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Democratizing Data in Large Enterprises

Meenal Iyer shares her thorough, effective, and clear strategy for democratizing data successfully and how that helps create a successful data culture in large enterprises
Jun 2022
Transcript

Photo of Meenal Iyer
Guest
Meenal Iyer

Meenal Iyer is the Sr. Director for Data Science and Experimentation at Tailored Brands, Inc. She has over 20 years of experience as a Data and Analytics strategist and she has built several data and analytics platforms, driving the enterprises she works with to be insights-driven. She’s also led data teams at various retail organizations, and has a wide variety of specialties in Data Science, including data literacy programs, data monetization, machine learning, enterprise data governance, and more.


Photo of Adel Nehme
Host
Adel Nehme

Adel is a Data Science educator, speaker, and Evangelist at DataCamp where he has released various courses and live training on data analysis, machine learning, and data engineering. He is passionate about spreading data skills and data literacy throughout organizations and the intersection of technology and society. He has an MSc in Data Science and Business Analytics. In his free time, you can find him hanging out with his cat Louis.

Key Takeaways

1

Data democratization is not just about data access, but about providing the end user a comfortable layer for which they can interact with data easily.

2

Alignment on a single definition for metrics for your organization is one of the single biggest low hanging fruits organizations can tackle when it comes to galvanizining a data culture.

3

Data leaders can continually accelerate data culture by communicating regularly with stakeholders, sharing the impact of their work, and creating internal evangelists by equipping them with the data skills to make their lives easier.

Key Quotes

How do we make data essentially a priority in every conversation with every initiative that takes place? Start by determining the success metrics for the initiative and for that initiative and how they will be measured. You can’t manage what you can’t measure. It is important for data leaders to have a seat at the table so they can be a part of conversations with key stakeholders about upcoming initiatives. Once they identify what those initiatives will impact across the organization, they encourage stakeholders to cascade that information down the chain so that is reaches all parts of the enterprise.

Inconsistency in metrics is when metrics are defined differently across different business units and are used in different forms. It can be hard to identify when this is happening, but it's vital that organizations align on a single metrics definition so they know exactly where they are starting from and what they are driving toward. Unfortunately, this is something that is rampant across organizations. Finance, Marketing, Sales, and more end up with their own definitions of each metric. Bring all those stakeholders together to align on one definition so the separate business units can all speak as one, rather than speaking as several different business units with different languages.

Transcript

Adel Nehme: Hi everyone. This is a Dell data science educator and evangelist a data camp. If you've been listening to data frame for a while now, you promptly know that data democratization is one of my favorite topics to discuss on the show every now and then I get to take a bird's-eye view with guests and discuss the broad ways organizations can become data-driven.

And this is one of these episodes today's guest is Muna. Manal is a data and analytics, strategist, and transformational leader with over 22 years of experience, building data analytics platforms and driving enterprises to be insights driven. She has a wealth of knowledge and specialties, including the illiteracy programs, data monetization, enterprise data analytics strategies.

The others she's led data teams, various retail organizations, such as Macy's tailored brands and more. And she's one of the few data leaders I've spoken to that can really articulate a simple coherent strategy for data democratization. Throughout the episode, we talk about the components of data, democratization, data, culture, and people, the importance of standardizing business metrics that you've data democratization, which we spoke about at length, how to enlist data champions is analytics leader.

And. If you've enjoyed this episode, make sure to rate, subscribe, and comment, but only if. Also, don't forget this week, we'll be hosting data, camp radar, our digital summit on June 23rd, during the summit of a variety of experts, including many people from different backgrounds, we'll be discussing everything r... See more

elated to the future of careers and data.

Whether you're recruiting for data roles or looking to build a career in data, there is definitely something for you. Seats are limited and registration is free. So secure your spot today on events.datacom.com/radar. The link is in the. Now onto today's episode. Now it's great to have you on the show.

Meenal Iyer: Likewise. Thank you so much for the opportunity to speak in this.

Adel Nehme: So I'm excited to deep dive with you on your work. Leading data science and theater brands, the importance of data democratization and how data leaders can really create a vibrant data culture. But before, can you give us a bit of a background about yourself?

Meenal Iyer: Absolutely. So, thanks again for letting me speak about data democratization. That is one of my favorite topics to speak about. So as terms of the background, I've been in the space for about 20, over 20 years and have experienced like the good, bad and ugly of the space worked across multiple industries, which gave me also the experience to solve for unique data problems I enjoy or enabling data enterprises to essentially become data driven.

And, you know, that has kind of become my motto to provide the right information to the right people at the right. I'm currently working at tailored brands. I had data science and experimentation here in addition to data now.

Topics
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