Upskill Employees on AI to drive Innovation

 

Companies are quick to hire full-time data scientists to solve their most complex problems when it comes to data innovation. The reality is that the success of implementing emerging technologies can lie with your current internal employees themselves.  One way to improve your success rate is by imparting your employee the requisite skills required to understand data and how to use it in their daily work life. Not only is upskilling your existing employees a benefit to the organization, but it also offers employees the opportunity to future-proof their careers.

Data Scientists are not the Only Answer

Even though the term “Data Scientist” has entered the daily vernacular of business terms, there is still no agreed certification that demonstrates qualification for the role. Most businesses hire a single data scientist (or small team) with the assumption that they have the means to understand the intricacies and nuances of the company because of their advanced statistical expertise. But to support a successful data initiative, subject matter experts will also be required.

This subject matter expertise will have to come from your existing internal employees. No one knows better than these employees the patterns of your business, where to find relevant data sources, and why specific levers can influence an outcome.

The Current Data Gap

So why are organizations looking externally for these skillsets? In short, employers are not developing the tools or providing the opportunities to inspire, motivate, and incentivize their workforce to learn and utilize these skills.

To allow your employees to take advantage of AI and machine learning benefits, you must implement training programs that will educate them on AI and machine learning principles. One way to get employees to adopt AI is by letting them work on problem-solving and analysis challenges cross-functionally. However, leaders need to recognize that many of the first ventures into Data Science will not materialize into considerable benefits. But what will be gained is an opportunity for the employees to become comfortable with the concepts and methodology that will eventually lead to sustained growth and profitability. A key element of improving your Analytics maturity lies in building a culture that supports experimentation and failure.

Conclusion

According to QuantHub, some 35% of organizations surveyed said they anticipate having the most difficulty finding appropriate skillsets for data science roles. And the problem will not improve anytime soon. For this reason, businesses need to look towards the same people they are relying on to handle today’s challenges and prepare them with the capabilities to address tomorrow’s as well.

 

 

 

 

What are the Different Job Roles within Artificial Intelligence?

 

Artificial Intelligence (AI) is everywhere around us. It has already been widely integrated into our daily lives by the smartphone in our pocket and the Apple Watch on our wrist. This technology has become an integral part of our everyday lives, and we are now interacting with it regularly. We are already living in the age of AI, which is projected to continue growing at an exponential pace. In turn, the number of job roles and the type of skills necessary to support AI initiatives at the enterprise is also increasing. The days of everyone calling themselves a “Data Scientist” are almost over, so let’s take a look at the various roles within AI.

Software Engineer

One pivotal role when discussing AI is the role of Software Engineer. At its core, AI requires data to perform – and without systems to capture this data (consistently and reliably), no algorithm or fancy model will provide valuable insights. Software Engineers are the first line of AI – developing tools and systems to make it easier to build machine learning and deep learning-based algorithms. Social Media apps, sensors, mobile apps, Internet of Things (IoT), analytics tools all have the potential to capture this valuable resource. These software applications (internal and external) have the power to make a new AI initiative within a corporation successful or painfully troublesome.

Data Engineer

If producing and capturing data is pivotal, then finding it and accessing it is indispensable. At the most basic level, the Data Engineer is responsible for analyzing and cleaning the data gathered from the various systems and tools used across an ecosystem. The Data Engineer is the all-around data specialist that prepares data and ensures that it can be consumed and utilized within the organization. By extracting information from various systems, transforming/cleaning data, and combining disparate sources to form a functioning database – the data engineer is the “hidden jewel” in AI. Often these individuals need to have an in-depth knowledge of the business processes that enable them to find hidden data treasures.

ML Engineer

Moving from simple data to predictive models is where the Machine Learning (ML) Engineer shines. The ML Engineer is responsible for developing and training models and algorithms using advanced statistical techniques and data science skills. They identify patterns in historical datasets, find the most influential factors and attributes to a particular outcome, and experiment with feature engineering to improve these models’ scalability and deployment. A discounted responsibility of the ML Engineer is related to business consumption. If the predictive model has exceptional predictive power, but business users are not utilizing its recommendation – “well if an algorithm makes a prediction in the woods and no one hears it…”. The ML Engineer must create the most accurate model possible using their advanced analytical skills and the best method for business users to trust and use the insight to run and optimize their business results.

