Discover data analysis

 Introduction :

You are on a journey as a data analyst. Consider all of the data that is created and available in an organization on a daily basis, ranging from transactional data in a standard database to telemetry data from services you use, to signals you receive from other places such as social media. The fundamental difficulty that businesses face today is understanding and utilizing their data to positively effect change inside the business, with data and information being the greatest strategic asset of a corporation. Businesses continue to struggle to make meaningful and effective use of their data, limiting their capacity to act.
Data analysis exists to aid organizations in identifying insights and unlocking hidden value in troves of data through narrative, eventually supporting businesses in overcoming these difficulties and pain points. 

Components of Data Analytics:

1. Descriptive Analytics: 

Based on historical data, descriptive analytics may assist answer questions about what happened. Descriptive analytics is a strategy for describing outcomes to stakeholders by summarizing huge datasets.

These tactics can assist measure the success or failure of major objectives by establishing key performance indicators (KPIs). Many businesses employ measures like return on investment (ROI), and specialized metrics are developed to evaluate performance in specific sectors.

Creating reports to offer a picture of an organization's sales and financial data is one example of descriptive analytics.

2. Diagnostic Analytics:

Diagnostic analytics can assist you figure out why things transpired the way they did. Diagnostic analytics approaches are a complement to basic descriptive analytics, and they employ descriptive analytics findings to figure out what's causing these occurrences. Then, performance indicators are looked into further to see why these occurrences have improved or deteriorated. This procedure is usually broken down into three steps:

1. Recognize any discrepancies in the data. Unexpected changes in a statistic or a specific market might be the source of these anomalies.
2. Collect information about the abnormalities.
3. Discover correlations and patterns that explain these abnormalities using statistical approaches.

3. Predictive Analytics:

Predictive analytics can assist in determining what will occur in the future. Historical data is used in predictive analytics approaches to discover patterns and decide if they are likely to repeat. Predictive analytical tools can help you predict what will happen in the future. Statistical and machine learning techniques such as neural networks, decision trees, and regression are among the methodologies used.

4. Prescriptive Analytics:

Prescriptive analytics can assist in determining which activities should be made in order to meet a goal or target. Organizations may make data-driven choices by utilizing predictive analytics insights. In the face of ambiguity, this strategy enables organizations to make educated judgments. Machine learning tactics are used in predictive analytics approaches to detect trends in massive datasets.

5. Cognitive Analytics: 

In a self-learning feedback loop, cognitive analytics tries to make inferences from current data and patterns, deduce conclusions based on existing knowledge bases, and then put these discoveries back into the knowledge base for future inferences. Cognitive analytics can assist you predict what will happen if things change and how you will manage these events.

Roles in Data: 

1. Business Analysist:

While a data analyst and a business analyst have certain similarities, the fundamental difference between the two positions is what they do with data. A business analyst is more familiar with the company's operations and is an expert in interpreting data obtained via visualization. Often, a single individual will be responsible for both the data analyst and the business analyst duties.

2. Data Analyst:

Through visualization and reporting technologies like Microsoft Power BI, a data analyst helps organizations maximize the value of their data assets. Data analysts are in charge of data characterization, cleansing, and transformation. Designing and creating scalable and effective data models, as well as enabling and applying sophisticated analytics capabilities into reports for analysis, are among their tasks. A data analyst collaborates with key stakeholders to identify relevant and essential data and reporting needs, after which they are entrusted with transforming raw data into actionable insights.

3. Data Engineer:

On-premises and cloud data platform technologies are provisioned and set up by data engineers. They are in charge of securing and managing the flow of structured and unstructured data from many sources. Relational databases, nonrelational databases, data streams, and file stores are some of the data systems they employ. Data engineers also make ensuring that data services work together safely and seamlessly.

Data engineers' primary tasks include ingesting, egressing, and transforming data from many sources using on-premises and cloud data services and tools. To discover and address data requirements, data engineers interact with business stakeholders. They come up with ideas and put them into action.

4. Data Scientist:

Advanced analytics are used by data scientists to extract value from data. They may do everything from descriptive to predictive analytics. Exploratory data analysis is a procedure used in descriptive analytics to examine data. In machine learning, predictive analytics is used to apply modelling approaches that can discover abnormalities or trends. These analyses are crucial components of forecasting models.

Data scientists' job includes more than just descriptive and predictive analytics. Some data scientists work in the field of deep learning, doing repeated trials with tailored algorithms to address complicated data problems.

5. Database Administrator:

The operational components of cloud-native and hybrid data platform solutions built on Microsoft Azure data services and Microsoft SQL Server are implemented and managed by a database administrator. A database administrator is in charge of the database solutions' overall availability, consistent performance, and optimizations. They collaborate with stakeholders to create and execute data backup and recovery policies, tools, and procedures.

A database administrator's job is distinct from that of a data engineer. A database administrator is responsible for monitoring and managing the overall health of a database as well as the hardware on which it runs, whereas a data engineer is responsible for data wrangling, or the process of ingesting, transforming, validating, and cleaning data to meet business requirements.

Tasks of Data Analyst:

1. Prepare:

Data preparation is the act of collecting raw data and transforming it into reliable and intelligible information. It includes tasks such as guaranteeing data integrity, fixing incorrect or erroneous data, detecting missing data, transforming data from one structure to another or from one kind to another, and even making data more legible.

Understanding how you'll receive and connect to the data, as well as the performance consequences of your actions, is part of data preparation. You must make judgments while connecting to data to ensure that models and reports meet and perform to established standards and expectations.

2. Model:

The process of identifying how your tables are connected to one another is known as data modelling. This is accomplished by defining and establishing connections among the tables. You can then improve the model by adding new computations and specifying metrics to enrich your data.

3. Visualize:

You get to bring your facts to life in the visualization challenge. The visualization task's ultimate purpose is to address business challenges. A well-designed report should provide a captivating story about the facts, allowing corporate decision makers to get the information they need fast. You may create a successful report that directs the reader through the material quickly and effectively, allowing the reader to follow a narrative into the data by utilizing proper visualizations and interactivity.

The reports generated during the visualization process assist organizations and decision makers in comprehending the data in order to make precise and critical decisions.

4. Analyze:

The analyze task is crucial for comprehending and evaluating the information presented in the report. As a data analyst, you should be familiar with Power BI's analytical capabilities and how to utilize them to uncover insights, spot patterns and trends, anticipate outcomes, and then convey those findings in a clear and understandable manner.

5. Manage:

Reports, dashboards, workspaces, datasets, and other features are all part of Power BI. As a data analyst, you're in charge of managing these Power BI assets, including monitoring the sharing and distribution of products like reports and dashboards, as well as guaranteeing their security.