Overview
In this episode of The Dashboard Effect, Jon Thompson of Blue Margin sits down with Eric Weiss and Mike Marotta, executives at Power ON, to discuss a capability that addresses one of the most persistent limitations of Power BI as a planning and forecasting tool: the inability to write data back into the model. Power ON turns what has traditionally been a one-way visualization platform into a two-way planning environment, and the implications for budgeting, forecasting, and scenario analysis are significant for organizations that have been exporting to Excel to do that work.
For finance teams, FP&A professionals, and data leaders evaluating how to bring planning and analytics into a single environment, this episode offers a clear account of what Power ON makes possible and why. See how Blue Margin’s Managed Data Platform helps organizations build the data foundation that tools like Power ON depend on to deliver reliable planning and forecasting capabilities.
What This Episode Covers
The Power ON Value Proposition (3:56 – 5:36)
Power BI has historically been a one-way street: data flows in and visualizations flow out, but users cannot input data directly into the model. Power ON transforms that architecture into a two-way interaction, allowing users to enter data, assumptions, and adjustments directly within Power BI rather than exporting to Excel to do the work and reimporting the results. For organizations that have been managing budgeting, forecasting, and planning across a combination of Power BI and Excel, consolidating that workflow into a single environment reduces friction, version control problems, and the reconciliation overhead that comes with maintaining two parallel tools.
Scenario Planning and Forecasting (7:07 – 8:36)
Power ON enables organizations to build sandboxes within Power BI where complex what-if scenarios can be modeled in real time. The ability to simulate the impact of inflation, supply chain disruptions, or other variables on business performance without leaving the reporting environment changes how quickly and confidently decisions can be made when conditions shift. Mike Marotta references a Fortune 100 life sciences client that realized a hundred million dollar impact on top-line margin using these capabilities, which puts a concrete scale on the potential value of scenario planning done well.
Simplifying Data Management (15:20 – 18:40)
Power ON reduces the technical barrier to working with Power BI by enabling Excel-like formulas rather than requiring users to learn DAX for data manipulation tasks. That accessibility extends to back-end operations like data population and row-level security management, which Power ON simplifies in ways that reduce the SQL expertise required to maintain a production environment. The result is a platform that a broader range of users can operate effectively without the depth of technical knowledge that full DAX and SQL fluency requires.
The Path to Data Maturity (24:26 – 27:06)
Power ON is positioned as a tool that accelerates the path to data maturity by reducing the time organizations spend on data collection and quality management rather than on the analysis and decision-making that the data is supposed to support. The goal, as the hosts describe it, is single-screen visibility into business performance: a consolidated view that gives leadership what they need to make decisions without navigating between multiple systems or waiting for reports to be assembled. Power ON is presented as a capability that makes that visibility more achievable faster than the alternatives typically allow.
Prioritize Data Quality (19:39 – 22:36)
Eric and Mike close the discussion with advice that applies regardless of which planning tools an organization adopts: data quality is a strategic asset, not an IT concern. Poor data quality produces poor decisions with consequences that scale with the size and scope of the decisions being made. Leaders do not need to be data experts to ensure their organizations are oriented around accurate, real-time numbers, but they do need to treat data quality as a leadership priority and leverage trusted partners and tools to maintain it rather than assuming the problem will resolve itself through technology alone.
Who It’s For
This episode is worth your time if you are a finance or FP&A leader who has been managing planning and forecasting across a combination of Power BI and Excel and wants to understand what consolidating that workflow into a single environment would require and deliver, a data or BI team lead evaluating write-back capabilities for Power BI and wanting a practitioner’s perspective on what Power ON makes possible, a business leader responsible for scenario planning and competitive response who wants to understand what real-time what-if analysis looks like in practice, or any organization that is spending more time on data collection and reconciliation than on the analysis and decision-making that data is supposed to enable.
Why It’s Worth a Listen
The one-way street limitation of Power BI is one of the most consistent pain points for organizations that use it as their primary analytics platform and then export to Excel for the planning work that requires data input. This episode introduces a specific and well-developed solution to that problem and makes the case for why solving it in Power BI rather than maintaining a parallel Excel-based planning process produces a more reliable, more efficient, and more decision-ready environment.
The scenario planning discussion is the most compelling illustration of the capability’s business value. The ability to model the impact of external variables on business performance in real time, without leaving the analytics environment, changes how quickly an organization can respond to changing conditions. The hundred million dollar margin impact referenced by Mike Marotta is a striking data point, and while that scale is not universal, the directional argument about the value of real-time scenario analysis is broadly applicable.
And the data quality advice that closes the episode is worth carrying into any conversation about planning tool adoption. The most sophisticated planning capability delivers unreliable results if the underlying data is not trustworthy. Leaders who invest in tools without investing in the data quality that makes those tools useful tend to find that the tools surface bad data more efficiently rather than producing better decisions. This episode makes that priority explicit, which is a useful anchor for any organization evaluating where to start.
