Unlocking the Power of Data: Expanding Your Horizons

In detective movies, detectives find clues and solve puzzles to make important discoveries.

Surprisingly, businesses also do detective work to understand what customers want and how they can improve. For businesses, clues come from information about how customers interact with them, how websites work, and other collected information. This information is called data, and businesses work hard to collect, analyze, and learn from it, just like detectives do with clues and evidence. Data is a bunch of information that can be organized and processed.

Data is super valuable to a business, but not everyone knows how to use it. Because more and more data is being created, there's a big need for people who know how to collect, understand, and analyze it. Businesses collect and process data to help them make decisions, and they need smart employees (like you!) to help with that.

Data is powerful because it gives companies the info they need to understand what customers want and how to make their business better. But if data isn't looked at or used correctly, it can be useless. Data professionals change the data into information that anyone in the business can understand, even if they don't know much about data.

Analytics is the process of finding meaning in data. The goal is to discover patterns and trends that businesses can use to make smart decisions. The useful patterns and trends found through analysis are called insights. Insights are really valuable. They're the main reason why data is collected so much, and why data professionals are so needed. Everyone wants insights from data, but not everyone knows how to get them.

Business analytics uses data and reporting to check how well a business is doing, find insights, and make decisions based on data. This job is done by a business intelligence analyst. In the next reading, you'll learn more about this role.

The Convergence of Business Analysis and Business Analytics and Data Analytics

Analytics, simply put, is the process of finding meaning in data. The aim is to uncover patterns and trends that businesses can use for well-informed actions, commonly known as insights.

There are various fields within analytics, such as business analytics, data analytics, and data science, all focusing on the same goal but within different concentrations. Business analytics employs data and reporting to investigate a business's performance, revealing insights for data-driven decisions. The previous reading clarified the difference between business analysis and business analytics. Now, we'll delve into the distinctions between business analytics, business analysis and data analytics.

Business Analysis: This field primarily focuses on identifying business needs and problems and finding solutions. It involves requirements gathering, process modeling, and ensuring that a project or system aligns with business objectives. Business analysts act as the bridge between stakeholders and IT teams, ensuring that technology solutions meet business needs.

Business Analytics: In contrast, Business Analytics involves the exploration of past data to identify trends, analyze the effects of decisions, and provide insights for informed future decisions. It uses statistical and predictive analysis, data mining, and multivariate testing to discover deeper insights and answer questions like "Why did this happen?" and "What might happen next?"

A Business Intelligence (BI) Analyst, is someone who uses data and statistical analysis to help companies make better business decisions. They gather, process, and analyze data from various sources like market research, sales figures, and financial reports. The insights gained from this data help them identify trends, patterns, and opportunities, which they present to management through reports, dashboards, and visualizations. BI analysts also play a role in designing and implementing tools and dashboards to support decision-making, often using software like Tableau.

Common responsibilities of BI analysts include:

1. Exploring past business performance data to drive strategic planning.
2. Using data to analyze business problems and propose solutions.
3. Developing and managing business intelligence solutions.
4. Visualizing key data and key performance indicators (KPIs) for informed decisions.
5. Designing, developing, testing, and deploying dashboards.
6. Creating SQL queries to extract and analyze data.
7. Applying research and evaluation standards to translate complex data into meaningful content.
8. Designing, creating, testing, and maintaining a portfolio of reports, scorecards, databases, and dashboards.
9. Delivering, reviewing, and distributing reports.

While industry terms may vary, understanding these responsibilities is crucial. In this certificate, you'll learn about related roles like business analyst and data analyst, aligning with fields such as business analytics, business analysis, and data analytics.

Data analytics, a subset of analytics, concentrates on exploring, manipulating, visualizing, and analyzing data to unveil insights and guide data-driven decisions. Much like business analysis and business analytics, job postings often blur the lines between a business analyst and a data analyst. To navigate these roles effectively, it's crucial to understand the difference between them.

An analyst, whether a business analyst or a data analyst, enhances a business by providing data-driven insights. They may act as a bridge between the business and IT. Both roles involve collecting, processing, and analyzing data to discern patterns and support decision-making. While a business analyst focuses on improving overall business operations, a data analyst concentrates on interpreting patterns and trends gathered from various sources to inform decision-making across different facets of a company.

