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Sachin Virmani
Sachin Virmani

shutterstock_561621778 (1)In the present day, where we have a huge amount of data generated in the digital space, it becomes imperative for non-profits to use the “meaningful” information from this humongous data and apply analytics to accomplish organizational-level objectives.

Why is data analytics important for non-profits?

Data analytics can empower non-profits with the tools to collect data from disparate sources in varied formats in ever-increasing volume and velocity. Along with collecting the data from such sources, two other activities, such as efficient processing of data and providing analytical capabilities simultaneously, do take place. All of these steps help to extract valuable organizational insights as the ultimate result. Although the concept of data analytics has been around for a while now, it has become more relevant in today’s times with the advent of more powerful tools which can harness the true potential of data.

How far have non-profits progressed on their analytics journey?

Here are the results of survey a conducted by a leading software service provider:

  • Most of the non-profit organizations (67%) are in the early stages of their journey of adoption of data and analytics, primarily limited to data warehousing and traditional business intelligence reporting.
  • Some non-profits (33%) have taken major strides in this endeavor by using advanced analytical techniques to progress in their core missions, and 78% of them have reported efficiency gains since then.

Predictive modeling for budgeting and forecasting – A common use case of advanced analytics for non-profits

A well-known use case where advanced data analytics can help non-profits is by optimizing internal budgeting and forecasting processes. Predictive modeling used for forecasting allows organizations to take better decisions by efficiently using their resources of time and money, which at times are limited in the context of non-profits.

How have non-profits been conventionally doing forecasting?

As a process, conventional forecasting is a time-consuming manual activity especially for businesses where the aim is first to collect the data from different divisions, systems, and stakeholders within the organization; collate that data in one centralized place, and then apply modeling techniques in spreadsheets to generate the forecasts.

What is predictive modeling?

At heart, predictive modeling involves an assortment of advanced statistical & mathematical techniques by modeling associations between variables in historical data, to predict or forecast the likelihood of occurrence of a future event.

How does predictive modeling help non-profits over traditional forecasting methods?

Predictive modeling eliminates the challenge of manually managing the financial planning, budgeting, and forecasting processes in an organization. It enables the concerned stakeholders to monitor the progress towards their organization’s end goals and to take corrective measures in real time.

The predictive model  an integral part of a data-driven analytical solution for forecasting 

As an enabler, predictive modeling is a core component of an end-to-end data analytics solution. The solution provides automated data ingestion, cleansing, standardization, and processing capabilities to consolidate data in a centralized data store. Sophisticated modeling techniques are then applied to the data to empower business folks to perform complex financial planning and analysis by using interactive dashboard & reporting capabilities. The prediction model is integrated with the overall solution, and it provides a user-intuitive platform so that even non-technical users can run the model to generate predictions.

Our experience at Nagarro of building a forecasting model using predictive modeling techniques for a US client serving the non-profits

In our recent experience with a US-based client, a leading software solution & service provider for the non-profits, we built a forecasting model to be used by client’s financial planning & analysis team to support its annual budgeting needs.

How did we build the forecasting model?

The model was created using revenue and transaction time series data as the source, originating from one of the client’s leading payment-related product streams. The overall solution has been built on the Cloud platform using data tools along with machine learning capabilities used for the modeling purpose. This repeatable solution runs end to end in an autopilot manner based on an automatic monthly schedule, starting from the data integration stage to the publishing of forecasting results, thereby removing the need of manual intervention in any phase of the forecasting process.

What key features does the forecasting model offer? How do they help?

The solution offers drill-down capabilities to look at actual, forecasted trends and variances at different aggregation levels and granularities like market groups, business verticals, and customers. In addition to this, it also provides a “self-service” platform for the end business users to manually adjust/override certain parameters like defining exclusion and inclusion criteria. As a result, end users have the flexibility to control the forecasting model externally and run it on demand, which is helpful in the “what-if” analysis. The machine learning-based prediction model also uses anomaly detection techniques to identify and exclude data sets termed as outliers, which can negatively impact the accuracy of trends during the forecasting process.

An affirmation from the client, validating the success of the forecasting model

As a testimony to the success of this project, the client plans to broaden the scope of this forecasting model by introducing more features and including additional product streams as data sources, thereby expanding the overall coverage of this solution in terms of managed revenue.