One of the overarching themes that quickly gets established when reading the Gartner essays on data/information architecture is their collective emphasis on creating new data-centric roles. With as much data being generated and collected by enterprises across different market segments it would behoove the enterprises to take Gartner’s advice seriously. For context, 2020 Seagate’s ‘Rethink Data’ report projected enterprise data collection to increase at a 42.2 percent annual growth rate. The more confounding data point noted in the same report was how 68% Of data available to businesses went unleveraged.

The idea behind the creation of these new data roles is to open streams of data locked inside the enterprise. This data is present within the enterprise but siloed under different business units. In addition to standing up the data roles and creating cross-organizational teams, Gartner also advises implementing a robust governance regime that is agile, flexible, and adaptive.

In my enterprise, the IT leadership has invested in various data-centric policies. Which includes employing a data warehousing lead alongside a dedicated data analytics team. However, unlike Gartner’s suggestion of having a CDO, the team rolls up to a manager who does not focus on data only. Let me know how your enterprise deals with data. Does it have data-centric roles and governance built around the use of data? Share your thoughts below.

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On a similar topic, WWE (the wrestling promotion) recently became part of TKO holdings. An unfortunate side effect of these mergers is the large number of jobs that get cut or consolidated. One of the casualties of this merger was WWE’s director of enterprise master data & governance, Amanda Bloom. Ms. Bloom posted a message on her LinkedIn profile. This message covers most of the topics we have read for L03. I wanted to share it here as an interesting side note.

 

 

While reading the L03 material, one of the Gartner research papers discussed data literacy. This term caught my attention, not because I heard it for the first time, but because of how it encapsulated the state of many enterprises’ workforce. And the day-to-day struggle of those tasked with maintaining, curating, and, most importantly, cleaning the data. In my own experience, I have encountered many datasets with invalid values and sometimes incorrect formatting. And periodically, spending countless hours cleaning up those dataset(s).

As enterprises evolve and legacy applications are scaffolded with new functionalities, the data inconsistencies can compound unless the solution is architected correctly. And this is where the term data literacy comes to the fore. In the same article, Gartner describes data literacy as the ability to read, write, and communicate data in context, including understanding data sources and constructs, analytical methods and techniques applied, and the ability to describe the use-case application and resulting value.

The question then arises: how do you increase data literacy in your team, business unit, or enterprise? One of the most effective Gratner’s suggestions involves establishing a communication platform that includes, for instance, a data dictionary and a business glossary to raise your organization’s data literacy level. According to Atlan, a data dictionary is a collection of metadata such as object name, data type, size, classification, and relationships with other data assets. A data dictionary acts as a reference guide on a dataset.

Establishing an enterprise-wide data dictionary can alleviate the overall data literacy of an enterprise. A centralized reference guide can also gradually improve data entry errors, which, according to HBR, can take up to 80% of data scientists’ time to clean up. With that being said, what is your enterprise’s data literacy like, share your thoughts below. I will leave you with this very apt meme shared by one of my coworkers during one of the projects we worked on.

P.S. I could not reliably find the source of this meme which is why I am sharing the image without attribution since it has been shared across multiple posts.