Medium Tableau Interview Questions

The level of detail of a view is the combination of dimensions placed on Rows, Columns and the Marks card. Each unique combination produces one mark. If only Region is in the view, Sales is summed per region; adding Category on Detail splits each region into category marks. Understanding view granularity is the foundation for LOD expressions and for explaining why numbers change when fields are added.

Tableau aggregates measures to the view level, using SUM, AVG, MIN, MAX, COUNT, COUNTD, MEDIAN and others. Aggregation can be changed per pill from its dropdown. To see every row, you can uncheck Aggregate Measures in the Analysis menu, which plots disaggregated rows, useful for scatter plots of raw records. For large data it can create many marks and slow the view.

Show Me is a panel that suggests chart types based on the fields you have selected, highlighting valid options such as bar, map, scatter or histogram and stating minimum field requirements. Automatic mark type lets Tableau pick a mark based on the pills, for example line for continuous dates. Both speed up exploration, but experienced analysts often build charts manually for full control.

A hierarchy groups related dimensions in order, such as Category to Sub-Category to Product, or Country to State to City. You create one by dragging a field onto another in the Data pane. In the view, a plus icon on the pill lets users drill down and a minus icon rolls up. Dates have built-in hierarchies from year to quarter, month and day.

Groups combine dimension members into higher-level categories, such as merging small states into Others, and are static. Sets define a subset of members as In or Out, either by manual selection, by condition such as SUM(Sales) above 1 lakh, or by Top N. Sets can be dynamic, used in calculations, combined, and changed by set actions. Groups are for labelling, sets for analysis.

A bin is a dimension created from a continuous measure by splitting values into equal-sized ranges, for example ages in buckets of 10. A histogram is the chart that counts records in each bin. Tableau can create both automatically through Show Me. You can choose bin size manually or tie it to a parameter so users change bucket width interactively.

Tableau applies filters and calculations in a fixed sequence: extract filters, data source filters, context filters, then sets, Top N and FIXED LOD, then dimension filters, then INCLUDE and EXCLUDE LOD and blending, then measure filters, then table calculations, totals and reference lines, and finally table calculation filters and trend lines. Knowing this explains why a FIXED calculation ignores a normal dimension filter unless it is added to context.

For a hierarchy, drag Sub-Category onto Category in the Data pane and name it, then drag Product below Sub-Category. For a group, right-click a dimension, choose Create, then Group, select members with Ctrl, and click Group, renaming it. Optionally tick Include Other to bucket remaining members. Both appear as new fields and can be used on any sheet.

Drag Customer Name to Rows and SUM(Sales) to Columns. Drag Customer Name to Filters, open the Top tab, choose By field, Top 10 by Sales Sum. For a flexible version, create an integer parameter and select it instead of the fixed 10, then show the parameter control. If other filters must apply first, add them to context so Top N works on filtered data.

First compare filters, date ranges and data refresh time between both sources. Check the aggregation used, for example SUM versus AVG, and whether joins have duplicated rows, which inflates totals. Use View Data to inspect underlying rows and COUNT the records. Check null handling and whether Excel has hidden rows or manual adjustments. Document the cause and fix it at the data source level.

Employee ID is numeric, so Tableau classified it as a measure. Right-click it in the Data pane and choose Convert to Dimension, and optionally Convert to Discrete. Now it acts as a label rather than an aggregated number. If you need the number of employees, use COUNTD(Employee ID) instead of SUM. Fixing default properties early avoids wrong numbers across all sheets.

Create a string parameter with the values Sales, Profit and Quantity. Create a calculated field: CASE [Measure Selector] WHEN 'Sales' THEN SUM([Sales]) WHEN 'Profit' THEN SUM([Profit]) ELSE SUM([Quantity]) END. Use this field on the axis, show the parameter control, and put the parameter in the title so users know which measure they see. Number formatting can be handled with separate calculations if needed.

Situation: a regional head could not understand a scatter plot of discount versus profit. Task: help him act on it. Action: I replaced jargon with a simple story, highlighted the quadrant of loss-making high-discount orders, added a clear title and tooltip, and walked through two examples. Result: he reduced discounts above 30 percent in two regions, and margins improved the next quarter.

Data blending combines data from two separate data sources on a sheet using linking fields, with one primary and one or more secondary sources. Secondary data is aggregated to the linking level and then joined like a left join. Use it when data lives in different systems and cannot be joined, such as sales in a database and targets in Excel. Relationships have replaced many blending use cases.

