Know My Company
Tell us about your interaction with smart technologies like AI and Cloud-based Ops platforms.
FactGem is an enabler of AI and Machine Learning technology that focuses on taking disconnected data sources, linking them, and providing context. Our work, then, gives your AI and Machine Learning technology a rich set of contextual information to run your models. By taking the data management portion out of the equation, teams can focus on creating new AI algorithms, rather than the time- and labor-intense data management.
How did you start in this space? What galvanized you to start at FactGem?
FactGem started about seven years ago with my Co-founder, Megan Kvamme. As a financier, she was interested in solving the problem of taking lots of data across 100s of spreadsheets and turning them into a cohesive model representative of a whole financial deal. Her quest to solve this challenge started FactGem.
At this point, I had been working in Big Data in the intelligence and defense industries for many years but was more interested in figuring out ways to make technology accessible to non-technologists, not just software engineers. I believe that we can work harder on the application side to make this technology available for the everyday user, reducing the time and cost-implementation of sorting through data.
What is FactGem and how does it leverage AI in its operations?
FactGem integrates your data sources in hours using our FactGem Data Fabric. We coexist with a business’s existing applications, so data can instantly connect according to rules and paths already set in place. Our visual model builder allows companies to see and digest data quickly, as opposed to the days or weeks it may typically take. This also helps eliminate the writing of expensive, time-consuming code meaning less manpower and money. We do this all before AI would be enacted, allowing your algorithms to have some contextual information.
What is the state of AI for IT Management in 2019? How much has it evolved since the time you first started here?
AI has a lot of challenges, and it typically requires a large amount of computing power. There are certain technologies that are leveraging GPUs to reduce the footprint. But for larger businesses, you’re still dealing with a significant computing component managed from the IT side.
On the other side of the equation, when businesses ask IT, teams, how AI or Machine Learning found a specific answer, it isn’t always known. We like to call this “Black Box IT.” Until people fully understand how AI solved a problem, we will not see widespread adoption. An IT manager will have to get used to having this tough conversation with customers and users.
How do you see the raging trend of including involving AI and Machine Learning in a modern CIO/ CMO’s stack budget?
As innovative leaders, you’d be negligent not to use AI or Machine Learning – or at least consider them to solve organizational challenges. Potential problems to keep in mind include:
- They are often expensive. Data wrangling, which FactGem can help take off your plate, is time-consuming and costly. This will dramatically affect the outcome and benefit of your company.
- There are far more job listings for data scientists than available data scientists, so hiring becomes a problem.
- These resources can be challenging. They are applying sophisticated techniques to solve problems that can cause a cultural shift or challenges within an organization.
While many organizations are receiving top-notch results, companies should approach this opportunity with eyes wide open and with realistic expectations.
How do you differentiate between technologies for Customer Success vs. Customer Support in IT Infrastructure Management? Who are you competing in this landscape?
The difference between Customer Success and Customer Support is defined organizationally, as each company will have its own definition. I’d define Support as upholding the existing offerings, status quo, and customer happiness. Taking customers’ needs and finding a solution.
Customer Success is more proactive and more involved in trends – recognizing that IT exists to serve a business function and does not live on its own. IT exists to serve a company or client’s end goals. Technologists must stay close to the business and proactively look for technologies to take to clients and say, “As partners in this endeavor, we believe these approaches will help us achieve our goal.”
FactGem fits into this nicely because we bridge the IT and business gap by creating and maintaining a function in their organization that is persistent in the IT infrastructure. This allows IT departments and business functions to work cohesively together – using the same terminology, the same models and the same goals.
What is the biggest challenge to Digital Transformation in 2019? How does FactGem contribute to a successful Digital Transformation?
The biggest challenge is bringing IT and businesses together. It isn’t a technology challenge; it’s a cultural challenge.
How do we align businesses and IT departments? How do we bridge that gap, so they communicate effectively to reach common goals? It becomes less about IT, and more about the processes in place. And to some degree, how accessible those technologies are for the business user. At FactGem, we are looking for ways to make this accessible and manageable to organizations, outside of their data scientists and IT managers. We’re demystifying this process so the people who best know the business can see, use, and implement the findings of the data.
