Difference Between Data Analytics and Machine Learning and Which Should Your Business Use?
With the fame of machine learning, many companies are now coming up of a demand for machine learning applications for their businesses. In most of such cases, the answer remains no. But why?
One of the benefits of the cloud is that it allows your business to get almost infinite storage, and processing power to get critical insights from the data your devices or sensors are collecting. Both big data analytics services and machine learning solutions can be powerful weapons in achieving this. But there is still a confusion if they mean and when is the best time to use one of two.
At some high point, machine learning takes a huge amount of data and generates some useful insights that can help the companies. This could mean improving processes, creating a better experience, cutting costs or even opening new business models.
Yet, many organizations can get several benefits from traditional data analytics as well, without using more complicated machine learning applications.
Traditional ways of data analysis are great at explaining us the data. We can easily generate reports, or models of what happened in the past or what is happening today and can draw useful insights to apply to our organizations.
Data analytics can help us quantify and track our goals, enable smarter decision making, and then providing the means for measuring success over time.
So, why we need machine learning?
Today's data models are typical traditional data analytics which is often static and of the limited use in mentioning fast-changing and unstructured data. When it comes to IoT, it is mainly important to identify relations between a few sensor inputs and external factors which are rapidly producing millions of data points.
While traditional data analysis needs a model built on past data and expert opinions to establish a relation between the variables, machine learning starts with the output variables e.g. saving energy and then automatically searches for predictor variables and their interactions with each other.
To conclude, machine learning is important when we know what we want but we don't know the important input variables to make that decision. So, we give the machine learning code the goals and then it leans from the data which points are important in achieving that goal.
A good example of machine learning is Google's application. Data centers of Google needed to remain cool, so they require a good amount of energy for their cooling systems to function appropriately. So, Google decided to increase its efficiency with big data analytics consulting and machine learning. For this, with 120 variables impacting the cooling system e.g. fans, speeds, pumps, windows etc. they built a model with a classic approach where they used analytics and machine learning and cut the consumption by almost 15%. This will save them hundreds of millions of dollars in the coming years.
Moreover, machine learning is also important for accurately predicting future events. Where the data models are built using traditional data analytics which is static, machine learning codes are constantly improving over time as more data is getting in and assimilated. This only means that the machine learning codes can predict the future, see what happens and compare them with the predictions, then just adjust to becoming more accurate and precise.
Well, the answer to the question is clear, you should try machine learning for your business to increase efficiency, save on costs and time.
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