Creating and Customizing Sage Intacct Dashboards

Simply Put Consulting recently posted an article “Sage Intacct Dashboards – Visibility and Insight into Business Financials” - an overview of dashboards and their flexibility and customizability for the endless variation that we see in companies and the world today.? This article will look at that customizability.

Sage Intacct Dashboards provide a real-time view of your business performance, allowing you to monitor key metrics and make informed decisions. The dashboards use components to show customizable snapshots of distinct parts of your business and help focus on the data that is most important to you, as well as displaying it in a way that is easy to understand. So, for the sake of this article let us discuss creating a customized dashboard for the new Chief Data Scientist.

We first need to answer the question: what does the Chief Data Scientist (CDS) want to see on his/her dashboard? Without getting lost in the weeds, let us look at a couple of the obvious metrics the company executives would want to analyze:

1.????? Project velocity – when you start a new project, you have objectives, and theories, yet you do not know how the project will turn out. Must of data science is a research process, with “no insight” as to a possible valid outcome. This metric would capture the project work and catalog the modeling datasets, features used, and initial results, so when you get a similar project down the road, you have a head start in terms of validated data, preliminary models, or effective approaches. Making these complex processes repeatable and trackable is important to building a high-velocity data team so you can move from “artisan thinking” to reproducible and reusable “modular system” thinking.

2.????? Deliver ROI (Return on Investment) – company executives have revenue expectations on their investments in data science – often big expectations. If a model fails, then investment in data science may disappear.? The goal is to set expectations and show results that improve over time. One way to show direct ROI is by using control groups in the production environment. This is going to help you show the value across the company, especially with senior business leaders outside of the data science or IT organizations. By creating a before and after comparison, the data science team can show a direct link between their project and revenue enhancement. Data science teams then expand upon traditional analytics departments by measuring the rate of change of a measured delta, rate of expansion, and projected ROIs of future initiatives.

Data science executives and professionals need to track and show the link between model performance and impact. Metrics should show how well the models predict or classify outcomes. Good components of a data science department tracking dashboard could include:

• Business impact: how much revenue was generated, costs saved, or improved customer satisfaction due to data science initiatives.
• Model adoption and utilization: how often are models used to inform decision-making by other teams?
• Employee/Team Metrics: Recruitment and retention, team engagement with a line of business owners, marketing and sales, a growing data science knowledge base so new and old team members know how work gets done, and consistent lunch-and-learns.
• Customer Engagement/Review: Utilize natural language processing (NLP) for sentiment analysis. Gain an understanding of how both the company and product are viewed by customers and competitors.

A data science leader would want to see areas such as team efficiency, strategic alignment, innovation, and employee performance and development. But for this article, we will stick to project velocity and ROI. So, let us get started!

How to create Sage Intacct Dashboards

1.??Go to the Dashboards module.

2.??Create a new dashboard for your ‘Chief Data Scientist (CDS)’ or click on the dashboard that you want to customize.

3.??Click on the "Appearance" tab.

4.??Here you can change the following settings:

• Number of columns: You can choose to display your dashboard in one, two, or three columns.
• Column width: You can adjust the width of each column.
• Colors: You can assign colors to separate groups of data.
• Font size: You can change the font size for the text on your dashboard.

5.??Click on the "Filters" tab.

6.??Here you can add filters to your dashboard. Filters allow you to narrow down the data that is displayed. For example, you could filter your dashboard to only show data for a specific period or department.

7.??Click on the "Permissions" tab.

8.??Here you can control who has access to your dashboard. You can choose to make your dashboard public, or you can restrict access to certain users or groups.

9.??Once you have made your changes, click on the "Save" button.

Now we need to customize our Sage Intacct CDS dashboard. To do this we will add components. Some good advice for components on a dashboard includes:

• Use a mix of charts and graphs to display your data. This will make your dashboard more visually appealing and easier to understand.
• Label your charts and graphs clearly. This will help viewers to understand what they are looking at.
• Keep your dashboard clean and clutter-free. Too much information can be overwhelming.
• Use colors to highlight valuable information.
• Update your dashboard regularly. This will ensure that your information is always accurate and up to date.

Let us get started creating our two components – one to show project visibility and another to track ROI on the models and projects delivered by the data science team.

Adding components, the high-level view

1.??Click the + button in the upper right corner of the dashboard. A pop-up window will appear with a variety of component types to choose from. These include:

a. Chart: Line graphs, bar charts, pie charts, etc., to visualize trends and relationships

b. Grid: Displays data in a table format, allowing for detailed analysis.

c. KPI: Highlights a single metric with a clear goal or target.

d. Trend: Shows the evolution of a metric over time in a line graph format.

e. Scorecard: Combines multiple KPIs into a single view for a holistic overview.

f.? Text: Add text boxes for explanations, titles, or disclaimers.

2.??Configuring the component. Once you have chosen a component type, click on it to open the configuration options.

a. This will vary depending on the chosen component, but typically includes:

b. Data Source: Select the specific data source for the component, like a specific report, subledger, or custom calculation.

c. Metrics: Choose the specific values to be displayed in the component.

d. Filters: Apply filters to focus the data displayed on specific segments or criteria.

e. Visualization Options: Customize the appearance of the component, like colors, labels, and chart types.

3.??Add more components to the dashboard to create a comprehensive view of the data science projects and work. Arrange them in a way that makes sense and is visually appealing. Use different component types to represent distinct types of data and insights.

4.??Click ‘Save’ then share the dashboard with other users or groups within Sage Intacct by granting them access permissions.

The project velocity component

The key piece to this component is data external to Sage. For example, the development pipeline you want to access is in Azure DevOps, this will require integration tools or platforms. This can be done via an iPaaS such as Tray.io or JitterBit Data Loader.

Or this can be done via an API-based integration. Sage Intacct offers a robust API allowing programmatic access to their data and most development platforms (Azure DevOps, Jira, etc.) provide an API.

There are a variety of metrics we could use to gauge the progress and identify bottlenecks of data science projects. We could use ‘Story Points’ which typically correspond to the effort of a developer in a single day. We could use ‘Model Deployment Rate’ which tracks the number of models deployed to production per unit of time, like a month.

The most popular would be ‘Cycle Time Metrics.’ A component using this type of metric would measure the time it takes for a task or feature to go from concept to completion. Common stages would be:

• Idea-to-deployment: Time from initial idea to deploying the model in production.
• Code-to-deployment: Time from writing code to deploying the model in production.
• Data-to-model: Time from acquiring data to building and training the model.

This component would then track the time taken for each stage and calculate averages or distributions. It would help identify bottlenecks in the workflow and areas for improvement.

The Project ROI Component

This component will use a more direct data feed from Sage Intacct.? When talking to the business owner about a data science project, it is important to frame how a model can be evaluated.? As stated above, it is often worthwhile to create a control group that will NOT be impacted by the new software.

As an example, let us suppose that the credit card line of business has asked the team to develop machine learning algorithms to detect fraud, an important project that protects the customer and the company.? One of the best methods to determine how well the model is working is to roll out the model for a select set of credit cards and compare how well the model prevents fraud against those credit cards that are NOT being protected by the model.? This component would compare the fraud losses between the protected credit cards and the unprotected credit cards.

If you are looking for a way to gain valuable insights into your business financials, you should consider using Sage Intacct dashboards.

Contact Simply Put Consulting to take your Sage Intacct Dashboards to the next level! https://simplyput.com/contact-us/

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