Build Vs. Buy E-Book - 10-11-22 Campaign

Modern Spend Analysis: Build vs. Buy

Starting Your Spend Analysis Journey: Build vs Buy

Companies often face the decision of building knowledge or tools internally versus going to a thirdparty. Often referred to as “build versus buy,” the dilemma raises the fundamental economic question of how to best allocate resources for achieving the optimal return on capital and time invested—would it be better to find a pre-existing software that provides a one-size-fits-all solution, or to build a highly customized, unique internal software that you’ll have to manage and update?

The build versus buy predicament has become a common one—especially when it comes to spend analysis. Organizations that decide to manage and build spend analysis in-house face the risk of building internal spend analysis competencies at the cost of having to manage tactical and often repetitive activities that can take precious time and resources away from more strategic tasks.

Doing spend analysis successfully also requires significant investment in high demand data science resources and emerging technologies related to machine learning and AI to create more repeatable processes and accurate results. But even with advanced business intelligence platforms and emerging tools in data automation and data science, utilizing human experience and knowledge in understanding spend data cannot be overemphasized for achieving success.

Companies can’t afford to continue putting off decision about spend analysis. According to a recent Deloitte study, 18% of CPOs regretted not accelerating digitization of spend data and analysis fast enough through investments in 2020—a pain that more CPOs will feel as competitors surpass them in the market.1

When it comes down to it, it’s less about build versus buy, and more about knowing how and when to the fill gaps within your organization based on the right combination of internal knowledge, technology, and third-party expertise.

Modern Spend Analysis: It’s Not About Build versus Buy, but Balance

50% 45%

of procurement leaders currently leverage intelligent and advanced analytics for cost optimization.

believe the quality of data is a major barrier to the effective application of their digital technology.2

Spend analysis is strategic:

Sometimes referred to as the new currency or oil of business, getting insights from procurement data has become the leading strategic and technology initiative for most procurement organizations. Because procurement’s focus on cost management is predicated on insights from spend data, getting spend analysis correct is necessary for maximizing procurement-related EBITDA impact.

In a study from Bain & Company,3 researchers assessed 22 different digital procurement solutions across source-to-pay, including spend analysis. The study found spend analysis was considered highly important to the business, and highly adopted, but ranked low in terms of satisfaction. Due to the traditional challenges around reliability and spend data accuracy, the research even recommended proceeding with caution when considering spend analytics.

With the expectation that improving spend visibility is essential for delivering better insights and even encouraging digital transformation efforts, organizations today are reassessing their strategic initiatives around spend analysis. In this context, the question of “build versus buy” for spend analysis has come front and center.

Spend visibility requires balance:

For those deciding on a new spend analysis solution, using a “build versus buy” framework with well-defined technology requirements could be considered a sound approach. By evaluating how software products in the market measure up against current requirements and any existing in-house solutions, you can quantify the trade-off between features and cost. Like other technology initiatives, if you develop a requirements analysis via RFI/RFP based on weighted scores, you can find answer whether it makes more sense financially and operationally for your company to build or buy.

However, using a software selection approach for spend analytics based on “features and functions” alone highlights a failure in the inherent understanding

of the wider spend analysis discipline. Deciding on a spend analysis strategy is not simply about buying technology—it’s also about assessing the skills and talent of internal resources in their ability to take on a combination of business processes and technical tasks for managing growing data management requirements related to your objectives.

A true assessment of spend analysis requires an honest and open view of an organization’s current spend data process capabilities in collecting, normalizing, and optimizing spend data combined with the current analytical tools for providing procurement-centric visibility.

With the expansion of data science, these data and technical skills necessary have grown to include advanced analytics, machine learning, and wider AI.

While technology plays a large part, it’s also about around organizational maturity around category knowledge and sourcing expertise for identifying new sourcing opportunities and the professional skills in communicating the value to the business. AI alone is not enough to give you the insight you need into your spend data.

