Organizations everywhere rely on data to make informed decisions and improve the bottom line through data management.  But with the vast amount of data today, things can quickly get out of control and spawn data gremlins (i.e., little pockets of disconnected, ungoverned data) that wreak havoc on the organization.

Remember those adorable creatures that transformed into destructive, mischievous creatures when fed after midnight in the 1984 comedy horror film Gremlins?  (see Figure 1)

Data Gremlins
Figure 1: Warner Bros. Pictures/Amblin E/Sunset Boulevard/Corbis via Getty Images

In the same way, data gremlins – aka “technical debt” – can arise if your systems are not flexible to enable finance to deliver.  Effectively managing data to prevent data gremlins from wreaking havoc is crucial in any modern organization – and that requires first understanding the rise of data gremlins.

Do You Have Data Gremlins?

How do data gremlins arise, and how do they proliferate so rapidly?  The early beginnings started with Excel.  People in Finance and operations would get big stacks of green bar reports (see Figure 2). Don’t remember those?  They looked like the image below and were filed and stacked in big rooms.

Green Bar Reports Stacks
Figure 2: Green Bar Paper

When you needed data, you pulled the report, re-typed the data into Excel, added some formatting and calculations, printed the spreadsheet and dropped it in your boss’s inbox.  Your boss would then review and make suggestions and additions till confident (a relative term here!) that sharing the spreadsheet with upper management would be useful, and voila, a gremlin is born. 

And gremlins are bad for the organization.

The Rise of Data Gremlins

Data gremlins are not a new phenomenon but one that can severely impact the organization, leading to wasted time and resources, lost revenue, and a damaged reputation.  For that reason, having a robust data management strategy is essential to prevent data gremlins from causing havoc.

As systems and integration got more sophisticated and general ledgers became a reliable book of record, data gremlins should have faded out of existence.  But did they?  Nope, not even a little.  In fact, data gremlins grew faster than ever partly due to the rise of the most popular button on any report, anywhere at the time – you guessed it – the “Export to .CSV” button.  Creating new gremlins became even easier and faster, and management started habitually asking for increasingly more analysis that could easily be created in spreadsheets (see Figure 3).  To match the demand in 2006, Microsoft increased the number of rows of data a single sheet could have to a million rows.  A million!  And people cheered!

Excel is not a database
Figure 3: ©Fox Television The Simpsons™

However, errors were buried in those million-row spreadsheets, not in just some spreadsheets, but in almost ALL of them.  The spreadsheets had no overarching governance and could not be automatically checked in any way.  As a result, those errors would live for months and years.  Any consultant who experienced those spreadsheets will confirm stories of people adding “+1,000,000” to a formula as a last-minute adjustment and then forgetting to remove the addition later.  Major companies reported incorrect numbers to the street, and people lost jobs over such errors.

As time passed, the tools got more sophisticated – from MS Excel and MS Access to departmental planning solutions such as Anaplan, Essbase, Vena Solutions, Workday Adaptive and others.  Yet none stand up to the level of reliability IT is tasked with achieving.  The controls and audits are nothing compared to what enterprise resource planning (ERP) solutions provide.  So why do such tools continue to proliferate?  Who feeds them after midnight, so to speak? The real reason is that these departmental planning systems are like a hammer. Every time a new model or need for analysis crops up, it’s “Let’s build another cube.” Even if that particular data structure is not the best answer, Finance has a hammer, and they are going to pound something with it.  And why should they do their diligence to understand the proliferation of Gremlins? (see Figure 4)

Mean Gremlin
Figure 4: Warner Bros. Pictures/Amblin E/Sunset Boulevard/Corbis via Getty Images

The relationship can best be described as “complicated.”  ERPs and data warehouses are secure, managed environments but offer almost no flexibility for Finance or Operations to do any sophisticated reporting or analysis that has not been created for them by IT.  Requests for new reports are made, often through a ticket system, and the better IT is at satisfying these requests, the longer the ticket queue becomes.  Suddenly the team is doing nothing but reporting, and that leaves Finance thinking, “How the heck is there a team of people in IT not focused on making the business more efficient?”

