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A Guide To Data

Our guide to data-driven decision making takes you through what it is, its importance, and how to effectively implement it in your organization.

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A Guide To Data Driven Decision Making: What It Is, Its Importance, & How To Implement It

A Guide To Data Driven Decision Making: What It Is, Its Importance, & How To Implement It What is data-driven decision-making?

Data-driven decision-making (DDDM) is defined as using facts, metrics, and data to guide strategic business decisions that align with your goals, objectives, and initiatives. When organizations realize the full value of their data, that means everyone—whether you’re a business analyst, sales manager, or human resource specialist—is empowered to make better decisions with data, every day. However, this is not achieved by simply choosing the appropriate analytics technology to identify the next strategic opportunity.

Your organization needs to make data-driven decision-making the norm—creating a culture that encourages critical thinking and curiosity. People at every level have conversations that start with data and they develop their data skills through practice and application. Foundationally, this requires a self-service model, where people can access the data they need, balanced with security and governance. It also requires proficiency, creating training and development opportunities for employees to learn data skills. Finally, having executive advocacy and a community that supports and makes data-driven decisions will encourage others to do the same.

Establishing these core capabilities will help encourage data-driven decision-making across all job levels so business groups will regularly question and investigate information to discover powerful insights that drive action.

The importance of data-driven decision-making

The amount of information collected has never been greater, but it’s also more complex. This makes it difficult for organizations to manage and analyze their data. In fact, NewVantage Partners recently reported that 98.6 percent of executives indicate that their organization aspires to a data-driven culture, while only 32.4 percent report having success. A 2018 IDC study also noted that organizations have invested trillions of dollars to modernize their business, but 70 percent of these initiatives fail because they prioritized technology investments without building a data culture to support it.

In pursuit to be data-driven, many enterprises are developing three core capabilities: data proficiency, analytics agility, and community. Transforming how your company makes decisions is no easy task, but incorporating data and analytics into decision-making cycles is how you will see the most transformative impact on your organization. This level of transformation requires a dedicated approach to developing and refining your analytics program.

Organizations benefiting from data-driven decision-making

Thanks to modern business intelligence, organizations are inching closer and closer to understanding the value of data-driven decision-making across all departments and roles. Here are a few examples of organizations that are effectively leveraging the value of their people and their data.

Lufthansa group increased organizational efficiency by 30%

Providence St. Joseph Health improved quality measures and cost-of-care

Charles Schwab Corporation increased their speed to business insights

Without our visual analytics solution, we would be stuck analyzing enormous amounts of data in spreadsheets. Instead, our dashboards provide clear actionable insights that drive the business forward.

DONALD LAY, SENIOR BUSINESS INTELLIGENCE MANAGER AT CHARLES SCHWAB CORPORATION

Try Tableau for free to create beautiful visualizations with your data.

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6 steps to effectively make data-driven decisions

These steps can help you can find the “who, what, where, when, and why” to make the most of data—for you, for colleagues, and the business. But keep in mind that the cycle of visual analysis isn’t linear. One question often leads to another, which may mean you need to go back to one of these steps or skip to another—eventually leading to valuable insights.

Step 1 - Identify business objectives: This step will require an understanding of your organization’s executive and downstream goals. This could be as specific as increasing sales numbers and website traffic or as ambiguous as increasing brand awareness. This will help you later in the process to choose key performance indicators (KPIs) and metrics that influence decisions made from data—and these will help you determine which data to analyze and what questions to ask so your analysis supports key business objectives. For instance, if a marketing campaign focuses on driving website traffic, a KPI could be tied to the amount of contact submissions captured so sales can follow-up with leads.Step 2 - Survey business teams for key sources of data: To ensure success, it is crucial to get inputs from people across the organization to understand short and long term goals. These inputs help inform the questions that people ask in their analysis and how you prioritize certified data sources.

Valuable inputs from across the organization will help to guide your analytics deployment and future state—including the roles, responsibilities, architecture, and processes, as well as the success measurements to understand progress.

Step 3 - Collect and prepare the data you need: Accessing quality, trusted data can be a big hurdle if your business information sits in many disconnected sources. Once you have an idea of the breadth of data sources across your organization, you can start data preparation.

स्रोत : www.tableau.com

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google data analytics professional certificate. Contribute to elmoallistair/google-data-analytics development by creating an account on GitHub.

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google-data-analytics/01_foundations-data/05_discovering-data-career-possibilities/graded-assessment/weekly-challenge-5.md

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Weekly challenge 5

Latest Submission Grade: 100%

Question 1

An online gardening magazine wants to understand why its subscriber numbers have been increasing. What kind of reports can a data analyst provide to help answer that question? Select all that apply.

Reports that predict the success of sales leads to secure future subscribers

Reports that compare past weather patterns to the number of people taking up gardening recentlyReports that show how many customers shared positive comments about the gardening magazine on social media in the past yearReports that examine how a recent 50%-off sale affected the number of subscription purchases

Analyzing historical data such as weather patterns, social media comments, and past sales would provide useful insights into the increase in subscription numbers.

Question 2

A doctor’s office has discovered that patients are waiting 20 minutes longer for their appointments than in past years. A data analyst could help solve this problem by analyzing how many doctors and nurses are on staff at a given time compared to the number of patients with appointments.

True

False

Analyzing staffing and patient numbers would likely provide useful insights about why patients are waiting longer for their appointment times and to help solve this problem.

Question 3

What is the process of using facts to guide business strategy?

Data programming Data visualization Data ethics

Data-driven decision-making

Data-driven decision-making is using facts to guide business strategy.

