In this post, you will learn how to use cross-filtering to improve the interactivity of your charts. In addition, we are also going to do a quick refresh on the drill-down feature in Google Data Studio.

Before we begin, if you want to explore different navigation features and how to make your reports user-friendly and visually appealing, you can take a look at our guide on Advanced Page and Report Navigation.

Now, let’s get started!

How does cross-filtering work?

In simple terms, cross-filtering lets you interact with one chart and apply that interaction as a filter to other charts in the report. When cross-filtering is turned on for a chart, you can filter the report by interacting with that chart in two ways:

• Clicking on the chart dimensions.
• Brushing or dragging a mouse across a line chart, time series, or area chart.

If you are used to applying filtering in all the regular controls, then you would easily notice that there’s not much difference with cross-filtering. For example, you can filter a Pie chart based on country dimensions and gets the same results as using a drop-down filter. Besides, you can also limit cross-filtering to certain groups, just like in other controls.

How to cross filter in Google Data Studio?

This feature is usually turned on for most of the connectors. However, it’s turned off for ads related connectors by default. For this reason, you need to turn it on manually. You can also turn off other connectors if you do not wish to use cross-filtering.

How to turn cross-filtering on/off?

To use this feature, you need access to a report with an active chart. For instance, in this example, we will use data from the Google Analytics sample set. All Google Data Studio users have access to this demo data set. Here is what you have to do:

• Click the “Edit” button in your report.

• Choose the desired chart. In this case, the chart type is a map.

• Select the “DATA” menu on the right and scroll to the very bottom.
• From there, use the toggle to activate cross-filtering.

How to reset chart filters?

If you wish to reset a chart filter to its default position, the steps are as follows:

• Right-click a chart and choose the “Reset” action/
• Un-select the selected dimensions
• Click anywhere on the border of the chart you are filtering.

Chart interactions limitations

• The Cross-filtering feature is not available for bullet charts or scorecards.
• You are not allowed to cross filter using the “others” category.
• Cross-filtering has the same limits, just like other controls when it comes to filtering across data sources.

Allow users to drill down into your data

Another popular way to add interaction to your charts is to allow users to drill down into your data. This feature provides you with a way to reveal additional details in levels within a chart.

For example, if you want to reveal sessions by country and city, you can enable drill down on the chart. Turning this feature on will save you from creating two different charts for countries and cities.

Once you enable drill the drill-down feature, users can:

• Drill down to lower levels. For example, from country to city
• Drill up to higher levels. For example, from city to country.

You can reduce the number of charts required by using the drill-down feature. Besides, it makes it easier for users to find useful insights by navigating to various levels.

How to activate drill-down for a chart?

• Open a report and select a chart.
• Navigate to the right menu and click on the “DATA” option.

•  Use the toggle to activate “Drill down”.

• Add levels of details by adding more dimensions. For example, you can perform the following geo breakdown:
  • Sub Continent
  • Country
  • City

• Now choose the default drill down and make sure that is placed as your first dimension. (This is the level that shows automatically when users open your report)

How does drill down work under the hood?

First, you need to define a dimension hierarchy when adding a drill-down feature to your chart. The hierarchy dictates the detail levels your chart can display when users drill up or down. Examples of hierarchies include;

• Geography (Country>>Region>>City)
• Date (Year>>Month>>day)
• Google Analytics Events (Category>>Action>>Label)
• Product (Department>>Category>>SKU)

Note that the order of the dimensions is very important for defining a hierarchy. The best practice is to define hierarchies, starting with the most general dimensions down to the most specific.

For example, defining a day hierarchy starting with month, year, then date could produce undesirable results. This is because it lacks flow.

Another example is starting with country>>region>>city hierarchy. Here users will get undesirable results or get confused because you start with a general level, then move to a more detailed level, and back again to a general level.

Final Thoughts on Cross-Filtering and Data Drill-Down

Cross-filtering and data drill-down are just a few ways to make your reports more interactive. There are other controls that can put the power in your users’ hands such as metric sliders, so make sure you take a look at our other articles in the blog.

I hope this was helpful! If you have any questions on advanced data blending, feel free to subscribe to my newsletter or take a look at one of our courses.

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If you want to learn how to build powerful data visualizations and unlock insights that can help you drive business results for your clients or employers, take a look at my full course on Udemy.

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The role of a data analyst is to collect and analyze data sets to assess, predict, and improve a business. Data analyst skills are derived from many disciplines including mathematics, statistics, and computer programming.

To be successful in the field of data analytics and land a great data analyst role, a combination of technical skills and business acumen will be necessary. As an analyst, you will be working with data in addition to business personnel, which means you will need a well-rounded skill set. If you are interested in becoming a data analyst, this article will explore a handful of essential skills you will need to stand out and be successful.

The data science and data analytics dictionary

On your way to entering the data analytics field, you will encounter a lot of terms, which look similar and interchangeable but unfortunately have completely different meanings. Before we get any further, let us make sure those terms are clear and the relationships between them are known:

• Big data. The field of big data deals with ways to analyze, extract information from, or otherwise deal with data sets that are too large or complex for traditional data-processing applications. To illustrate just how “big” big data is, Domo estimates that in 2020, every person will accumulate 1.7 megabytes every second.

• Data science. In 2012, the Harvard Business Review deemed the job of a data scientist as “The Sexiest Job of the 21st Century,” which has been going from strength to strength even until today. In essence, data science is an interdisciplinary field that combines math and statistics, specialized programming, advanced analytics, and storytelling to uncover and explain the business insights buried in data. Often, data science uses complex machine learning algorithms to build predictive models.

• Business intelligence (BI). Data preparation, data mining, data management, and data visualization are all included under the umbrella term, business intelligence.

• Data mining. This is the process of finding patterns, correlations, and anomalies within a large set of data to discover business intelligence that can drive business growth.

• Data warehouse. Also known as an enterprise data warehouse, this is a type of data management system that acts as a central repository for and supports the development of business intelligence activities, especially data analytics.

• Artificial intelligence (AI). In contrast to the natural intelligence displayed by humans, artificial intelligence is demonstrated by machines.

• Machine learning (ML). The concept of machine learning is, in fact, a branch of artificial intelligence and computer science that uses data and algorithms to simulate the way humans learn, gradually increasing its accuracy.