The answer suggested by the mega ERP vendors is to stop doing that.  End users don’t really need that data, that level of granularity or that flexibility.  They need to learn to simplify and not worry about trivial things.  For example, end users don’t need visibility into what happens in a legal consolidation or a sophisticated forecast.  “Just trust us.  We will do it,” ERP vendors say.

Here’s the problem:  Finance is tasked with delivering the right data at the right time with the right analysis.  What is the result of just “trusting” ERP vendors?  Even more data gremlins.  More manual reconciliations.  Less security and control over the most critical data for the company.  In other words, an organization can easily spend $30M on a secure, well-designed ERP system that doesn’t fix the gremlin problem – emphasizing why organizations must control the data management process.

The Importance of Controlling Data Management and Reducing Technical Debt

Effective data management enables businesses to make informed decisions based on accurate and reliable data.  But controlling data management is what prevents data gremlins and reduces technical debt.  Data quality issues are perpetuated by the gremlins and can arise when proper data management practices aren’t in place.  These issues can include incomplete, inconsistent or inaccurate data, leading to incorrect conclusions, poor decision-making and wasted resources.

One of the biggest oversights when dealing with data gremlins is only focusing on Return on Investment (ROI) and dismissing fully burdened technical debt (see Figure 5). Many finance teams use performance measurements like Total Cost of Ownership (TCO) and ROI to qualify that the solution is good for the organization, but rarely does the organization dive deeper – beyond the performance measurements to include opportunities to reduce implementation and maintenance waste. Unfortunately, this view of performance measurements does not account for hidden complexities and costs associated with the negative impacts of data gremlin growth.

Technical Debt is MORE than the Total Cost of Ownership

Technical Debt and TCO
Figure 5: Technical Debt is More than the Total Cost of Ownership

Unfortunately, data gremlins can occur at any stage of the data management process, from data collection to analysis and reporting.  These gremlins can be caused by various factors, such as human error, system glitches or even malicious activity.  For example, a data gremlin could be a missing or incorrect field in a database, resulting in inaccurate calculations or reporting.

Want proof?  Just think about the long, slow process you and your team engage in when tracking down information between fragmented sources and tools rather than analyzing results and helping your business partners act.  Does this drawn-out process sound familiar?

The good news is that another option exists.  Unifying these multiple processes and tools can provide more automation, remove the complexities of the past and meet the diverse requirements of even the most complex organization, both today and well into the future. The key is building a flexible yet governed environment that allows problems and new analyses to be created within the framework – no Gremlins.

Sunlight on the Data Gremlins

At OneStream, we’ve lived and managed this complicated relationship our entire careers.  We even made gremlins back when the answer for everything was “build another spreadsheet.”  But we’ve eliminated the gremlins in spreadsheets only to replace them with departmental apps or cubes that are just bigger, nastier gremlins.  Our battle scars have taught us that gremlins, while easy to use and manage, are not the answer. Instead, they proliferate and cause newer and bigger hard-to-solve problems of endless data reconciliation.

For that reason, OneStream was designed and built from the ground up to eliminate gremlins, including the need for them in the future (see Figure 6).  OneStream combines all the security, governance and audit needed to ensure accurate data – and does it all in one place without the need to walk off-prem while allowing the flexibility to be leveraged inside the centrally defined framework.  In OneStream, organizations can leverage Extensible Dimensionality to provide value to end users to “do their thing” without having to push the “Export to .CSV” button.

OneStream Unified Platform
Figure 6: OneStream Unified Platform Capabilities

OneStream is also a platform in the truest sense of the word.  The platform provides direct data integration to source data, drill back to that data, and flexible, easy-to-use reporting and dashboarding tools. 

