Created with underwriters in mind, the Accelerator for Broker Portfolio Analytics allow users to see who the biggest players are, which brokers incur the biggest costs and greatest returns, and how brokers compare when internally benchmarked across a series of key KPIs.

You can [connect with The Information Lab directly](https://www.theinformationlab.co.uk/customer-success/) to find out how their experts can help you with your own Broker Analytics.

### Answer Key Business Questions
- How have our key broker performance indicators changed relative to last year?
- How have brokers individually contributed to GWP, NILR, RRV, TA change?
- What is the distribution of scores between different brokers?
- What contribution did the top 10 brokers make vs the rest and who are the top 10?

### Monitor and Improve KPIs
- GWP, £, Gross written premium
- Live policy count, int, the number of live policies
- Net incurred loss ratio (NILR), %, Net written premium ÷ incurred claims
- Brokerage, %, Total acquisition costs ÷ gross written premium
- Renewal rate variation (RRV), %, Net written premium internal share ÷ as if policy premium
- Technical adequacy (TA), %, Net written premium internal share ÷ benchmark net premium

### Required Data Attributes
- Policy ID, (string), unique identifier of each policy
- Broker name/ ID, (string), unique identifier of each broker responsible for the policy
- Inception date, (date), the date the policy was launched
- Gross written premium internal share, (currency) £
- Benchmark net premium, (currency), £
- Committed RRV, (int)
- Incurred claims internal share, (currency), £
- Total acquisition cost, (currency), £
- As if policy premium, (currency), £
- Net incurred loss ratio (NILR), (int) %, Net written premium ÷ incurred claims
- Technical adequacy (TA), (int) %, Net written premium internal share ÷ benchmark net premium
- Contract status type, (string), (bound, enquiry or quote)
- Entry Status, (string), (Cancelled, Live, NTU, Partial)
- Net written premium internal share, (int), £


Broker Portfolio Analytics

Explore and contrast how brokers have contributed to KPIs

Localize listing name

Insurance

Financial Services

Free

The Accelerator for Investment Portfolio Performance enables a Fund Manager to review a Fund’s performance over time--as well as to drill down into an individual company’s metrics.  Fund Managers can get a grasp on key metrics like a company’s Sales and EBITDA over time, or they can gain qualitative information, like value drivers and a company’s analytical journey through these dashboards. Designed and created by a Tableau Ambassador, this accelerator uses visual best practices and expert-level user design that are tailored to Financial Services and Private Equity decision makers. 

[Get in touch with Lovelytics directly](https://lovelytics.com/our-services/data-visualization/) to find out how they can help you. 

### Answer Key Business Questions
- What is a Company’s Sales and EBITDA over time? 
- What is the valuation of each company within a Fund and how has that changed over time? 
- What is a Fund’s Committed Capital and what is the quarterly rate of return? 
- What is the story behind each company and some of their value drivers that make them profitable?

### Monitor and Improve KPIs
- EBITDA
- Sales
- Initial Rate of Return (IRR)
- Multiple of Invested Capital (MoC)
- Committed Capital
- Distribution to Paid-in (DPI)

### Required Data Attributes
Fund Level Attributes
- Committed Capital (Integer)
- Distribution to Paid-in (Integer)
- Gross Initial Rate of Return (Integer)
- Net IRR (Integer)
- Gross MoC (Integer)
- Net MoC (Integer)
- Acquisition Date (Date)
- Business Description (String)
- Company (String)
- Currency (String)
- Current Initiatives (String)
- Fund (String)
- Industry (String)
- Key KPIs Story (String)
- Status (String)

Monthly Attributes
- EBITDA Budget Local (Integer)
- EBITDA Budget USD (Integer)
- EBITDA Local (Integer)
- EBITDA USD (Integer)
- Sales Budget Local (Integer)
- Sales Budget USD (Integer)
- Sales Local (Integer)
- Sales USD (Integer)

