(US & Canada) VIDEO | Deploy Trust Monitors Across Pipelines to Reduce Dark Data — FirstEigen CEO

Seth Rao, CEO of FirstEigen, speaks with Sreeram Potukuchi, Director of Enterprise Data at Republic Services and CDO Magazine Editorial Board Member, in a video interview about building a data trustability platform, the challenges with ensuring data trustworthiness, the importance of a data trust score, how everyone in a business is a stakeholder, the need for accountability, and a glimpse of change.

Rao begins by stating that the main focus of the company is creating a data trustability platform. He adds that as the amount of data being collected rises, mistakes tend to occur, leading to a lack of trust when consuming the data.

To solve this, FirstEigen made the process easier by using AI and ML, while also partnering with Gartner, IDC, Eckerson, Giga, and more. Rao notes that this is not limited to one industry and his organization has worked with companies in different sectors, such as financial services, banking, pharmaceuticals, retail, manufacturing, healthcare, streaming, and more.

Delving further, Rao states that the most common issue people encounter is a lack of trustworthiness concerning data, from the endpoint to the whole pipeline. This forms a substantial risk, as it can lead to bad decisions about supply chains, mortgages, financial management, and sales investments. As a result, the greatest danger is the doubt surrounding the data across the system.

In continuation, Rao opines that to measure the trustworthiness of data, one must measure the data trust score at every point in the data pipeline, which is the equivalent of currency for data. Without the ability to quantify and measure data, one cannot solve a problem.

Companies have avoided attempting to calculate and set up data trust metrics due to the laborious, four to eight-week process of setting up a single data table, says Rao. With an average mid-size company dealing with 5,000 tables, it would take 400 years to set up data trust metrics and monitors. To solve this problem, people must figure out a way to automate the process, he asserts.

Furthermore, Rao states that the data trust score is not a permanent number. Citing an example, he says that if a burger company is trying to manage the trustworthiness of its supply chain data, having a couple of extra burgers on the shelf would not cause major harm to the business.

Thus, the company might determine that it has a data trust score of 80% for this particular use. However, accounts payable at the same burger company may not be content with that degree of certainty and instead want to be 99.99% certain when paying out money.

Explaining further, Rao affirms that the data trust score is an indicator of where the data has been and can be used for comparison. He maintains that if a successful company has a historical data trust score of 80, it can survive managing the supply chain.

Such companies could further increase their data trust score to reduce costs, have more control over their inventory, and better manage their vendors; however, the finance arms of companies need to ensure a very high score even to begin with.

Referring to a nuclear power plant, Rao emphasizes that monitoring the pressure, temperature, and radiation levels in every pipeline every second is key for controlling a nuclear plant and avoiding a "meltdown."

Comparing businesses with the nuclear plant, Rao suggests that companies must first deploy trust monitors across their pipelines so that dark data is reduced to less than 20%. He advises that a cultural change is necessary for people to start asking for data trustability. Further, he advises companies to track their data and pay attention to changes and errors in it.

Thereafter, Rao affirms that everyone in the ‘nuclear power plant’ is a stakeholder in the plant functioning successfully, even the janitor. Similarly, everyone in a business — finance, sales, and operations — is a stakeholder in making correct decisions and not having to do double the work by fighting to fix incorrect data.

Likewise, in a bank, insurance company, or any other financial services organization, the risk team is a stakeholder for being able to confidently analyze the risk of the organization, says Rao. However, he notes that accountability has become too dispersed, causing difficulty in understanding the risk underlying the organization.

Highlighting the evolution aspect, Rao shares that, when the IT team sends data to the business team, the business team now inquires whether the data has gone through FirstEigen’s data validation and monitoring tool DataBuck.

This reveals that a cultural shift is taking place as the IT team has adopted it and the business team has seen the benefits. More than that, everyone in the organization has a shared responsibility for ensuring good data throughout the pipeline, Rao concludes.

CDO Magazine appreciates Seth Rao for sharing his insights with our global community.