That allows IT to eliminate the non-value add cost of authoring reports AND the gremlins – all in one fell swoop.

Finally, our platform is the only EPM platform allowing organizations to develop their functionality directly on the platform.  That’s correct – OneStream is a full development platform where organizations can leverage all the platform resources of integration and reporting needed for organizations to deliver their own Intellectual Property (IP).  They can even encrypt the IP in the platform or share it with others.

OneStream, in other words, allows organizations to manage their data effectively.  In our e-book on financial data quality management, we shared the top 3 goals for effective financial data quality management with CPM:


Data gremlins can disrupt business operations and lead to severe business implications, making it essential for organizations to control their data management processes before data gremlins emerge.  By developing a data management strategy, investing in robust data management systems, conducting regular data audits, training employees on data management best practices and having a disaster recovery plan, organizations can prevent data-related issues and ensure business continuity.

Learn More

To learn more about how organizations are moving on from their data gremlins, download our whitepaper titled “Unify Connected Planning or Face the Hidden Cost.” 

Download the White Paper

Machine learning (ML) has the potential to revolutionize Enterprise Performance Management (EPM) by providing organizations with real-time insights and predictive capabilities across planning and forecasting processes.  With the ability to process vast amounts of data, ML algorithms can help organizations identify patterns, trends and relationships that would otherwise go unnoticed.  And as the technology continues to evolve and improve, even greater benefits are likely to emerge in the future as Finance leverages the power of ML to achieve financial goals.

Join us as we examine Sensible ML for EPM – Future of Finance at Your Fingertips.

The Shift to Intelligent Finance

For CFOs, whether artificial intelligence (AI) and ML will play a role across enterprise planning processes is no longer a question.  Today, the question instead focuses on how to operationalize ML in ways that return optimal results and scale.  The answer is where things get tricky.

Why?  Business agility is critical in the rapidly changing world of planning.  To think fast and move first, organizations must overcome challenges spanning the need to rapidly grow the business, accurately predict future demand, anticipate unforeseen market circumstances and more.  Yet the increasing volumes of data across the organization make it difficult for decision-makers to zero in on the necessary data and extrapolate the proper insights to positively impact planning cycles and outcomes.  Further exacerbating the problem, many advanced analytics processes and tools only leverage high-level historical data, forcing decision-makers to re-forecast from scratch whenever unforeseeable market shifts hit.

But with AI and ML, business analysts can analyze and correlate the most relevant internal/external variables.  And the variables then contribute to forecasting accuracy and performance across the Sales, Supply Chain, HR and Marketing processes that comprise financial plans and results.

Those dynamics underscore why now is the time for Intelligent Finance.

Over the coming weeks, we’ll share a four-part blog series discussing the path toward ML-powered intelligent planning.  Here’s a sneak peek at the key topics in our Sensible ML for EPM series:

Figure 1: Sensible ML Dashboard

Regardless of where you are in your Finance journey, our Sensible ML for EPM series is designed to share insights from the experience of OneStream’s team of industry experts.  We recognize, of course, that every organization is unique – so please assess what’s most important to you based on the specific needs of your organization.


The aspiration of ML-powered plans is nothing new.  But to remain competitive amid the increasing pace of change and technology disruption, Finance leaders must think differently to finally conquer the complexities inherent in traditional enterprise planning.  ML has the potential to greatly improve EPM by providing organizations with real-time insights and predictive analytics.  However, organizations must overcome challenges (e.g., ensuring good data quality and selecting the right ML algorithm) to achieve success.  As ML continues to evolve, increasingly more organizations are likely to leverage its power to drive better financial and operational outcomes.

Several challenges lie ahead for organizations of all sizes, but one of the most important decisions will be implementing the right ML solution – one that can effectively align all aspects of planning and elevate the organization toward its strategic goals.  Sensible ML answers that call.  It brings power and sophistication to organizations to drive transparency and increase the velocity of forecasting processes with unprecedented transparency and alignment to business performance.