Question 4

Fill in the blank: A business task is described as the problem or _____ a data analyst answers for a business.

solution comment

question

complaint

A business task is described as the problem or question a data analyst answers for a business.

Question 5

Data-driven decision-making is using facts to guide business strategy. The benefits include which of the following? Select all that apply.

Getting a complete picture of a problem and its causesUsing data analytics to find the best possible solution to a problem

Making the most of intuition and gut instinct

Combining observation with objective data

Data-driven decision-making enables companies to use data analytics to find the best possible solution to a problem, complement observation with objective data, and get a complete picture of a problem and its causes.

Question 6

It’s possible for conclusions drawn from data analysis to be both true and unfair.

True

False

Sometimes, a conclusion may be true, but it’s unfair because it doesn’t represent all groups or it ignores social context and other systemic factors.

Question 7

Fill in the blank: Fairness is achieved when data analysis doesn't create or _____ bias.

resolve

reinforce

constrain highlight

Fairness is achieved when data analysis doesn’t create or reinforce bias.

Question 8

A gym wants to start offering exercise classes. A data analyst plans to survey 10 people to determine which classes would be most popular. To ensure the data collected is fair, what steps should they take? Select all that apply.

Collect data anonymously.

Survey only people who don’t currently go to the gym.

Increase the number of participants.Ensure participants represent a variety of profiles and backgrounds.

Ensuring participants represent a variety of profiles and backgrounds, collecting data anonymously, and surveying more than just 10 people would all help ensure the data analysis is fair.

स्रोत : github.com

Google Data Analytics (General terms Course 1) Flashcards

Study with Quizlet and memorize flashcards containing terms like What is data?, What are the processes for data analytics?, A clothing retailer collects and stores data about its sales revenue. Which of the following would be part of its data ecosystem? and more.

Google Data Analytics (General terms Course 1)

5.0 (3 reviews) Term 1 / 47 What is data?

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Definition 1 / 47

A collection of facts used to draw conclusions, make predictions, and assist in decision making.

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Created by whaddupeli

Terms in this set (47)

What is data?

A collection of facts used to draw conclusions, make predictions, and assist in decision making.

What are the processes for data analytics?

Ask, Prepare, Process, Analyze, Share, Act

A clothing retailer collects and stores data about its sales revenue. Which of the following would be part of its data ecosystem?

The database of salves revenue, records of its inventory, the cloud that stores its database

What is the process of guiding business strategy using facts?

Data-driven decision-making

Fill in the blank: Curiosity, understanding context, having a technical mindset, data design, and data strategy are _____. They enable data analysts to solve problems using facts.

Analytical skills Question 4

The owner of a skate shop notices that every time a certain employee has a shift, there are higher sales numbers at the end of the day. After some investigation, the owner realizes that since the employee was hired, the store earns 15% more each month. In this scenario, the manager used which quality of analytical thinking?

Correlation

An advertising firm has used insights from its analytics team to create a strategy for improving sales. Now, they implement a plan to increase annual revenue. The firm is at which step of the data analysis process?

Act

A data analyst adds descriptive headers to columns of data in a spreadsheet. How does this improve the spreadsheet?

It adds context

Gap analysis is a process that could help accomplish which of the following tasks? Select all that apply.

Increase the efficiency of a car manufacturer based on its current assembly process.

Reduce a company's carbon footprint based on its current emissions.

Improve accessibility for an educational app based on its current functionality.

This is a selection from a spreadsheet that ranks the 10 most populous cities in North Carolina. To alphabetize the county names in column D, which spreadsheet tool would you use?

Sort Range

Imagine you are sharing your data with a company stakeholder. Why might you display data with a data visualization instead of a table? Select all that apply.

It's aesthetically pleasing.

It helps them identify trends more quickly

It's easy to understand.

Phase: Ask

Ask questions to define both the issue to be solved and what would equal a successful result

Phase: Prepare

Build a timeline and collect data

Phase: Process

Clean data to make sure it was complete, correct, relevant, and free of errors and outliers

Phase: Analyze

Analyze the clean data

Phase: Share

Communicate findings and recommendations with the team

Phase: Act

Determine what action is needed for "success"

Data Ecosystem

Group of elements that interact with one another to produce, manage, store, organize, analyze, and share data

Cloud

Place to keep data online, rather than on a computer hard drive

Data Science

Creating new ways of modeling and understanding the unknown by using raw data

Data Analysis

The collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making

Data Analytics Science of data

Data Driven Decision Making

Using facts to guide business strategy

Subject matter experts

people familiar with the business problem

What are / Identify the real-world examples of how a company might make data-driven decisions.

Suggesting new music to a customer based on their listening history

Scheduling a certain number of restaurant employees based on the average number of lunch-goers per day

Choosing e-commerce solutions based on customer shopping preferences

Gut instinct

An intuitive understanding of something with little or no explanation

Analytical Skills

The qualities and characteristics associated with solving problems using facts

Context

the condition in which something exists or happens

Technical Mindset

The ability to break things down into smaller steps or pieces and work with them in an orderly and logical way

Data Design

how you organize information

Data strategy

The management of the people, processes, and tools used in data analysis

Analytical thinking

Identifying and defining a problem and then solving it by using data in an organized, step-by-step manner

Visualization

A graphical representation of data

Root cause

The reason why a problem occurs

Gap analysis

A method for examining and evaluating the current state of a process in order to identify opportunities for improvement in the future

stakeholders

people who have invested time and resources into a project and are interested in the outcome

Formula

a set of instructions that performs a specific calculation using data in the spreadsheet

function

स्रोत : quizlet.com