Understanding the ABC of data analytics

In the field of analysis, many analysts are stocked of what to do based on the knowledge gap of the dataset made available to solve the problem. I will be discussing some of the best practices of a day-to-day analysis problem in this article from a start to a finishing process.

There is a major lifecycle that is important for data analysis. I call them the ABC of data analysis.

1. Understanding the Business Problem.
2. Knowledge about the dataset.
3. Exploring the dataset.

What data is in simple words

According to Wikipedia, Data are individual facts, statistics, or items of information, often numeric. In a more technical sense, data are a set of values of qualitative or quantitative variables about one or more persons or objects, while a datum is a single value of a single variable.

In simple words, data is the endpoint that describes a situation, occurrence, event to inform stakeholders (users, employers, client) about what to know, what to do, then most importantly how to DO IT (figures and facts).

Let’s get to understand data with a case study, as a business owner who intends to open a new outlet which is already available in Lagos, Nigeria in Akure, Nigeria, you will need data to become (information) to know where to locate the outlet, the possibilities of earning income and making a profit, you need to use the relevant information, insights to get facts (why, where, how questions).

The place of data has shown that things can get better based on the level of information, observation available.

The place of analyzing data

The need for analyzing for businesses, organizations, or individuals is basically to SOLVE an underlying question or problem. Let’s look at some of the underlying questions clients, organizations, businesses could need responses to.

1. Who is the best staff of the year?
2. Which of our branches is making the highest sales with profit?
3. Which of the staff(s) should be promoted?

The data processed (analyzed) will give a rider to decision-making, also look for meaningful information that can serve as evidence (fact).

The simple things to understand about analysis

Data analysis is important in business to understand problems facing an organization, and to explore data in meaningful ways. Data in itself is merely facts and figures.

Data analysis organizes, interprets, structures, and presents the data into useful information that provides context for the data.

2 keys for Data Analysis (Description and Prescription)

The main attention of this article is about describing and prescribing data, the ABC of data analysis is about knowing that the principal key to data analysis is to describe and recommend based on the relevant data.

At this level, I will be referencing the data analysis or analytics which is also seen as the business intelligence part life cycle earlier highlighted, it a moment that reveals underlying factors about a product, sales, and many others.

1. Understanding the Business Problem

Here, the first step as a data analyst, you need to know what the company stands for, intend to achieve, how they are having working (business environment and best practices)

Every organization knows what they want, knows the expected solution when they see one. be concerned about reading their minds.

The other part, there are companies who do not have a whole lot of information to share with you our detailed as expected, they require that you develop a working KPI or an idea which they can adopt. The place of domain knowledge (making research about a subject matter for knowledge gathering) is essential in the analysis process.

2. Understanding the dataset

Of course, the number one need for data analysis is FUEL, the fuel to work with is DATA. getting the dataset is not enough, understanding the data is the key, you need to work with variable description documents if provided or you generate one from the business profile document you have or have researched upon.

At the starting point, every feature in your dataset is worth checking through and understanding how they possibly could solve the analytical problem.

3. Explore the dataset

The technical know-how for analysis lies here, the analytical tools and framework will be expected.

Many programming languages such as R, Python are useful for analysis, I personally work with python and I encourage you to check out the possibilities of analyzing with python. There are python libraries that works the wonders, let look at some of them.

Numpy is used for creating arrays and performing some mathematical operations.

Pandas is one of the widely used libraries that will help generate a whole lot of insights. some of the activities of pandas are groupby, merging, concatenating, joining, understanding series and dataframe, working with data, input and output data.

Matplotlib and Seaborn are also python libraries toolkit for visualizing your insights for a clear representation of your findings. the use of lines, graphs and plots are useful for easy understanding for your employers or clients.

As a data analyst, it is important to know that there are keys that open the possibilities of your proficiency in the journey of data analysis. Let’s get deep into understanding the two keys.

Key 1:Description: The first task of data analysis is to find patterns, generate insights from the data, in the process of generating insights, there are some manipulations you are expected to do.

The data cleaning process helps you to identify the issues with your dataset, what to do during the processes.

From the data, you should answer questions both asked and unasked by your employer or clients, dig into the features, explore them.

Some final result could be,

1. There are 15 pieces of equipment in the fashion department that have lost value.
2. based on the data explore, more than 50% of the staff are due for promotion
3. 10% of the student failed because the lecturer tends to be unfriendly

Key 2: Prescription

The prescription or recommendation phase suggests the possibly what is next or a rider for companies on what next to do based on the important insight generated.

Some of the possible recommendations will be,

1. We recommend that the 15 pieces of equipment in the fashion department be sold at a lower cost each and purchase 2 new standard pieces of equipment.
2. The management should endeavor to promote a number of staff for more efficiency in their roles.
3. We encourage that a teacher orientation program is held to address teacher behavioral attitudes to students to enhance student performances.

Conclusion

In a short, we have discussed the identify ABC of data analysis (business, data, explore), the keys that make a data analytics a successful data analyst (description, prescription).

Data Analysis is completed when information is gotten by the management and recommendation are available to help the decision-making process.