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Key data analyst skills

1. Problem-solving and critical thinking

As a data analyst, you are going to be solving problems and interpreting answers every day. The role of an analyst is to present a question or concern and use data to infer a solution and produce actionable insights. If you are determining the functionality of data or the significance of a trend, you will need problem-solving and critical thinking skills.

2. Communication

Insightful discoveries are useless if you can’t communicate them to the rest of your team clearly. Therefore, in addition to analytical skills, data analysts must possess effective communication skills to do this.

As an analyst, you will be collaborating with a team or meeting with stakeholders often. Your ability to communicate successfully will be tested and will need to be strong. You will have to explain a complex idea or describe a detailed analysis, and if you are not able to do so, your findings will be pointless.

3. Data cleaning and preparation

An important step in the data analysis process is the cleaning and preparation of the data that you are working with. This means taking raw data from one or more sources and reformatting, combining, and correcting it to create an accessible data set that offers insight for an analysis. If the data is not properly prepared, the analysis will be affected, and the results will be biased.

The responsibilities you will have while cleaning and preparing data may include:

• Standardizing data formats
• Identifying missing or inconsistent data
• Merging, splitting, or joining input sets
• Removing outliers

The role data preparation takes in the analytics process is crucial to ensuring the quality and accuracy of your work. Homing in on this key skill is vital for a successful career in the industry.

4. Statistical knowledge

While you don’t have to have an expert level of statistical knowledge, a fundamental understanding is key to interpreting the data you are working with.

The level at which you will need to know statistical analysis will vary by role and data type. However, every professional in the field of data science should have a base level of statistical knowledge. A few concepts that will provide an advantage when analyzing data include:

• Regression analysis
• Standard deviation
• Coefficient of variation
• Correlation coefficient
• Covariance
• Inferential statistics
• Dummy variables

Having insight into how statistics are and will be affected by the data you’re analyzing will help you as an analyst. This is not necessarily about becoming a data scientist, but rather about ramping up your technical skills and understanding. There are courses available that can teach you fundamental statistical concepts and theories.

5. Microsoft Excel

Most office workers are familiar with Excel, but data analysts need to be expertly skilled and able to use the program’s more complex features.

Excel’s analytical and modeling tools will guide you in organizing, cleaning, and manipulating data sets. The program also offers tools to help identify and visualize patterns and trends. Excel has been a cornerstone of the industry for years and is an essential skill you will have to use every day.

6. SQL

Structured query language (SQL) is used to communicate with a set of structured data, allowing for data or subsets of it to be extracted or combined while providing a means to update, organize, and modify data structures. This is normally executed through a SQL query in a relational database such as Google BigQuery or Microsoft SQL Server.

Most data analysts will have to use SQL to retrieve data for their analysis, so it is an essential tool to have under your belt. Many data analyst positions will require having SQL knowledge and may even have a technical screening to demonstrate your skill using this language.

SQL is one of the simpler languages to understand and is an excellent place to begin when learning how to use programming languages.

7. Statistical programming

While knowing SQL is key for this industry, you will also need to learn statistical programming languages. Python and R are powerful programming languages that are popular in the industry. They are both open source and free to use, functioning as a means to complete advanced analysis and manipulation of large data sets.

In addition to Excel and SQL, understanding Python and R will drastically increase your efficiency and accuracy as a data analyst. While knowing both is ideal, learning at least one of these two statistical languages will help you become a well-rounded analyst.

R was designed specifically for use in data science and analysis. Python, however, was created as a multi-purpose tool and happens to have some application to data analytics. Choosing which statistical programming language you learn will fall under personal preference depending on your interests and field of work.

8. Python

Python is a general-purpose programming language that recently began adding libraries for data analysis and machine learning. Over time, Python has become the preferred tool for data analysts due to its ease of use and versatility. Compared to R, Python is much easier to learn and has a simple syntax that makes collaboration more convenient. Python is often a mandatory programming language in most computer science degrees or certificates.

9. R

R is a programming language that was created specifically for data analysis and is preferred by some analytics for this reason. The R language has packages that offer useful tools for preparation, manipulation, data visualization, and predictive analytics.

10. Data visualization and visual storytelling

As a data analyst, being able to access and analyze data is only part of the work that needs to be done. Once you have finished the analysis and are ready to share your results, your responsibility is now to determine how the data should be presented. If you cannot create an impactful and comprehensive visual from your data, you will not be able to communicate your findings effectively.

Moreover, even though the right answer or interpretation might be obvious to you, your audience could still end up confused. Being able to identify the trends and patterns of your data and deciding how to illustrate them is a crucial aspect of data analysis. Therefore, good presentation skills are mandatory for all data professionals.

A tool like Google Data Studio can be a great starting point for learning how to visualize and communicate key business findings to those who do not understand the complex data.

Job seeking tips for a data analyst role

Data analysts are growing in demand every year at an above-average rate. As we already established, a data analyst position requires a wide range of technical skills and domain knowledge. However, the benefits of becoming a data analyst are great, to name a few:

• Ability to work with large brands and their unique data
• Great entry-level salaries and opportunities for growth and specialization
• Freedom to work from anywhere, as the nature of the job is remote-friendly

If you have a knack for critical thinking and enjoy working with data and statistics, becoming a data analyst may just be the perfect fit. If you are interested in looking or applying for a data analyst job, check out data analyst resume examples for inspiration and as a point of reference for your own portfolio.

What are some common data analyst responsibilities?

In a data analyst job description, you are likely to find quite a few of the technical skills that we described earlier, but you should also look for the data analytics specialization and client industry.

For example, it might be a data analyst job that focuses on marketing and advertising analytics, in which case you would be using Facebook Ads, Google Search, DoubleClick, and StackAdapt as data sources. Plus, data manipulation and visualization would probably be key responsibilities.

However, another role might require a stronger data science background, where the analyst would have to build a model from first-party/CRM data.

Indeed, the data analytics world is vast, and asking about the specific tasks and client industry during your interview is highly recommended. This will shed light on the specific data analytics skills that you might need to be successful in the job.

What are some job title examples in the data analytics field?

The data analytics field is very broad. Not only can you be involved in a wide area of industries, but there are also many applicable job titles:

• Data scientist
• Data engineer
• Business analyst/business analytics
• Health data analyst
• Analytical lead

If you have any questions, feel free to reach out on LinkedIn or subscribe to the datastudio newsletter for more updates. In the meantime, happy learning!

The post 10 Data Analyst Skills for a Successful Data Analytics Career in 2022 appeared first on Data Studio.

