Why Your BI Software Isn't That Intelligent_Scale Spend Save_GADs

Why Your “Business Intelligence” Software Isn’t That Intelligent

Why Your “Business Intelligence” Software Isn’t That Intelligent

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Quick History Lesson

In a perfect world, business leaders would have psychic abilities and regularly adjust their behavior in order to optimize the future. Business Intelligence (BI) software has historically been known as the next best thing, touted to make predictions about a business’s future based on its past behaviors. However, Business Intelligence software is not really all that intelligent…

While it delivers more powerful analytics, it lacks what is needed to make sense of the data without human insight.

Spend is always just spend—it happens or it doesn’t happen. The moment spend stops is the moment business stops, which is why it is vital to accurately keep track of its pattern. General BI software cannot analyze at the same level of specialized spend analysis tools – leaving gaps in your business insights.

A business’s past can be attributed to many moving parts such as the political climate, weather, tariffs, changing preferences of its customers, changing preferences of its customers’ customers, health and personal lives of its employees, and so much more.

Spend, the only guaranteed thing to occur every single day of business, is the only metric on which we can comprehensively and tangibly measure a business’s past, present, and future.

Why Your “Business Intelligence” Software Isn’t That Intelligent

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“Intelligence” by definition is “the ability to acquire and apply knowledge and skills,” yet BI software used by procurement teams (Tableau, Qlik, etc.) is not built to acquire and apply knowledge (data) and skills (strategy). In other words, BI software isn’t that intelligent and can perniciously misguide a business via the application of limited or false human knowledge.

In order to get the value you need out of BI tools, so much more cost goes into:

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Consolidating a company’s fragmented sets of accounting data (i.e., invoice details) and procurement data (i.e., purchase order details).

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Organizing that data to see how spend varies – if the fragmented data sets could even be reconciled.

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Hiring an expensive third-party firm to review the static sets of data and pinpoint savings opportunities such as minimizing tail spend – if tail spend was even documented.

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These expensive, time-demanding third-party firms would audit multiple departments for “spend analysis” and possibly only achieve insignificant savings using scraped-together, unreliable data sets at the point of no return. Following tons of inquiries, they would possibly discover orders that were made by nonexpert purchasers across departments. Limited by ERPs and Excel, they would manually slice and dice the data into categories, cleanse and standardize the data row-by-row and column-by-column. Then, they’d be able to infer some spending trends but, ultimately, the analysis would be descriptive at best, merely providing sums and averages.

Smaller procurement organizations would make do by pulling data from an ERP, accounts payable and homegrown data-warehouse integrations, but larger, more complex organizations typically needed data from additional sources to make the most strategic sourcing decisions, which is challenging without advanced analytics tools. Today, spend analytics software of good quality are not just spend analytics tools; they are also data-management platforms that enable organizations to work toward the future rather than be stunted by the past.

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Why Spend Analytics Solutions

Spend analytics (SA) software solutions like SpendHQ can provide visual dashboards that update dynamically and can even integrate APIs that continuously pull data from third-party sources relevant to the business (i.e., commodity cost indices, labor rates) for deeper insights. Leading tools in this space have built-in algorithms that cleanse, normalize, and classify existing datasets – the most common hurdle for businesses and a prerequisite to effective analytics. Spend analysis can conduct correlative root-cause analysis beyond sums and averages, visually indicate areas of heavy, mid, and tail spend across or within categories, identify trends across time, and can even make commodity cost predictions.

Hiring a firm to perform periodic statistics is no longer a necessity, especially if organizations are equipped to uphold best practices in data management. The initial and complex transition from spreadsheets and disparate datasets to spend analysis technology can

Why Your “Business Intelligence” Software Isn’t That Intelligent

require onboarding support from providers or third-party guidance; but, once implemented, it should function as a regular compass, providing guidance not just to procurement, but for C-Suite, finance, risk and operations, and even corporate development teams. Spend analysis standardizes and centralizes your spend data, making it accessible to everyone on your team.

There are a lot of factors that go into choosing the right spend analysis tool for your organization and the various choices out there carry unique benefits and threats. Let’s explore the pros and cons of using three different forms of spend analytics solutions: homegrown tools, source-to-pay suite providers (with spend analysis as a subfunction), and standalone best-of-breed providers.

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Homegrown Tools Pros: Homegrown solutions are built from the ground up within organizations and are typically deeply embedded into business processes. They might use Tableau to query and present data that is entered and referenced teamwide.

