Lauren Stiebing:

Hi, I’m Lauren Stiebing, and welcome to this episode of the Career Success Podcast. Today we’ve invited Sharon Joseph back as our guests to discuss with us how we should be thinking, and using data in the next phase she calls, “Data transformation”. Data can be quite intimidating. Today we hear a lot about data science, which is the intersection of computer science business, or domain expertise, math, and statistics. So, Sharon, how would you explain the evolution of data, and why is it important today?

Sharon Joseph:

Yeah. Sure, Lauren. So I look at data transformation in three areas, and let’s also take a step back to what people do know, and that is digital transformation has been around for the last two decades, where people were just starting to get digitally savvy. And now, more than ever, people really had to do that in the last few months if they weren’t a company that was already accelerated, they had to. And in addition, if you were a person that didn’t understand how to operate all these tools, et cetera, you’re forced into doing it. The reason I came up with this concept of data transformation is I think we’re headed into that same space where people are going to have to be using information and data more than ever.

Sharon Joseph:

So, three things. One is this, the kind of information you’re processing. Do you know if that information is reliable, and where’s it coming from? That’s one big area.

Sharon Joseph:

The second one is on testing your hypothesis and thinking through it. So what analysis needs to be done, because rarely Lauren, are you given information that’s already ready to go. And if you do get information that’s ready to go, you should always be questioning it because that information can’t be the only piece that’s being pulled together for an insight or a hypothesis. So I’m always about testing it.

Sharon Joseph:

And then the third thing I like to look at is how is this going to drive a business, and competitively differentiate? Because who cares if you’re looking at data if you’re not going to be able to do something with it, because there’s so much information out there, you need to be able to decipher quickly. And I think the companies and those that are going to be leaders, who are going to be successful in the next decade, are the ones who are able to work through these three steps really quickly, and seamlessly and are able to pull teams together that create this data culture.

Sharon Joseph:

And why is it important? I know based on the top consulting companies globally, the BCGs, the Bains, the McKinseys, Accentures, Deloittes, that they’ve done a lot of information digging and research on this, and it ranges from being two to five times more likely to outperform their competitors if they are a data driven leader. So that means you’re using it frequently in your day-to-day work, you’re using the information to execute ideas, you’re using it faster to make decisions and then you’re also, just overall, it’s driving into your PNL and you see financial performance improvements.

Sharon Joseph:

And then the other thing as well for data leaders is that there’s just going to be a gap in the marketplace. So we have a lot of people. So Gen Xers and Boomers who have been working in these major Fortune 100 companies, who have the experience, and the expertise, but they weren’t learning Python. And they weren’t learning all these tools that are now out there to just be able to manage and drive insights and data a lot faster. And then you have all these Gen Z’s, and these millennials, those Gen Ys that are coming out of school and they have tons of coding experience. But they have no strategy or expertise in these industries. So there’s this big gap right now. Those are kind of my summaries.

Lauren Stiebing:

Yeah, and how important would you say it is for a senior individual to learn Python?

Sharon Joseph:

Yeah. I don’t think that anyone … I’m not saying that you shouldn’t go out and learn it, and I definitely have my coders on my team who excel at that, and I just need to know how to ask the right questions, right. And that’s like any leader. We don’t expect CEOs to learn all of the nitty gritty when I was in Fortune 100 about AC Nielsen and IRI. So I don’t expect leaders today to do that. I expect them to know the tools and be well adept at how do these tools work. And that’s the big scary part for a lot of leaders now is these things that are coming out like Jupyter Notebook, or even just Python and predictive analysis, pattern recognition, all the coding language is intimidating. But I think it’s just understanding where computer science sits versus the math and statistic versus what they have is strategic expertise.

Sharon Joseph:

So I think they all work in conjunction together and you just have to like anything, be able to talk to it and understand where we are and where you are as a company on the data transformation journey.

Lauren Stiebing:

Yeah. And how is big data being used across industries at the moment?

Sharon Joseph:

It’s actually unbelievably crazy that people have been using this for decades, right? So companies have been using it for decades. I’m just going to give some examples of sort of data mining that’s been happening. So if you think about credit card management in financial institutions, they really had to understand who is going to be the best candidate to get a certain amount of dollars on their credit card, right? So if you’re going to get a credit card, should I be giving somebody $1,000 to start with, or should I give them a $12,000 credit card opportunity?

Sharon Joseph:

And so these are the types of things that have already been used. Also within banking, it was around client leads. So if you’re doing commercial banking and you need to be able to determine which clients or customers can I bring into the business who are in need of a loan, then you have to be able to figure out like what’s the size of the business and all these elements that right now through the analytics and the data that you can get into, you can find that a lot faster. So I think in early 2000, that’s when this whole concept of data science came out and that’s how long companies have been using it. And then from your perspective, just kind of recruiting and human resource management, you can look at how do you find the top-performing talent? How do you keep the top-performing talent? How do you recruit the top-performing talent?

Sharon Joseph:

So there’s multitudes of examples of how this whole concept of data mining and big data is being used and AI more than ever. And I was listening to a few talks recently with regard to what’s the biggest trend that’s going to be happening across businesses. And if they’re not on it, AI is one of the biggest ones, because it can use to predict so many things within your business. And if you don’t have that included in your business model, you’ll be behind. And those are the tools that I think are going to surface a lot in the next few months, as people are really thinking through, how do we leverage data even further?