Data analysis is vital for the success of any business. It’s the process of collecting, modeling, and analyzing data to obtain useful insights for decision-making. Without data analysis tools, it would be hard for businesses to make effective and data-driven decisions. 

Luckily, we are in a digital era with plenty of options available. Although it’s good to have many options, most businesses end up using the wrong combination of tools or simply get overwhelmed. This article will guide you through the best five data analysis tools accompanied by relevant data analytic techniques that can help your business grow.

1. Google Data Studio (data visualization)

Google Data Studio is a free data analytics tool that turns data into fully customizable interactive dashboards and reports that are easy to read and share. The tool integrates automatically with many popular data sources, making it one of the most sought data visualization platforms.

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Features

Google Data Studio allows you to connect, visualize, and share data easily.

• Connect. You can add data from hundreds of data sources such as Facebook Ads, Google Analytics, Instagram, Amazon, Alibaba, Google Sheets, MySQL, PostgreSQL, Youtube, Google Ads, and many more. Plus, if you don’t find the data connector you need, you can always use a data automation tool like Supermetrics.
• Visualize. Google Data Studio can uncover a lot of hidden patterns through data visualization. It can change how you view your data and even lead to more successful decision-making. Also, it’s a great way to present to other viewers who might have trouble interpreting raw data.
• Share. You can easily share your reports with other groups to view with a click of a button. Additionally, you can control what the groups can edit or view, and all the control is in your hands.

Business scenario

A digital marketer or data analyst might face a hard challenge in turning raw data from different sources into one easy-to-read and shareable report. In the past, businesses did this task manually by hiring programmers to link reports every time. However, thanks to Google Data Studio, merging data from different sources now takes a split second.

Google Data Studio does a lot more than just data visualization. It contains features that allow you to transform data to obtain valuable information for decision-making. For example, you can use formulas for advanced analytics such as calculated formulas and CASE Statements to manipulate data as needed.

Some data sources such as Google Analytics may provide ambiguous raw data. As such, you might need to group different elements to get a cleaner report. You can use Google Data Studio CASE statements to do this.

For example, you can group different Facebook traffic channels into one using this CASE statement:

CASE
WHEN Source IN ("facebook", "m.facebook.com", "l.facebook.com", "facebook.com", "lm.facebook.com") THEN "Facebook"
ELSE "Not Facebook"
END

Or differentiate between paid social traffic and organic social traffic using this CASE statement in Google Data Studio:

CASE WHEN Medium IN ("social-ad") THEN "Paid Social"
WHEN Medium IN ("social") THEN "Organic Social"
ELSE "_Other"
END

2. Google BigQuery (data analysis)

Google BigQuery is a fully managed data analytics and warehouse platform that uses a serverless computing model and a built-in query engine to process petabytes of data in minutes. 

It’s a flexible, powerful, and fast data warehouse integrating with other Google Cloud Platform services. Google BigQuery is, in simple terms, a platform that you can use to analyze a huge pool of data. It features an inbuilt machine learning capability and supports ANSI SQL syntax querying.

Features

• Database. It collects, stores, and organizes data from different sources.
• Integrations. Spark integration, data preparation, and machine scaling.
• Processing. Real-time, streaming data workloads and processes batch, in multi-tenant, singular, or cloud-based systems.
• Data transformation. It facilitates real-time and high-volume data analysis. Users can query data using query languages such as SQL.
• Connectivity. Integrate data from many different sources. Apache Hadoop, Apache Spark, and Data Lake.
• Operations. Governed discovery, data visualization, data workflow and notebooks.
• Data management. Data integration, data compression, data quality, data analytics, machine learning, and data lake analytics.
• Deployment. On-premise and on-cloud deployment options.
• Security. Authentication, data security, role-based authorization, encryption, and audit logs.
• Storage. It can store columns and tables. It also supports multiple data types such as hashes, sets, sorted sets, and lists.
• Availability. Auto sharding, auto-recovery, and data replication.
• Support. Google BigQuery supports various operating systems such as Windows, Mac, and Linux.

Business scenario

When comparing a company’s performance, you need to analyze a ton of data. This can consume a lot of time, even if you use a powerful CPU. For example, you may want to sort data from a table with millions of rows and export filtered data to a new CSV file. Since Google BigQuery operates on the Google Cloud, the process will compute in a matter of minutes.

As a test, you can use the following PHP code below to export your table to a CSV file: 

use Google\Cloud\BigQuery\BigQueryClient;
// $projectId  = 'The Google project ID';
// $datasetId  = 'The BigQuery dataset ID';
// $tableId	= 'The BigQuery table ID';
// $bucketName = 'The Cloud Storage bucket Name';
 
$bigQuery = new BigQueryClient([
	'projectId' => $projectId,
]);
$dataset = $bigQuery->dataset($datasetId);
$table = $dataset->table($tableId);
$destinationUri = "gs://{$bucketName}/{$tableId}.json";
// Define the format to use. If the format is not specified, 'CSV' will be used.
$format = 'NEWLINE_DELIMITED_JSON';
// Create the extract job
$extractConfig = $table->extract($destinationUri)->destinationFormat($format);
// Run the job
$job = $table->runJob($extractConfig);  // Waits for the job to complete
printf('Exported %s to %s' . PHP_EOL, $table->id(), $destinationUri);

3. Microsoft Excel (data analysis)

Microsoft Excel is a data analysis and visualization software developed by Microsoft. Excel makes use of spreadsheets to organize and analyze numbers. Besides, it also features graphing tools, calculation pivot tables, and macro codes to help analyze and visualize data seamlessly. Microsoft Excel is a widely used platform by companies of all sizes around the world. According to Statista, Office 365, which features Excel, controls 38% of the global office suite market share.

Features

• Add header and footer
• Find and replace command
• Password protection
• Data filtering
• Data sorting
• Edits results automatically
• Built-in formulas
• Formula auditing
• Create charts
• VBA (Visual Basic for Applications) programming
• Pivot tables
• Store up to 1 million records and connect to a variety of databases
• Grouping
• Data validations
• Worksheet and workbook protection options.

Business scenario

You can analyze data in Excel by using the Analyze Data button. In a business, financial operations are critical. You need to calculate profits, losses, means, interests, and so much more. 

Excel is the powerhouse of all these business calculations. It features thousands of formulas, functions, and macro codes to help you get the job done fast. You can also visualize data as line graphs, pie charts, and bar graphs, to name a few.