Joins merge tables at row level before analysis and can duplicate values. Blending queries each source separately, aggregates the secondary to the linking fields, and combines results per sheet, so it works across sources but has limitations such as no COUNTD from secondary in some cases. Relationships define flexible links and let Tableau choose joins per view while keeping each table's grain.

A union stacks tables with the same structure on top of each other, adding rows rather than columns. It is used for monthly or regional files such as Sales_Jan, Sales_Feb and Sales_Mar. Tableau supports manual unions and wildcard unions that pick up files or sheets matching a name pattern, and adds Table Name and Path fields to identify the source of each row.

Hyper is Tableau's in-memory data engine used for extracts since version 10.5. It is a columnar engine that loads and queries large datasets quickly, supports incremental refresh, and handles billions of rows on good hardware. Extracts in .hyper format improve dashboard speed, reduce load on source databases, and allow aggregation and filtering at extract time to keep the file small.

A full refresh rebuilds the entire extract from the source, guaranteeing accuracy but taking longer. An incremental refresh adds only new rows based on a column such as an increasing ID or a date, which is fast but does not capture updates or deletes to old rows. Teams often schedule incremental refreshes daily and a full refresh weekly to keep data correct.

An extract filter limits which rows go into the extract file, reducing size permanently until the extract is redefined. A data source filter applies to every sheet using the source, live or extract, and is useful for security or removing test records. Both run before context filters. Extract filters improve storage and speed, while data source filters control the data visible to the workbook.

Tableau Prep Builder is a visual tool for cleaning, combining and shaping data before analysis. A flow includes input, clean, aggregate, pivot, join, union and output steps, and shows data profiles to spot nulls and outliers. Output can be a hyper extract, CSV, or published data source. With Prep Conductor on Server or Cloud, flows can run on schedule as a repeatable pipeline.

On the data source page, select the month columns such as Jan, Feb and Mar using Ctrl, right-click and choose Pivot. Tableau creates two fields, Pivot Field Names and Pivot Field Values. Rename them to Month and Sales. This converts wide data into long format, which works better for time-series charts and filters. The same is possible in Tableau Prep.

Create an extract from the Data menu, choose Extract Data, and tick Incremental refresh. Select a column whose values always increase, such as Order ID or Load Timestamp. Publish to Server or Cloud and create a schedule for incremental refresh, plus an occasional full refresh. Ensure the source does not update old records, or those changes will be missed.

Connect to both sources. Build the view with the primary source, for example Region and Month from sales. Switch to the targets source in the Data pane, check the link icon next to Region and Month so they are active linking fields, then drag Target onto the view. Tableau aggregates targets to those fields. Make sure the linking field names and values match exactly.

The join type or grain caused duplication. If Returns has multiple rows per order, an inner or left join repeats each order line, so SUM(Sales) inflates. Fix it by using a relationship instead of a join, aggregating Returns to order level before joining, or using an LOD to deduplicate. Always check row counts before and after a join to catch this problem early.

Use a wildcard union on the folder with a pattern such as Sales_*.xlsx so new files are included automatically on refresh. Alternatively, build a Tableau Prep flow with a wildcard input and schedule it with Prep Conductor. Ensure every file has the same column names and types. Use the Path or Table Name field to derive the month if it is not in the data.

Situation: while refreshing a weekly revenue dashboard I noticed one region had dropped 60 percent overnight. Task: confirm whether it was real. Action: I checked row counts in the source, found a failed ETL load for two days, informed the data engineering team and added a warning banner. Result: the leadership meeting used corrected figures, and we added an automatic row-count check to the pipeline.

Most operations involving a null return null, so [Sales] + null is null. Use ZN to convert null numbers to zero, IFNULL to replace nulls with a value, and ISNULL to test for them. Aggregations such as SUM ignore nulls. In charts, nulls may appear as an indicator at the bottom right, and you can choose to filter, show at default position or hide them.

SUM over SUM gives a weighted ratio that reflects total profit against total sales at whatever level the view shows. AVG of a row-level ratio gives every order equal weight, so a tiny order with high margin distorts the result. Business users expect the weighted ratio, which also matches totals in finance reports. Always build ratios as aggregate calculations.

DATEPART returns an integer part of a date such as month number. DATENAME returns the name, such as March. DATETRUNC truncates to a level, giving a date such as the first of the month. DATEADD adds intervals, DATEDIFF computes the difference between two dates in a unit, MAKEDATE builds a date, and TODAY and NOW return the current date and time.

