QueBIT Blog: xP&A Data Hub Primer for the CIO

Posted by: Ann-Grete Tan

Jun 29, 2021 8:43:05 AM

xP&A – eXtended Planning & Analysis - a term coined in 2019 by Gartner, represents an evolution of traditional Financial Planning and Analysis (FP&A) towards integrated, continuous enterprise planning breaking down barriers between finance and operational functions including HR (Human Resources), Supply Chain and Sales. Companies are embracing xP&A in order to achieve strategically valuable benefits like better coordination, improved forecast accuracy, reduced cycle times and more agility. CIO

While successful xP&A adoption depends primarily on rethinking business processes and how data travels through the enterprise to support them, selecting the right planning software platform and system architecture to support the effort is a necessary prerequisite. This is where the IT function has an opportunity to add tremendous value. 

The most common planning software platforms are built on multi-dimensional (OLAP) database technologies, which power the modelling and analytical reporting required for planning and forecasting activities. Ideally the platforms are flexible and enable less technical business users to configure and modify calculations and reports with relative ease. This is important because business requirements in this space can be fluid, and financial analysts need to be able to react quickly in response to changes in the business environment or corporate strategy. This need for agility is the reason why spreadsheets continue to be the most prevalent planning tools in the business world, despite their many well-documented shortcomings. 

If a planning software platform does not meet the “configurable-by-the-business” requirement, users will be driven back to spreadsheets because they will feel there is no other way to get their jobs done. 

Hence, most planning software purchases are driven by the business, rather than by IT. Back when FP&A requirements were simpler, many companies would simply select planning software platforms offered by the same ERP (Enterprise Resource Planning) vendors who supplied their accounting and financial close solutions. The bar they were aiming to clear was a low one: find a better way to churn out financial reports and analyses, and reduce dependency on manual, uncollaborative, insecure and error-prone spreadsheets. 

Today the bar is much higher. As an xP&A “integrated continuous planning” mindset takes hold, companies increasingly see their cross-functional planning functions as strategic assets that can drive performance and competitiveness. It is no longer just about reporting on the past, but about pro-actively anticipating what is to come. AI (Artificial Intelligence)-based predictive techniques are being applied to look to the future, and high-performance modelling engines are used to explore and compare multiple what-if scenarios on a continual basis. The kinds of data pressed into service to support these activities has expanded (along with their volumes) while timelines for delivering analyses have been compressed. 

Spreadsheets are no longer a viable “Plan B” when every planning initiative invariably involves a significant data management effort, and when turnaround time is expected to take only hours or days, as opposed to the weeks or months that were acceptable before. 

Forward-thinking companies are therefore taking a collaborative, systemic approach to selecting the right planning software platform in which IT contributes expertise on data infrastructure and integration, while the business focuses on the business problems it wants to solve. Doing this effectively depends on how well this team comes together, and the willingness of its members to listen and understand the priorities and concerns of the other function. 

For example, the business will typically prioritize flexibility, ease of use, and independence (from IT) which may seem at odds with IT concerns about stability, governance and security. 

Leadership, especially IT leadership, plays an important role in finding constructive ways to bridge these gaps. Since the biggest gaps relate to data management – an area that business folks rarely have much experience in - incorporating a relational “data hub” as a component of the overall xP&A roadmap system design from the start is a significant step in the right direction.


As mentioned above, planning software platforms are typically built on OLAP technologies to easily support multi-dimensional analysis and reporting, hierarchies and drillable “roll-ups”, multi-dimensional calculations, and more.

Meanwhile the data used to support the modelling and analyses are always sourced from external systems which can range from ERP, HR, CRM systems and data warehouses, to spreadsheets. Since most of these operational systems are transactional, many of them are built on relational databases which are managed by IT departments or SaaS vendors either in corporate data centers or (increasingly) in the Cloud. Regardless of which planning software platform you choose, a data strategy will be needed to bring together data from disparate sources, and transform, map and aggregate as needed so as to prepare it for planning and analysis. Note that this is not a one-time activity, and data strategies and data models must be accompanied by designed business processes to support ongoing maintenance and governance. These are all areas where IT expertise and experience are invaluable.

