Over the past few years machine learning has continued to prove its worth to enterprise.
Over 70% of CIOs are pushing digital transformation efforts, with the majority of those focusing specifically on machine learning.
Almost the same number (69%) believe decisions powered by data are more accurate and reliable than those made by humans.
Still, some companies struggle to get value from their machine learning processes. They have trouble finding talent, and their projects are slow to reach ROI.
The problem isn’t with machine learning - it’s with the company’s approach.
The Pitfalls of Reinventing the Wheel
Sometimes companies get so caught up in new technology that they forget what business they’re in.
They don’t need to build complex data science systems or experiment with new types of algorithms or push machine learning as a science forward.
What they need is to extract actionable insights from their data. Companies should be aware of and maintain their data infrastructure, but that isn’t their primary focus. Their focus is running their core business.
However, the majority of companies approach machine learning with a misguided idea of what makes it work.
They assume their specific business needs mean they have to start from scratch, to build a machine learning solution from the ground up.
These companies get bogged down by mechanics without enough thought for how the output will be put to use.
As a result, they wind up building the wrong kind of infrastructure for their machine learning project. One common place this flawed infrastructure shows is in the type of talent chosen.
Companies go straight for high level data engineers who build machine learning software.
That’s a large - and often costly - mistake. In an enterprise context, data engineers aren’t as useful as applied machine learning experts with experience in turning data into decisions.
Imagine a business traveler looking for the fastest route to a meeting in a new town. Would they have better luck getting directions from a civil engineer or a taxi driver?
The civil engineer knows how to build functional roads, but they don’t necessarily know a specific city’s streets or layout.
The taxi driver knows how to use the streets to get results: arriving at the meeting in time despite traffic, construction, and seasonal issues.
This might sound like a silly example, but it’s exactly what businesses do when setting up machine learning programs.
They focus too much on the “how” (building data systems) and not enough on the why (what business goals the system needs to fulfill).
In other words, they think they need civil engineers when what they really need is a good seasoned taxi driver.
The result is wasted resources and higher program failure rates. A big enough failure can also risk future projects when leaders blame the technology rather than the flawed execution.
Why Companies Get Stuck in A Rut
There’s a very good reason why otherwise smart people make mistakes with machine learning: it’s complicated.
Artificial intelligence and machine learning are incredibly complex topics with thousands of sub disciplines and applications.
There is no “catch all” job description for someone who can do all kinds of machine learning.
Those few people with experience in several phases of the data-to-decisions pipeline are high-level, in-demand experts who very probably won’t take an average enterprise position.
On top of this, executives aren’t always sure what type of talent they need because they aren’t clear on what their data science needs are.
They hire data engineers, give them vague directions to “increase efficiency”, then get frustrated when they don’t see results.
Even the best machine learning system can’t create value without working towards a goal.
Getting More by Doing Less
Laying the groundwork for successful machine learning is a case of “less is more”.
Don’t get caught up in high-level, experimental machine learning which seeks to advance the science unless there’s a good business reason (and for enterprise purposes, there almost never is).
A PhD in artificial intelligence and experimental mathematics is not necessary to run a productive enterprise machine learning program.
Instead, find the right experts: statisticians, data intelligence experts, applied machine learning engineers, and software developers with experience in machine learning software.
The truth is, most businesses won’t need to build a machine learning program from scratch. There are many tried and tested solutions available that can be customized to fit a specific company’s needs.
Better yet, they’ve been tested by others at their expense. These tools remove the need for those high-level machine learning construction experts.
Practical talent choices and existing machine learning tools can make the difference between project success and failure.
Using them helps companies get to data quality assurance and usable results faster, meaning the project reaches ROI sooner. The project is more likely to succeed, and future projects will have an easier time winning support within the company.
In short, don’t hire the civil engineer to build roads when there are several existing routes to get where the company is going. The taxi driver is usually the better choice for the job.
Staying on Target
Most importantly, remember the core business and focus on tools that support that instead of distracting from it.
Always build machine learning systems around business objectives. Have specific issues or opportunities to address with each tool, and be sure everyone on the team understands the goal.
When machine learning is treated as a tool rather than a goal, companies are much more likely to see value from their investment.
There’s a wealth of machine learning tools out there to use- but sometimes it’s hard to manage incoming data from different software.
Concepta can help design a solution to put your data in one place.