Quarterly Metrics
- 3rd Party Debt (Integer)
- Capital Leases (Integer)
- Cash (Integer)
- Debt Investment (Integer)
- Debt Other (Integer)
- Equity Invested (Integer)
- Equity Investment (Integer)
- Factoring (Integer)
- IRR (Integer)
- Multiple of Investment (Integer)
- Net Fund Investment USD (Integer)
- Realized Value USD (Integer)
- Revolved (Integer)
- Term Debt (Integer)

Investment Portfolio Performance

Insights into the financial performance of the companies in your investment portfolio

Private Equity

The Accelerator for Tableau Cloud Migrations enables Tableau Server Admins to connect to the underlying PostgreSQL database within Tableau Server and identify content that should be prioritized and/or does not need to be migrated to Tableau Cloud. This can be helpful in any migration project, as it will ensure that you are capturing all your data during the migration process–and, migrating only what is required.

[Connect directly](https://web.zuar.com/accelerator-support-submission-page-tableau-cloud-migration) with Zuar to learn more.

### Answer Key Business Questions
- Who is using Tableau the most frequently/infrequently and will be impacted the most/least by migration?
- What projects should be prioritized, and which could be archived?
- Which workbooks are accessed the most often? Which workbooks are no longer in use?
- How many data sources do you have that do/do not need to be migrated?

### Monitor and Improve KPIs
- Current Viewcount for Workbooks
- Workbook Freshness (Based on Last Refresh Date)
- Datasource Freshness (Based on Last Refresh Date)
- Active and Recent Users (Based on Last Login)
- Empty or Stale Projects

### Required Data Attributes
Data can be accessed in your Tableau Server environment by following [these instructions.](https://help.tableau.com/current/server/en-us/perf_collect_server_repo.htm).
- Created At ( Projects) (date)
- Id ( Datasources) (string)
- Id ( Workbooks) (string)
- Login At ( Users) (date)
- Name ( Datasources) (string)
- Name ( Workbooks) (string)
- Name ( Users) (string)
- Name (Hist Projects) (string)
- Name (Hist Projects) (string)
- Owner Name ( Projects) (string)
- Updated At ( Datasources) (date)
- Updated At ( Workbooks) (date)
- Id (Hist Datasources) (int)
- Id (Hist Workbooks) (int)
- Revision (Hist Workbooks) (int)
- View Count (int)



Tableau Cloud Migration Planner

Identify the content that does/does not need to be migrated to Tableau Cloud. 

English

ITSM

Zuar’s Accelerator for Financial and Revenue Intelligence enables you to understand your overall business performance, how and where your business is growing this year, what strategic growth could look like in the future, and how the inventory of the products that you carry will change–and impact your business–over time.

From the summary KPIs in the Financial Intelligence Toolkit, you can navigate to the 
- YTD Growth Watch, where deep details about growth across key sectors of your business enable you to optimize how your organization operates;
- Strategic Growth Forecast, where you can see the relationship between orders and revenue growth and provide more accurate and data-driven sales forecasts to your leadership;
- And the Inventory Supply Planner, where you can make sure that inventory stock levels will enable your revenue goals.

[Zuar](https://www.zuar.com/contact) built this Accelerator from their work with CPG and Retail customers: [click here](https://www.zuar.com/contact) to connect with them to learn more.

### Answer Key Business Questions
- What is revenue year-to-date, and how does it compare with the prior period?
- What is revenue across critical sectors of our business, as well as month-over-month? 
- What customers are driving growth, and which are at risk?
- What’s our forecasted revenue, and how do we expect different segments of our business to change?
- How do we expect the sales and orders from each customer to change?
- What are inventory stock levels, and what are projections for our product lines in the future? 
- What is our inventory on hand, and what’s the forecast for each of our product lines for the next six months?
- How much revenue is at risk from depleted stock levels?

