INTRODUCTION
Have you ever wanted to export large amounts of data from IMPLAN? Or compare data from multiple regions without having to download data from each region individually? Or even create your own custom data table? If so, you’re in luck with IMPLAN’s Data Library feature!
IMPLAN’s Data Library is the most efficient and comprehensive tool for regional economic analysis. With it, you can analyze large datasets, utilize pre-built dashboards, build your own data queries, and easily compare data across time and regions, all with the click of a button!
What’s better than having access to data for regions included in your IMPLAN subscription? Having access to data for the entire US model in IMPLAN! With IMPLAN’s Data Library, users have access to National level data all the way down to the county level, regardless of your subscription.
Data Library provides users access to a warehouse for the majority of IMPLAN’s underlying data. It is a tool that can be used for viewing, comparing, and analyzing data across regions, industries, and time. Data Library is a separate feature from IMPLAN’s Input-Output application, so while you cannot perform economic analysis using it, you can use Data Library to enhance your analysis and reports.
WHERE CAN I FIND DATA LIBRARY?
Data Library can be accessed in two places in the IMPLAN application.
- From the IMPLAN home screen by selecting the Data Library tile.
- From the IMPLAN home screen, locate the Side Navigation Bar on the left and click the notebook icon.
DATA LIBRARY SPECIFICS
DATA ASSETS
There is a wide range of data at the national, state, and county level that can be found within Data Library. This data includes:
- Commodity Data
- Core Competency Data
- Deflator Data
- Demographic Data
- Employment & Wages by NAICS (Imputed CEW Data)
- Environmental Data
- Industry Data
- Occupation Data
- Tax Data
- Trade Flow Data
- US Foreign Trade Data
Each of these data assets provides detailed information regarding different Industries and Commodities over time within the IMPLAN application.
DASHBOARDS
One of the most common use cases of Data Library is building your own customized data sets. When you open the Data Library application, you’ll see a navigation bar at the top of the screen that provides you with options for getting started with your data query. Before we get into how to use Data Library, let’s go over some of the specific terms and definitions associated with Data Library that are found on the navigation bar.
The first tab on the Top Navigation Bar is Dashboards. Dashboards are pre-built collections of data tables and visualizations that consolidate multiple data sets from the Data Library into a single view. Dashboards provide information on different types of data, including Industry, Commodity, Employment, Wages, and many more! These pre-built dashboards offer a helpful starting point for your analysis.
There is also a subsection for user-defined dashboards where you can build your own dashboard for specific, custom data queries and save them to your account. Creating a dashboard allows you to compile visualizations and data tables based on your data queries, and even create a research summary.
EXPLORES
The second tab on the Top Navigation Bar is Explores, which offers the most flexibility for custom data querying. Explores allow you to create unique data queries and visualizations to answer your specific research questions. There are three key concepts that are important to understanding Explores in Data Library: query, dimension, and measure.
- Query: a question using data, displayed in Looker as charts/tables and visualizations
- Dimension: a qualitative field of a data query or report. These are characteristics of the data that will be displayed in the query (examples: Region, Industry)
- Measure: a quantitative field of a data query or report. These are the data points that will be displayed in the query (Total Employment, Total Value Added, Kilograms of Greenhouse Gases)
When creating a data query through an Explore, you will be selecting the type of data you want to look at (e.g., industry level, commodity level, occupation, etc.).
After selecting the type of data you want to look at, you can decide which components you want to include in your query. This is where the dimensions and measures come into play:
- Dimensions are the qualitative aspects of your query (e.g., state, county, Industry code, Commodity code, etc.).
- Measures are your actual data values (e.g., Output, Value Added, Employee Compensation, etc.).
You can select multiple dimensions and measures within your query to build a unique data set.
After selecting all the measures and dimensions you want to use in your data query, you can finalize the look of your data table by pivoting the dimensions in your query. Pivoting the dimensions in your data query allows you to put each dimension on either the x-axis or y-axis of your table. This gives you the ability to improve the look and flow of your data table. You can pivot the dimensions of your data query by clicking on the L-shaped arrows next to each dimension. Only the dimensions–not the measures–can be pivoted in a data query. This is because the dimensions are the qualitative aspects of your data query, and those can be presented on either axis of your data table.