AI Business Strategist

We can now capture data, access data, and even make unique predictive models, but just because it can be built – should it be built? The AI Business Strategist is an often-neglected role when enterprises are instituting AI for the first time. This role is less about the technical aspect of AI and more about the softer side of AI. The AI Business Strategist should be a senior individual who understands what AI is capable of (Art of the Possible) and recognizes the business impact it can have across an organization (Transformational). They know the business goals and can garner executive sponsorship to experiment with minimum viable products (MVP). They have the business acumen to identify and prioritize the first AI projects an organization should pursue based on their analytics maturity and data fluency. In the simplest terms, an AI Business Strategist can help an organization launch successful AI initiatives that can demonstrate positive ROI.

Conclusion

When considering the ongoing progress of AI within the enterprise, it’s essential to take a step back and look at the big picture. AI is the latest technological advance that’s changing the way business is being conducted today. Companies are leveraging AI across a broad spectrum of functions, enabling them to provide a superior customer experience and deliver a higher return on their investment. Organizations must understand how AI will impact many of the current jobs and ensure they consider all the roles that will enable a successful AI implementation.

 

Data is Not the New Oil

 

Perhaps in the past few years, you have heard the adage “Data is the New Oil”! Given the exponential growth opportunities that are possible with Data, I can see why so many people have embraced this phrase. However, in a few respects, this could not be further from the truth.

Why the Phrase works

The phrase was first coined by Clive Humby in 2006. Michael Palmer expanded upon the quote to say that like Oil, Data is “valuable, but if unrefined, it cannot really be used”.

I grew up in the ’80s in Houston, TX, and from my earliest years, I was the biggest Houston Oiler fan (“Luv Ya Blue”), and at that time and in that city, you could see how Oil was King. Oil was lucrative, and fortunes could be made if one had the means to extract it, refine it, and find use cases for it (i.e., gas, plastics, chemicals).

Similarly, over the past decade, fortunes have been made by those savvy enough to do the same with Data. Today, the list of Fortune 500 companies continues to be disrupted by these businesses – Google, Amazon, Facebook, etc. So it is understandable why many continue to use the analogy.

Where the Phrase breaks down – Availability and Reusability

Availability

Oil is not available to just anyone. Companies with deep pockets have to scour the earth for it, and if you happen to live in a place that dinosaurs tended to frequent – well, then you are in luck. But if dinosaurs would not be caught dead in your neck of the woods (see what I did there), well, sorry no fortune for you.

Data does not have the same challenges. Data is everywhere and available to anyone that has the forethought and means to capture it. Individuals, Communities, and Organizations of all sizes have the potential to begin acquiring and leverage this valuable resource. Needless to say, collecting it is not always easy, and refining it does take a unique set of skills. However, Data is available across all geographies like Oil could never be.

Reusability

The other challenge with Oil is that it is a non-renewable resource. You use it once, then “poof” it’s gone. Sure, you could look for ways to increase the efficiency of its use, but it cannot be reused.

Data is not only reusable; the value that you can extract grows the more you use it!

The same datasets can be used across various functions, analyses, and predictive models. Combine one dataset with another, and you now have new insights that could not be leveraged before –> 1 + 1 = 3. With the proliferation of Artificial Intelligence, your ability to reuse the Data is imperative to identify patterns and learn from historical events.

Conclusion

Overall, I understand why people continue to use the phrase “Data is the new Oil.” But because of its Availability and Reusability, Data can be much more lucrative to many more organizations, communities, and people than Oil alone could ever be.

Unfortunately, I do not see a future where Houston will rename their NFL football team the Houston “Data.” Perhaps the Houston “QuantJocks”?