### Monitor and Improve KPIs
- Revenue YTD (Dollars)
- Change in Revenue over Prior Period (%)
- Change in Sales (Dollars and %)
- Change in Orders (PO Count and %)
- Change in Average Order Size (Dollars/PO and %)
- Change in Order Frequency (Days Between Orders)
- Current Inventory in Units
- Replenishment Rate in Units per Month
- Sales Forecast in Units per Month
- Revenue at Risk due to Stock Shortages
- Projected Ending Supply Balance
- Projected Supply Balance Over Time
- Months of Stock at Current Rate of Sale

### Required Data Attributes
Strategic Forecast Summary: Order Detail, detail for the last 12 months + Opportunity detail for the next 12 months
- Date (date)
- Customer (string)
- Account Manager (string)
- Channel (string)
- Region (string)
- Order ID (string)
- Record Type (Order or Opportunity) (string)
- Days Since Last Order (int)
- Sales Total (from Orders) (currency)
- Sales Forecasted (from Opportunities) (currency)

YTD Growth Watch: Order Detail, starting from the beginning of the prior year.
- Date (date)
- Customer (string)
- Channel (string)
- Account Manager (string)
- Region (string)
- Product Line (string)
- Product Name (string)
- Sales Total (Dollars)

Inventory Planning
- Product Line (string)
- Product Name (string)
- Product ID (string)
- Quantity on Hand (int)
- Price (currency)
- Stock Par Max (Units) (int)
- Stock Par Min (Units) (int)
- Date (date)
- Item Quantity Forecasted (Units) (int)
- Replenish Quantity Expected (Units) (int)


Financial and Revenue Intelligence Toolkit

Support your revenue goals with the right inventory and organizational plans

Procurement

Retail

Supply Chain & Manufacturing

Corporate Finance

Sales

This Accelerator for Return Fraud Monitoring leverages retail sales and returns data to monitor returns activity across all products and all store locations to identify potential fraud. The dashboard uses historical trends to predict return activity and clearly identifies areas that fall outside of those expected values. Actionable data provides managers with the insights that they need to take corrective action to prevent additional losses and ensure employee compliance.

[Connect with Cleartelligence to learn more.](https://www.cleartelligence.com/tableau-aceelerator-return-fraud-monitoring)

### Answer Key Business Questions
- What were the actual returns in a given period and how does that compare to the expected returns? 
- Are there excess returns, which could signify fraud, occurring in any single store location or group of locations? 
- Are excess returns more prevalent in certain products or groups of products? 
- What days and times of the week are returns most common? 
- Which associates are processing the most returns? 
- How much of the returns are confirmed invalid (open box fraud), or potentially invalid (returned without receipt)?

### Monitor and Improve KPIs
- Total Sales 
- Total Returns 
- Expected Returns 
- Excess Returns 
- Excess Returns by Location 
- Excess Returns by Product Category 
- Excess Returns by Product 
- Distribution of Returns by Time & Day 
- Distribution of Returns by Associate 
- Percentage of Returns by Return Type

### Required Data Attributes
- Associate Number (String) 
- Product Category (String) 
- Product ID (String) 
- Product Name (String) 
- Product Sub-Category 
- Return Status (String) 
- Store ID (String) 
- Store Location (String) 
- Time (Time) 
- Transaction Date (DateTime) 
- Transaction Type (String) 
- TransactionID (Integer) 
- Return Quantity (Integer) 
- Sales (Decimal) 
- Sales Quantity (Integer)

### Build Your Data Source
The data source for the Return Fraud Monitoring analysis is comprised of a single transactional table with all purchases and returns from a retailer's operational data store. Records are aggregated by transaction and product, so for each product in a single transaction, there is one row of data.

You'll need to query the appropriate schema in your own data store in order to analyze these KPIs for your own business.

Returns and Purchases are identified by a field named TransactionType. 

Other attributes included in the data source include the Transaction ID, Product ID, Product Name, Product Category, Product Segment, Store ID, Store Location, Transaction Date, Transaction Period, Customer ID, Customer Name, Order ID, and Store Associate. 
There are two measures unique to Returns: Return Status and Return Quantity. There are two measures unique to Sales: Sales Quantity and Sales.

There is a secondary table, with a relationship set on Period, that includes total hours worked by Store Associate by Period.


Return Fraud Monitoring

Monitors retail organization's returns for fraud

**Tableau on Firebolt**

The Firebolt connector for Tableau delivers sub-second query speeds on live connections, thus enabling truly interactive analytics experiences without needing to compromise on data volume, freshness, or latency.