Learn More

To learn more about the value of Sensible ML, download our whitepaper titled “Sensible Machine Learning for CPM – Future Finance at Your Fingertips” by clicking here.  And don’t forget to tune in for additional posts from our machine learning blog series!

Download the White Paper

Today, confidence in data quality is needed more than ever.  Executives must constantly respond to a multitude of changes and be provided with accurate and timely data to make the right decisions on what actions to take.  It’s, therefore, more critical than ever that organisations have a clear strategy for ongoing financial data quality management (FDQM).  Why?  Well, better outcomes result from better decisions.  And high-quality data gives organisations the confidence needed to get it right.  This confidence in data then ultimately drives better performance and reduces risk.

Why Is Data Quality Important?

Data quality matters for several key reasons, as shown by research.  According to Gartner,[1] poor data quality costs organisations an average of $12.9 million annually.  Those costs accumulate not only from the wasted resource time in financial and operational processes but also – and even more importantly – from missed revenue opportunities.

As organisations turn to better technology to help drive better future direction, the emphasis placed on financial data quality management in enterprise systems has only increased.  Gartner also predicted that, by 2022, 70% of organisations would rigorously track data quality levels via metrics, improving quality by 60% to significantly reduce operational risks and costs.

In a report on closing the data–value gap, Accenture[2] said that “without trust in data, organisations can’t build a strong data foundation”.  Only one-third of firms, according to the report, trust their data enough to use it effectively and derive value from it.

In short, the importance of data quality is becoming increasingly clearer to many organisations – creating a compelling case for a change in financial reporting software evaluations.  There’s now real momentum to break down the historic barriers and move forward with renewed confidence.

Breaking Down the Barriers to Data Quality

Financial data quality management has been difficult, if not virtually impossible to achieve for many organisations.  Why?  Well, in many cases, legacy corporate performance management (CPM) and ERP systems were simply not built to work together naturally and instead tended to remain separate as siloed applications, each with their own purpose.  Little or no connectivity exists between the systems, so users are often forced to resort to manually retrieving data from one system, manually transforming the data, and then loading it into another system – which requires significant time and risks lower quality data.

There’s also often a lack of front-office controls that leads to a host of issues:

All of that contributes to significant barriers to ensuring good data quality.  Here are the top 3 barriers:

  1. Multiple disconnected systems – Multiple systems and siloed applications make it difficult to merge, aggregate, and standardise data in a timely manner.  Individual systems are added at different times, often using different technology and different dimensional structures.
  2. Poor quality data – Data in source systems is often incomplete, inconsistent, or out of date.  Plus, many integration methods simply copy data complete with any existing errors – lacking any validations or governance.
  3. Executive buy-in – Failing to get executive buy-in could be due to perception or approach.  After all, an effective data quality strategy requires focus and investment.  With so many competing projects in any organisation, the case for a strategy must be compelling and effectively demonstrate the value that data quality can deliver.

Yet even when such barriers are acknowledged, getting the Finance and Operations teams to give up their financial reporting software tools and traditional ways of working can be extremely challenging.  The barriers, however, are often not as difficult to surmount as some believe.

The solution may be as simple as demonstrating how much time can be saved by transitioning to newer tools and technology.  Via such transitions, reducing the time investment can result in a dramatic improvement in the quality of the data through effective integration with both validation and control built into the system itself.

The Solution

There are some who believe Pareto’s law applies to data quality: Typically, 20% of data enables 80% of use cases.  It is therefore critical that an organisation follows these 3 steps before embarking on any project to improve financial data quality:

  1. Define quality – Determine what quality means to the organisation, agree on the definition, and set metrics to achieve the level with which everyone will feel confident.
  2. Streamline collection of data – Ensure the number of disparate systems is minimised and the integrations use world-class technology with consistency to the data collection processes.
  3. Identify the importance of data – Know which data is the most critical for the organisation and start there – with the 20%.  Then move on when the organisation is ready.

