Machine learning empowers organizations to make better and more accurate data driven decisions.
It further allows them to solve problems that traditional analytics approaches could not solve. However, machine learning is not the be all and end all of analytics.
It encounters many of the same challenges as different analytics methods. Let’s discuss some common mistakes that need to be avoided by organizations to successfully incorporate machine learning in their analytics strategy.
- Machine Learning Mistake 1
- Machine Learning Mistake 2
- Machine Learning Mistake 3
- Machine Learning Mistake 4
Machine Learning Mistake 1
Inadequate Infrastructure For Machine Learning
Many organizations face a big challenge in managing the different aspects of infrastructure surrounding machine learning.
Regularly used database management systems can sometimes fail under the variety and load of data that organizations look to collect and analyze today.
How to fix it?
Keeping the following things in check can ensure that your infrastructure is built to handle machine learning.
A suitable organization-wide storage solution that is capable of meeting the data requirements and is capable of maturing with the technology advances should be designed. Data structure, usage and digital footprint should be considered while designing.
A scalable, secure and powerful computing infrastructure allows data scientists to power through data preparation techniques and various models to reach the most ideal solution in the fastest time possible.
- When to use SSDs (soli state hard drives) – When tasks are I/O intensive like data preparation or software analytics that is disk enabled.
- When to use GPUs (graphical processing units) – When tasks are computationally intensive which can be run in parallel like matrix algebra.
When it comes to distributed computing tasks where data is split across various connected computers, this helps reduce execution times.
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You need to ensure that you use a distributed environment that is ideally suited to machine learning.
Computing and storage resource consumption can prove to be very dynamic when it comes to machine learning.
This requires high amounts in few intervals and low amounts in others. Elasticity in infrastructure caters for optimal use of computation resources and financial expenses that are limited.
Machine Learning Mistake 2
Data Quality Problems
The improvement of algorithms is often seen as the glamorous side of ML; however, the truth is that maximum time is spent preparing data and dealing with quality problems.
Quality of data is key to obtaining accurate results from your models. A few common data quality issues include:
- Data which contains a huge amount of misleading or conflicting information – Noisy data.
- When data consists of inconsistent values, categorical and character features with multiple levels, missing values, it is known as – Dirty data.
- Data consisting of very few real values, it consists mostly of zeroes and missing values – Sparse data.
- Biased or incomplete data – Inadequate data.
As seen in the above examples, a lot can go wrong in data collection and storage processes. However, steps can be taken to mitigate these issues:
How to solve it?
Data governance and security. Data security issues should be addressed right at the beginning of a machine learning exercise, particularly when there is a support requirement from other departments.
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Data governance plans should consider the usage, storage and reusability of algorithms.
Data preparation and integration. After data collection and cleaning has been done, the data should be transformed into a logical format for consumption by machine learning algorithms.
Exploration of data. A professional and productive exercise should begin with a specific business need and should result in quantifiable results.
The ability to query, summarize and visualize data prior to and even after ML models are trained. They should also be able to build algorithms with the new addition of new data.
Machine Learning Mistake 3
Implementing Machine Learning Without Data Scientists
A glaring challenge that is faced is the shortage of deep analytics talent. With this being the case, the requirement for employees who can consume and manage analytical content becomes even greater.
Recruiting and retaining these technical experts who are in-demand has become the point of focus for all organizations.
The most skilled analytics professionals that need a unique combination of mathematics, domain expertise and computer science are data scientists.
The price tags that an experienced data scientist commands are extremely high, and they require engaging projects to keep them going.
How to solve it?
Build and develop a centre of excellence for analytics. Centres of excellence will work as an in-house analytics consultancy.
The centre of excellence will allow for consolidation of all analytics talent in one place and allows for the use of analytical skills across the organization in an efficient manner.
Relationships building with universities will allow organizations to tap into a reserve of fresh talent.
Creating an internship program or recruitment program with universities is one way to go about recruiting fresh talent.
Universities today also have programs that pair students with organizations to solve business problems.
Talent development from within the organization. Invest in data science training for students who have a natural aptitude for problem solving and mathematics.
Analytics should be made a lot more approachable. If you have user friendly data visualization tools and if your data is easy to explore, problems can be solved by people (not data scientists) in the business with just the data.
Machine Learning Mistake 4
Implementation Without Strategy
Data-driven organizations today have developed successful analytics platforms over the years.
A challenge is deciding when to incorporate newer and complex modelling methods into your analytics strategy. The move to machine learning may not even be required until the business needs and IT evolve.
How to solve it?
Machine learning should be positioned as an extension to the analytical processes in place.
Traditional regression may be used by banks for their regulated dealings. Using ML to predict when a regression model is gong stale and when it needs to be refreshed is very useful.
Below are a few effective techniques for businesses that want to give modern machine learning a try:
When it comes to anomaly detection, there is no particular approach to solve a real business problem.
However, several machine learning algorithms are known to boost the detection of outliers, fraud and anomalies.
Many markets have massively different segments. Take the case of healthcare where every person in a treatment group may/will require specialized attention.
In such cases, the application of a predictive model to the group or to each patient will result in more efficient and targeted care.
This is known as the Model Factory Approach and using this to build models automatically will definitely see a gain in efficiency and accuracy.
Rather than using one particular model, the combination of several models will result in better predictions.
These are known as Ensemble models. Ensemble modelling algorithms such as super learners, random forests etc have shown great promise.
However, using custom combinations of pre-existing models can lead to better and improved results.
To effectively use machine learning in business, a clear understanding of ML in the broader scheme of things when it comes to the analytics environment is required.
Having familiarity with the applications of machine learning that are proven is required.
Another key factor is to analyze the challenges that may be faced while using machine learning. Keeping an eye on the leaders in the field of machine learning will help greatly to avoid pitfalls.