A Beginners Guide To The Data Analysis Process
Summary
TLDRThis video script offers a comprehensive guide to the data analysis process, outlining five key stages: defining the question, collecting data, cleaning data, analyzing, and sharing results. It emphasizes the importance of understanding business objectives, using various data types, and employing tools like Databox and Tableau for analysis and visualization. The script highlights the crucial role of data cleaning and the analyst's responsibility to communicate findings clearly to influence business decisions.
Takeaways
- 🔍 The first step in the data analysis process is defining the objective, which involves formulating a hypothesis and determining how to test it.
- 🤔 Understanding the business and its goals is crucial for a data analyst to frame the problem correctly and identify the right data to solve the business problem.
- 📝 Data can be categorized into first, second, and third-party data, each with different sources and levels of relevance and reliability.
- 🛠 Tools like Databox, DashaRoo, Grafana, Freeboard, and Dashbuilder are useful for creating dashboards to visualize data at the beginning and end of the analysis process.
- 📈 After defining the objective, a strategy for collecting and aggregating the appropriate data is necessary, which includes determining the types of data needed.
- 🧼 Data cleaning is a critical step that involves removing errors, duplicates, outliers, and irrelevant observations to ensure high-quality data for analysis.
- 🕵️♂️ Data analysts spend a significant amount of time—up to 70 to 90%—cleaning data, emphasizing the importance of this step for accurate analysis.
- 📊 Various data analysis techniques exist, including univariate, bivariate, time series, and regression analysis, each serving different analytical goals.
- 📚 Descriptive, diagnostic, predictive, and prescriptive analyses are the four categories of data analysis, each providing different types of insights into the data.
- 🗣️ Effective communication of findings is essential, using reports, dashboards, and interactive visualizations to present data insights clearly and unambiguously.
- 🛠 Tools like Google Charts, Tableau, Datawrapper, and Infogram facilitate the sharing of data insights without requiring coding skills, while Python libraries like Plotty, Seaborn, and Matplotlib cater to those with programming knowledge.
Q & A
What are the five key stages of the data analysis process mentioned in the script?
-The five key stages of the data analysis process are: 1) Defining the question, 2) Collecting the data, 3) Cleaning the data, 4) Analyzing the data, and 5) Sharing the results.
What is the importance of defining the objective in the data analysis process?
-Defining the objective is crucial as it sets the direction for the entire analysis. It involves formulating a hypothesis and determining how to test it, which helps in framing the problem correctly and identifying the right data to solve the business problem at hand.
Can you provide an example of how a data analyst might reframe a business problem?
-An example given in the script is when senior management asks, 'Why are we losing customers?' A data analyst might reframe this to 'Which factors are negatively impacting the customer experience?' or 'How can we boost customer retention while minimizing costs?'
What are the three categories of data sources mentioned in the script?
-The three categories of data sources are first party, second party, and third-party data.
What is first party data and how is it typically collected?
-First party data is data directly collected by the company from its customers. It often comes in a clear and structured form, such as transactional tracking data or information from a customer relationship management (CRM) system.
How does second party data differ from first party data?
-Second party data is the first party data of other organizations. It is usually structured and can be obtained directly from the company or from a private marketplace, and while it is less relevant than first party data, it tends to be reliable.
What is third-party data and where can it be sourced from?
-Third-party data is collected and aggregated from numerous sources by a third party. It often contains a lot of unstructured data or big data and can be sourced from industry reports, market research, open data repositories, government portals, or firms like Gartner.
Why is data cleaning considered a crucial step in the data analysis process?
-Data cleaning is crucial because it ensures that the data is of high quality and free from errors, duplicates, or outliers. This step is important for accurate analysis and can prevent incorrect conclusions, which might lead to poor business decisions.
What percentage of time does a good data analyst typically spend on data cleaning?
-A good data analyst typically spends about 70 to 90 percent of their time on data cleaning.
Can you explain the four categories of data analysis mentioned in the script?
-The four categories of data analysis are: 1) Descriptive analysis, which identifies what has already happened, 2) Diagnostic analysis, which focuses on understanding why something has happened, 3) Predictive analysis, which identifies future trends by analyzing historical data, and 4) Prescriptive analysis, which allows for making recommendations for the future.
