The demand for organizational data capabilities is driven by data being more than just a byproduct of business activity. How does a high performing, progressive organization identify the best candidate for a data job?
One challenge is to identify the part of the data world one requires capability for, such as data architecture or data engineering, data analysis or data science, data governance or data management, or data operations or data strategy—not to mention data privacy, data security and a whole host of functional data disciplines.
Another is to know that it is about more than skills. Good data professionals are technically adept; verifiable proficiencies are critical, but they are mere tablestakes in the long game. In contrast, great data professionals tend to also have a passion for the people the data represents. This kind of passion is instrumental in sustainable data success because most commercial data, “is not merely an asset to be exploited. It is … a reflection of a human being’s life, and that it should therefore be treated with respect, not just as a thing to be processed.”
More Than Bits and Bytes
As a mirror of people’s lives, data can be demographic —contact details and products bought—or transactional—the engagement with a product or service. Great candidates treat all data with respect by, for example, being inherently protective about privacy and security and being mindful that data value is proportional to its quality. Candidates that only see data as bits and bytes unnecessarily discard this implicit line of defense against bad data activities.
Great candidates know that customer profiling and targeting is disrespectful and intrusive if the customer does not know what data are being collected and how they are being used. Lack of organizational operational transparency makes it difficult for customers to determine whether their legal rights are being violated or whether the organization practices an ethical obligation to treat the customer’s data with respect. Red flags emerge when candidates readily speak of data exploitation without understanding the data or contextualizing the phrase and when candidates speak about data fusion without discussing the potential liability inherent in third-party data.
Given the tablestakes of technical expertise and knowledge, the greater the sense of data purpose, the more likely candidates conceptually understand their place in the data value chain, rather than being just a cog in a vast milieu in a state that could be deemed risky. Great candidates know to tread gently while pursuing purpose, meaning that data must be used with care. The damage to the global human social fabric (e.g., by the activities of Facebook and Cambridge Analytica) reminds us why it is so important to tread gently with respect to data: it impacts people. Furthermore, candidates who are able to tell data-derived stories in a way that adds two-way value—to the organization and to the customer—are highly desirable; it is not only about what is in it for the organization, there also needs to be something good in it for the customer.
Documenting the Data Repository
The temptation or management pressure to perform today’s requirements without thinking about tomorrow is a great failing in today’s data world. One way of thinking about tomorrow is documenting the data landscape for future custodians of customer data. Another way is to increase value by abstracting the raw data by taking an architected, systems approach to the data milieu. Great candidates advocate creating and maintaining operational metadata as a means of documenting the data repository and building its sustainability, and documenting how the myriad components of a data ecosystem are meant to work together beyond technology. Such candidates understand the delicate balance between data priorities, impact, cost, effort, maintainability and usability.
The most eminently usable data environments are created by data professionals with the wisdom to recognize that the data management literature presents scarcely a fraction of the reality of an organizational data environment. This wisdom includes recognizing the place of lore—unwritten organizational knowledge and experience—and that the data stored in the organization’s repositories make up a tiny part of the knowledge that exists within the organization. The predisposition to manage expectations, create alignment, define objectives and apply the power and influence of the relationships between the various data disciplines and the roles that action them are all characteristics of great candidates.
Great candidates recognize that putting this all into play on premises is an entirely different proposition to putting it into play in the cloud, and have accumulated the wisdom to navigate the various traps inherent in sailing those great organizational data oceans. In turn, hiring organizations should know that characteristics like these are influential in helping to control its enterprise risk profile.
Editor’s note: For further insights on this topic, read Guy Pearce’s recent Journal article, “Beware the Traps of Data Governance and Data Management Practice,” ISACA Journal, volume 6 2022.