Modern Spend Analysis: Build vs Buy

The Elements of Modern Spend Analysis

assessing internal

reasonably manage current spend analysis requirements against internal capabilities (build), if going to an outside party is a better option (buy), or if some hybrid approach is the best option to fill the gaps and created the desired balance.

CORE COMPETENCIES:

Assessing Internal Capabilities for Modern Spend Analysis

1. People: Do internal skills & resources exist to manage against the iterative category requirements?

One of the first areas to address for spend analysis is people in the context of procurement talent and analytical skills. As an upstream procurement function, spend analysis requires expertise in category management and a fundamental understanding of supply markets and market trends combined with the ability to link spend patterns to business strategies.

Foundationally, this requires knowledge and experience in creating the optimal category taxonomies necessary to organize spend in a way that enables identification of new strategic sourcing opportunities and for tracking compliance risks associated with suppliers that impact downstream transactional procurement efforts and savings realization.

Many organizations often struggle to secure acceptance from stakeholders when comes to spend analysis. There are several factors that can contribute to this, including 1) inexperience or frequency of change in the supply base within the organization and 2) underestimating the nature of category data management and iterative maintenance of spend taxonomies.

In circumstances where change is frequent (consider mergers and acquisition or private equity), managing spend category changes and requirements may be even more difficult to determine. This contributes to continuing inconsistencies, miscategorized spend data, and inaccurate or uninformed reports back to business stakeholders. But against these data-intensive tasks, category managers and sourcing professionals are now also asked to manage against the wider strategic procurement projects related to total cost of ownership, supply assurance, and demand management.4

Therefore, when assessing spend analysis in the context of “build versus buy,” procurement leaders need to identify the people skills component of spend analysis, by understanding if internal category skills and knowledge of spend taxonomies are meeting business requirements, or if they would benefit by being augmented with additional outside experience related to category knowledge and wider sourcing and procurement experience. In this regard, third parties like SpendHQ can truly augment procurement teams by leveraging proven best practices and methodologies to determine the new category and sourcing opportunities.

Standardizing Category Management

A $4 billion technology manufacturer experienced dramatic changes in its industry and acquired three new organizations, tripling its volume of indirect spend. At the time, this technology organization conducted spend visibility manually through macros and spreadsheets as it was unable to secure investment in a spend analysis tool previously.

Manual processes of managing over 200 spend categories across 10 different backend systems resulted in a lack of consistency in data normalization and classification efforts and created additional pressures on the procurement team to find a better way to manage spend.

Through SpendHQ, the organization leveraged outside category management expertise as part of service-driven spend analytics solution to analyze over $2B in spend each year and identify new savings opportunities. By moving away from their previous

“build” to the “buy” SpendHQ solution, the organization realized a 30% increase in spend classification accuracy with a focus on a sub-category strategy.

Moreover, improved category accuracy and data visualization in one tool now acts as a source of truth for all category management efforts. What once took 1-2 hours to compress and filter into a single report for a Category Manager now took as little as 15 mins, a 400% improvement in efficiency across approximately 200 sourcing projects conducted annually.

2. Process: Can procurement rely on the availability of data scientists or fund its own team?

With data visibility a growing priority, organizations rely on data analysts to solve underlying data process challenges related to sales, product, and operational areas. But as the modern-day saviors for whipping digital transformation projects into shape, the high demand for these resources has created short supply globally. With a 35% annual growth in demand for data scientists, and trends of data analysts also replacing legacy roles, finding the in-house talent necessary to build a spend visibility tool is a challenge most companies don’t have the luxury of time to take on.5 And the recent KPMG CIO Survey revealed that companies that have not already started to meaningfully invest in business intelligence driven by AI are rapidly losing the chance to do so before falling behind competitors.6

So even with these advanced technologies in hand, IT on its own may not be prepared to handle the specific

nuances of spend analysis and spend data. In this regard, using third-party resources for improving data optimization may be a better alternative for procurement.