Excel uses VBA code which makes data analysis easy. The VBA code can format your spreadsheets, search for items, filter items, send emails, and much more. Let’s look at some useful VBA codes for data analysis.

VBA code for creating a chart

Sub CreateChart()

Dim MChart As ChartObject

Set MyChart = ActiveSheet.ChartObjects.Add(Top:=60, Left:=100, Width:=500, Height:=300)
MyChart.Chart.SetSourceData Range("C4:D10")

End Sub

VBA code for analyzing negative numbers 

Sub highlightNegativeNumbers()
Dim Rng As Range
For Each Rng In Selection
If WorksheetFunction.IsNumber(Rng) Then
If Rng.Value < 0 Then
Rng.Font.Color= -16776961
End If
End If
Next
End Sub

4. Microsoft Power BI (data visualization)

Microsoft Power BI is a data analysis and visualization platform that integrates apps, software services, and connectors to analyze and share insights. BI stands for Business Intelligence. Power BI allows organizations to visualize and share data easily, and the best thing about Power BI is that it comes as an Android app, desktop application, and an online SaaS.

Features

• Attractive methods for visualizing data
• Microsoft Power BI connects to a wide range of data sources
• Datasets filtration
• Customizable, printable, and shareable dashboards
• Flexible tiles
• Navigation pane
• Informative reports
• Natural language question box
• DAX data analysis function
• Help and feedback buttons
• Office 365 app launcher

Business scenario

Power BI is one of the most useful data analysis tools for business. You can study the insights, trends, and business intelligence. This tool features artificial intelligence that allows you to form queries using your own words. 

Besides, it also automatically discovers interesting insights from your data, schedules reports, and sends notifications if anything changes. You can use data analysis expressions to solve data analysis problems and calculations. 

Data analysis expressions, popularly referred to as DAX, are a collection of constants, functions, and operators. You can use them in an expression or formula to generate new insightful information.

Examples of useful DAX functions in Microsoft Power BI are as follows:

The FILTER function

Expression - FILTER(<table>,<filter>)

For example, you want to find the number of shipped products above $300 from your list of products. In this case, you will combine two functions. The first is the COUNTROWS function that counts the number of rows. Next, add the FILTER function to complete the expression.

COUNTROWS(FILTER('Shipped', 'Shipped'[Shipped] > 300))

• The first Shipped represents the table.
• The second parameter, ‘Shipped’ [Shipped] > 300)) indicates a true/false boolean expression that checks each row in the table.

The ALL function

Expression - ALL(<table> or <column>

Like the filter function, the ALL function can work together with another function for more specific results. This function overrides any applied filters and returns all the values in the rows or columns in a given table.

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The RELATED function

Expression - RELATED(<column>)

If you have two tables with related data, you can use the RELATED function to track the relationship. This function executes a LOOKUP function to evaluate values in the other table regardless of whether another filter has been applied.  However, this only works in a row context.

5. Looker (data analysis)

Looker is a browser-based business intelligence software (BI) that helps businesses analyze data and develop insightful visualizations. It is a cloud-based platform that provides businesses with a unified source of business analytics. Looker embeds custom data apps, workflow integrations, and analytics to provide truthful and powerful sources. It is compatible with databases such as MySQL and Oracle.

Features

• Intuitive, easy-to-use reports interface
• Offers a variety of chart and graph formats
• Tack KPIs visually using Scorecards
• Offers several self-service features like calculated fields, data column filtering, data discovery, collaboration, search, and auto modeling
• Looker offers advanced analytics like big data services, data visualization, and predictive analytics
• Allows integration of APIs to communicate with other software
• Looker offers various metrics such as engagement, sessions, entry and exit packages, retention, return, funnels, conversions, and custom event tracking
• Reporting: It offers real-time, predictive, and custom reports
• Connect to various external and internal data sources

Business scenario

Looker generates and submits SQL queries against a database connection. Fundamentally, the SQL queries are generated based on a LookML project. A lookML project describes relationships between columns and tables in the database.  

You don’t have to be a guru in SQL to generate a powerful Looker model, and you only need a slight understanding of SQL to write LookML. It is easy to learn even for a non-tech-savvy person.

Here is a video tutorial for more LookML details from the Looker team. It’s easy to import local and remote projects using LookML.

LookML to import a local project

# This project
project_name: "my_project"
 
# The project to import
local_dependency: {
  project: "my_other_project"
}

LookML to import a remote project

remote_dependency: ga360_block {
  url: "https://github.com/llooker/google_ga360"
  ref: "master"
  override_constant: connection {
	value: "importing_project_connection"
  }
}

Choosing the best data analysis tools

There are many data analysis tools available today, which makes it difficult for businesses to choose the best ones. If you want to make the best choice, you need to understand your needs first before picking a specific data stack. Look for features that will make your work easy to collect, transform, share, and visualize data. Remember, it is not about the number of tools you have at your disposal but the quality of insights you produce, which can help you grow your business.

The post 5 Data Analysis Tools to Help Your Business Grow appeared first on Data Studio.

As some of you might know, I graduated high school from Nord Anglia Education in China. At the time, I was exploring universities in the United States as my next step, which normally require SATs. However, I was studying International Baccalaureate (IB), which as a program does not cover the SAT material or preparation.

I started asking around how I can prepare for the SATs online and get resources. Luckily, a teacher of mine, who was British told me “Have you heard of Alison? They have free online courses on any topic”, so I created an account and there it was “SAT Math Exam”, the first course I ever took online.

This was 2011 and I still have my certificate today. When the Alison folks reached out to me, I immediately remembered the name and recalled the little memory.

With that we are launching our first course on their platform called “Introduction to Data Visualization” in our pursuit to providing free access to high-quality education!

Plus, the course uses Google Data Studio, as the main tool to teach data visualization! Feel free to share this with friends and colleagues, who are just starting out their journey in data visualization. More courses to come soon, so stay tuned!

Introduction to Data Visualization – FREE Course – Google Data Studio

I hope this was helpful! If you have any questions, feel free to DM me on LinkedIn or subscribe to my newsletter for more updates. In the meantime, happy learning!

The post Introduction to Data Visualization with Google Data Studio – FREE Course appeared first on Data Studio.

Apart from allowing users to input data in the calculated fields, parameters also allow you to create custom report templates. They operate in the same way as variables in programming languages. Variables pass values to the reports defined by a user.

