Is your company ready to implement Machine Learning?
Machine learning is here and it is here to stay. Machine learning is significantly influencing all the Industries especially those that are in the IT segment. Whether your company is in IT or non-IT machine learning is sure to affect you in some way or the other.
Advantages of adopting machine Learning for your Company:
Machine Learning essentially include algorithms that are fed data based on which they can take decisions on their own, in a way ‘Learning’ on their own. Machines can do this learning faster and efficiently than us humans, this improves the efficiency of any work that is required to be done by the machines.
Machine learning systems help in understanding the exact requirements of the customers and thus increase the customer satisfaction, this in turn improves your customer base.
Customer support is significantly improved due to machine learning with the advent of chatbots that help customers with their problems.
Machine learning can help in forecasting of the future conditions in your business and thus help you tackle the situation that could have occurred unplanned.
Machine learning systems can scan and shortlist resumes based on the skills required and thus help in better HR functioning.
We shall have a look at a few points to consider when deciding
‘Is your company ready to implement machine learning’:
Do you Have sufficient data?
Data is the most important input for machine learning and thus it is necessary that you have data and that too a significant amount of it so that you have the best utilisation of the machine learning systems.
How is your data organised?
Now that you have confirmed that you have significant amount of data. It is to be noted that what form is your data in? Is the data structured and usable? Is it clean and has been standardised into a consistent set?
Where is the data stored?
With significant increase in the quantity of data it is to be taken note of where the data is stored. If your data is spread across multiple locations, how efficient access can take place? Do you have proper control over your data?
What are the costs involved?
Increase in data leads to the requirement of much larger storage. This brings along with it the burden of increased hardware and computational costs. How often does the analysis of data need to occur? Who does the data analysis.? This work usually requires a skilled data scientist who can coordinate and control the working of the systems. It is also to be taken into consideration that a lot of learning algorithms and code are available publicly for anybody to use. All the trade-offs between the costs and the benefits are to be analysed to make a decision on whether your company is to implement machine learning
If you have thought out and analysed the company situation here are a few steps that can help in making your company machine learning ready:
Systematize your business processes: Every work requires taking some decisions that are to be taken frequently and perpetually. Data can be collected about this decisions and choices and this can be used by machine learning to do the decision making effectively
Concentrate on the simple and well-defined problems: Machine earning can be useful when the problem statement is well defined. This increases the decisions effectiveness.
In case of complex problems: In case of complex problems, machine learning can be used to break up the problem and find the best solutions for each sub components.
In a business it is very important to update itself to the latest arrivals in technology to keep up in the market. It may not take long to see Machine Learning become a part of every business out there, it is a good idea to be ready to implement machine learning in your business. The sooner the better.
About the Author
DataFactZ is a professional services company that provides consulting and implementation expertise to solve the complex data issues facing many organizations in the modern business environment. As a highly specialized system and data integration company, we are uniquely focused on solving complex data issues in the data warehousing and business intelligence markets.