In a scenario where every margin point is contested, discussing " how to transform data into profit and productivity in the purchasing area" has gone from being futuristic rhetoric to an immediate agenda item for purchasing, finance, and supply chain managers. Low-quality data, generic records, and a lack of visibility into expenses directly compromise EBITDA, decision-making, and competitiveness.

At the same time, pressure is growing for automation, AI, and process digitization. However, without a complete, standardized, and governed database, no technology delivers its full potential. AI starts to "go haywire," automations are lost, and the purchasing area continues to operate reactively.

This article, based on a webinar conducted by CH | Astrein in partnership with Paradigma Business Solutions , shows in a practical way how well-managed data translates into recurring savings, increased productivity, and more strategic decisions – with real-world examples from major Brazilian industries.

Throughout this text, you will see how to act on five main fronts: data quality, visibility and spending analysis, strategic negotiations and compliance, automation and digitization, and supply chain integration. All converge on the same goal: transforming data into measurable and sustainable profit and productivity .

What does "Transforming Data into Profit and Productivity" mean in practice?

In practice, transforming data into profit and productivity means treating information as an economic asset, not as a "byproduct" of the ERP system. It means making sure that:

• Each registered item must be unambiguous, comparable, and traceable.

• Each purchase order should be based on reliable data, not generic descriptions.

• Each negotiation should provide evidence of volume, supply alternatives, and price history.

• Each expense report should clearly point out where the greatest opportunities for savings lie.

When this happens, results begin to appear in very concrete areas:

• A 2% to 5% reduction in purchasing costs through data quality and standardization alone.

• A drastic reduction in emergency purchases, rework, and returns.

• Reducing the purchase cycle from weeks to just a few days.

• Increased team productivity, as they can now dedicate time to strategic negotiations, rather than correcting registration information.

  
  

Data ceases to be merely a "record" and becomes raw material for decision-making , directly linked to EBITDA, cash flow, and competitiveness.

Why data quality is the foundation of financial results.

CH | Astrein has been working for over 30 years in the standardization and management of master data, having cleaned up more than 40 million items and built a community with more than 5 million unique items, 200,000 suppliers, and 280 corporate clients. This scale allows us to demonstrate, in practice, that data quality is a results-oriented project, not just an organizational one.

In one of the cases, a large food and fertilizer company had:

• High volume of generic and duplicate items.

• Lack of consistent categorization.

• Difficulty in structuring category management and strategic negotiations.

Following a project focused on sanitation, standardization, and governance, the company:

• It reduced purchasing costs by approximately 5%, in an annual volume exceeding R$ 2 billion.

• Migrated to a new ERP system with consolidated and reliable data.

• It began operating with far fewer emergency purchases and specification errors.

  
  

In another industry, focused on general materials, the impact was measured by productivity :

• Order processing costs fell from 50 to 17 (internal index).

• The average purchase lead time has been reduced from 5–15 days to 1–3 days.

• Incorrect purchases fell from 10% to 1%.

• The annual savings reached 4.9% of the expenditure of the unit analyzed.

  
  

These cases show that, before talking about AI, blockchain, or advanced automation, it's necessary to address the basics: complete descriptions, correct categorization, elimination of duplicates, appropriate unit of measurement, correct tax classification, and continuous governance of records.

Five fronts for transforming data into profit and productivity.

1. Quality and governance of master data

It all starts with how your company describes and organizes materials, services, and suppliers. When the registration includes terms like "cable," "maintenance," "as per sample," or "outside the box," the purchasing department is left "in the dark."

A mature approach involves:

• Clear technical descriptions with standardized attributes.

• Elimination of duplicate and generic items.

• Adoption of international categorization standards (such as UNSPSC).

• Inclusion of tax, application, and unit of measure attributes.

• Review of legacy items and creation of clear rules for new registrations.

  
  

At CH, this is supported by a framework with over 72,000 description standards, a specialized technical team, and a central concept: the Golden Code , a "CPF of the item" that connects internal codes from different companies to a single technical identifier.

With the Golden Code and the data community:

• An updated item in one client can be used by the entire community.

• Changes in NCM codes or tax attributes are monitored and replicated.

