Introduction to Data Analysis
Data analysis is defined as the technique that analyse the data to enhance the productivity and the business growth by involving process like cleansing, transforming, inspecting and modelling data to perform market analysis, to gather the hidden insight of the data, to improve business study and for the generation of the report based upon the available data using the data analysis tools such as Tableau, Power BI, R and Python, Apache Spark etc.
It refers to the technique to analyze data to enhance productivity and grow business. It is the process of inspecting, cleansing, transforming and modeling the data.
Why we Need Data Analysis?
We need Data Analysis basically for the reasons mentioned below:
- Gather hidden insights.
- To generate reports based on the available data.
- Perform market analysis.
- Improvement of business Strategy.
Who is a Data Analyst?
Data analyst is a person who collects data from various sources and the structure and models to find a pattern to generate the report. Various industries try to gather a diverse set of data to create a model out of it. For example, manufacturing sector companies record various parameters like queue status for manufacturing unit and how it can be synchronized with other units such as quality assurance, packaging, and storage unit to ensure minimum downtime. The idea here is to reduce the idle use of a resource which will boost productivity without impacting the cost. Just like the manufacturing industry other industries like the gaming industry keep track of the rewards for their user and food delivery companies can keep track of the eating habit of the people in certain demographic structures.
Basic Steps Required
Now we are going to discuss some basic steps of Data Analysis:
Step 1: The primary task here would be to profile the data. In the current structure, most of the traditional industry is not even aware of the data they already have as in earlier days there was no clear distinction between interaction data and transactional data. Therefore, the biggest challenge in the case of implementation of Machine Learning or AI implementation is finding out where the data lies and how the data lies. This involves data profiling with a huge amount of data and finding out properties like data correctness, data completeness, null percentage and above all relevance and categorization of the available data.
Step 2: Then we need to store those data using any unstructured data storage method. This is same as processing the unstructured data via big data infrastructure already in place. The storage infrastructure of the modern era is different from traditional RDBMS. Now the big data infrastructure can extract information from unstructured data like a Facebook comment or a message sent via email.
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Step 3: The next step would be to build a model after the categorization and grouping of data. Once a data model has been prepared then the system will start extracting information.
Step 4: Once the data starts flowing then various data like interaction data and transactional data can be correlated and processed to establish a pattern that will not only be able to create a report on historical data but also will be able to define a clear strategy for the future when fed into an AI engine.
Types of Data Analysis
The various types are explained below.
1. Descriptive Analysis
This kind of analysis tells the business what actually went right and what went wrong example coma when a restaurant gets to know that those users who ordered the pizza once, they kept reordering but there is no reorder for their risotto. it gives the restaurant hint that they should improve the recipe of their risotto and keep the focus on pizza to keep the business running.
2. Diagnostic Analysis
This tells you why something happened if you take an example of BlackBerry, the data shows as the iPhone market starts booming with their touch screen phones with no keypad, the sales of BlackBerry mobile phones declined and made this company lose its market share significantly. This is a real-life example of Diagnostic analysis.
3. Predictive Analysis
This kind of analytical strategy tells a business what is likely to happen. Another real-life example of this would be the case of Kodak. Where they were very late to realize that eventually the film photography will be extinct and the new future would be digital so their predictive analysis failed and others like Nikon, Canon, Sony captured the market. Kodak was so late to jump into the digital camera market, it was already over for them.
4. Prescriptive Analysis
This Analysis is to understand and describe the future course of action to grow or sustain the current business. generally, companies use machine learning techniques and algorithms to define the business rules going forward. An example of this could be of any telecom company understands that as the phones are getting better at computation therefore call will be less prioritized and the focus will increase on consumption of mobile data.
Popular Tools Used
Let us look into some widely used data analysis tools along with some tools which are market leaders in this segment:
- Tableau: It can create a data visualization, dashboard, and analysis report after connecting to various data sources. This tool works on unstructured data therefore compatible with Big Data.
- Power BI: Previously it was an extension to MS Excel later it became a separate tool. It is lightweight and gets updated frequently.
- R and Python: If you like custom coding and customization then R and Python is the option for you. R is better for statistical analysis while Python is having out of box data analysis libraries inbuilt.
- Apache Spark: Apache Spark is a fast, lightweight and large-scale data processor that executes data from big data clusters and can process a huge chunk of data rapidly.
Conclusion
We can say that the use of data properly can give a new set of insight to any business which will ensure effective utilization of the resource, a better understanding of the customer and market which eventually will result in business growth.
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