User functions such as USERNAME, FULLNAME, ISMEMBEROF and USERDOMAIN return details of the person viewing the workbook on Server or Cloud. A calculation like [Manager Email] = USERNAME() or ISMEMBEROF('North Team') can be used as a data source filter set to true. Each user then sees only their rows. This is a common way to implement row-level security in Tableau.

The formula mixes a row-level field with an aggregated one, such as IF [Sales] > AVG([Sales]). Either aggregate everything, for example IF SUM([Sales]) > 1000, or make everything row level. If you need to compare a row with an overall average, use an LOD such as [Sales] > {FIXED : AVG([Sales])}, which returns a row-level value that can be mixed safely.

Right-click the date field, choose Default Properties, then Fiscal Year Start, and select April. Tableau then shows FY quarters and years based on April. For a label such as FY 2025-26, create a calculation: IF MONTH([Order Date]) >= 4 THEN STR(YEAR([Order Date])) + '-' + RIGHT(STR(YEAR([Order Date]) + 1), 2) ELSE STR(YEAR([Order Date]) - 1) + '-' + RIGHT(STR(YEAR([Order Date])), 2) END.

Try changing the data type to Date first; Tableau may parse it automatically. If not, use DATEPARSE with the pattern: DATEPARSE('dd-MM-yyyy', [Date String]). For extracts and most databases this works reliably. Alternatively use MAKEDATE with INT of SPLIT parts. Then check for nulls, which indicate rows with a different format that need cleaning.

Situation: a client needed a what-if pricing dashboard in one week. Task: I had not used parameter actions before. Action: I studied official help pages, built a small prototype on sample data, and asked a senior to review it before applying it to real data. Result: the dashboard launched on time and the client used it in a pricing review meeting.

With no dimension, FIXED computes at the table level, returning grand total sales for the whole data source, after context and data source filters. It is useful for percent of total: SUM([Sales]) / MIN({FIXED : SUM([Sales])}). Since it repeats on every row, wrap it in MIN, MAX or AVG in the view rather than SUM, which would multiply the total.

FIXED LODs with dimensions return row-level values that can be used as dimensions, for example a customer's first order year used for cohort analysis. Tableau puts them in measures by default when the result is numeric, but you can convert them to dimension. INCLUDE and EXCLUDE results are measures because they depend on the view. Converting FIXED to a dimension is common for bucketing.

LOD expressions are computed in the data source as part of the query, so they can be used as dimensions, in filters and inside other calculations. Table calculations run on the aggregated results already in the view, so they depend on the view layout and addressing. LODs are better for fixed levels such as customer totals, while table calcs suit running totals, ranks and differences.

If you used a quick table calculation, the total is computed on the filtered view, so it changes. Replace it with a FIXED LOD for the total, which ignores dimension filters, or use EXCLUDE for the filtered dimension. Make sure any filters that should affect the total are added to context. Test with and without filters and document the behaviour.

FIXED returns a value repeated on every row at the fixed level. If the view is at a finer level, or at the total level, SUM adds the repeated values, multiplying the result. Use MIN, MAX or AVG to bring back the single value, or make sure the view level matches the FIXED dimensions. This is a frequent mistake when using table-level totals.

Calculate category max: {FIXED [Category] : MAX({FIXED [Category], [Product Name] : SUM([Sales])})}. Then flag: IF {FIXED [Category], [Product Name] : SUM([Sales])} = [Category Max] THEN 'Top' END and filter to Top. A simpler alternative is a RANK table calculation computed along products within each category, filtering rank equal to 1.

Situation: the marketing team planned to increase spend on a segment they believed was growing. Task: check the trend before budget approval. Action: I built a cohort analysis using FIXED first purchase date and found repeat purchase rates in that segment had fallen for three quarters. Result: they shifted part of the budget to retention campaigns, and repeat purchases improved the next quarter.

Addressing fields define the direction the calculation moves along, such as across months. Partitioning fields define the groups where the calculation restarts, such as each region. In Compute Using, checked dimensions are addressing and unchecked ones are partitioning. For a running total of sales by month restarting each year, month is addressing and year is partitioning.

Table Across computes left to right across the whole table, Table Down top to bottom, Table Across then Down moves across then wraps, Pane options restrict the calculation to each pane, Cell computes within a single cell, and Specific Dimensions lets you tick exact addressing fields. Specific Dimensions is the most reliable because it does not change when the layout is rearranged.