At its core, a fully integrated CPM software platform with built-in financial data quality (see Figure 1) is critical for organisations to drive effective transformation across Finance and Lines of Business.  A key requirement is providing 100% visibility from reports to data sources – meaning all financial and operational data must be clearly visible and easily accessible.  Key financial processes should be automated and using a single interface would mean the enterprise can utilise its core financial and operational data with full integration to all ERPs and other systems.

Figure 1: Built-In Financial Data Quality Management in OneStream

The solution should also include guided workflows to protect business users from complexity by guiding them uniquely through all data management, verification, analysis, certification, and locking processes.

OneStream offers all of that and more.  With a strong foundation in financial data quality, OneStream allows organisations to integrate and validate data from multiple sources and make confident decisions based on accurate financial and operating results.  OneStream’s financial data quality management is not a module or separate product but built into the core of the OneStream platform — providing strict controls to deliver the confidence and reliability needed to ensure quality data.

In our e-book on Financial Data Quality Management, we shared the following 3 goals for effective financial data quality management with CPM:

  1. Simplify data integration – Direct integration to any open GL/ERP system and empower users to drill-back and drill-through to source data.
  2. Improve data integrity – Powerful pre-data and post-data loading validations and confirmations ensure the right data is available at every step in the process.
  3. Increase transparency – 100% transparency and audit trails for data, metadata, and process change visibility from report to source.

Delivering 100% Customer Success

Here’s one example of an organization that has streamlined data collection and improved data quality in the financial close, consolidation, and reporting process leveraging OneStream’s unified platform.

MEC Holding GmbH – headquartered in Bad Soden, Germany – manufactures and supplies industrial welding consumables and services, cutting systems, and medical instruments for OEMs in Germany and internationally.  The company operates through three units: Castolin Eutectic Systems, Messer Cutting Systems, and BIT Analytical Instruments.

MEC has 36 countries reporting monthly, including over 70 entities and 15 local currencies, so the financial consolidation and reporting process includes a high volume of intercompany activity.  With OneStream, data collection is now much easier with Guided Workflows leading users through their tasks.  Users upload trial balances on their own vs. sending to corporate, which speeds the process and ensures data quality.  Plus, the new system was very easy for users to learn and adopt with limited training.

MEC found that the confidence from having ‘one version of the truth’ is entirely possible with OneStream.

Learn More

If your Finance organisation is being hindered from unleashing its true value, maybe it’s time to evaluate your internal systems and processes and start identifying areas for improvement.  To learn how read our whitepaper on Conquering Complexity in the Financial Close.

Download the White Paper

Financial data quality management (FDQM) has often been developed as merely an afterthought or presented as simply an option by most corporate performance management (CPM) vendors.  In reality, it should be the foundation of any CPM system.  Why?  Well, having robust FDQM capabilities reduces not only errors and their consequences, but also downtime caused by breaks in processes and the associated costs of inefficiency.

Having effective FDQM means providing strict audit controls alongside standard, defined, and repeatable processes for maximum confidence and reliability in any business user process.  Effective FDQM also enables an organisation to shorten financial close and budgeting cycles and get critical information to end-users faster and more easily.

Continuing our Re-Imagining the Close Blog series here, we examine why financial data quality management is an essential requirement in today’s corporate reporting environment.  Poor data quality can lead to errors or omissions in financial statements, which often result in compliance penalties, loss of confidence from stakeholders, and potentially a reduction in market value.

Increasing Interest in Financial Data Quality Management

Organisations are now realising that FDQM is critical.  Why?  Finance teams must ensure not only that the financial reporting gets out of the door accurately but also that all forward-looking data and guidance is fully supported by ‘quality’ actual results.

