The Ultimate Guide to Data Analytics: Techniques and Tools

What's Data Analytics?

It is defined as the collection, transformation, and organization of data to;

Draw conclusions

Make predictions

Drive informed decision-making.

It converts raw data into actionable insights, being a sub-category of data analytics, it deals specifically with extracting meaning from data.

**Data Analytics **incorporates other disciplines as a whole including but not limited to; data science, data engineering, machine learning, etc.(which is beyond the scope of this article).

Data is a collection of facts.

Importance of Data Analytics

It helps in decision-making by providing insights that inform better decisions by identifying trends and patterns in data, thus making business decisions based on facts.
Understanding market trends, customer preferences, and industry dynamics enables them to gain a competitive advantage.
By analyzing data, businesses can identify inefficiencies and areas for improvement, thus helping optimize operations, reducing costs, and enhancing productivity.
It helps understand customer behavior and preferences, improving customer satisfaction and driving loyalty.
By analyzing historical data, organizations can identify potential risks and implement strategies to mitigate them thereby enhancing stability and security.
It fosters innovation by uncovering new opportunities, and market trends thus aiding in developing new products, services, and business models.
It supports evidence-based planning and strategy formulation, hence ensuring business strategies are grounded in reality.
Enhances customer experience by analyzing customer feedback and interactions.

Types of Data Analytics

There are 4 types of data analytics, which help organizations make data-driven decisions based on factual data and not biases or intuitions.

_Descriptive Analytics- _It tells us what happened. Focuses on summarizing and interpreting historical data to understand what has happened in the past.

_Techniques; _data aggregation, data mining, and visualization in the form of reports, dashboards, and summary statistics.

Diagnostic Analytics- Tells us why something happened. It involves deeper analysis to identify the causes of trends and anomalies discovered during descriptive analytics.

Techniques;_ drill down, data discovery, data mining, and correlation.
_
It is useful in problem-solving and identifying root causes.

Predictive Analytics- Tells us what will likely happen in the future by using statistical models and machine learning algorithms to analyze historical data and make future predictions.

Techniques; _regression analysis, time series analysis, and machine learning algorithms like decision trees and neural networks.
_
**Prescriptive Analytics- **Tells us how to act. It goes beyond the prediction to recommend actions that can influence desired outcomes. It utilizes optimization and simulation algorithms to suggest the best causes of action based on predictive analytics insights.

Techniques; decision analysis, optimization models, and simulation modeling.

This type of analysis helps organizations, make data-driven decisions by providing actionable recommendations and insights on how to achieve set goals.

The Data Analytics Process/ Phases

For a data analyst to analyze data to make informed data-driven decisions, one must go through these phases, not in any particular order, because it varies as per industry specifics, while some phases are morphed together as per the goal of the data at hand.

Ask- Understanding and knowing what is the problem first;

**_-Define the problem to be solved.

-Understand stakeholder's expectations.

-Focus on the problem at hand.

-Embrace collaboration with stakeholders and the line of communication should be open._**

Questions to ponder during the Ask Phase

_-What are my stakeholders saying their problems are?

-After understanding the problem, how can I help in solving it?_

Prepare- Here, you decide on the data collection techniques and tools you are going to use to resolve the problem at hand;

_-What do I need to figure out how to solve this problem?

-What research do I need to do/ gather?_

Process- After gathering data, perhaps you need to clean up the data to get rid of any inconsistencies whatsoever, for example, use spreadsheet and SQL functions to find duplicate data and also check for possible bias;

_-What data inconsistencies might get in my way of getting the best possible answer to the problem I am trying to solve?

-How to accurately clean data to get rid of all inconsistencies?
_
**Analyze- **You'll want to think analytically about your data. At this phase, you might sort and format your data to make it easier to;

**_-Perform calculations

-Combine data from multiple sources.

-Create tables with my results
_**
Questions to ponder;

_-What story is my data telling me?

-How will my data help solve this problem?_

**Share- **This is where you use visualizations in the form of dashboards, charts, and graphs to tell your data story. This will help your organization to;

_**-Make better and informed decisions.

-Lead to stronger outcomes.**_

*Questions to ponder;
*
_-How can I make what I present to the stakeholders engaging and easy to understand?

-What would help me understand this if I were the listener?
_
Act- As the name suggests, this is the action stage. It involves providing recommendations to stakeholders on the findings to make data-driven decisions;

-How can I use the feedback I received during the share phase to actively meet the stakeholders' needs and expectations?

Data Analytics Skills and Tools

Skills

**Statistical Analysis **eg mean, mode, median, and standard deviation to validate hypothesis and data insights.
Data cleaning and preparation- this involves dealing with data inconsistencies to prepare quality data for analysis.
**Programming **for eg python and R which are essential for data manipulation, analysis, and visualization. However, python and R are used more extensively in the data science field.
Data visualization- This is what is used to showcase your final data story after findings, for eg using tools such as Tableau, Power BI, and programming libraries such as matplotlib and seaborn.
Machine Learning- Knowledge of machine learning algorithms and their applications including supervised and unsupervised learning techniques, using tools like scikit-learn to validate models.
**Critical thinking and problem-solving- **Ability to formulate hypotheses and validate them using analysis. Problem-solving skills to tackle complex data-related skills.
**Domain Knowledge- **Understand the specific industry or domain to contextualize data analysis and generate relevant insights. Being aware of industry-specific metrics, KPIs, and business processes. -You can drive datasets from sites such as Kaggle.

Tools and Technologies for Data Analytics

Data analytics tools

_Spreadsheets eg Excel and Google Sheets for basic data analysis, cleaning, and visualization.

Statistical software eg R for advanced statistical analysis and data manipulation._

*Data Visualization tools
*
_Tableau, Power BI

Python libraries eg matplotlib, seaborn._

Database Management Systems for managing and querying structured data eg SQL, PostgreSQL, Oracle, MSSQL. NoSQL DBs for holding unstructured or semi-structured data.

Machine Learning and AI tools
For example Scikit-Learn for data mining and analysis.

Data Analytics Career Paths

-Other career paths include;

**_

Machine Learning Engineer (Senior-Level).
Data Architect (Senior-Level).
Chief Data Officer (Executive-Level). etc. ** NB: As I had mentioned earlier at the beginning of this article, these roles are not set in stone to be as it is, the roles vary as per the industry metrics for example one can be hired to be a **_financial analyst** in a bank, etc.