Note: Explores in Data Library do not automatically save once you create them. There are two options for saving an Explore in Data Library in order to use it again and/or make edits to it:
- Saving the Explore as a Dashboard: The Explore will then show up under the Dashboard > User Defined Dashboards tab
- Saving the Explore as a Look: The Explore will then show up under the Looks tab
LOOKS
In Data Library, a Look is a data query or report that is saved by the user. It is a convenient way for a user to use and edit data queries that they have previously created. In order to make an edit to a data query that has been saved as a Look, click the gear icon on the top right-hand corner of the screen and select “Explore from here.” This will take you back to the Explore page, where you can make edits to your data query.
DATA LIBRARY TIPS AND BEST PRACTICES
There is so much data available for use in Data Library that it can feel overwhelming when you’re starting from square one…but it doesn’t have to! Here at IMPLAN, we have outlined some tips and best practices below in order to help you be a pro at utilizing Data Library.
TIP #1: SAVE YOUR EXPLORES PERIODICALLY
Explores in Data Library do not automatically save while you are working or when you close the IMPLAN application. It’s important to always save your progress periodically by saving your Explore as either a Look or a Dashboard. This will allow you to use the Explore in the future, or make edits to pull additional data into or eliminate data that is no longer needed from your query in the future. Making these saves allows you to ensure that none of your progress is lost in the case of a technical issue.
TIP #2: ALWAYS USE FILTERS
When you're working with Explores in Data Library, there are preset filters set by IMPLAN for each data asset category. These filters depend on the data asset category, but usually cover things like Data Year, Dollar Year, Aggregation, and Dollar Type. These are very basic categories and cover large amounts of data. When minimal filters are used in an Explore, IMPLAN will pull all the data that fits under the filters that are included. For example, if there is no state-specific filter applied in the Filters screen, IMPLAN will pull data for the entire US.
To make the most out of your Data Library experience, it is important to use filters to specify the data you’re looking for. Be sure to use filters for Industries, Commodities, States, or Counties to narrow your results to specific regions or sectors.
TIP #3: UTILIZE THE "EXPLORE FROM HERE" OPTION ON DASHBOARDS AND LOOKS
The “Explore from Here” function on the top right hand corner of data tables in Dashboards and Looks in Data Library allows you to make edits to existing Dashboards and Looks. This allows you to use a pre-built Dashboard as a starting point for your data query and to make edits to your own data query that you’ve created. The large amount of data in Data Library can be overwhelming, so using the “Explore from Here” option on a pre-built Dashboard is a great place to start, and allows you to personalize your data query.
TIP #4: UPDATE THE ROW LIMIT IN YOUR DATA TABLE
Once you create an Explore and run your data query, if you’re pulling large amounts of data, you might notice a warning that says “Row Limit reached. Results may be incomplete”. No need to panic! There are a few solutions we have here at IMPLAN to help you clear this warning.
The default for the number of rows visible in a data query is set to 500. This means that in your data query, there will only be 500 rows that will be visible in your data table. IMPLAN has the capacity to show up to 5,000 rows in your data table. All you need to do to clear this warning is update your Row Limit on the right-hand side of the data table to 5,000, and that should help clear the warning message.
But what if you have a data query that is over 5,000 rows? That leads us to our final tip!
TIP #5: DOWNLOAD YOUR DATA TABLE IN ITS ENTIRETY
Even though IMPLAN has the ability to only show up to 5,000 rows in your data table, you can create a data query that has more than 5,000 rows of data. However, you won’t be able to see the data table in its entirety in the application. In this case, you can choose to download your data table into an Excel file to view it in its entirety. To do this, you should click the gear icon in the top right hand corner of your screen, and select Download.
From there, you’ll see a pop-up that allows you to specify the format of your download, as well as some other specifications. It is important that when you are downloading your data table from IMPLAN, you select the option that includes “All results.” This will tell IMPLAN that you want to download all the results from your data query, and it will not adhere to the 5,000 row limit that is in the application.
CONCLUSION
As you can see, there are a lot of uses and functionalities of IMPLAN’s Data Library. But let this be an advantage and not a deterrent! It’s a powerful, comprehensive tool that can be used to strengthen an analysis and provide additional context for your inputs.
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Written March, 11, 2026