**About Firebolt**

Firebolt is the cloud data warehouse for builders of next-gen analytics experiences. Combining the benefits and ease-of-use of a modern architecture with sub-second performance at terabyte scale, Firebolt helps data and dev teams deliver data experiences that end users love.

Built on a cloud-native decoupled storage and compute architecture, Firebolt eliminates traditional challenges associated with provisioning, scale and resource utilization. Firebolt’s optimized storage format and tightly coupled indexing techniques make even the most challenging workloads fast and efficient, without the need for lengthy and customized engineering efforts.

Firebolt

Firebolt is the cloud data warehouse for next-gen analytics experiences.

Palantir Foundry is the world’s leading platform for data-driven operations and decision making. This technology, forged through years of direct experience with complex institutional data challenges, re-unifies companies around their central mission. It enables them to become fully digital "connected companies". Once data has been integrated, cleaned, and validated, the Palantir Foundry JDBC driver provides a SQL interface to Foundry datasets to easily access data and visualize it in Tableau dashboards.

Palantir Foundry

A Tableau Connector Plugin for Palantir Foundry

The Couchbase Analytics Tableau connector provides familiar and secure native Tableau connectivity so that you can quickly visualize and analyze your operational JSON data. It uses Couchbase Analytics tabular views to annotate your data with type and relationship information for Tableau and provides an easy-to-use SQL-based interface to support your analytics-driven query needs. This connector works with Couchbase Server v7.1 and later.

Couchbase Analytics

The Couchbase Analytics Tableau Connector enables visual operational data against JSON data

The ClickHouse ODBC connector is an Apache 2.0-licensed Tableau connector for ClickHouse tables. You will need to install the ClickHouse ODBC driver as a prerequisite. Once installed you can connect to ClickHouse servers and access tables using the full power of Tableau.

Altinity Connector for ClickHouse

Enable Tableau access to ClickHouse data via ODBC.

Denodo is the leader in data virtualization providing agile, high performance data integration and data abstraction across a broad range of data sources, and real-time data services at half the cost of traditional approaches.

Tableau already includes a native connector for Denodo, which uses the PostgreSQL ODBC driver to connect to Denodo. This connector you are downloading relies on the JDBC driver of Denodo. Use this connector if the native connector does not suit your environment.

This connector is compatible with Denodo 8.0 and Denodo 7.0.

Denodo JDBC

Tableau Connector for Denodo that uses the JDBC driver.

Simple Tableau Extension that allows users to create styled backgrounds for their Dashboard objects and containers. Leverages Bootstrap CSS, Tailwind CSS color, and additional extended classes.

Addition documentation can be found here [Github Link](https://github.com/FsuLauncherComp/BootstrapTableau) 

Documentation

TabCSS

Sunburst charts help us visualize hierarchical data and provide quick summaries. 

But in most cases, when the breakdowns are more at each level, inference gets tougher. That's the problem we are solving with this Dynamic Sunburst Chart by adding the ability to drill down. 

After a quick configuration, the extension will visualize your data as a Sunburst Chart, and any interaction on the chart will drill it down to the next level. 
This helps the user get both the high-level and individual element-specific from the same chart or widget. 

Dynamic Sunburst Chart

Dynamic sunburst chart helps you visualize hierarchical data instantly and explore with drill-downs

Insight into data requires users to build performant data models. Key and unique constraints provide structures to access data efficiently. Key Finder mines a data source to automatically find all valid key and unique constraints. Sample data and various measures of validity help users choose the key and unique constraints helpful for tasks such as managing entity and referential integrity, defining index structures, identifying dirty data, and finding all ids that reference the same entity.

### Use cases:
1. **Data Integration:** Key Finder is capable of automatically discovering keys that hold exactly on your dataset and checking the degree to which any other attribute combinations hold as keys. This information serves as a foundation for finding correct foreign keys and joining tables.
2. **Data Quality:** A very common problem that exists in production databases is data duplication. The same entity will often appear more than once in a table with different auto-generated ids. Key Finder makes it easy to quickly discover if this problem is present in your data.
3. **Performance Optimization:** One of the most important aspects of building a performant data model is choosing the right indexes to speed up data access. Key Finder quickly finds and suggests all attribute combinations that may serve as indexes.