The three major uses of parameters in Google Data Studio include:

1. Displaying results depending on what a user inputs.
2. Sending back custom parameters to connecters such as Facebook and Google Ads.
3. Sending back custom parameters to Google BigQuery (a SQL query).

Depending on your knowledge level of parameters, feel free to directly scroll to the section that interest you the most.

How to configure a simple parameter in your data schema?

In this section, we are going to connect a Google Sheets file and create a parameters within it.

Connecting Google Sheets to maximize flexibility when setting up a parameter

1. Open the Google Data Studio Dashboard.
2. Click the “+ Create” a button on the right upper corner and select a data source from the pop-up menu.

3. Of the available options, select Google Sheets.

4. You will see all Google Sheets available. Choose any that you want to work on. I recommend a simple sheet, without too many columns and data.

5. Now click the “Connect” button to link the data source and generate a data schema.
6. A page such as the one below will appear, which is where you will have to define the parameters. In our case, Google Data Studio has generated the default “Record Count” and also we have a “Text” dimension. Of course, your configuration might be different.

How to create a parameter?

I know you are eager to add and configure your parameter, but first, you will need to understand the following concepts:

• Parameter ID: Data Studio uses a  parameter ID internally to identify the parameter.  Report viewers will not see the parameter ID. It is set by default. Changing parameter ID will break the calculated fields.
• Parameter name: This is the name that refers to the parameter in the list of available parameters.
• Permitted values: It specifies the values that a user can input.
• Data type: Parameters can store Boolean, numbers, and text values.
• Default value: It specifies the automatic starting parameter value.

Now, you are ready! (sorry the intro took so long). Let’s continue from where we left off.

7. Click on “Add a Parameter” on the top right corner in the data schema.

8. A parameter form such as the one below will pop up. Fill in the “Parameter name”. In the “Data type” drop-down, you can use a Boolean, Number, or Text.

9. The next step will require a permitted data setting that is based on the selected data types. We have used a whole number in this example, and that’s why we have three options: “Any value”, “List of values” and “Range”.

You can add any value to the first option. But in the list of options, you can only select from the provided values. Lastly, you can set a range of permitted values by choosing a range. Users will have to choose only a value that falls in the range that you set. This option also allows you to set a default value.  However, for our example, we are going to use any value option.

10. Save your settings by tapping the blue “Save” button. This will generate your parameter.
11. Go back to the previous page by clicking the arrow next to “All fields”.

The parameter that we have just created will appear in the data schema.

How to configure parameters within a calculated field in Google Data Studio?

So far, we have created a parameter called “Results”. But I need to add a new field with the payment amount using the calculation mechanism. Here are the steps to follow when configuring a field:

12. Add a new field by clicking on the upper right corner.

13. Rename the field using a descriptive name.

This is the section where we need to define the field pattern of calculation. Since we need to calculate the total payment in our case, then we can use the following formulae.

New payment Field = Record Count * Results

The next step is to implement the formula in the field.

14. Click the “+” button that appears upon hovering on the parameters.

Note that you cannot mix “Text” and “Numbers” when using the operators. After clicking the “+” button, you would be able to see the field in the formula tab. Next add “*” (for multiplication) and the last remaining field to complete the formula.

15. Save your work to complete the schema.

16. Navigate back to the previous field by clicking the arrow next to all fields. Its located at the far left corner.

17. The “New Payment Field” is now showing in the schema.

18. To make changes, click the @ button next to the parameter that you want to edit. Click save after making any changes and go back to the previous page.

Advanced editing techniques to modify parameters further in Google Data Studio

How to show or hide parameters in reports?

You can show or hide parameters in your reports once you have created a data source. To do that, you can:

1. Edit a data source that includes parameters.
2. Select the parameter.
3. Click on the three dots located to the right of the parameter.
4. Click show parameter/hide parameter.

If you hide the parameter, it will be automatically removed from the list in the reports properties panel. Any components that use this parameter will be broken. The calculated field will use the default values for the formula when you hide a parameter.

How to copy a component with parameters?

If you copy a component with set parameters, then all the values in the property panel are copied as well. You can edit the component to ignore the copy’s parameters

How to fix invalid parameters?

Sometimes the parameter configuration may change, causing charts using it to break. For instance, if the ability to override a value is removed, the report will no longer provide the required data. All the charts impacted by this change will show an error.

You can fix this. Simply locate the invalid parameter and click fix invalid values on the parameters section of the component. Note that you can set the parameters at the group level, page, report, and on individual charts.

How to override default parameter values?

Google Data Studio offers two different methods to override default parameter values:

Method 1. Edit individual components in a report to override the default parameters

1. “Edit” the report.
2. Click on any component that includes parameters.
3. Click on the bottom-left of the data tab to change the default values.

Method 2. Edit parameters at the page or report level.

1. “Edit” the report.
2. Choose a file to edit File > Page or Report Settings > Current Page Settings.
3. Change the parameter values.

How to change parameters using the report link in Google Data Studio?

Advanced users and developers can set their parameters using the report’s URL. These type of parameters can configured programmatically. The corresponding values of Google Data Studio parameters are encoded and passed as encoded JSON strings.  Since they are appended to the URL, you can edit them by following these steps:

1. “Edit” the report.
2. Click on Resource > Manage report URL parameters.
3. Check the “Allow to be modified in report URL” box.

This page also loads the other URL parameters. You can edit the name by clicking the edit pencil icon. Make sure that all the URL parameters are unique. Edit the parameter name does not affect how it is used in your report.

Final Thoughts

With parameters, Google Data Studio has now brought in a new way to make data visualization more engaging. Users can customize the reports by adding their own values into calculations and charts. All this is made possible by using parameters. They are used for displaying results in calculated fields based on the user input. You can also pass them back to a community connector or BigQuery via a custom SQL query. It allows users to add new values or change the input using a slider, text input, or a dropdown. Besides, the values can also be set by configuring the report URL.

I hope this was helpful! If you have any questions, feel free to DM me on LinkedIn or subscribe to my newsletter for more updates.

Dynamic Dashboards and Data Analysis with Google Data Studio – 2021

If you want to learn how to build powerful data visualizations and unlock insights that can help you drive business results for your clients or employers, take a look at my full course on Udemy.

The post How to use parameters in Google Data Studio in 2021? appeared first on Data Studio.