An old expression used around financial reporting captures this idea well – ‘if you put garbage in, you get garbage out.

garbage in-garbage out- 741x610px

A lack of robust data integration capabilities creates a multitude of problems.  Here are just a few:

In many cases, legacy CPM and ERP systems were simply not built to work together naturally and tend to remain separate as siloed applications, each with their own purpose.  Often, little or no connectivity exists between the systems, and users are forced to resort to manually retrieving data from one system, manually transforming the data, and then loading it into another system.

As organisations get increasingly more complex and data volumes grow ever larger, it is only natural to turn attention to the quality of the data being input.  Not to mention, as more advanced capabilities are explored and adopted – such as artificial intelligence (AI) and machine learning (ML) – organisations are being forced to first examine their data and then take steps to ensure effective data quality.  Why are these steps necessary?  Simply put, the best results from AI, ML, and other advanced technologies are entirely dependent on good, clean quality data right from the start.

What’s the Solution?

A fully integrated CPM/EPM platform with FDQM at its core is critical for organisations to drive effective transformation across Finance and lines of business.  A key requirement is 100% visibility from reports to sources – all financial and operational data must be clearly visible and easily accessible.  Key financial processes should be automated and using a single interface would mean the enterprise can utilise its core financial and operational data with full integration to all ERPs and other systems.


The solution must also include guided workflows to protect business users from complexity by guiding them uniquely through all data management, verification, analysis, certification and locking processes.

Users should be able to achieve effective FDQM and verification through standardised and simplified data collection and analysis with reports at every step in the workflow.  The workflows must be guided to provide standard, defined, and repeatable processes for maximum confidence and reliability in a business user-driven process.  What’s the end result?  The simplification of business processes and a reduction in errors and inefficiencies across the enterprise.

Why OneStream?

OneStream’s strong foundations in the FDQM arena allow for unparalleled flexibility and visibility into the data loading and integration process.

OneStream’s Financial Data Quality Management is not a module or separate product but is a core part of OneStream’s unified platform – providing strict controls to deliver confidence and reliability in the quality of your data.  How?  Financial data quality risk is managed using fully auditable system integration maps, and validations are used to control submissions from remote sites.  Data can be loaded via direct connections to source databases or via any file format.  Audit reports can be filtered based on materiality thresholds – ensuring one-time reviews at appropriate points in the process.

In essence, OneStream’s unified platform offers market-leading data integration capabilities with seamless connections to multiple sources.

OneStream Integration Connectors offer direct integration with any open GL/ERP or other source systems, providing the following benefits:

100% Customer Success

AAA logo-customer-417x253px

AAA Life Insurance implemented OneStream to support all their financial consolidation, reporting, budgeting, and analysis needs.  Ever since AAA Life has streamlined the financial close process and dramatically improved not only visibility into data but also transparency into results.  They can now drill down from OneStream’s calculated or consolidated numbers all the way back to the transactional ERP system to get rapid answers to critical questions.

The dream of ‘one version of the truth’ is entirely possible with OneStream.

Learn More

To learn more about how you can re-imagine the financial close with the unrivalled power of OneStream’s Intelligent Finance Platform, download our whitepaper.  And don’t forget to tune in for additional posts from our Re-Imagining the Financial Close blog series.

Today’s CFOs and controllers need to manage their critical, enterprise-wide financial data and processes as effectively as possible. That data needs to be timely, accurate and easily accessible for insightful reporting and analysis to maintain a competitive edge. Accordingly, their corporate performance management (CPM) solutions need to be robust, scalable and provide full integration with their ERP, HCM, CRM and other systems.


Whether you’re shopping around for an EPM solution or looking to switch over to a new solution — OneStream’s drill-through capability is the functionality you never knew you needed.


Many vendors claim to handle complex consolidations, but there are several significant differences between the underlying engine that is needed to run financial data aggregation and the engine used to run straight aggregation or simple consolidations. Any vendor that uses Microsoft Analysis Services, Oracle Essbase, Cognos, TM1, SAP Hanna or a similar straight aggregation engine will face numerous challenges to address core financial consolidation requirements. Let’s examine this more closely.