Additionally, Key Finder automatically calculates metadata for sampled tables such as the number of null values and the number of distinct not-null values in the original table.

### Licenses

**Free** – This is a demo version of the extension available when you access it through the Tableau Exchange. It has some limitations compared to the full version that make it suitable for testing and evaluation purposes only. The restrictions for the demo version are: tables that are sampled cannot contain more than 1 million rows, samples are only stored for 1 hour and permanently deleted after that. Additionally, the demo version runs on our servers and cannot be deployed to the infrastructure that you manage.

**Paid** – None of the restrictions of the demo version apply to the full version. Please contact us if you are interested in purchasing the full version.

To find out more about **pricing** and watch a **demo video** of this dashboard extension, please visit our [website](https://viadotto.tech).

Key Finder

Key Finder mines keys and unique constraints from a given data source.

The Inovista Animator is a web based application that allows users to import SVG images and create intelligent animations that change their behavior based on external data.
This Tableau extension allows a user to take a data-driven animation created in the Inovista Animator and load it into Tableau. Variables in the animation can be linked directly to Tableau data values or defined as user editable properties.

In the Animator, a full range of animations are available including Movement, Rotation, Scaling, Path Animation, Text Animation, Clipping, Masking, Text Offsets, Morphing and Wave Motions. Animation can also be driven by events such as mouse clicks.
 
Highlights:

This extension will take any animation created in the Inovista Animator and convert it into a Tableau component. Animations may be charts or infographics driven by Tableau data or simply decorative items such as moving logos.

The animations can be exported from the Animator as JSON files or they can be copied to the clipboard. In Tableau, the JSON file can be loaded or can be pasted directly from the clipboard.

In the Animator, all aspects of the animation can be modified by creating variables, for example, how much of an image to display to simulate a bar chart. Each variable can be defined as either a data item or property. In Tableau, data items are linked to Tableau data and properties can be set by the user. 

Examples of properties may be fonts or colors. 
Once defined, data values can be updated by a filter and/or parameter change. 
View a short YouTube video to learn more:

The Inovista Animator for Tableau Extension: [https://youtu.be/OH9anhm7FZQ] 

View a short introduction to the Animator: [https://youtu.be/nCvLoHKSIxo]

Animator for Tableau

Building a data model in Tableau requires users to identify relationships and their performance options manually. Relationship Finder mines the data sources to automatically find all relationships that are valid in the logical model. The relationships can be used to join tables by linking column combinations. Sample data and various measures of validity help users choose relationships and their performance options for building a data model.

### Use cases:
1. **Data Integration:** Relationship Finder automatically discovers all relationships that are valid in your datasets. This information can be used to pick the right join type to combine tables in the physical layer of the Tableau data model, define single and multi-column relationships in the logical layer, and find linking fields to join multiple data sources.
2. **Data Quality:** Relationship Finder is capable of discovering relationships that are not fully valid. Finding such relationships will help quickly discover referential integrity violations.
3. **Performance Optimization:** Relationship Finder recommends the optimal performance options for relationships in the logical layer. This allows Tableau to pick the optimal approach for join and aggregation, which in turn significantly speeds up data access.

Additionally, Relationship Finder automatically calculates metadata for sampled tables such as the number of null values and the number of distinct not-null values in the original table.

### Licenses

**Free** – This is a demo version of the extension available when you access it through the Tableau Exchange. It has some limitations compared to the full version that make it suitable for testing and evaluation purposes only. The restrictions for the demo version are: tables that are sampled cannot contain more than 1 million rows, samples are only stored for 1 hour and permanently deleted after that. Additionally, the demo version runs on our servers and cannot be deployed to the infrastructure that you manage.  

**Paid** – None of the restrictions of the demo version apply to the full version. Please contact us if you are interested in purchasing the full version.       

To find out more about **pricing** and watch a **demo video** of this dashboard extension, please visit our [website](https://viadotto.tech).

Relationship Finder

Relationship Finder mines relationships from data sources to find ways of joining them.

Tableau Exchange

Dashboard Extensions