⊲ Built specially to accommodate a business’s unique needs and spend categories. ⊲ Can harbor useful and historical data that can be exported into Excel, which most procurement professionals, across all levels of seniority, are educated on . ⊲ Often centered around supply chain management, collecting important data for both spend analysis and inventory tracking. ⊲ There is an increasing amount of spend analytics solutions entering the market meant to easily integrate with homegrown solutions and apply algorithms, circumventing the need to “reinvent the wheel.” ⊲ Microsoft (Excel) users may benefit from new functionality over time (as suggested by Microsoft’s current investments in spend management and AI). ⊲ On-premises solutions can be reconfigured for the cloud for more dynamic data practices.

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Cons ⊲ Homegrown solutions are often built in Excel or Tableau – meaning analysis requires lots of spreadsheets and pivot tables and only offer general analysis.

⊲ Supplier diversification and category savings opportunities using external data sources/APIs will be missed or there will have to be extra coding legwork.

⊲ Ill-equipped to efficiently merge datasets if a merger or acquisition occurs.

⊲ Without dedicated intervention, data will remain fragmented and siloed leading to misunderstandings and frustration between employees and with customers; data will ultimately be an ongoing source of pain when it should be a useful asset.

⊲ Any new talent will need to learn and improve on existing code/architecture, often without much documentation to work from. ⊲ Data transformations require a time-consuming audit of information and rigorous context-gathering (i.e., a supplier changes all their product SKUs, reconciliation of too many free-form data fields). ⊲ Employee turnover: sticking with a homegrown solution requires a dedicated IT team to longterm handle the maintenance, development, and augmentation of the solution – ideally, an IT team with strong employee retention that is educated on the solution architecture and knows context.

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Suite Tools Pros: Procurement suite tools offer end-toend support from source to pay (plus AP, payment, and treasury management capabilities in some cases). Suites are fully integrated into the business and update dynamically, hosting the bulk of all relevant procurement data. Spend analysis tools are typically not a central focus of the application but, rather, an appendage to the larger solution.

Why Your “Business Intelligence” Software Isn’t That Intelligent

⊲ Suites enable collaborative workflows and easier project management (usually enhanced by chat box functionality). ⊲ Integrated features and one-stop-shop for customer support frees up IT resources for other company projects. ⊲ There are a couple of suites that do offer automated, out-of-the-box opportunity identification and “what-if” analysis capabilities and with enough investment in spend analytics capabilities might start to offer more advanced category-specific “what-if” analysis.

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Cons: ⊲ Suites usually offer old-school “black box” AI mappings, which means low-quality data (i.e., duplicates, typos) consistently entered cannot be detected or fixed by the end user without retraining or running all mappings through manual override rules. → Manual overrides will often require support from IT or the provider which will create spend analysis bottlenecks.

⊲ Pre-built and immutable formulas make it challenging to build analysis around one-off factors and load temporary datasets for custom views in a temporary workspace. → Example: an end user might want to inspect the fluctuating availability of a niche commodity and see whether the current pricing and build-ahead will force an undesirable increase in overtime spend. A suite will likely not accommodate HR data into its spend analytics module.

⊲ Suites will have to accommodate a growing number of organizations that only want standalone spend analytics tools which means they would have to invest in or partner with best-of-breed spend analysis tools that do not rely on suite data. ⊲ Suites tend to have out-of-the-box reports and taxonomy that pre-configures dashboards, which means users have less flexibility to generate customized reports that represent their organization’s specific KPIs or categories… Meaning they may have to tediously use Excel after all.

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⊲ At most, there are suites with “what-if” analytics built in using Tableau-powered front ends but outof-the-box category intelligence. Across-the-board opportunity analysis requires a time-consuming customization project. ⊲ A suite’s end-to-end solutions are all connected; when one solution malfunctions, so do the others. ⊲ If a suite has added capability through the acquisition of specialist providers, integration can be a long, drawn-out process. Clients might become tethered to suites; a suite will have a monopoly on multiple business processes which means less flexibility to explore new solutions in the market. → This also means less negotiation leverage year after year (with a subscription model) as the suite, broadly integrated with the business, will become mission critical. ⊲ Suites do not enable the depth of taxonomy that best-of-breeds offer; users will lose out on time that could be spent collecting competitive, in-depth category data.

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Best-of-Breed Tools Best-of-Breeds (BoBs) center their innovation around a niche area of procurement. Spend analytics BoBs offer category-specific savings levers, should-cost modelling, and predictive insights using more advanced AI than suites (and homegrown tools) given its focus.