• Photos, attributes, and manufacturer alternatives are shared, enriching everyone's listings.

  
  

Without governance, sanitation becomes a one-off project, and the problem returns within a few months. With continuous governance, data quality becomes a permanent asset.

2. Visibility of expenses and management by category.

With cleaned-up data, it's possible to move beyond "other/miscellaneous" and advance to a clear view of spend analysis .

• How much is spent by category, region, unit, and supplier?

• Which categories have the highest volume of transactions and deserve immediate attention?

• Where there is excessive fragmentation of suppliers.

• Which items have the potential for consolidation to achieve economies of scale?

  
  

From there, category management comes into play using matrices such as Kraljic/Strategic Sourcing :

• Strategic items: high criticality and high financial impact → robust contracts and well-managed risk.

• Bottleneck items: few suppliers → contingency plans and development of alternatives.

• Leverageable items: many suppliers and high spending → reverse auctions, volume consolidation, aggressive negotiations.

• Non-critical items: low impact → maximum automation, structured catalogs, and a focus on operational efficiency.

  
  

When the purchasing department masters this information, it can justify decisions based on data, prioritize initiatives, and build a credible and traceable savings pipeline.

3. Strategic negotiations, purchasing community and cross-referencing

Once the registration process is standardized and expense visibility is consolidated, it opens up opportunities for structural gains in negotiations .

Two concepts stand out:

1. Cross-reference (OEM vs. actual manufacturers)

   • Many replacement parts are purchased as if they were exclusive to the machine manufacturer (OEM), but in practice they are produced by specialized manufacturers (bearings, valves, connections, etc.).

   • By correlating OEM codes with the original manufacturers, it is possible to buy directly from them, reducing the price by 20%, 40%, or even more than 70% in some cases.

   • The CH data community concentrates this intelligence, enabling the identification of reliable technical alternatives.

     
     

2. Joint negotiations and community procurement (Blockchain Procurement)

   • Using the Golden Code, it's possible to identify the same item being purchased by different companies within the community.

   • A Blockchain Procurement solution allows you to consolidate this volume and bring a joint demand to market, significantly expanding your bargaining power.

   • In a case study involving electrical materials and bearings, consolidating demand based on the lowest price offered in the community resulted in an additional reduction of 25% for electrical components and 32% for bearings, without any loss of quality.

     
     

In this context, data ceases to be merely a snapshot of the past and becomes the basis for collaborative purchasing strategies , both inside and outside the organization.

4. Compliance, supplier risk and tax security

Turning data into profit also means avoiding losses, fines, and reputational risks .

Critical points:

• Correct tax classification (NCM, TIPI, Supplementary Law 116).

• Adherence to tax reform and monitoring of daily changes in regulations.

• Supplier risk management (financial, reputational, socio-environmental).

• Documentation, audit trails, and governance regarding registrations and purchasing decisions.

  
  

CH works with updated NCM (Brazilian Customs Nomenclature) databases and mandatory attributes (including DUIMP/import requirements), ensuring that the technical description contains the necessary elements for proper tax classification. A simple item like "chair" can have different NCMs and tax rates depending on the material and application; without a correct description, the risk of error is high.

On the vendor front, solutions like Web for Link enable:

• Structured supplier approval with clear criteria (A, B, C).

• Reputational, fiscal, financial, and socio-environmental analyses (background check).

• Expanding the base of qualified suppliers, integrated into the community of items.

  
  

The result is a purchasing department that negotiates better, is exposed to fewer risks, and is prepared for internal and external audits.

5. Automation, digitization, and intelligent use of AI

Only after cleaning up data, standardizing registrations, and structuring categories can automation and AI deliver their full potential.

Paradigma , through SRM 360 and modules like the Shopping Avatar , shows how this connects in practice:

• Automatic quotation generation based on purchase requests and pre-configured rules.

• Automatic invitation of relevant suppliers, cross-referencing item category with supply category.

• Automatic portfolio division by category, specialty, or strategy.

• Automatic consumption of contracts and catalogs in recurring requests.

• Dashboards showing SLAs, volumes, savings, and area performance in real time.

