Data Analyst


Data Analytics

What is data analytics?

Most companies are collecting loads of data all the time but, in its raw form, this data doesn’t really mean anything. This is where data analytics comes in. Data analytics is the process of analyzing raw data in order to draw out meaningful, actionable insights, which are then used to inform and drive smart business decisions.

A data analyst will extract raw data, organize it, and then analyze it, transforming it from incomprehensible numbers into intelligible information. Having interpreted the data, the data analyst will then pass on their findings in the form of suggestions or recommendations about what the company’s next steps should be. A company can make a plan of action for increasing its profit through data analytics.

It’s all about finding patterns in a dataset which can tell you something useful and relevant about a particular area of the business how certain customer groups behave, for example, or how employees engage with a particular tool. By seeing the data a pattern followed a business can profit from it.

Data analytics helps you to make sense of the past and to predict future trends and behavior's; rather than basing your decisions and strategies on guesswork, you’re making informed choices based on what the data is telling you.

Data Analytics is a fast growing industry in the current scenario where every company, organization, online sites etc. are generating large amounts of data every second. Million of raw and meaningless is produced every second and need sorting and cleaning to become a meaningful and useful data which can be used for benefits and profits.

Difference between Data Analytics, Data scientist, Data Engineering and Business Analytics

*Data Engineering coverts raw data into usable data

*Data analytics uses this data to generate insights. To draw graphs and diagrams to depict these insights in a meaningful manner.

*Data Scientists use Data Analytics and Data Engineering to predict the future using data from the past

*Business Analysts uses these insights and predictions to drive decisions that benefit and grow their business.

Difference between Data Analytics and Data Scientist

 Data analysts typically work with structured data to solve tangible business problems using tools like SQL, R or Python programming languages, data visualization software, and statistical analysis. They clean and organize raw data.

Data scientists often deals by using more advanced data techniques to make predictions about the future. They might automate their own machine learning algorithms or design predictive modelling processes that can handle both structured and unstructured data. This role is generally considered a more advanced version of a data analyst.

Data analysis requires basic statistic, Excel, SQL and data visualization tools like power bi whereas Data Scientist requires tools like advance statistics, Hadoop, Spark, Machine Learning and data modelling.

Data Analytics in real world

Nowadays since large amount of data is generated in every field and walks of life the role of data analytics is becoming very crucial. There are lacks of data generated every second and it is very vital to use this raw data and make a meaningful dataset.

In the real world too there are many examples were we are now seeing data analytics come into play. Some of them are stated below.

1.Analytics in Tennis Has Been an Evolution, Not a Revolution. A new era of data analysis has given players deeper insights into their opponents’ games and a strategic advantage. Player can see the data and analysis it and make some strategies to tackle his/her opponent. They can analysis their opponents weakness and strengths also.

2. Aditya Birla Capital (ABC) will continue to focus on customer acquisition, data and analytics which will be key drivers of growth to gain customer wallets for the financial services. Aditya group is working on analysis of data to gain profits for its company.

3. IKEA one of the top furniture and home care company around the globe has always been known for providing the best experiences for its customers. The retail giant uses both qualitative and psychographic data to understand its customer’s behaviors on a deeper level and offer them the best experience. For instance, they observed that most of their clients go to the store with their kids which often makes it harder for them to shop. To solve this issue they implemented supervised play areas so that parents can shop without distractions. 

4. The amount of data generated in the banking sector is skyrocketing. This data is estimated to grow by 600-700 percent by the end of the next year. With big data analysis, banking officials can easily study, and analysis the data and detect any illegal activities being carried out like: 

·        Misuse of credit or debit cards 

·        Credit hazard treatment 

·        Transaction clarity 

·        Customer statistics alteration 

·        Money laundering 

·        Risk mitigation 

·      Cheque frauds

Next Post »

No comments:

Post a Comment

Please do not enter any spam link in the comment box