Why is it important for data analysts to present their findings clearly and unambiguously?
-Clear and unambiguous presentation of findings is important because it influences the direction of the business. Decision makers rely on these insights for making strategic decisions, and honest communication ensures that conclusions are scientifically sound and based on facts.
Outlines
📝 Defining the Data Analysis Objective
In this introductory segment, Will outlines the initial step of the data analysis process, which is defining the question or problem statement. He emphasizes the importance of understanding the business's goals to frame the problem correctly. Using the example of a fictional company, 'Top Notch Learning,' he illustrates how to identify the core issue affecting customer retention. Will also introduces the concept of business acronyms and KPIs, and suggests tools like Databox, DashaRoo, Grafana, Freeboard, and Dashbuilder for creating dashboards to track business metrics throughout the analysis process.
🔍 Collecting and Classifying Data Sources
Will proceeds to explain the second step, which involves collecting and aggregating appropriate data. He categorizes data into three types: first-party data collected directly by the company, second-party data which is the first-party data of other organizations, and third-party data collected from various sources. Will discusses the benefits and sources of each data type, such as CRM systems for first-party data and industry reports for third-party data. He also mentions tools like Salesforce DMP, SAAS, Xplenty, Pymcore, and Dswarm for managing data collection strategies.
🧼 Cleaning and Preparing Data for Analysis
The third step, as described by Will, is data cleaning, which is essential for ensuring high-quality data analysis. Key tasks include removing errors, duplicates, and outliers, as well as structuring data for easier manipulation. He highlights that data analysts often spend a significant portion of their time on this step. Will recommends tools like Open Refine for basic cleaning and Python libraries such as pandas for more complex data sets. He also mentions enterprise tools like Data Ladder for data matching.
📊 Analyzing Data and Drawing Insights
In this part, Will delves into the actual analysis of the cleaned data, discussing various techniques such as univariate, bivariate, time series, and regression analysis. He categorizes data analysis into descriptive, diagnostic, predictive, and prescriptive analysis, each serving different purposes in understanding past events, diagnosing problems, forecasting trends, and making future recommendations. The choice of analysis technique depends on the insights one hopes to gain.
📈 Sharing Results and Influencing Decisions
The final step discussed by Will is sharing the results of the data analysis. This involves not only presenting the raw data but also interpreting the findings in a clear and unambiguous manner. Will stresses the importance of honest communication and covering all evidence to ensure scientific soundness in conclusions. He suggests using reports, dashboards, and interactive visualizations to support findings. Tools like Google Charts, Tableau, Datawrapper, and Infogram are recommended for those without coding skills, while Python libraries like Plotty, Seaborn, and Matplotlib cater to those with programming knowledge.
🎓 Further Learning Opportunities in Data Analytics
In the concluding part of the script, Will offers a resource for further learning in data analytics, recommending a short course by CareerFoundry that viewers can sign up for free through a link provided in the description. He also invites viewers to watch another video he made on data analytics, suggesting a continued learning path for those interested in the subject.
Mindmap
Keywords
💡Data Analysis Process
💡Problem Statement
💡Hypothesis
💡Business Metrics
💡First Party Data
💡Second Party Data
💡Third Party Data
💡Data Cleaning
💡Descriptive Analysis
💡Predictive Analysis
💡Prescriptive Analysis
💡Data Visualization
Highlights
Introduction to the five key stages of the data analysis process.
Defining the question as the first step in data analysis, emphasizing the importance of a clear problem statement.
Understanding the business and its goals to frame the problem correctly.
The role of a data analyst in identifying the core problem and formulating a hypothesis.
The example of Top Notch Learning to illustrate defining the right problem statement.
Importance of identifying the right data sources to solve the business problem.
Explanation of first, second, and third-party data in the context of data collection.
Tools for data collection, including DMPs and open-source software.
The crucial step of data cleaning to ensure high-quality data for analysis.
Key data cleaning tasks such as removing errors, duplicates, and outliers.
Tools for data cleaning, including open-source options and enterprise solutions.
Different types of data analysis techniques, including univariate, bivariate, time series, and regression analysis.
