Machine Learning vs Data Analytics: A Brief Comparison

Data is also called the new ‘oil’ of this century. Meaning data is as precious for the functioning of a business in the 21st century as crude oil was at the start of the 20th. Much as oil has become an essential part of human civilization, data is also proving to become one. Activities related to its collection, manipulation, and presentation are gaining more and more prominence. 

Since businesses are increasingly being more and more dependent on data, new techniques to handle the data above have evolved. Data Science, Data Analytics, Machine Learning, Data Engineering and others are some fields of studies. These train an individual in specific data handling techniques for a specific role in the data handling process. 

Machine Learning and Data Analytics are two such related but different fields, and before exploring the question – machine learning vs data analytics, a basic understanding of the terms is necessary.

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Data Analytics – What is it?

Inferring by its name, one would think that data analytics must be related to the act of ‘analyzing’ data, and he would be correct. Data Analytics is the ‘analyzing’ of data, but analyzing is a very broad term, so let’s briefly get an overview of what this ‘analyzing’ involves and how it works.

• Collection of data – A set of figures and associated parameters are collected. Data analytics does not cover the collection of actual data but rather complies with the collected data from various sources. For example, four companies have conducted a similar survey in 4 different regions; data analytics compile all four similar datasets into one file in the database for processing.
• Processing of data – Data processing is how data related to particular specified parameters are extracted from the raw database file. This extraction is performed by utilizing certain functions embedded in data processing software or by running a script (program) on the data entries. E.g., if one wants to find the age of the people who participated in the four surveys, he would process the data solely on the parameters of age.
• Data cleaning – The next step is to clear the duplication of entries, errors or incomplete data from the ‘data pool’ related to those parameters. To achieve these certain limits, benchmarks and formats are present in the system. For example, the applicant’s previous survey age limit should be positive and below 120;  the algorithm would eliminate any negative entry or entry exceeding 120.
• Application Statistical and modelling techniques – The calculation of  KSI (The key statistical Indicators) of the data, and modelling of certain graphs, charts, tables etc., visual communicators and others. E.g. For the above survey the respondents average age in the survey for the region, 1,2,3,4 can be depicted in the form of a chart.

Moving on to the other half of the question, machine learning vs data analytics.

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Machine learning – What is it?

Again, as evident from the name, it involves how the machine learns by itself. The problem is that machines are not as sentient as humans; thus, machine learning involves the algorithms or codes that would amend themselves according to the feedback requested and input/data received.

One such example of machine learning in everyday use is E-mail clients, which classify some of the received e-mails as ‘spams’; here, the input is the content of the e-mail. For feedback, the algorithm may scan the document for certain parameters such as ‘sale’, ‘offer’, etc. and combine it with the information whether the sender is in the receiver’s contact list. Other factors such as the mail being cc (carbon copy) or bcc to many people would decide the feedback as being ‘spam’ or ‘not spam. Over time, the algorithm may include more words to scan for in its database by analyzing the receiver’s e-mails manually marked as ‘being spam’ and moving the e-mails from frequent ‘spammers’ directly into the ‘trash bin’.

Several models are available for implementing machine learning, with new models experimented on and released each year. Part of it has to do with rapid advancements in the hardware types of equipment and digitization processes. Some of the popular models are –

• Artificial Neural Networks – A collection of various Machine Learning programs interacting with each other.
• Decision tree model – A logical progression of tasks. With several branches of outcomes for several different inputs or logical conditions.
• Regression analysis – Developing a relationship between input and output and tailoring the output to match their averages.

This ability of a program/algorithm to apply its learned knowledge is very beneficial to the industry. Some of its applications are automated chat boxes on websites, automating the user’s routine tasks, prediction based on data, checking receipts, theorem proving, optimization of the process based on feedback.

Now that both the terms are clear, comparing them.

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Machine Learning vs Data Analytics 

A quick comparison between machine learning vs data analytics is done on the following parameters – 

• Modification in the algorithm/ program

For any modification in the algorithm of Data Analytics, the changes have to be entered manually. Whereas for machine learning, the changes are made by the algorithm without any external intervention.

• Handling raw data 

One thing that Data analytics does phenomenally better is data handling. All sorts of data handling are possible – It can prune data by removing faulty, repeated, empty data sets and arranged in a neat table, graphs and whatnot. Moreover –  Data can be filtered by a certain parameter or variable. It can make certain variables correlated with each other. Statistical functions such as – moving averages, skewness, medians, modes, etc., can also be obtained from the data.

On the other hand, Machine learning cannot handle raw data. It makes sense, because Data analytics has been around far longer than Machine Learning, so instead of designing Data Analytics algorithms into machine learning, one can separately use a data analytics tool. However, several softwares provide the functionalities of both into one package.

• Feedback

There is no such concept of ‘feedback’ in Data Analysis; it more or less operates on the ‘input-output basis. One enters the input (data), selects a suitable modifier (function) and gets an appropriate output (result). There is no modification in the modifier (function) based on the result.

On the other hand, Machine learning follows the same routine. After generating the output, the algorithm can make changes by analyzing the relationship between the input and the user’s interactions.

• Predicting

Data Analytics cannot make predictions based on a data set. It may model the data establishing various correlations between variables and represent them but cannot estimate the next set of variables based on the trends in a number of the previous set of variables. 

Machine learning, on the other hand, can do it effortlessly. All it needs is a large enough collection of previous datasets for analysis. Machine Learning finds application in data analytics for this specific purpose only.

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• Applications

Data analytics has a highly specific purpose – to collect, clean, process and model the data. 

As such, it has comparatively limited applications. Some applications include providing information to help in the management’s decision-making, Serving as a proof of opinion, delivering facts to the public, and compiling the financial statements and others.

On the other hand, a machine’s ability to adapt without any external help has tremendous applicability. Machine learning is applicable in any field where there is a need for ‘customization’ of the process according to an individual or the elimination of manual processes favouring an automated one. One such example of its usage is in data analytics itself.

That being said, Machine learning is a comparatively new field of study. As such, there is a lot more to be done in terms of innovation, applicability and marketability of the machine learning techniques. SO, for a common task, the industry is biased towards data analytics than machine learning.

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• Examples of software suits 

Sometimes, the software contains both data analytics tools and machine learning tools to make data manipulation easier. However, due to the large scope of Machine learning, several suites are available for several purposes.

For Data analytics, a host of software suites are available, including Microsoft Excel, Apache Open Office Spreadsheets, Julia, ROOT, PAW, Orange, KNIME, MATLAB ELKI, Google Sheets and more.

There are hosts of software suites for machine learning, the most common of them are – Amazon Machine Learning Kit, Azure Machine Learning, Google Prediction API, MATLAB, RCASE, IBM Watson Studio and KNIME, to name a few.

After a brief study of the answer to the question machine learning vs data analytics, written above, one can easily observe that machine learning is a much more potent tool and flexible tool with diverse applications. However, one can also conclude that they both have a specific role in the business industry. There are some functions, such as processing raw data, that only data analytics can perform and then there is a certain function such as Prediction that only machine learning can perform.

So, each one has its importance and applications, and although sometimes one may work better than the other for a specific task, they both are much needed by the industries.

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