Where do you see AI/Machine Learning and other smart technologies heading beyond 2020?
Most people today do use some form of Machine Learning – a prime example is Google Maps. You just don’t think about it, like many technologies that work well. “Explain-ability” will continue to be important – more organizations are going to demand that.
The Good, Bad and Ugly about AI that you have heard or predict.
Recently, a study took place with leading cancer research hospitals where they applied AI to make recommendations alongside doctors. Many of the treatments recommended were preposterous, and people couldn’t explain the recommendations. It caused a huge press storm, a canceling of budget, and reevaluating the future.
This will continue with tech. Like quantum computing, to capitalize on types of things that happen, people want to explain the WHY behind it. This is referred to as explain-ability and it is becoming increasingly important in the AI field. In addition, several ethical questions arise.
AI will eventually provide the best, cheapest solutions to big questions. It will depend on the management of the technologies, and the answers managers can provide.
What is your opinion on “Weaponization of AI/Machine Learning”? How do you promote your ideas?
This depends a lot on your goals. My background is in intelligence and defense. If you’re a nation-state, AI and Machine Learning are probably something you care about and have been researching. If I want to hack your infrastructure, one way is absolutely machine learning to infiltrate countermeasures.
Like everything else, it’s a matter of perspective. While it’s a significant issue, you could say that about any other major technology that has come into existence. For example, how social media affected the election or the Brexit vote. There will always be that debate.
The Crystal Gaze
What Cloud Analytics and SaaS start-ups and labs are you keenly following?
I take a different approach to this. I like to follow the big players like Microsoft, IBM, Google, and see what is coming out of their research departments. That will ultimately inform what moves out of research and into production. I like to see what that tipping point is that warrants an investment in technology. I’m a believer in casting a wide net and seeing what exciting ideas are popping up. There is too much happening now to focus so narrowly.
What technologies within AI/NLP and Cloud Analytics are you interested in?
There is a move in the AI and Machine Learning community to take existing algorithms and procedures, like neural networks, and rethink them to better leverage existing data technologies, such as graph and data structures.
FactGem is a heavy user of graphs and data structures. We use it to create contextual data to power analytics and AI capabilities. What will surprise people is that traditional neural networks, such as a system that is designed to model a human brain or something similar, does not look like reality. It’s only in the last few years that people have started using sophisticated data structures and databases to rethink neural networks to get more interesting results.
As a Tech Leader, what industries you think would be the fastest to adopting Analytics and AI/ML with smooth efficiency? What are the new emerging markets for these technology markets?
Like most technologies, we’ve adopted two market segments:
- First, where you can make a strong impact with minimal risk. For example, using machine learning for customer product recommendations: “We saw you looking at this shirt, these pants go well with it.” This is a fairly low-risk example. If you continuously make good recommendations, it will strongly affect your revenue. If not, people will simply not buy that product.
- On the other side are industries that have been hit with hard problems, and they don’t have a solution. So, the risk might be high, but the result could be tremendous. An example of this is pharma companies using this approach. These companies typically have far more data about treatments and potential new drugs, and they’re making progress using machine learning.
What’s your smartest work-related shortcut or productivity hack?
I’m a big believer in getting out of the office and out from under your workload to get new ideas and get inspired. Time outside, reading, and podcasts outside my vertical industry help me disengage and remove myself from the box and find new inspiration. Right now, I’m liking NPR TED Radio podcasts.
Tag the one person in the industry whose answers to these questions you would love to read
Elon Musk and Bill Gates
Thank you, Clark! That was fun and hope to see you back on AiThority soon.
Clark Richey is the Chief Technology Officer at FactGem. He has over 20 years of experience designing and developing software, primarily for the defense and intelligence sectors. He has also taught in the master’s program at Loyola University and undergraduate program at UMBC. Clark has investigated non-traditional methods and technologies that use data more efficiently for over 10 years.
FactGem integrates your data sources in hours through the FactGem Data Fabric. We don’t disturb existing applications. Data instantly connects according to the business rules that you create in our visual model builder. You no longer have to write expensive and time-consuming code to enforce business rules for your data. Easily and rapidly engage with your data to expose the gems that you have been missing.