Key functions are RUNNING_SUM, RUNNING_AVG, WINDOW_SUM, WINDOW_AVG, WINDOW_MAX, LOOKUP, PREVIOUS_VALUE, INDEX, FIRST, LAST, SIZE, RANK, RANK_DENSE, RANK_PERCENTILE and TOTAL. They operate on aggregates, so arguments must be aggregated, such as RUNNING_SUM(SUM([Sales])). WINDOW functions accept start and end offsets for moving calculations.

For values 100, 90, 90, 80: RANK gives 1, 2, 2, 4 (competition ranking). RANK_DENSE gives 1, 2, 2, 3 without gaps. RANK_MODIFIED gives 1, 3, 3, 4. RANK_UNIQUE gives 1, 2, 3, 4, breaking ties by order. Choose based on business rules, for example dense ranking for leaderboards where ties should share a position without skipping.

LOOKUP(expression, offset) returns the value of an aggregate from another row in the partition, relative to the current row. LOOKUP(SUM([Sales]), -1) returns the previous period, useful for period-over-period difference. Positive offsets look forward. FIRST() and LAST() can be used as offsets to compare every row with the first or last period.

The table calc was probably set to Table Across or Table Down, which depends on layout. When fields move between Rows and Columns, the direction changes. Edit the table calculation and set Compute Using to Specific Dimensions with the correct addressing fields. Then the calculation stays correct even if users or developers reorganise the sheet.

Normal filters remove rows before the rank is computed, so rank recalculates on the filtered set. Either replace the filter with a table calculation filter such as LOOKUP(MIN([Product]), 0), which hides rows after ranking, or compute rank with an LOD-based approach. Explain both behaviours to the user and confirm which rank they actually need.

Place MONTH(Order Date) discrete on Columns and YEAR(Order Date) on Color with SUM(Sales) on Rows. Add a quick table calculation Percent Difference, compute using Year, relative to previous. Each month now shows growth versus the same month last year. Alternatively calculate with LOOKUP(SUM([Sales]), -1) partitioned by month and addressed by year.

Situation: I published a dashboard where a percent of total used Table Across and gave wrong numbers after a layout change. Task: fix it and rebuild trust. Action: I informed the stakeholder immediately, corrected the calc with Specific Dimensions, added a validation sheet comparing totals, and re-shared. Result: the issue was fixed within an hour, and I now test calcs after every layout change.

Reference lines mark a constant, average, median or parameter value across an axis, such as a target. Reference bands shade a range between two values, such as acceptable delivery time. Distributions show percentiles, quantiles or standard deviations. They are added from the Analytics pane or by right-clicking an axis, and can be computed per table, pane or cell.

The Analytics pane offers summarise options such as constant line, average line, median with quartiles, box plot and totals, model options such as average with confidence interval, trend line, forecast and clustering, and custom reference lines, bands, distributions and box plots. Dragging an item onto the view adds it at table, pane or cell scope.

Tableau uses exponential smoothing models to forecast continuous time series. It chooses the best model, with or without trend and seasonality, based on the data and shows prediction intervals. You need a continuous date and a measure. Options include forecast length, aggregation level and ignoring the last incomplete period. It suits quick projections, while complex forecasting needs Python or R integration.

Tableau has built-in geocoding for countries, states, cities, postal codes and more, so assigning a geographic role generates latitude and longitude. You can build symbol maps with circles sized by a measure or filled maps coloured by a measure. For Indian data, district or custom regions may need spatial files such as shapefiles or GeoJSON, or custom geocoding imports.

Drag Sales to Columns and Profit to Rows, then drag Customer Name to Detail so each mark is a customer. Add Region to Color if needed. From the Analytics pane, drag Cluster onto the view; Tableau uses k-means to group customers, and you can set the number of clusters. Save clusters as a group to reuse them in other sheets.

Select Sales and Target with a dimension such as Region, then choose bullet graph from Show Me. Tableau puts Sales as bars and Target as a reference line with distribution bands. If the roles are reversed, right-click the axis and choose Swap Reference Line Fields. Add colour for achieved versus missed using a calculation comparing SUM(Sales) with SUM(Target).

Create a pie on the first axis using a measure on Angle and a dimension on Color. Put two copies of MIN(0) on Rows, set the second to circle mark, make it smaller and white, and select Dual Axis with synchronised axes. Place a total label on the inner circle. Use donuts only for two to four segments; bar charts are usually clearer.