Lauren Stiebing:

How would you suggest to go about finding the right tools for your business?

Sharon Joseph:

Yeah, so I think one of the things I look at is what are you trying to achieve from a business strategy? So it always comes back to how are you performing versus your competitors? And do you really understand that? So we kind of do it in [inaudible 00:07:12] anyways, in my business, as what we call a deep dive. And you get into, what’s the first thing that you would look at always? And that’s hey, how am I stacked up against my competitors?

Sharon Joseph:

The next thing is what goals and objectives do you have within your business and what are you lacking? And for the most part, every company that I’ve worked with usually has kind of five or six big things that they’re trying to achieve. So it’s competition, innovation, speed to market, brand awareness, client solutions, their product portfolio, or the product offering their team performance and then strategy. Depending on those pieces or elements, it’s when you kind of take a company like myself, or if you’re one of the Fortune 100s, they’re taking the top consulting companies and saying, where are the biggest gaps versus ourselves in the competition, and where we’re going to grow?

Sharon Joseph:

And for you in particular, I know we talked about like human resource tools. One of the biggest companies out there from a large company perspective is SAP. And so I was kind of digging in because you were asking me some questions with regard to, how do we find the best talent? Or how do we keep the best talent? Or how would we look at predicting personality assessments? Et cetera. So I just went to kind of talking to some SAP leaders, as well as doing some research in their app center. And so they have this massive app center online that you can go to. And some of the software they have, the first one I thought of for you was specifically human resource companies is something called Plum. And it makes predictive talent decisions by using AI and industrial and organizational technology in order to quantify the potential of their talent and kind of talk about it in three ways. And I thought, because I’m so deep in data, it’s probably better to explain it in ways that you’d understand.

Sharon Joseph:

So one is around just the talent assessment. So they take a survey and then you get stacked up in terms of how your assessment is, right, which is nothing new. The second thing is then measuring the job needs as well. So what’s the competency between, or behind that job that you’re trying to fill, and then it’s a match score. So it’s a score between your talent and the individual to figure out what’s their likelihood of being successful. And then once you have a lot of that going on in your organization, and you’re kind of putting Plum into the early stage of recruitment, then you’re able to predict even out further and then determine other things within the company as well within talent. So I think once you build those in, it’s like a layer of predictive data that helps you successfully source talent. So I think that’s one example.

Sharon Joseph:

Another one that I quickly looked at, there are two other ones actually. One’s called Censia and it’s a talent AI transforming acquisition software. So it’s finding the ideal candidate for open positions you have in a matter of seconds. So everything you know, everybody wants real time information. And then Eightfold.ai was another one that’s kind of focused more on like how successful and how well you can keep the individuals in your company and keep them long term. And then also it’s around hiring talent. So I think all these tools are coming out to better enable companies to make decisions based on key pieces of information, data, skillset, and then using what I had mentioned earlier, which is this whole operational information, then meshing it with experiential. So the why behind why people are doing things.

Sharon Joseph:

And then I think there’s like massive amounts of opportunity in all these other tools we have. So if you think of Glassdoor, you can check sentiment. I think more people nowadays want to know what company they’re going to get involved with. So as an employee, you’d want to say is company X the best fit for me? And pulling all that information together in a platform for a potential future employee is really important because they want to know, is that going to be somewhere I want to stay for awhile? Or am I going to be in and out in a year because I’m not really in tune with their culture? And I think you can do a lot of that checking sentiment and seeing a team health factor or score will help employees make better decisions.

Sharon Joseph:

And then tools like Slack and Zoom, which we’re all on now, I think you can check on sentiment there. I know there’s tools that are already tapping into Slack and one of them is Moodbit. And you can, I think, also see productivity and efficiency of teams eventually. So what I predict is that you’re going to start looking at who are the best teams, who are getting results and start analyzing their Slack channels and what they’re doing in Slack and their communication methodology to then predict out how to have the best performing teams. I get really excited about that one because I think that team performance is really important these days, right? We’re all going online and [inaudible 00:12:05].

Sharon Joseph:

The other one is zoom. I think that we’re all on Zoom now, if you could do a lot more voice recognition, and I know some companies here in New York that are working on that and they were telling me, “Sharon, voice recognition and AI, that’s the next big thing.” And I was like, “Oh, that’s really interesting.” And I believed it, but now more than ever, I believe that because we’re now forced into conversations on the phone on things that are being recorded, you want to go back to that and analyze if you sold something or if you were trying to retain a customer, what conversations had to happen in order to actually have a successful result? And by understanding that I think those are implications for even things like call centers, right? So you’d be able to analyze those calls and understand the likelihood of someone being successful and use it for training and development.

Sharon Joseph:

So I think there’s just so much going on in this space that it’s really an exciting time to be working in it. And no one should feel intimidated because I’ve been working in it for my whole career, I’ve been in data and analytics because marketing and sales at large Fortune 100s, you had to. However everybody who’s been working at those large companies has that experience and anyone who hasn’t, is more entrepreneurial knows that you just have to be a little scrappy with stuff, right. And that’s this whole concept where we are in data transformation.

Lauren Stiebing:

Well, Sharon, thank you so much for joining us. I’m sure our listeners have found this very educational. Thanks again.

Sharon Joseph:

Yeah. Thank you, Lauren. Always great to be on talking with you and we love to talk about what’s the future hold. And so here we are again, thank you.

Lauren Stiebing:

Exactly, thanks.