Given the focus on procurement and spend analysis as a discipline and years of experience, third parties like SpendHQ are “practitioner-driven” and already use the most advanced PAML and analytic technologies configured specifically for spend data optimization.

Working with hundreds of client use cases on top of millions of dollars of spend, a neophyte data science team or IT department not familiar with procurement simply cannot match the experience and knowledge of a more cost-effective business analyst experienced in spend analysis. Thus, leveraging a third-party may come at lower price point by providing a guarantee of access to scarce “data science” knowledge resources skilled in both procurement and advanced analytic technologies that may not otherwise be accessible to a procurement team.

Creating Balance for Process

This multi-billion-dollar global pharmaceutical provider is a recognized innovator in its industry. As a result, the company wanted to be ahead of its competition and was an early adopter of emerging digital technologies to clean its data company-wide. Enabling areas like artificial intelligence, machine learning, and RPA across the wider procurement organization was the goal. To accomplish this, the company engaged a management consulting firm with experienced data scientists to roll out an advanced analytics and machine learning platform as a pilot company-wide to integrate with its existing tools.

As part of this effort, an engaging and innovative CPO got procurement involved in this pilot where she saw this as a unique opportunity to improve their spend data optimization by using machine learning and AI techniques in the advanced analytics platform. However, financial constraints due to revenue pressures facing the industry forced consolidation

and reassessment of all IT spending in emerging technology areas, and the pilot was soon canceled. Additionally, a new, more conservative CPO was brought in to help reign in expenses, and the focus was turned to improving existing procurement systems before new technologies would be introduced.

By contrast, through working with a third-party like SpendHQ, the organization could have had immediate access to its proprietary data insights related to their optimization challenges and spend analysis visualization and still have the option of leveraging their existing BI tools as part of the cost-driven initiative pushed by the new CPO—without the prohibitively high price tag that stalled their project to build internally.

Driven by AI and having analyzed $2T+ in spend since inception, SpendHQ guarantees 97% accuracy in spend data. The company’s initial budget in the cost of using consulting data science resources would have covered over three times the cost for implementing the SpendHQ solution, including normalizing and categorizing over 10-15 different data sources from various systems and would include engaging spend analytics-as-a-service for a three-year period.

By partnering with a third-party like SpendHQ in the same period of the pilot, the company would have been well on its way to identifying new sourcing opportunities using those same sought-after machine learning and AI techniques with improved data optimization process and access to improved spend analysis visualization as part of the three-year commitment.

3. Technology: Can spend analysis just be built on BI?

Many organizations, as part of a “build versus buy” framework, have invested in advanced business intelligence (BI) technologies to replace the use of manual spreadsheets or homegrown tools. Aligned with the acquisition of data scientists as part of the business process of data, the generalized BI strategy is based on using a single visualization tool to analyze business data across the board, hoping to succeed in multiple areas—whether it’s sales, marketing, finance, or any other function, such as procurement.

In some cases, the decision to use BI tools is warranted. For instance, some of the largest technology providers in the market are focused on developing state-of-theart data visualization techniques, such as predictive modeling using BI tools. Moreover, solutions from bigname providers such as Microsoft Power BI, Qlik, and Tableau are frequently recognized by industry analysts for offering the latest and greatest in visualization that promotes self-service analytics and are often integrated as part of source-to-pay suites used by procurement.

But BI tools are like a Swiss Army knife and can do anything you ask. Since they are not designed for a specific business function out of the box, they often fail to provide the deep insights necessary for discrete business function initiatives like spend analysis. It’s no wonder that research from Gartner finds more than 87% of organizations have low business intelligence and analytics maturity. And, unfortunately, many BI tools lack the category management and taxonomy context that more specialized solutions provide without internal resources with expertise building in those capabilities.

As mentioned earlier, spend analysis is really a “technology-enabled service” and not just software. So, a BI-centric approach to spend analysis without addressing people and process will frequently miss the mark for most procurement organizations when complexities in spend data arise. Moreover, internal IT groups—even with data science resources in hand—will lack expertise in spend data management, leaving procurement on its own to build and maintain a complex spend data management operation.

