Interview: Kenneth Viciana, Equifax on Data Lake & Other Strategies for Insights Culture

Kenneth Viciana has extensive Data Management experience in the Financial Industry. He has successfully led and delivered many strategic data initiatives at Fortune 500 Companies. Kenneth's primary management areas of focus include; Data Warehousing, Data Quality, Data Governance, Big Data, and Business Intelligence. He is driven to deliver powerful data management solutions in support of generating corporate revenue.

In his current role at Equifax, Kenneth leads a team that is responsible for Enterprise Data Strategy, and transforming Big Data into valuable insights that are the catalyst for accelerating the delivery of solutions to real life problems.

Here is my interview with him:

Anmol Rajpurohit: Q1. What are the key responsibilities of Enterprise Data Strategy team at Equifax?

Kenneth Viciana: The Enterprise Data Strategy Team has three main responsibilities at Equifax.

1. ‘Insights Support’- We generate insights from Big Data to solve customer pain points. To accomplish this we leverage our unique differentiated data assets, analytical capabilities, technology stack, and industry expertise. A good example of insights support work is helping a bank achieve on goals such as customer retention, household expansion, and growing their footprint. Equifax Decision 360® equips us to deliver actionable intelligence on the bank’s customer base/footprint.
2. ‘Data Innovation’- This work stream is tasked with proactively finding value and opportunities within our data. The work in this space often starts with data mining, and drawing observations from the data. Additionally, work in this area can be born out of a hypothesis which we test/validate within the data.
3. ‘Data Evaluations’- Our data inventory is our foundation to support insights delivery, and we are constantly looking for new data assets to provide additional value. My team supports this effort by testing prospective data assets to determine the achieved lift. We frame our findings along with a recommendation which enables an informed decisioning path.

 
AR: Q2. What were your motivations behind building a Data Lake? What challenges did you face while building it?

KV: The motivation to building out the ‘data lake’ was enabling innovation within our Data & Analytics team. This environment provides a hub with all readily available data and the proper tools to efficiently analyze the data. It empowers our analysts to draw observations from the data, test hypotheses, and generate insights.

In any large scale implementation the requirements gathering phase presents a challenge. You want to ensure all core requirements are accounted for in the build. Additionally selecting the tools for the environment can be quite an undertaking. We approached the tool selection process as a proof-of-concept opportunity; we provided prospective vendors with requirements and gave them the opportunity to demonstrate how their tool(s) were equipped to deliver on the requirements.

AR: Q3. How would you describe the Equifax Decision360 approach? What is its value proposition to your customers?

KV: Equifax Decision 360® is our collaborative approach to help our customers address key business needs. It starts with the ability to create deeper, connected insights that drive better business decisions, augmented through the ability to deliver those insights at the moment of opportunity so we can help our clients maximize customer value across their business and throughout the lifecycle.

AR: Q4. Can you elaborate the term "Insights Culture"? It's one thing to have an Analytics center of excellence (i.e. to delegate Analytics responsibilities to a dedicated team), but totally another to have everyone in the organization imbibe data-driven thinking. How can we achieve the latter?

KV: Creating an Insights Culture starts with establishing a collaborative work environment based on innovation. Our team is staffed with inquisitive analysts that are challenged to find value and opportunities in the data. We regularly have brainstorming/whiteboard sessions focused on generating new ideas and hypotheses, that are then tested and validated in the data. The work is very interesting and the team has fun while remaining focused on supporting the delivery of insights.

 
AR: Q5. What are biggest bottlenecks for value delivery in Big Data projects at enterprises? How would you approach these bottlenecks?

KV: Big Data projects present various challenges, but that’s what makes it interesting!

One major challenge in this space is providing transparency around the data that is available. The value of Big Data is being able to draw pertinent observations (from Big Data) that can be translated into insight. In a fast moving environment where new data assets are consistently boarded and enhancements are applied, our data consumers/customers need to be informed of these changes.

An additional challenge is information governance. By this I mean managing data access and usage. All data sources are not created equally, and each has its set of contractual/legal regulations that need to be applied. This effort seeks to provide a framework and foundation conducive to keeping the organization compliant.

Second and last part of the interview

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