As technology is ever-evolving, the data generated from more users, more software, and improved software, is accumulating at an exponential rate. In the past few years, data analytics are emerging as an area of significant interest. Enterprises have access to all this information generated by applications, websites, and indirect sources… so what is one supposed to do with it all? Does it truly benefit companies to dive deeper into this information and source out what it says? In our eyes, absolutely.
Lunavi has recently brought Jeff Thomas on board as our new Director of Data Analytics. By expanding our data analytics segment, we are positioning ourselves to better understand and serve customer needs, and what better way than to bring on an expert?
Mr. Thomas is from Springfield, Illinois but currently resides in El Paso, Texas with his family. Jeff has Bachelor’s and Master’s Degrees in Computer Science, but it wasn’t until he entered the workforce that he found his niche with data and analytics. Fast forward 18 years, and he has built an impressive career in his field. Having started out as a developer in the healthcare industry, Jeff worked in data analytics and AI Machine learning and worked his way into leadership.
His diligence and dedication allowed him to work on innovative projects such as working with IBM Watson on developing cancer cocktails, to building Business Intelligence, data warehousing, and data analytics practices from the ground upas he did for multiple companies such as GECU Credit Union. When Jeff isn’t busy innovating on new projects in data analytics, he is spending time with his family or doing CrossFit, which he has been dedicating time to for 12 years! We are excited to have him on our team and want to extend additional thanks to Jeff for taking the time to chat with us.
Lunavi is definitely good at application development, and I like the way we structure our processes, they’re pretty perfect.
I saw that the area of expertise that was missing is in data analytics, so I saw that I could take a piece of Lunavi’s portfolio and build it into data analytics. I want it to be just as fulfilling as Lunavi’s application development, so that’s my current goal.
Also, with Lunavi offering managed services, there is a lot of opportunity to do managed services through data analytics, which was a new perspective I brought up in our interview. When companies want to build out an enterprise data warehouse or build out their data analytics it can get expensive, and a lot of people don’t have the resources for it. That’s something I discussed with Mike as well as a long-term goal to work towards.
For one, I think it’s exciting that people are finally getting into it. I think that people thought it was just a buzzword and it was almost beat to death in the beginning with terminology. It scared people away as a million dollars was spent only to get nothing out of it because they just followed the wrong path. So, I do think that’s the most exciting piece, people actually getting into data and analytics right now.
One of the nicest things about data and analytics is the tool sets are always evolving. Microsoft is making huge strides and a lot of these companies making these strides are making it easier and more affordable to get involved.
If you can help groom a company and take them from just descriptive analytics to predictive, prescriptive, and AI, you can help them make big decisions. A lot of people think AI is magic and it’s really not. That’s the other exciting part, people are starting to understand what it is and how it works.
With AI, you can ask me a question and I can essentially answer that question simultaneously for a million people once I’ve trained the model. You build an AI model and you tell it how to react, then it evolves off that and continues learning. You have to train the model to come up with the outcome you want, and people are learning what they can do with Machine Learning.
The more exciting piece of that is that it used to take a team of data scientists to build a model like that and now you can do it with a small team or even one person can manage two to three models.
I think the next five years are really going to be driven by more people using it. There are a lot of people moving in that space and it’s going to force data to evolve across the world.
There’s going to be a lot more scrutiny on data and how people use it, so I think that there is going to be more regulation that comes with it. That’s going to help, because if a client comes to you and says, “I want to be able to pre-approve people for loans,” a data scientist can do that, that’s their job. If you do that and run the list to the marketing team, you’re going to have fireworks going off. There’s nothing wrapped around that, no compliance.
If you work with someone that understands compliance and regulation, you can help them think outside the box. If you have a client ask you to help them do pre-approvals, I would come in and ask what compliance measures need to be met. Do we have any regulatory issues to worry about? Where are you geographically located [as it pertains to your data]? How do we do this for our customers in away that is responsible and ethical, and they like the outcome?
The simplest terms can give us specific results, and a model can run for years, and it needs to be managed in that time to maintain those regulatory outlines.
The very first thing I look at is what that person has for data analytics now. I have met people that don’t have a single enterprise data warehouse, and that’s an essential piece to building a report on where you’ve been, where you’re at now, and where you’re trying to go. That’s descriptive, predictive, and prescriptive analytics.