Pros: ⊲ BoB providers have built their AI on the foundation of millions of mis-mapped spend across their entire client database – meaning they should be prepared and able to do the heavy lifting to get your data clean and accurate. ⊲ BoBs can speedily harmonize and analyze data across various sources as part of onboarding process. ⊲ Organizations that already have multiple integrated software solutions are set up with additional do-ityourself capabilities. ⊲ BoBs have a strong focus on developing data partnerships and API integrations (e-marketplaces, sell-side systems, market indexes, niche data providers with intel on supplier ESG, credit scores, etc.).

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⊲ Can configure customized algorithms (and taxonomies) that provide quick answers to immediate questions such as “Should we prioritize certain suppliers within specific regions?” ⊲ Clients are set up with scheduled data pushes and pulls so that questions about spend can be answered on and ongoing basis and those answers can be automatically presented in shared dashboards. ⊲ The advanced data classification, modeling, and anomaly detection built into most BoBs can help detect fraud. ⊲ The time-consuming process of external-data imports and multiple, convoluted pivot tables is completely eliminated. ⊲ BoBs stay ahead of homegrown and suite solutions as they work toward more predictive and prescriptive analytics features. ⊲ Spend analytics providers differentiate themselves through existing data partnerships and keep growing data partnerships to provide standout value.

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Cons: ⊲ Like all other solutions, BoBs cannot completely ensure against rogue spend not being documented, but can only incentivize data entry with strong UI/ UX and change management that embeds the use of spend analysis dashboards into all buying decisions. ⊲ AI capabilities are nascent and still evolving and have not reached full potential. ⊲ Data enrichment will always require the development and maintenance of data partnerships outside the client organization’s control. ⊲ Spend analytics providers will be a few steps behind suites when it comes to expanding their global procurement and supplier network.

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Choosing SpendHQ for Spend Analytics

Businesses always assume an amount of risk and rarely expect perfection. As done historically with BI tools like internally developed solutions or external suite platforms, users can take diligent steps to collect, clean and analyze spend data as best they can. The cost, however, will inevitably be their time; it could be IT’s time, HR’s time hiring new IT employees, nearly everyone’s time as outside firms step in to summon data, or a quarterly roundup of executives discovering and stitching together process holes.

Productive spend analysis will be the responsibility of multiple stakeholders even though it has the potential to solely live with procurement, and procurement will never conduct data-driven business nearly as fast (or as accurately) as teams using BoB spend analysis solutions.

SpendHQ is a BoB procurement provider that sees high-quality category data as being critical to running an intelligent and adaptive business and sees categoryspecific data management as subpar or even non-existent within homegrown, suite and even other BoB solutions.

Why Your “Business Intelligence” Software Isn’t That Intelligent

A standout provider for category-level intelligence, SpendHQ helps even entry-level analysts identify millions of dollars of savings in everyday categories (i.e., travel, IT, professional services, facilities, corporate, transportation). With SpendHQ’s Category Management solution comes deep, ready-to-be-used opportunity algorithms and dashboards answering “what-if” questions across 40+ categories with the option to add customized and changeable taxonomies so that future category sourcing does not have to be limited by past data practices.

SpendHQ is a procurement analytics platform for sourcing and procurement professionals. We empower our customers to enhance and grow their strategic sourcing capabilities with a suite of spend visibility,

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supplier/risk management and savings analytics tools powered by leading AI and machine learning technologies. Born out of the consulting firm, Insight Sourcing Group, SpendHQ combines procurement intelligence with world-class spend analytics to deliver realized savings to procurement teams of Fortune 500 and SMB organizations across the world.

Dashboards can be optimized for finance, presenting working capital needs against different payment terms, and providing a “total cost management” solution with the ability to spot category savings opportunities.

Contract-compliance views allow category-spend and suppliers to be managed continually throughout a contract as opposed to at renewal time or just quarterly.

Essentially, spend analysis software should not just benefit procurement—it should and can enable close collaboration between procurement and all executive stakeholders. Legal, AP, Finance, procurement, corporate development, or any Center of Excellence should all benefit from regular analytics reports that pull from cohesive, accurate data.

After all, if data is used for intelligence, then a business with misleading data can become intelligently (and irreversibly) misled.

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Schedule a demo with a SpendHQ expert today to see how your organization can use your data to drive strategic goals.

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Why Your “Business Intelligence” Software Isn’t That Intelligent

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