The terms "business analyst" and "data analyst" are often used interchangeably, and responsibilities can vary between companies. When exploring these roles, it's essential to inquire about specific responsibilities during interviews. Although the skills required for both roles are similar, the key difference lies in their usage. For instance, a data analyst commonly relies on SQL as a primary tool, while a business analyst might use it selectively for specific projects. Many consider the business analyst role as a stepping stone toward becoming a data analyst due to their substantial similarities.

Data Analytics Lifecycle

Businesses use different types of analytics to achieve their goals and address challenges based on the specific questions they seek to answer. There are four main types: descriptive, diagnostic, predictive, and prescriptive analytics. Each type excels at answering particular questions. The table below outlines how these analytics approaches differ in addressing problems and explains why an analyst might opt for one method over another depending on the nature of the question they aim to solve.

The data analytics lifecycle is a repeating process designed for businesses to effectively manage analytics tasks and align them with their objectives. It consists of six stages. Initially, data is collected, and project objectives are outlined. The data is then processed to suit the project's needs. Following this, the team plans the model. After finalizing the plan, the model is applied to the data to extract insights. A story is crafted from these insights to communicate with stakeholders. If the findings are well-explained and useful, the business implements the recommendations, often leading to future projects. This cycle continues.

Project Management Basics for Analytics

Project management involves planning and executing a project from start to finish to achieve its goals. For business analysts, understanding project management is crucial as they often collaborate with project managers or manage projects themselves. This knowledge ensures effective collaboration and project success. Business analysts differ from project managers as their focus is on the scope of the solution, while project managers concentrate on the overall project scope. Business analysts may use project management topics and skills like project lifecycles, project charters, Agile methodologies, and planning.

The Project Lifecycle and Phases?

1. Initiating the project: In this stage, the project gets formal approval. The business analyst, along with key team members, defines the project scope, identifies stakeholders, and estimates factors like cost and time.
2. Analyzing the problem: In this stage, the business analyst works to comprehend the current situation and strives for stakeholders to share a common understanding of the problem.
3. Gathering requirements: Here, the business analyst documents and shares detailed project requirements, validating and gaining approval from stakeholders.
4. Developing the solution: This stage involves the team developing a prototype of the solution and presenting it to stakeholders.
5. Testing and implementing: The business analyst collaborates with the testing team in this phase, ensuring the solution aligns with the earlier elicited project requirements.

Agile vs. Waterfall: Approaches to Project Management

The Agile approach differs from traditional project management, known as waterfall. In the waterfall methodology, all project work must be finished before reaching a final outcome.

In the diagram, notice that the waterfall methodology leads to a single outcome. Project work in each phase progresses toward this one final result. In contrast, the Agile approach produces multiple outcomes. There might be an outcome after each sprint, allowing the team to learn before moving on to the next sprint.

In many projects, stakeholders may not have a clear picture of the business needs initially. Even if they do, these requirements are often not well-communicated at the project's start. Due to this lack of clarity, a waterfall approach is often avoided. Using this approach might involve developing solutions based on under-developed business requirements, which is less than ideal.

In an Agile approach, the solution remains flexible and easily adaptable. Involving stakeholders in several rounds of iterations and delivering solutions incrementally increases the likelihood that the project will meet the business requirements.

Agile Planning: Essential Components

Planning is a constant activity in Agile, with six distinct levels of planning outlined, ranging from the highest level of strategic planning to the most detailed level of daily planning. For business analytics, focusing on at least the three levels below is beneficial:

1. Project Planning: Conducted at the project's initiation.
2. Sprint Planning: Conducted at the start of each sprint.
3. Daily Planning: Conducted every day.

The image below illustrates an example of the three levels of Agile planning specifically tailored for a business analyst.

Why is Data Literacy Important for Business Analysts?

Data literacy is the capacity to understand, analyze, and convey information about data. Understanding data involves grasping its uses, methods, and outcomes within a given context. Analyzing data requires skills to perform and interpret various analyses. Communicating data entails presenting findings in written and verbal formats.

Data literacy is crucial for business analysts to communicate effectively and make well-informed decisions for their business. Enhanced understanding of data enables business analysts to align business decisions more effectively with the outcomes of their analyses.

Imagine having the ability to not only analyze data but to present it in a way that captivates your audience, making complex information crystal clear. That's precisely what the Tableau Business Intelligence Analyst Professional Certificate offers – a key to unlocking the power of data visualization and taking your analytical skills to the next level.