The below lecture is from my course on Data Studio. There, you can see how to enable the so called “report publishing”, as it is not turned on by default. In essence, report publishing lets you control when viewers see the changes you make to a report. Up until now, all viewers were seeing changes in real-time, which is not always good.

Transcript of the report publishing video:

Now, let’s build on top of the version of history that we explored in the last lecture and look at something called publishing settings, which will be in your menu here. Now, before we click on it. I just wanted to remind you that when you’re editing a dashboard, a report in Google Data, you by default, the edits are live. So what I’ve done here to demonstrate that is I have the report open and I have it in edit mode and here I have it in view mode. So just to quickly show you right now, there is my profile here. It shows that who is part of the report right now and who is looking at it. So the second user is, again, me. But if I move this table up. Immediately live.

That table moves up here in the U.S., so you really don’t have a lot of flexibility if you are a agency or a business that wants to work on a draft version and then publish the final piece. So to prevent you from editing the live, there is an option in the letters to you to actually have a draft and a published version. So there is this basic version control there that is absolutely super useful. I’m just going to move this back here and go to file and go to publication publishing settings. And he would really ask you, do you want to turn on the manual publishing control so you can control when the viewers are seeing the published version and you’re doing changes on the draft?

Continued…

So this is by default, not turned on. I’ll turn it on here and I’ll click safe. So what happens is you get this little button here, draft version, published version. I’ll close this window and let’s see what’s happening in the draft. So I’ll click and chart. And I want a bar chart here. I’ll expand it. It’s not going to be a lot of data, just we have the medium, for example, here, so all is good. Now, as a simple chart, let’s look at what’s happening in the published. So I clicked on the published, but unfortunately, my chart is not there and that’s because I haven’t really published the draft. So go back to the draft mode.

The chart is here, but the customer is not able to publish to see it. So by clicking on the draft version, we have two options. We can either publish here and push the final of the draft into production or the published version, or there’s another way to do it and click on edit come to see version history. And this is the familiar version of history that we explored in the previous lecture. You have this green box here that says current published version and then current version based on the timing. So what I could do is click and publish.

Continued…

And published here. And all changes have been published. So that’s what we’re getting now, I’ll close that. And all of you and so right now I’m doing the draft and the published versions are the same. So we I have a little bit more control over what the client is seeing. And if I want to do some sort of changes and I’m doing the changes while there’s a presentation going on, the actual dashboard is not chaotically changing and things are moving out. So it it really allowed us to present data in a better way for your clients.

I hope this was a helpful snippet on version control! If you have any questions, feel free to DM me on LinkedIn or subscribe to my newsletter.

The post How to enable report publishing and use version control in Data Studio? appeared first on Data Studio.

How to setup the Chrome UX (CrUX) report?

What should I look at in the CrUX dashboard?

The CrUX dashboard includes a lot of details, but there are three aspects of the user experience that Google is focusing on in 2020:

• Largest Contentful Paint (LCP): this one measures how long it takes for your page to load the largest image or text block visible within the viewport. According to Google, the LCP should be under 2.5 seconds to be considered “good”.
• First Input Delay (FID): this one measures the time it takes for your website to be interactive when a users have triggered an action (eg. button click). Here a “good” is considered less than 100 milliseconds.
• Cumulative Layout Shift (CLS): this one measures how stable your website layout design is. For a “good” experience, pages should have a CLS of less than 0.1.

Here is a quick example of a Google Search Console that I have been managing. I have been flagged for two out of the three metrics mentioned. Essentially, Google is telling me that I need to fix certain pages to improve the UX. To take it a step further, the CrUX dashboard can enhance this view by providing rolled up metric performance distribution and device breakdown.

I hope these were helpful tips for the SEO folks out there! If you have any questions, feel free to DM me on LinkedIn or subscribe to my newsletter ?

The post How to use the Chrome UX (CrUX) report in Google Data Studio for SEO? appeared first on Data Studio.

How to set up budget pacing with a bullet chart?

If you have a single metric that is of great importance, you can use a bullet chart to set a target or a benchmark. For example, if you are running a paid media campaign, you would want to know how much you are spending against your budget. Let’s say the campaign is a flash sale that spans only three days and you need to monitor budget closely.

In this case, to configure a benchmark/target for your bullet chart you need to do the following:

1. The first step is to set the “Range Limits”. You want to make sure Range 1 starts at 0 as an entry point. Range 2 and 3 should reflect the relative scale of your metric. If you know you are spending only $750, then you might want to set Range 2 at $500 and Range 3 at $1000. Another tip is to have Range 2 be 50% of Range 3, this creates a more visually appealing chart.
2. This is the easy part. You simply pick a value for a target and check the box. The target value would appear on your chart immediately as a black line.

In addition, you can make any design changes to the coloring, axis and value rounding by navigating to “Style”.

How to add targets and benchmarks and conditional formatting to a table?

Now, you can set targets and flat benchmarks within a table as well. Unlike the bullet chart, this method is not just about a rolled-up view, but rather a more detailed representation of your data. In the scenario below, we have a “Month” dimension and a “Spend” metric. Here is how to re-create the chart:

1. Switch the metric under “Column #1” to Bar. This will provide you with a more clear view of the metric’s scale.
2. Tick the “Show number” box to add the value next to the bar.
3. Lastly, tick the “Compact numbers” bar. If your values are in the millions, this is definitely a must. Otherwise, you might get a bit cross-eyes, when looking at large datasets.

Lastly, you can go ahead and check the “Show target” box, which will allow you to input your target/benchmark.

It is worth mentioning that you can repeat this process by adding additional metrics to your table (eg. impressions, clicks, CTR and etc.). The target/benchmark works on a column basis, so you can have a dedicated target/benchmark for each column.

Optional: Enhancing your targets and benchmarks with conditional formatting

As a final touch, consider using conditional formatting. If you are new to it, I have a detailed intro video/article on the topic, so you can take a look. Ideally, you would want to create a rule (#1 below) that highlights certain abnormalities in the data, which are also correlated to your targets or benchmarks.

In this case (#2 above), I have my benchmark set at $5000, so by creating the conditional formatting rule, I am able to color the “Spend” fields every time they go above $5000. In this way, it would be much easier for me to find the anomalies in the data when I look at the table.

What are chart reference lines and how to use them for dynamic sales targets?