Create the KPI measure and a comparison calculation, for example growth versus last month. Build an indicator calculation: IF [Growth] > 0 THEN '▲' ELSE '▼' END. Use a text mark showing the value, growth and indicator, and colour the indicator using a calculated field for positive and negative. Remove gridlines and borders and place the sheet in a dashboard container.

Explain that a pie with 15 slices is hard to read because people compare angles poorly. Suggest a sorted horizontal bar chart showing values and percentages, which makes ranking clear, or group the smallest categories into Others and show the top five. If they still want a pie, limit it to a few slices. Offer both and let them choose based on readability.

Click the unknown indicator and choose Edit Locations. Tableau shows unmatched names; correct spellings such as Bengaluru versus Bangalore, set the correct country and state context, or map them manually. Make sure State is in the view to resolve duplicate city names. For permanent fixes, clean names at the source or add latitude and longitude columns.

Situation: a sales head wanted a 3D pie chart for regional share. Task: deliver what he needed while keeping the chart readable. Action: I made both versions, showed how the bar chart made the rank and gap between regions obvious, and kept his preferred colours. Result: he chose the bar chart for the monthly review and asked me to apply the same style to other reports.

Since version 2020.1, parameters can refresh their list of values or current value from a field when the workbook opens. For example, a date parameter can default to the latest date in the data using a calculation like MAX([Order Date]), and a list can pull current region names. This removes manual maintenance when new values appear in data.

For quick filters, All Values in Database shows every value of the field regardless of other filters. Only Relevant Values shows only values that remain after other filters, creating cascading filters such as states within the selected region. Relevant values need extra queries and can slow dashboards, so use them carefully on large data. All Values in Context is another option.

Create an integer parameter Top N with a range 5 to 50 and step 5. Drag Product to Filters, open the Top tab, choose By field, select Top and pick the Top N parameter, by SUM of Sales. Right-click the parameter and Show Parameter. Add region to context if Top N should apply within the selected region.

Go to Dashboard, Actions, Add Action, Filter. Choose the map as source sheet, select the target sheets, set run on Select, and choose what happens when clearing the selection, such as Show all values. Under Filter, choose Selected Fields and map State to State. Test by clicking a state and checking that the bar and trend charts update.

Create a date parameter with dynamic current value from a calculation such as MAX([Order Date]) or TODAY()-1, refreshing when the workbook opens. Filter the data with a calculation [Order Date] = [Selected Date] set to True. Show the parameter control. Users get yesterday by default and can pick another date without editing the workbook.

Measure first with performance recording. Reduce the number of quick filters and replace some with dashboard filter actions. Change Only Relevant Values to All Values in Database where cascading is not needed. Add an Apply button to multi-select filters, convert high-cardinality filters to wildcard search, and move fixed restrictions to data source filters. Re-test after changes.

Create a float parameter Price Change with a slider range of -20 to 20 percent. Build calculations: New Revenue = SUM([Sales]) * (1 + [Price Change]) and New Profit = New Revenue - SUM([Cost]). Optionally include an elasticity parameter for demand change. Show current versus projected profit side by side, and label assumptions clearly on the dashboard.

Situation: a supply chain team asked for an inventory dashboard without clear details. Task: understand what they needed. Action: I met users to ask which decisions they make, which KPIs and filters matter, refresh frequency and who will use it, then sketched a wireframe and confirmed it. Result: the first version was accepted with only minor changes, saving rework time.

Situation: finance wanted revenue after returns while sales wanted gross revenue on the same dashboard. Task: satisfy both without confusion. Action: I arranged a short call, documented both definitions, and added a parameter to switch between gross and net with a clear label and tooltip definitions. Result: both teams used the dashboard, and the definitions became part of our data dictionary.

A URL action opens a web page, a file or an email link using field values from the selected mark. For example, clicking an order can open the order details page in an internal system with the Order ID passed in the URL, or clicking a company can open a search. URL actions can open in a new tab or inside a web page object in the dashboard.

Double-click the sheet title and use Insert to add fields or parameters, for example Sales for <Region> in <Parameters.Year>. For multiple selected values, the title shows All or the value list; for cleaner text, create a calculation that returns the selection or All, using a FIXED or table calc approach. Dynamic titles reduce confusion during presentations.

