Learning About Data Analytics

Data Analytics is used for informed decision making, personalized advertisement, predict market trends, and assess potential risks.

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What is Data Analytics?

Data analytics is the process of analyzing raw data to extract information which you can use to make informed and smart decisions about your company and it’s future.

Data analytics can also be said to the process of your company using data collected from the past about a product and how it was received to plan what your next product is going to be and how you are going to market it.

Using data analytics to analyze the interest of certain customers and what they need or want can help your company create targeted campaigns or ads to push them to buy your product or service.

Why is Data Analytics important?

The whole core of what data analytics is all about is the gathering of any type of data and analyzing it to make sense of it and extract information. Data analytics can also be used to make educated predictions and to simulate the feasible outcomes of decisions.

In business, data analytics is used by companies to determine their direction of growth, how a new product is being perceived by the customers, whether or not they need to change their current tactics. As today’s market is always changing, data analytics is used to keep track of current and new trends so as to adapt to their audience.

Companies also use data analytics in other to determine cost-effective methods and find more efficient ways of doing business that takes up less resources.

In finance, they use data analytics to predict market trends and assess potential risk. And it is also used to keep track of transactions so as to detect fraud. It is also used to determine if an individual or a company is qualified to receive loan.

Scientist also use data analytics in weather forecast.

Data analytics is also used in many other places just like how it is used in our everyday lives like our fitness trackers. Our twitter feed is also the result of using data analytics to analyze our likes and retweets and finding posts like them.

Methods of Data Analytics

There are two methods of data analytics. They are:

1. Qualitative Data Analysis: this is the analysis of data that is non-numerical (not numbers), the data can be collected by conducting interviews, conducting focus groups, surveys, and other similar methods. An example of qualitative data analysis is the Google search engine optimization, which is done based on ranking factors. The ranking factors are the qualitative data.

Methods of Qualitative Data Analysis

• Content Analysis: analyzes the presence of certain words or ideas and the measure of their recurrence.
• Narrative Analysis: This involves analyzing and interpreting peoples stories to know more about or to understand something in the the story that was told.
• Thematic Analysis: Thematic analysis involves analyzing and interpreting patterns of data to group them or to find a pattern.

2. Quantitative Data Analysis: This is the analysis of data that is numerical or number based or data that can easily be converted into numbers. The data can be used for statistical analysis or mathematical calculations. It is collected using surveys, polls, questionnaires, and other similar methods.

Types of Data Analytics

1. Descriptive Analytics: In this type of analytics, data collected over a period of time is gathered and analyzed. This can be done to measure your increase in ROI, to learn learn how your customers have gradually accepted your product, etc.
2. Diagnostic Analytics: This type of analytics takes the results of the descriptive analytics to find out why the results happened.
3. Predictive Analytics: Like the name suggests, predictive analytics answer the question, “What is Going To Happen?”.
4. Prescriptive Analytics: After the prediction of the predictive analytics, prescriptive analytics are done as the analytics answer the question “What Should be Done?”. All the past data is gathered and analyzed so that you know what your company should do in the time ahead.

Data Analytics Techniques

• Regression Analysis: This is a method used to determine the relationship between two or more variables. It can be used to predict how changing one variable may affect the other. It is also primarily used to analyze data which consist of numbers.
• Factor Analysis: This method takes the data that is available in many variables and then shrinks the data.
• Cohort Analysis: In this method the data is broken in to small groups with each group sharing a common trait. The groups are called cohorts. This allows your data analytics to be able to dive into specific groups.

Data Analytic Tools

• Microsoft Excel: This is a spreadsheet software that is able to analyze, compute, store, or filter your data.
• Tableu: This is a powerful tool that is used for data analytics and visualization. it can analyze vast amount of data.
• Microsoft Power BI: This is an analytics and visualization tool that is able to turn multiple pieces of data in to something understandable.
• Rapid Miner: This is a powerful tool for data mining

Advantages of Data Analytics

1. Informed and Better Decision Making: By using data analytics, your company is able to make better decision. Because all your decision will be backed by analyzed data, thus cutting down your company's losses.
2. Increased Efficiency: Data analytics can help your company increase efficiency and cut down production and distribution time, by showing where in the production and distribution chain is time wasted and how to fix it.
3. Increased Security: By using data analytics to analyze your company’s security system, you are able to find and fix the faults in the system before they even become an issue.
4. Product Personalization: Data analytics can help your company by motoring social media platforms to gather information about a potential customer, so that you can personalize the product you advertise to him.
5. Accurate Analysis and Measurement of ROI: The data analytics can help to monitor, analyze, compute, and measure your companies ROI.

Disadvantages of Data Analytics

1. Breach of Privacy: The privacy of the customers is not assured as when data analytics on them is carried out, it gathers every single piece of information on them it can find out. Which means that their information is visible to the companies.
2. Cost: The cost of data analytics can be high because some of the analytics tools have to be paid for and some of them need a professional in the field before they can be used.
3. Lack of Quality Data: Data analytics needs a lot of data for the analytics to able to be carried out. While your company might have a lot of data, it might not be what is needed for a successful analytics to be carried out.

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