“Reference lines” are a bit more advanced than what we have seen so far and can, for example, help you visualize monthly sales or average store revenue against a specific sales target. The extra functionality here is that you can pick the target to either be a constant or a calculated value. Let’s unpack this.

Firstly, create a normal bar chart and navigate to “Style”. From there, simply select “Add a reference line” and you would get two options here:

Reference Lines with a Constant Value

Nothing new under the sun here, you can add a constant “Value” and that shows up on your bar chart. You have the ability to “Label” the reference line and formatted it, but it is fairly similar to the other methodologies we explored.

Reference Lines with a Calculated Value

This is the really cool part! If you select the “Calculated value” under “Type”, then you can create a dynamic target that changes based on a calculation. For example:

1. I have picked “Spend” as my metric, which is essentially the sum of my monthly media spending.
2. Then, I have selected “Average” as a calculation. Hence, every time I look at the chart and slice the data (by date, campaign and etc.), the reference line is going to fluctuate based on the average value of my “Spend” for that particular filtering. In that way, I can spot months that were below/above the average without doing any extra work.

Here is another example. You can create two reference lines and configure their calculations to the following to create a range:

1. Max – This is going to display the largest value in your bar chart.
2. Min – This is going to display the lowest value in your bar chart.

These are all great ways to create benchmarks and targets. Also, don’t forget, the “Calculation” field includes a few additional options that can be useful:

• Median
• Percentile

How to use Google BigQuery to create dynamic budget pacing tables?

This is the most advanced one from the lot, so you might find it a bit tricky. However, I want to show you how the introduction of BigQuery can make things much more flexible. In the video below (part of my course), you would be able to see how by writing a few SQL queries, you can JOIN a “Planned” and “Actual” media spend tables, which then can be transformed into a dynamic pacing chart in Data Studio. Enjoy!

I hope this was helpful! If you have any questions, feel free to DM me on LinkedIn or subscribe to my newsletter ?

Want to learn more about Google Data Studio and BigQuery?

If you want to learn how to build powerful data visualizations and further analyze marketing data with SQL take a look at my courses on Udemy. Each course includes practical hands-on exercises that will give you a chance to play around with real datasets.

Dynamic Dashboards and Data Analysis with Google Data Studio

Google BigQuery for Marketers and Agencies

The post 3 Ways to Use Google Data Studio for Benchmarks and Sales Targets appeared first on Data Studio.

Therefore, in this post, I want to show you how to use the newly released “Exchange” and “App/URL” dimensions in Data Studio to automate your inventory reporting and make finding insights much easier.

Bonus: If you read until the end, I have shared a free dashboard template too.

How to add “Exchange” and “App/URL” dimensions to your report and why?

The first thing you want to do is, connect DV360 to a Data Studio report. This can be done by clicking “Add data” in any new Data Studio report. Then pick the “Exchange” and “App/URL” dimensions in a table, as they are going to be your main inventory dimensions. You can add the standard “Impressions”, “Clicks”, “Revenue (Advertiser Currency)” and “Revenue eCPM (Advertiser Currency)” as metrics. A quick reminder, the metrics that contain Revenue in them are essentially your media cost. This is just how DV360 displays them.

The beauty of this table is that you are not only using the direct data connector, but you can also find great insights easily. For instance, you can see that you are buying the inventory of a certain website via different exchanges. In our example, “website2.com” is bought by both “Index Exchange” and “DoubleClick Ad Exchange”, but the CPM for each of those exchanges varies. This is valuable information, especially if that one website is responsible for a large portion of your campaign spend.

How to combine subdomains and app inventory from the same publisher?

Often, DV360 splits out subdomains and apps when reporting on inventory. You might be buying from the same publisher, but your “App/URL” dimension will show separate lines. In the example below, we have “publisher.com” as the main digital property, then a subdomain for the news section of the publisher’s website – “publisher.news.com” and two separate lines for the Android and iOS apps.

In this case, you can use CASE statements to group the subdomain and app inventory from the same publisher. This will allow you to see the real media spend and performance of the publisher. If you are not too familiar with CASE statements and Regular Expressions, I have written an introductory article on the topic. Following the example, the statement would look something like this:

How to explore the device type availability of an Exchange?

The dimension “Device Type” type is also available in the DV360 connector. For example, this allows you to see the number of impressions, bought and served per device, per exchange. This could prove to be quite powerful when you are planning a device-specific campaign.

*NEW fields available in the DV360 connector

The Google Data Studio Team is constantly working on adding additional dimensions and metrics to the DV360 connector, the most recent ones being:

• Line Item Type
• Inventory Source Type
• Creative Type
• CM Post-View Revenue
• Inventory Delivery Method
• Insertion Order Status
• Line Item Status

Display & Video 360 (DV360) Inventory Report in Data Studio

I have put together a dashboard template that combines everything I have mentioned so far. If you are trying to directly connect your Display & Video 360 instance, some of the metrics/dimensions might show as broken. I am working on a way to improve the experience, but in the meantime, you just have to replace them manually. Once that is done, the dashboard works perfectly fine!

I hope this was helpful! If you have any questions, feel free to DM me on LinkedIn or leave a comment below.

The post Automate Display & Video 360-DV360 inventory reporting with Data Studio appeared first on Data Studio.

Summary

1. CASE Functions in Data Studio
   1. Scenario 1: How do I re-create my Google Analytics “Custom Channel Groupings” in Google Data Studio?
2. REGEXP_EXTRACT and REGEXP_REPLACE Functions in Data Studio
   1. Scenario 1: How to extract a UTM parameter into a separate dimension with REGEXP_EXTRACT?
   2. Scenario 2: How to remove parameters from URLs with REGEXP_REPLACE?
3. CONCAT and IMAGE Functions in Data Studio
   1. Scenario 1: How do I use CONCAT to combine dimensions?
   2. Scenario 2: How do I use CONCAT with IMAGE to display images in a table?
4. DATE_DIFF Function
   1. Scenario 1: How do I calculate the difference between two dates using DATE_DIFF?
5. LOWER/UPPER Function
   1. Scenario 1: How to use LOWER and UPPER functions to standardize text strings?

Let’s dive into the world of functions!

1. CASE Functions in Data Studio

In a previous post, I wrote an introductory article with a lot of examples on how to use CASE statements in Google Data Studio, so if you are starting out, I would recommend you to look at that first. For this scenario, I want to focus on a more complex use of the CASE function.

Scenario 1: How do I re-create my Google Analytics “Custom Channel Groupings” in Google Data Studio?