A relational data hub is a built-for-purpose platform that leverages relational database, data virtualization (where appropriate), orchestration, and ETL (extract-translate-load) technologies specifically to support the xP&A roadmap. It is a component of the overall planning system, and is integrated with the planning software platform that you select. “Integration” can mean a variety of things including direct ODBC/JDB connections, REST API calls and orchestrated file movements using secure FTP.

The data hub performs multiple critical tasks - it aggregates the data from all systems, standardizes it with dates and codes, and transforms it into palatable formats. Integrated xP&A cannot work without proactive data management using a relational data hub. It helps build foundations for future data use cases, helps trace the data, aligns the correct tool with different use cases, integrates and cleanses the data, maintains hierarchical structures, and supports ad-hoc reporting. 


Every planning software platform including Anaplan, IBM Planning Analytics and Workday Adaptive Planning comes with tools that support data integration. They all have some ETL capabilities, and they are all able to store data to some degree. 

However, because the main purpose of a planning software platform is to support data collection, reporting and analysis in planning and forecasting use cases, their data management toolsets are narrowly focused. 

Examples of how this can manifest include:

  • Slower data load performance for larger data sets.
  • Complex scripting needed to load other-vendor data (for planning software platforms from ERP vendors).
  • Limited capacity for the storage of larger quantities of detailed data to support AI-based predictive modelling, for example for predictive demand forecasting.
  • Poor ability to audit mappings and transformations, adding to data validation and reconciliation challenges. 

Designing your xP&A system architecture to include a relational data hub from the outset mitigates these kinds of limitations while ensuring that you are embarking on your journey will all the tools and capabilities you will need along the way. The business climate can be very variable - as was demonstrated dramatically by the COVID-19 pandemic – and strategic priorities can easily change, so xP&A is best tackled in a step-wise agile fashion, with each step adding incremental but discernible value. As your organization progresses, adding more data sources and more models, the qualitative and cost-savings benefits of having the data hub will become increasingly self-evident.


  1. Why do we need a relational data hub despite having an in-house data warehouse?  

xP&A use cases generally require specialized data models that are not usually available in the enterprise data warehouse. Creating a distinct xP&A data hub makes it easier to accommodate changing and fluid data requirements without disrupting other data consumers. Thanks to advancements in data virtualization technologies, standing up a data hub does not necessarily mean that data will be duplicated at scale.

  1. Who will own the data hub and manage it?  

The data hub can reside on any standard relational database, on-premises or in the cloud. Most IT teams are already proficient in managing such databases so additional training is not necessarily required.  

On the other hand, data-related responsibility including governance and validation is best owned by the business who understand the provenance and meaning of the data, and whose job it is to perform interpretative analysis to support decision-making. Code mapping tables and other data transformation definitions should similarly be in the domain of the business. 

The ideal ownership structure is therefore a partnership between the business and IT, with the business having clear ownership of the qualitative aspects of the data and IT supporting the operation of the technology. 

One option to ease the burden of ownership is to use QueBIT Euclid Studio (ES). ES is a combined ETL and predictive modelling solution designed to simplify the integration of the xP&A relational data hub with any leading planning software platform, while augmenting the platform’s capabilities with robust, enterprise-proof predictive analytics. Its purpose is to reduce the drain on limited IT resources, while making it easier for the business owners to have ownership over their data and models.


While xP&A initiatives are often initiated by the CFO (or elsewhere in the business), success depends on understanding it as a cross-functional digital transformation effort, as opposed to a mere “planning software platform” purchase. Business and Finance functions may lack the data integration and system architecture experience to have a realistic view of the skills and effort required to fulfil their vision, while planning software vendors will invariably downplay the fact that every company is unique in its data and applications architecture, and that not all data can be guaranteed to be “ready to go”. 

IT is singularly qualified to partner with the business and provide advice in this area. ITs experience in designing data infrastructure to support other initiatives is extremely relevant in creating a relational data hub to support the xP&A vision, and also in evaluating the relative strengths of the competing planning software platforms in the area of data integration as it pertains to your application and data landscape. 


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