Before data can effectively convey a narrative, it must undergo a process that renders it suitable for storytelling. Data analysis encompasses the identification, cleaning, transformation, and modeling of data to unearth meaningful and valuable insights. Subsequently, this processed data is shaped into a narrative through reports, facilitating analysis to support crucial decision-making processes.

Overview

In an increasingly data-driven world, storytelling via data analysis has emerged as a vital element for businesses of all sizes. This significance is evident in the continued demand for data analysts within organizations.

Businesses guided by data-driven approaches base their decisions on the narrative derived from their data. However, many organizations encounter the challenge of not fully utilizing data to its maximum potential. Data analysis is, and should be, an integral part of every organization, playing a pivotal role in evaluating customer sentiment, conducting market and product research, and uncovering trends and other valuable data insights that can impact the business.

Although the data analysis process centers around activities like cleaning, modeling, and visualizing data, it’s crucial not to underestimate the significance of the concept and its impact on businesses. When delving into data analysis, key analytical components are typically categorized into the following:

• Descriptive
• Diagnostic
• Predictive
• Prescriptive
• Cognitive

Descriptive Analytics

Descriptive analytics plays a crucial role in addressing inquiries about past events using historical data. Techniques in descriptive analytics involve summarizing extensive semantic models to articulate outcomes to stakeholders.

Through the establishment of key performance indicators (KPIs), these methods contribute to monitoring the accomplishment or setback of vital objectives. Various metrics, such as return on investment (ROI), are widely employed across industries, with specialized metrics tailored to monitor performance in specific sectors.

An illustrative instance of descriptive analytics involves generating reports to offer insights into an organization’s sales and financial data. This process aids in presenting a comprehensive view of historical performance.

Diagnostic Analytics

Diagnostic analytics is instrumental in addressing inquiries regarding the reasons behind specific events. This type of analytics serves as a complement to fundamental descriptive analytics, utilizing insights derived from the latter to uncover the root causes of events. The examination of performance indicators is extended to delve into the factors contributing to the improvement or deterioration of these events. Typically, this diagnostic process unfolds in three stages:

Identify anomalies in the data. These anomalies might be unexpected changes in a metric or a particular market.

Collect data that’s related to these anomalies.

Use statistical techniques to discover relationships and trends that explain these anomalies.

Predictive Data Analytics

Predictive analytics plays a pivotal role in addressing inquiries about future events. This type of analytics employs historical data to identify patterns and assess the likelihood of their recurrence. Utilizing various statistical and machine learning techniques like neural networks, decision trees, and regression, predictive analytics tools offer valuable insights into potential future occurrences. By examining historical trends, these techniques enable organizations to make informed predictions about what may happen in the future.

Prescriptive Analytics

Prescriptive analytics is instrumental in providing guidance on the actions that should be taken to attain a specific goal or target. Through insights derived from prescriptive analytics, organizations can make decisions grounded in data, offering a valuable approach for decision-making in uncertain situations. These techniques often leverage machine learning as a strategy to identify patterns within extensive semantic models. By scrutinizing past decisions and events, organizations can estimate the likelihood of various outcomes, aiding in making informed decisions based on data-driven insights.

Cognitive Analytics

Cognitive analytics strives to make deductions from available data and patterns, forming conclusions based on existing knowledge bases. This information is then incorporated back into the knowledge base, creating a self-learning feedback loop. The aim is to understand potential outcomes if circumstances change and to devise strategies for handling such situations.

Unlike structured queries relying on a rules database, cognitive analytics involves unstructured hypotheses gathered from various sources and expressed with varying confidence levels. Successful cognitive analytics heavily rely on machine learning algorithms and incorporate various natural language processing concepts. These concepts enable the interpretation of previously untapped data sources, such as call center conversation logs and product reviews, enhancing the depth of insights.

Think of Examples

By utilizing descriptive analytics through reporting and data visualizations, a retail business examines purchasing patterns from previous years to predict potential popular products in the upcoming year. The analysis may include investigating supporting data to comprehend the reasons behind the popularity of a specific product and assess whether the trend is continuing, aiding in decisions regarding product stocking.

For instance, a business might identify a product that gained popularity during a specific timeframe. This analysis can be instrumental in evaluating the impact of marketing efforts or online social activities on the increase in sales.

A fundamental aspect of data analysis is establishing trust in the data. This involves capturing data from reliable sources and transforming it into a consumable, meaningful format that is easily comprehensible, supporting the decision-making process. Data analysis empowers businesses to gain a comprehensive understanding of their data through data-driven processes, fostering confidence in decision-making.

As the volume of data expands, the demand for data analysts increases. A skilled data analyst possesses the ability to organize information, distill it into relevant and understandable insights, and navigate the complexities of data overload, ensuring meaningful interpretation and application of the data.

Data Roles

The following sections highlight these different roles in data and the specific responsibility in the overall spectrum of data discovery and understanding:

• Business analyst
• Data analyst
• Data engineer
• Data scientist
• Database administrator

Business Analyst:

A business analyst distinguishes themselves from a data analyst by being closely aligned with the business and specializing in interpreting data derived from visualizations. In some instances, the roles of a data analyst and business analyst may be consolidated into a single role.

Data Analyst:

Data analysts play a crucial role in helping businesses extract maximum value from their data assets using visualization and reporting tools like Microsoft Power BI. Their responsibilities encompass profiling, cleaning, and transforming data. They design and construct scalable semantic models, integrating advanced analytics capabilities into reports for analysis. Working collaboratively with stakeholders, data analysts identify pertinent data and reporting requirements and translate raw data into meaningful insights.

Data Engineer:

Data engineers focus on provisioning and configuring data platform technologies, both on-premises and in the cloud. They manage and secure the flow of structured and unstructured data from various sources, utilizing platforms like relational databases, nonrelational databases, data streams, and file stores. Ensuring secure and seamless integration across data platforms is also a key responsibility of data engineers.

Data Scientist:

Data scientists engage in advanced analytics to extract value from data, covering a spectrum from descriptive analytics, achieved through exploratory data analysis, to predictive analytics using modeling techniques for anomaly detection or pattern recognition. These analytics are integral components of forecast models.

Database Administrator:

Database administrators are responsible for implementing and managing the operational aspects of cloud-native and hybrid data platform solutions built on Microsoft Azure data services and SQL Server. They ensure overall availability, consistent performance, and optimizations of database solutions. Collaborating with stakeholders, database administrators identify and implement policies, tools, and processes for data backup and recovery plans.