October 16, 2019

Gaurav Shroff joined our team in September 2019 as a Manager in our Data Science area, where he has been tasked with building out and enhancing our analytics capabilities. Gaurav has a unique background that has equipped him with the valuable combination of business acumen and technical expertise to fit this challenging role.

We recently sat down with Gaurav for a short Q&A about his new role and the data science challenges many companies are facing today.

Tell me about your job.

Gaurav: My job is to enhance analytic insights, by mining the data that we get from our clients, in order to create unique and actionable insights quickly. I’m helping our team tap into things that may not be part of our normal analysis – situations where we have to dive a little bit deeper to find a correlation between disparate sets of data. There are often valuable insights that aren’t readily visible in a standard analysis.

Could you give us an example?

Gaurav: Something that is common, but is difficult to do without software, is a SKU rationalization report. In our current Catalyst implementations, we have the price/volume mix calculation. If you’re just looking at a bunch of numbers, it may be hard to follow because you don’t have the calculations right there readily available. But if you can translate that text to a visual image, it represents the same data in a different way. Then you can actually understand what’s going on with very little effort. It’s kind of democratizing the value of the report. 

Our software allows us to slice and dice so many different types of data, so we’re helping clients leverage that in ways that make them more profitable. Instead of just doing a standard analysis, where we’re concentrating on financials in a traditional way, let’s start to think a little bit more out-of-the-box to find those pockets of opportunity inside the data.

What pain point do you think you’ll be helping most clients solve?

Gaurav: 90 percent of the time it’s, “I don’t know what I have.” We have so many systems in our businesses that collect different types of data. Understanding the data that’s available and how to correlate all of that information is a challenge for many businesses.

I probably spend 80 percent of my time figuring out where the data is coming in, how it’s related and the format. Many times, there are issues where we have to reformat the data or create new sets of data from current sets, to shape it in a way where we can derive insights from it.

For example, it could be that their inventory system has always been on a spreadsheet. Naming conventions may not match what’s in QuickBooks or the accounting system, because they’ve always done it a certain way, and they know the business intimately. We, as humans, can correlate that data and draw out insights. But when you start to automate, you have to be stricter about how you treat your data – how you collect it and how people enter it.

Sometimes it takes a complete system change. Other times it’s being more disciplined and training. Sometimes it’s a combination of both. It’s a huge challenge. And that’s what MGMT3D and EBM Software do for clients. We take data from these systems and we put it in a format that is ripe for picking out actionable insights.

What excited you about the Catalyst software?

Gaurav: It was just the ease of use. Once the hard part of loading all the data was done, the software makes it quick and easy to build reports. It’s hard for most software to be able to pull that data out of an ERP as quickly as we’re able to do it with Catalyst.

Also, the simplicity surrounding it. We’ve learned over time with Apple products that simplicity of use doesn’t necessarily mean they’re not complicated under the hood. There’s a lot more thought that needs to be put into the product in order for it to be user friendly like this, and that was very attractive to me.

In your opinion, what are the top three data science challenges most companies are facing today?

Gaurav: Number one is clean data. That’s certainly the number one challenge. I think number two is having a cultural mind shift towards being able to use that data across an organization. The tone really needs to come from the top on that.

The third is just constant training and upskilling. Making sure that your frontline staff understands the value of having clean data and having the right data. You have to start with the right question. Then we can start to figure out what we need.

The other thing you have to figure out is if there is bias in data. Because we can collect all the data we want, but if the method of collecting is biased, then are our insights going to be biased as well. So, understanding bias and accounting for that is going to be important for producing data that’s ultimately useful.

There’s a great book by Cathy O’Neil called, Weapons of Math Destruction, which talks about the perils of big data. These are actually huge problems that affected many, many people – all rooted in biased data and biased data models.

How can we help our clients solve these challenges?

Gaurav: I think the first thing that we can do is understand that Rome wasn’t built in a day. I think making incremental suggestions, on how data can be stored and organized can really help them understand the challenges that we face when working with their data. But our clients are so busy running their businesses, I think our value is that they don’t have to worry about it when they give it to us.

I actually think that’s the biggest way we can help with our clients. We’re going to take that burden off you. We’re going to make some suggestions that will make it easier for all of us in the future. Maybe we need to work towards that together, but at the end of the day, they brought us in to take care of a problem that we definitely have the ability to solve.

Do you have data issues you’re still trying to figure out?  Have you collected a mountain of data, but you’re still trying to gauge exactly how to tap into the insights it holds? Let us help. Contact us today to schedule a discovery call and let’s take the first step toward figuring out what you have and how we can turn it into insights that drive profitability.