I have met with people who didn’t have any of that and wanted to do AI, but how are you going to feed the model? Knowing where you’ve been is what builds up to where you could potentially go, so that’s the very first thing I do is ask “where are you at in your data journey?”
Another piece of that is data governance, just to throw that out there. Data governance is big, you know, how you manage these AI models. How do you manage these AI models? Who’s watching them? What are our calculations for turnover? What are our calculations for the marketing world? How do we say that was successful or unsuccessful? Building out all those measures and KPIs are an important part of data governance.
They used to not fit in. Right now, let’s say someone is 100% starting from scratch and you don’t have data analytics practices, we go to the cloud from the start. We could scale up or scale down, with the cloud you can do anything. It’s very simple, very manageable.
On the other hand, let’s say you have a client that already has something in play and they’re using some old tool sets or tool sets they would have to migrate over to the cloud. So, there could be a hybrid possibility where you may already be using SQL Server Integration on-prem – we can’t put that into the cloud. We can use that to populate data in the cloud though, then everything else will happen.
We have a third area where it doesn’t matter if they’ve started or not, they aren’t comfortable with the cloud and they would rather do everything on-prem. So how do we handle this?
The cloud is a growing opportunity with scalability and the cost is so much cheaper. In the beginning, it sounds like a big cost for a lot of people. If they look at purchasing a piece of hardware and having to replace it every three years, and you break that down with service and maintenance, along with everything else you have to manage it’s a no-brainer. For those that don’t want to move to the cloud, it’s a conversation about why and how you can bridge the gap to help them make the move.
One of the biggest things that I think people in general struggle with is they don’t know where to start. They think it’s going to be crazy expensive, and they think to themselves “I don’t have the resources for this.” It does take a lot of time to build that up, and it can take a lot of money to get resources on-site. Something that I think we can offer them is a way of building that base through consulting.
We can offer a way to build it without in-house resources and so they don’t have to worry about designing and running it. I think a lot of the costs are assumption based. We could either get you started and guide you on getting those resources or we could do the managed services side where we manage the entire suite for you, and you don’t have to worry about any of that and it’s more cost-effective.
So, for the people I’ve talked to, that’s the biggest thing, they don’t have the resources, they don’t know where to start, or they think it’s going to be too expensive. Or they get a horror story from somebody they know or somebody who’s tried it and failed. I think it’s all relative and a conversation to figure out those answers, with us for example, would help alleviate something they heard or something they may or may not have witnessed.
Depending on the industry, one of the things that you’ll be able to see is all your trending data. I’m going to be very general here because you can see all your trends. A lot of people are still referencing Excel documents. You hope that nobody made a mistake in that Excel document, and you hope that the data is right. You go through a hope phase when you’re looking at the manual processes people put together. You have that element of human error.
In the Business Intelligence space, if you had Power BI, for example, you’d be able to see all your trending data, and you’d have a single source of truth. You also know that the data you’re seeing is 100% accurate – there aren’t any questions to ask, it’s already been validated.
One of the things you could also do in a data analytics space is pulling benchmark data from other companies. You could pull that in and look at the competitive analytics of your company and a competitor. Where are they succeeding? Where am I not succeeding? What are people saying about ‘xyz?’
These platforms give people data at their fingertips on a phone, an iPad, or a computer where they can have trusted data right now and can answer questions quickly. I’ve been able to answer questions at a moment’s notice for senior leaders with this type of data on the spot. That’s the biggest advantage of having this data, being very general of course.
For the predictive piece, they say “we’re trending up, we think that we’re going to increase revenue by 3.6% this next year.” It’s all right there and it’s already been validated so they can trust it immediately. You don’t have to ask anybody and don’t have to rely on intuition or somebody who is very optimistic and says you’re going to have a 10% increase and end up with a 3%increase. What if you can plan to know other expenses and things that make the10% increase happen? You save yourself and the company from making bad decisions.
Yeah, I think anybody can dive into this space regardless of how small or big the company is, how small or big the project is – there is always value in data and a lot of people don’t recognize it until it’s too late. So, identifying that there could be value or there will be value in your data is key.
As you can see, the realm of data analytics is evolving but it will take the help of experts to fully understand its capabilities. The possibilities are endless with data and analytics.
Thank you for reading, we hope you learned something new about the world of data and analytics. If you or your company needs assistance in diving into the data analytics world, contact us today, and get to work with Jeff Thomas and our Digital Consulting team!
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