CONTAINS is case-sensitive in many sources, so Phone or PHONE will not match. Use CONTAINS(LOWER([Product]), 'phone') to normalise case. Also trim extra spaces and check for nulls. If you need complex patterns, use REGEXP_MATCH where the data source supports it. Test the result by counting matched rows and reviewing samples.

Site roles define the maximum capabilities a user can have. Creator users can connect to data, build in Desktop and Prep and publish. Explorer users can explore and web edit existing data sources, with Explorer (can publish) allowing publishing. Viewer users can view and interact with dashboards. Site Administrator roles manage users and content. Permissions on content further restrict what each user can do.

A published data source is a data connection with calculations, metadata and refresh schedules stored on Server or Cloud, which many workbooks can connect to. It provides a single version of truth, central refresh, and permissions, and can be certified by data stewards. Changes to calculations update all connected workbooks. It is a key practice for governance in enterprise Tableau.

Subscriptions email users an image or PDF snapshot of a view or dashboard on a schedule, such as every Monday at 9 AM. Data-driven alerts notify users when a measure crosses a threshold, such as inventory below safety stock, checked when data refreshes. Both help business users who do not open dashboards daily, but too many can cause email fatigue.

Use the performance recorder to find slow queries and rendering. Use extracts with only needed fields and rows, aggregate data, reduce marks and sheets, limit quick filters and relevant values, prefer integer and boolean calculations, avoid heavy custom SQL, use fixed dashboard size for caching, and hide unused fields. Workbook Optimizer in Desktop also suggests improvements based on best practices.

Add Navigation objects or buttons from the Objects pane, set each to go to a target dashboard, and style them with text or images. Place them in a horizontal container at the top of each dashboard for consistency. Use Go to Sheet actions for drill-through from charts. Hide sheets and use Show Sheets as Tabs off when publishing, so users rely on the custom menu.

The dashboard probably uses Automatic or Range size, so objects reflow on different resolutions. Set a fixed size suitable for the audience, such as 1366 by 768 or 1200 by 900, or use Range carefully with tiled containers. Avoid floating objects that overlap. Create a device layout for phones if needed and test on common screen sizes before sharing.

Check the background task and job history for the error message. Common causes are expired or changed database passwords, firewall or VPN issues, schema changes like renamed columns, timeout because the query is too large, or Backgrounder overload at peak time. Fix credentials, update the data source, optimise the query or move the schedule. Set up failure email notifications.

Compare data connection, since Server may use a live connection through a slower network or different credentials. Record performance on Server using the performance recording parameter. Check concurrent load, VizQL and cache settings, the number of quick filters and marks, and whether extracts are refreshing at the same time. Ask the Server admin for resource usage and background job schedules.

Use Tableau's embedding API or the Tableau web component to embed views in the portal, with single sign-on such as SAML or connected apps for authentication so users do not log in twice. Ensure users have licences and correct permissions, apply row-level security, set the dashboard size to fit the portal, and use the API to pass filters or parameters from the page.

Control capabilities through permissions: allow Download Summary Data but deny Download Full Data, or deny downloads entirely for some groups. Remove sensitive fields from the data source or hide them, and apply row-level security so downloaded data is limited to the user's scope. Provide a dedicated crosstab sheet with approved fields for export and document what is allowed.

Situation: the operations team tracked delivery delays in spreadsheets updated weekly. Task: give them daily visibility. Action: I built a dashboard on a scheduled extract with delay KPIs, a city map and a drill-down to late orders, and trained the team leads. Result: they spotted delays a day earlier, and late deliveries reduced by about 12 percent over two months.

Situation: an earlier dashboard I built had low usage after launch. Task: improve adoption on the next project. Action: I involved users in design, ran a short training session, added a how-to-use panel, set subscriptions for managers, and checked usage statistics monthly to remove unused views. Result: weekly active users doubled compared with the earlier dashboard, and feedback guided the next release.

Situation: my dashboard needed customer data that was spread across three systems. Task: get a reliable, single table. Action: I wrote clear requirements with grain, fields and refresh needs, shared sample SQL, and reviewed their view together, testing counts against source systems. Result: they built a curated table, my dashboard became simpler and faster, and we reused the table for two other reports.

Show and hide buttons let users collapse or expand a floating container, such as a filter panel or help text, by clicking a button. They save space on crowded dashboards and keep the main view clean. You enable it from the container menu with Add Show/Hide Button, and style it with images or text. They also work well for mobile layouts.