossover between the two worlds. Assigning the right personnel to the right data requires not just a knowledge of the data, but also who your people are. Successful implementing a data strategy requires a surprising amount of interpersonal communication between individuals with various roles at different levels. Even in the context of data management and governance, traditional managerial tools are still very much relevant.