The four categories of data analysis: descriptive, diagnostic, predictive, and prescriptive.
The importance of sharing results effectively with stakeholders using reports, dashboards, and visualizations.
Tools for interpreting and sharing findings, catering to different experience levels.
The impact of data analysis on business decisions and the importance of honest communication.
CareerFoundry's data analytics short course mentioned for further learning.
Transcripts
Hi my name is Will and i'm going to give
you a step-by-step guide to the data analysis process, let's go!
in this video we're going to go through the five key stages of the data analysis process we're
going to give you an overview and an introduction to each of these stages as well as looking at some
of the tools you'll use to undertake these stages so let's dive into step one defining the question
the first step in your data analysis process or any data analysis process is to define your
objective in data analytics terms this is called the problem statement defining your objective
means coming up with a hypothesis and figuring out how exactly to test it you can start by asking
what business problem am i trying to solve now i know this might sound straightforward but it can
actually be trickier than it seems for instance your organization's senior management might pose a
question such as why are we losing customers it's possible though that this doesn't get to the core
of the problem a data analyst job is to understand the business and the business's goals as a data
analyst you need to understand this in enough depth that they can frame the problem the right
way to give you a practical example let's say you work for a fictional company for example we'll
call it top notch learning this fictional company top notch creates custom training software for its
clients in this example top notch is excellent at securing new clients but unfortunately top
notch has much lower repeat business as such as a data analyst your question might not be why are we
losing customers but which factors are negatively impacting the customer experience or even better
yet how can we boost customer retention whilst minimizing costs now you've identified the problem
you need to find which data is going to help you solve this issue this is where your business
acronym comes in again for instance perhaps you've noticed that the sales pipeline for new
customers is very slick but the production team is extremely inefficient knowing this you could
hypothesize the sales process actually wins a lot of new clients but the customer experience well
it's kind of lacking could this be the reason that customers aren't coming back what sources of data
will help you answer this question as a data analyst considering all these things will help
you define the question and help you solve the problem at hand there's also a number of tools
that can help you define your objective defining your objective is mostly about soft skills so
business knowledge and lateral thinking but you'll also need to consider business metrics and key
performance indicators and these are called KPIs monthly reports can help you track problem points
in the business there are lots of tools out there on the market that can analyze this business data
tools like Databox and DashaRoo there's also free open source software like Grafana freeboard and
Dashbuilder these are fantastic for producing simple dashboards both at the beginning and at
the end of the data analysis process so that was step one defining the objective onto step two step
two collecting the data once you've established your objective you'll need to create a strategy
for collecting and aggregating the appropriate data a key part of this is determining which
data you need this might be quantitative data or numeric data eg sales figures or monthly reports
or qualitative descriptive data such as customer reviews all of this data fits into one of three
categories first party second party and third party data let's explore each one briefly now
what is first party data first party data is data that you or your company has directly collected
from customers it might for example come in the form of transactional tracking data or information
from your customer relationship management system your CRM system whatever it source
first party data is usually collected in a clear and structured way other sources of first party
data might include customer satisfaction surveys focus groups interviews or direct observation
let's talk about second party data to enrich your analysis you might want to secure a secondary data
source second party data is simply the first party data of other organizations this might
be available directly from the company or from private marketplace the main benefit of second
party data is that it's usually structured and although it's less relevant than first
party data it tends to be reliable examples of second party data include website app or social
media activity like online purchase history or shipping data so lastly what is third-party data
third-party data is data that has been collected and aggregated from numerous sources from a third
party often but not always third-party data contains a lot of unstructured data or big data
many organizations collect this big data to create industry reports or to conduct market research the
research and advisory firm Gartner is a good real world example of an organization that collects big
data and then sells it on to other companies open data repositories and government portals are also
sources of third third-party data let's take a moment to look at some of the tools that you
can use to collect data once you've devised this data strategy ie you've identified what data you
need and how best to go about collecting it there are many tools that you can use to help you one
thing you'll need regardless of industry or area of expertise is a data management platform or DMP
a DMP is a piece of software which allows you to identify and aggregate data from numerous sources
