The Core Use Cases For Sigma’s Input Tables
Input Tables create an entirely new paradigm for working with your warehoused data and reimagine how data analytics and business teams leverage external human context with live production data to achieve their goals faster at massive scale.
What are Input Tables?
Input Tables are a new feature allowing you to create and manage warehouse tables directly from Sigma. These tables live in your cloud data warehouse and are created, collected, edited, and controlled via the Sigma interface. Input Tables are similar to traditional spreadsheet tables in that they can be directly edited, linked to existing tables for dropdowns, and controlled like a typical spreadsheet with copy/paste and other common functions. Input Tables provide a powerful new way for analysts and business teams to interact with live production data in a way not previously possible.
Data Modeling & Analysis
By combining real-time data and manual human inputs, you can now perform completely new types of analysis.
For example, analysts can incorporate historical data and external factors such as market trends, seasonal variations, or customer behavior into input tables to create more predictive models from their internal functions and formulas. These models can be used to reduce risk, forecast potential outcomes and identify new business opportunities. Input tables also enable users to experiment with different scenarios and test the effects of various inputs into their models by adjusting a parameter and observing the results. Analysts and Business teams can work together to optimize these models to improve their accuracy and relevance, ultimately providing new mechanisms for truly practicing strategic, data-driven decision-making.
Some examples of data modeling and analysis with Input Tables include:
- Prototyping data models: Input Tables enable users to create and test data models quickly by combining data from different sources and incorporating manual inputs, allowing for faster iteration and refinement of models.
- Designing charts: By utilizing input tables, analysts can design and customize charts to visualize data more effectively, facilitating a better understanding of trends, patterns, and correlations in the data from sample or example data models prior to building from production data.
- Geographic coverage quantification: Input tables help organizations quantify their geographic coverage by combining location data with other relevant metrics, enabling them to identify gaps in coverage and optimize their expansion strategies.
- Cohort analysis: Input tables facilitate cohort analysis by allowing analysts to group users based on shared characteristics, such as acquisition date, product usage, or demographics, enabling them to track and compare the performance of different cohorts over time and make data-driven decisions to optimize customer retention and engagement.
Business Performance Management
Input tables offer a powerful solution for business performance management. Combining external business data like with real-time performance information, users work directly with their data to better understand and manage the business.
- Employee performance measurement: By combining data from HR systems with manual inputs, organizations can track and assess employee performance.
- Territory modeling: Input Tables facilitate the optimization of sales territories by combining historical sales data with geographic and demographic information.
- Pipeline modeling: Input Tables help organizations track and analyze their sales pipeline, combining real-time data from CRM systems with manual inputs to provide insights into the pipeline's health and identify areas for improvement.
- Validating account scoring models: Input Tables enable businesses to test and refine their account scoring models by combining data from various sources with manual inputs, ensuring models accurately reflect customer value and prioritize the right accounts.
- Territory planning: Organizations can develop effective sales strategies by modeling different territory assignments and analyzing the impact on revenue, ensuring balanced and optimized territory allocation.
- Goal setting: Set realistic and achievable goals by combining historical data with forecasts and manual inputs, promoting a more data-driven approach to goal-setting and performance management.
- Sales planning: Enable organizations to plan and track sales activities, combining real-time data from CRM systems with manual inputs for sales projections and targets, allowing for better resource allocation and decision-making.
- Market position data viewing: Provide businesses with a comprehensive view of their market position, empowering them to make informed decisions regarding product offerings, pricing, and competitive strategies.
- Comparing expected vs. actual values: Facilitate the comparison of expected and actual performance metrics, helping organizations identify areas where they are under- or over-performing, and adjust their strategies accordingly.
- Managing business goals: Enable organizations to set, track, and update their business goals, consolidating real-time data with manual inputs.
Risk Assessment and Financial Modeling
More relevant now than ever, a business's ability to identify, reduce, and plan for risk while modeling the potential impact on its financial health is an essential process.s. Organizations use input tables to build comprehensive financial models that incorporate both historical data and real-time information.. A bank's ability to monitor and manage its loan portfolio uses Input Tables to identify potential vulnerabilities and adjust lending practices.. By running stress tests and simulating different scenarios, banks can proactively prepare for adverse market conditions.
Some more examples of risk assessment and modeling are:
- Measuring against budget: Compare actual financial performance with budgeted figures, identifying variances and potential risks that may impact profitability and growth.
- Churn risk identification: Analyze customer behavior, transaction data, and other relevant factors to identify patterns that indicate potential churn risks.
- Portfolio analytics: Enable financial analysts to assess and manage investment portfolios by combining historical performance data with manual inputs for market trends, risk factors, and other relevant data points. This comprehensive analysis allows for better risk mitigation and informed decision-making in portfolio management.
- Customer margin improvement: Input Tables allow for in-depth analysis of customer-related financial data, enabling organizations to identify opportunities for margin enhancement and increase overall profitability.
- What-if scenarios: Facilitate the creation and evaluation of multiple financial scenarios by combining historical data with manual inputs for future assumptions
- Uploading and using sales targets: Streamline the process of uploading and integrating sales targets into financial models, which in turn aids in generating more accurate sales forecasts and aligning sales strategies.
Where can I learn more about Sigma features and use cases?
Our online documentation is a great way to get high-level information on product features along with as much fine detail as you want.
Sigma QuickStarts provide “step-by-step” guides to using Sigma, exploring specific features and use-cases.