Working with a third-party spend analysis provider like SpendHQ provides the necessary three sources of value not attainable from generalized BI tools. Even though BI tools may be highly customizable and claim to deliver the latest “bells and whistles” in the world of analytics, the effort to accommodate spend requirements makes BI tools on their own too difficult and expensive for procurement teams to manage without procurement expertise backing them.

These include:

Spend Data Management

Sourcing

and

Ultimately, a procurement organization’s value is in driving savings and compliance, not developing tools for data manipulation and visualization. And if they must rely on a third-party for analytics support, it is critical that their partner has deep procurement expertise, combined with the visualization capabilities that offer practitioner-based insights with the flexibility to configure custom dashboards, reports, and queries.

Creating balance for Technology

This $2 billion insurance provider owned by a private equity firm needed to implement a source-to-pay suite. As part of their spend analysis needs, they considered leveraging the spend analysis tool offered in the suite that integrated a popular BI tool for its visualization used by other portfolio companies within its private equity firm.

Initially, procurement saw the value of using the spend analysis tool from the source-to-pay suite, but were concerned that they were relying too heavily on one provider and wanted the flexibility to use their own solution. Moreover, because of the rapid change in their heavily regulated industry, procurement felt there would always be a need to rationalize spend from several different solutions, including an instance of their sourceto-pay solution leveraged by other portfolio companies, as well as several other ERP and T&E platforms used across the company.

Within months of adopting the spend analysis as part of the source-to-pay suite, the procurement team came across several issues.

By contrast, working with a third-party like SpendHQ, the organization would be able to kill two birds with one stone. With practitioner-based visualization as part of the tool, the organization could leverage the best data management practices in place. Furthermore, data would be coming from SpendHQ that is already properly normalized and categorized. And, on top of the existing out of the box pre-configured dashboards and reports, SpendHQ could offer the flexibility in using an analytics BI sandbox that provides advanced users capability to configure complex mashups and custom dashboards and reports in a seamless blending of both data in memory and in database, with multiple tables and the use of cutting-edge BI reporting capabilities needed for specific industry reporting requirements.

Modern Spend Analysis: Build vs Buy

Solving the Build vs. Buy Challenge for Spend Analysis

Like many other areas in business, there is often a fine balance between deciding to do something yourself or look to others for their expertise. The “build versus buy” decision is a significant one that many companies face when addressing any fact of their software needs. When it comes to areas of managing data, however, taking a holistic approach including people, process, and technology around data is the best way to meet the needs of the business.

For those in the process of deciding to build or buy a spend analysis tool, the advice is not to limit the thought process to an either-or situation, but to understand the value of spend analysis in its entirety and where a third-party can fill in the gaps. As technology moves quickly to advance analytic efforts, understanding the skills needed to maintain emerging technologies has also become a bigger part of deciding on keeping a task like spend analysis inhouse. Organizations need to understand that with the scarcity of funds and talent, building core competencies becomes an increasingly difficult task.

As a provider backed by decades of experience, SpendHQ has found that the best approach to spend analysis varies from one company to the next. Some organizations leverage all the spend analysis solutions we offer, while others prefer to rely on SpendHQ for the spend data-intensive services and their internal teams for the rest. Regardless of approach, the strongest recommendation is to focus on what you are consistently capable and effective at delivering within your internal procurement and IT teams and to partner with a trusted solutions provider like SpendHQ for creating balance in those areas you aren’t.

SpendHQ is currently helping hundreds of procurement organizations and their teams across almost all industries solve the compliance challenge. Currently analyzing over $5B in spend each year to identify new savings opportunities and to manage compliance,

SpendHQ was built by procurement professionals familiar with your vendors and your spend profile. To learn more about how SpendHQ could better support your organization today, schedule a demo today.