In Google Analytics, users have the capability to create “Custom Channel Groupings” that allow them to sort how users have arrived at their website (eg. direct, organic search and etc). However, the Google Analytics native connector for Data Studio does not offer those groupings. Therefore, you have to create them yourself with a CASE, which is actually a positive, because Data Studio gives you much more flexibility with functions when compared to the “Custom Channel Groupings” in Google Analytics.

As usual, you would have to create a new calculated field at the data source-level. Since the “Custom Channel Groupings” are going to include an extensive list of “Source/Medium” attributes, your CASE might get a bit crowded. Nothing to worry about, I have already pre-written the code for you, but here are a few callouts:

• All CASE statements follow a sequential order when executing. For example, if your rule for “Paid Search” is generic, that might pick up the “Organic Search” data before the specific rule for “Organic Search” has had the chance to run.
• You can use OR and AND combinations within your CASE to make things more digestible. I highly recommend that.
• Always have a back-up rule! For example, you might have “Social Source Referral=Yes”, but if Google Analytics misclassifies your social channel – “l.facebook”- as “Referral”, you need to have that addressed with a regular expression.

CASE 
WHEN (Source="(direct)" AND Medium="(none)") OR (Source="(direct)" AND Medium="(not set)") THEN "Direct"
WHEN (Source="google" AND Medium="cpc") OR REGEXP_MATCH(Medium,"(cpc|ppc)") THEN "Paid Search" 
WHEN Medium="organic" OR REGEXP_MATCH(Source, ".*(^google|baidu|bing).*|.*(search).*") THEN "Organic Search" 
WHEN Social Source Referral="Yes" OR REGEXP_MATCH(Source,".*(social|fb|facebook|twitter|linkedin|pinterest).*") THEN "Social" 
WHEN REGEXP_MATCH(Source, ".*(mail|outlook).*") THEN "E-mail" 
WHEN REGEXP_MATCH(Source, ".*(youtube).*") THEN "Video YouTube"
WHEN Medium="affiliate" THEN "Affiliates" 
WHEN (Source="dfa" AND Medium="cpm") OR REGEXP_MATCH(Source,"(display|banner|native|.*(doubleclick).*)") THEN "Display (Campaign Manager)"
WHEN Medium="referral" THEN "Referral" 
ELSE "(Other)" 
END

2. REGEXP_EXTRACT and REGEXP_REPLACE Functions in Data Studio

Scenario 1: How to extract a UTM parameter into a separate dimension with REGEXP_EXTRACT?

If you have multiple campaigns and want to see how each one is contributing to your business, the REGEXP_EXTRACT function can be a real time-saver. Here is an example, the URL below includes a campaign name that we want to extract, as that part of the UTM is valuable to us.

https://datastudio.ca/?utm_source=dfa&utm_medium=cpm&utm_campaign=summer_sale

In this case, you would have to write the formula below. Firstly, selecting the dimensions that you want to extract from – “URL (with utm)”. After that, the regular expression simply says “starting from campaign= match everything until you reach another &“.

Once you apply the function, the result is pretty cool. We have extracted the campaign name in a separate and much cleaner dimension.

Scenario 2: How to remove parameters from URLs with REGEXP_REPLACE?

Building upon the previous function, let’s assume that you want to remove all parameters to get the original URL. This is often used when you want to only see the traffic to a landing page and ignore all parameters. In our case, you can write the formula below. Once again, the regular expression says “look for anything after ?“. Then, the third argument specifies what we want our replacement to be. We simply leave that blank.

The final table would look something like this and our URL is now much more visible.

3. CONCAT and IMAGE Functions in Data Studio

Scenario 1: How do I use CONCAT to combine dimensions?

This is a function that could prove to be extremely useful, especially if you are working with Campaign Manager (CM) or Google Ads. For many organizations, “naming conventions” are important, as they allow for easy data parsing. Well, with the CONCAT function, you can combine a few pre-existing fields into a single and more powerful dimension. Here is how it works:

Luckily, the function has a simple syntax. You type your first dimension, in this case, “Campaign Name” and then a delimiter. The delimiter specifies how dimensions will be separated from each other. A few common delimiters are “|”(pipe) and “,”(comma).

The result is a compact and more informative dimension that combines all of the three fields. Remember, you can add as many dimensions as you want, as long as the final field is useful to you.

Scenario 2: How do I use CONCAT with IMAGE to display images in a table?

A more advanced and exciting use of the CONCAT function is nesting it inside an IMAGE function. For this scenario, I have used the product data from the Google Analytics Demo Account. All we are doing here is, using “Product SKU” as a dynamic dimension since the product code is used in the URL. You can apply this same methodology with your eCommerece data set, as long as you have a dynamic element in the URL.

Once you have created the IMAGE calculated field, you can add it to a table. You will see the images showing-up immediately. A little caveat, the IMAGE dimension is only supported in tables, not other chart types.

4. DATE_DIFF Function

Scenario 1: How do I calculate the difference between two dates using DATE_DIFF?

This is a familiar one for those of you who use SQL on a regular basis. The function returns the difference between two date/time values in a numeric format. An interesting scenario where I found this function useful is paid media for eCommerce. We often look at the time a user was exposed to an ad and the time he/she actually made a purchase. This gives us a sense of what we would call “time-lag”, which indicates that the user needs some time to think before committing to a purchase. This is exactly where DATE_DIFF kicks-in.

The formula is straight-forward, you just have to include the two date fields that need to be subtracted.

5. LOWER/UPPER Function

Scenario 1: How to use LOWER and UPPER functions to standardize text strings?

Last but not least, this function allows you to standardize your text dimensions. For example, imagine you have an online form on your website with a free-form input field that asks “How did you hear about us?” and the input field is free-form. The user might input any type of capitalization, so to clean up the mess, we can use LOWER.

You can also use the UPPER function in a similar fashion, converting all provinces to the standard uppercase abbreviations.

I hope this was helpful and you can now use all of these Data Studio functions in your own projects! If you have any questions, feel free to DM me on LinkedIn ?

Dynamic Dashboards and Data Analysis with Google Data Studio – 2021

If you want to learn how to build powerful data visualizations and unlock insights that can help you drive business results for your clients or employers, take a look at my full course on Udemy.

The post Top 5 Most Useful Calculated Fields and Functions in Google Data Studio appeared first on Data Studio.