before they're manipulating them segmenting them and so on there are many DMPs available some
well-known enterprise DMPs include salesforce DMP, SAAS and the data integration platform Xplenty if
you want to play around you can also try some open source platforms like Pymcore or Dswarm on to step
three cleaning the data once you've collected your data the next step is to get it ready for analysis
this means cleaning or scrubbing it and this is crucial to make sure that you're working with
high quality data key data cleaning tasks include removing major errors duplicates or outliers all
of which are problems when you aggregate data from numerous sources removing unwanted data
points so extracting irrelevant observations that have no bearing on your intended analysis bringing
structure to your data or general housekeeping so for example fixing typos or layout issues
which will help you map or manipulate your data more easily and finally it helps filling in major
gaps as you're tidying up you might notice that important data is missing once you've identified
these gaps you can go about filling them a good data analyst will spend about 70 to 90 of their
time cleaning data this might sound excessive but focusing on the wrong data or analyzing error in
this data will severely impact your results it might even send you back to square one so whatever
you do don't rush this step let's have a look at some of the tools that you can use to clean
your data cleaning data manually especially large data sets can be incredibly daunting but luckily
there are many tools available to streamline this process open source tools such as open refine are
excellent for basic data cleaning as well as high level exploration however free tools offer limited
functionality for very large data sets now i know this sounds like a data zoo but python libraries
such as pandas and some r packages are better suited to heavy data scrubbing you will of course
need to be savvy with languages alternatively enterprise tools are also available for example
data ladder which is one of the highest rated data matching tools in the industry there are many more
why don't you see which data cleaning tools you can find online share your free tools in the
comments below so that was step three cleaning the data on to step four analyzing that data
finally once you've cleaned your data now comes the fun bit analyzing it the type of data analysis
you conduct largely depends on what your goal is but there are many techniques available univariate
or bivariate analysis time series analysis and regression analysis are just a few you might
have heard of more important than the different types though is how you apply them this depends
on what types of insights you're hoping to gain broadly speaking all types of data analysis fit
into the four following categories descriptive analysis which is analysis which identifies what
has already happened this is a common first step that companies do before proceeding with deeper
explorations diagnostic analysis where the focus is on understanding why something has happened
it is literally the diagnosis of a problem just as a doctor uses the symptoms to diagnose the
patient's disease predictive analysis which is where you identify future trends by the analysis
of historical data predictive analysis is commonly used by businesses to forecast future growth and
lastly prescriptive analysis which allows you to make recommendations for the future
this is the final step in the analytics part of the process but it's also the most complex
this is because it incorporates aspects of all the other analyses that we've described today
step 5 sharing your results you've finished carrying out your analyses you have your insights
the final step of a data analysis process is to share these insights with the wider world or at
least with your organization's stakeholders this is actually more complex than just sharing the raw
results of your work it involves interpreting the outcomes and presenting them in a manner which is
digestible to everybody that's in the room since you'll also present your work to decision makers
it's very important that the insights that you share are 100 clear and also unambiguous for
this reason data analysts usually use reports dashboards and interactive visualizations to
support their findings how you interpret and present results will often influence the direction
of the business depending on what you share your organization might decide to restructure to
launch a new product or close an entire division that's why it's very important to present all the
evidence that you gathered and not to cherry-pick data ensuring that you cover everything in a clear
and concise way will prove that your conclusions are scientifically sound and based on facts on the
flip side it's important to highlight any gaps in the data or to flag any insights that might
be open to interpretation remember that honest communication is an important part of the process
it will help the business but it will also help you to excel at your job there's a ton of tools
for interpreting and sharing your findings these tools are suited to different experience levels
but popular tools that require no coding skills include Google Charts, Tableau, Datawrapper and
Infogram if you're familiar with python and r there are also many data visualization
libraries and packages available for instance check out the Python libraries Plotty, Seaborn
and Matplotlib whichever data visualization tools you use make sure that you polish up your
presentation skills too visualization is great but communication is key so hopefully now you
have a better idea of the data analysis process CareerFoundry have an awesome data analytics short
course and you can sign up for free by the link in the description thanks for joining us today i hope
this video has been helpful here's another video i made about data analytics which is just for you
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