At the age of data —we produce more data than ever before— companies are still unable to take advantage of its value. Companies' attempts to become data-driven are frustrated by problems that a new professional role, the Data Owner, could solve.
Foto: Negative Space
A recent study by NewVantage Partners places the data world in a paradoxical situation; it concludes that 62.2% of companies have not yet managed to foster a data-driven culture. Forbes magazine seems to agree according to their research in which the business leaders surveyed state that their organizations are becoming less and less data-driven.
This is particularly disconcerting when we consider that the amount of data generated annually continues to increase year after year and, in fact, the business intelligence consultancy IDC was already warning in 2018 that by 2025 we would have produced 175 trillion gigabytes of data. Therefore, and against all odds, it seems that data is moving faster than people and that we produce more data than we are able to take advantage of. It is certainly food for thought and we should question what is preventing organizations from leveraging technology to make their businesses more data-driven.
From the Recycling Industry to Data Companies: The Constraint of Working With Poor-Quality Material
In this article, Ryan Gross, vice president of Pariveda Solutions, makes an unusual but apt comparison about data companies and the recycling industry: "The costs of producing something usable are extremely high due to high cost of cleaning up the mixed mess of bagged plastic, cardboard, trash, and metals that is dumped onto the recycling plant. When you assume that a business can take whatever you give to it and be consistently profitable, you are setting yourself up for failure. [...] Looking back at the data side, the conditions are actually a little worse than in recycling, because data teams aren’t just being asked to produce raw materials."
Moreover, Gross identifies 3 problems that prevent companies from becoming data-driven:
1. Those who control data within an organization, through the creation or selection of operational systems, do not know what it can be used for and, therefore, do not optimize data collection —compiling unnecessary information or more data than needed—.
2. Due to the above factor, people who create data solutions find themselves between a rock and a hard place, which means that those who use data to make business decisions do not trust their data.
3. No one is responsible for estimating the impact of problems 1 and 2. Thus, over time, data quality becomes an invisible cost that no one pays attention to.
All of this means that data companies are forced to engage in a battle in which they are at a disadvantage: extracting valuable information or insights from unclean, absent and misinterpreted data sets. In other words, it's not that data companies are inefficient, it's that they are working with very unproductive material, just like in the recycling industry.
The Data Owner: From the Recycling Industry to the Manufacturing Industry
Sean McCall, vice president of Pariveda Solutions, believes that the solution to this problem is to create a new professional profile: the Data Owner, whose main function would be to maximize the value of the data.
The Data Owner would control the data supply chain —being in charge of a specific data domain: sales, customer, marketing, etc.— and would demand the necessary changes and requirements from data scientists to increase its value.
Gross goes further and defines the functions of a Data Owner:
- Define and build the business strategy for their data domain in collaboration with the data owners of other domains.
- Suggest the changes needed in data sources by providing requirements related to data collection and modeling.
- Design data evaluation methods that answer questions and validate intuitions about their data domain.
- Prioritize problem solving within their domain over other tasks.
- Interact with data consumers in their domain to provide additional context for data usage and ensure that metadata and documentation accurately reflect reality and are useful to self-service consumers of the data.
He also establishes the skills that a Data Owner must have:
- In-depth knowledge of their data domain and the business area to which it applies.
- User-centered design skills and understandment of the needs of data consumers.
- Strategy orientation and ability to value and defend their ideas.
- Capacity to control the complexities of data products, having a solid understanding of statistics and the industry.
- Ability to communicate with technical (software developers, data architects, data engineers) and business people.
- Basic understanding of data modeling practices that allow them to advocate for appropriate data inputs.
What Does Data Owner Solve?
Having a Data Owner would solve the 3 problems mentioned at the beginning of this article which, according to Gross, are preventing companies from finally becoming data-driven. Let's remind them:
1. The first problem is that the people who currently control the data within a company do not know what to use it for and, therefore, collect and work with more data than needed or, even worse, the wrong data. The Data Owner would solve this by ordering the capture of specific data based on concrete business goals and strategies.
2. The second problem is that the creators of data solutions do not trust the data. The Data Owner would prioritize working on data quality over producing new data sets.
3. The third problem is that, within organizations, there is no one to evaluate and analyze the impact of the low quality of the data available. In this sense, a Data Owner would have to justify the data roadmap by building a compelling business case for its application and prioritize it over other initiatives.
In brief, it seems unquestionable that companies need a new professional role that understands both the data, the business and the value that data can bring to the business. It is actually data what is pointing that we are doing something wrong and that, if we do not solve the underlying problems, we will continue to collect poor quality data instead of building value with the right material.