  
  

With well-structured data:

• Automation reduces the time spent on operational activities by 30% to 40%, and this can reach 60% in companies with a high volume of spot orders and manual processes.

• The purchasing team will now prioritize strategic negotiations, scenario analysis, and building business cases, instead of "putting out fires."

• When AI is applied (decision-making agents, recommendations, predictive analytics), it consumes fewer resources, makes fewer mistakes, and provides suggestions that are much more aligned with the reality of the business.

  
  

AI without quality data is costly and frustrating. AI built on a clean foundation leverages productivity and provides a competitive advantage.

How to take the first step in your business.

Although the landscape seems broad, getting started doesn't have to be complex. A pragmatic approach is:

1. Mapping the starting point

   • Identify the current quality level of the records (generic items, duplicates, inconsistencies, questionable NCMs).

   • Assess the level of expense visibility: how much is categorized as "other/miscellaneous"?

     
     

2. Calculate the potential ROI.

   • Use market benchmarks (e.g., 2% savings on revenue in indirect materials or 10%–15% savings per category with spend analysis) to estimate impact.

   • Apply these percentages to your purchase volume to build an annual return scenario.

     
     

3. Structuring a data quality project with governance.

   • Define the scope (direct materials, indirect materials, services, suppliers).

   • Involve critical areas (purchasing, maintenance, tax, IT, finance).

   • To ensure that, after the cleanup, there will be an ongoing process of registration and review.

     
     

4. Choosing the automation and SRM strategies that best fit the current situation.

   • Start with what generates the quickest impact: portfolio segmentation, automated invitations, use of contracts and catalogs.

   • Evolving towards intelligent agents, recurring reverse auctions, and collaborative planning with suppliers.

     
     

5. Build the business case for the C-level executives.

   • Translate gains in time, quality, and savings into a direct impact on EBITDA.

   • Show "before and after" comparisons: processing cost, purchasing cycle, emergency purchases, error rate.

   • It's important to highlight that every R$1 saved on purchases goes directly into the bottom line, unlike every R$1 of additional revenue, which is subject to taxes, costs, and business expenses.

     
     

When data, processes, technology, and people move in the same direction, the discourse of " Transforming Data into Profit and Productivity" ceases to be a promise and becomes a routine management practice.

Conclusion: Data as a strategic ally for the purchasing area.

The purchasing area is at a turning point. Between spreadsheets, urgent demands, and pressure to reduce costs, it's tempting to look only at the short term. But the cases presented show that the biggest "quick win" lies precisely in structuring what many companies neglect: master data, governance, and visibility.

Data quality projects, combined with SRM and automation solutions, have already generated savings of millions of reais in different segments, in addition to increasing team productivity and the credibility of the purchasing area with the board of directors and the council.

The next step, therefore, is not to ask whether it's worth investing in data, but rather how much it costs to continue operating with poor databases, low visibility, and poorly integrated processes. Companies that tackle this issue now will be better positioned to explore AI, advanced automation, and collaborative purchasing models.

If your organization truly wants to elevate the strategic role of purchasing and supply chain management, the path lies in a clear and disciplined agenda of Transforming Data into Profit and Productivity .

Bonus tip

Before initiating a major sanitation project or deploying a new platform, choose a pilot category (e.g., MRO materials, electrical, or bearings) and complete the full cycle: sanitation, categorization, strategy definition, structured negotiation, and workflow automation. Use the results from this category as an internal showcase to approve subsequent steps and scale the scope more quickly.

FAQ – 5 frequently asked questions from purchasing managers

1. Where do I start if my registration is very poor?

Start with a quick diagnosis: identify the largest spending volumes, quantify generic and duplicate items, and choose a relevant category to run a cleanup and governance pilot. Based on the learnings, expand to the rest of the database.

2. How long does it take to see financial results?

In general, data quality and spend analysis projects begin to generate savings within 6 to 12 months, especially when combined with structured negotiation actions (contracts, auctions, volume consolidation).

3. Do I need AI to turn data into profit?

Not necessarily. AI enhances what is already structured, but the main initial gains come from standardization, governance, and relatively simple automations. AI acts as an accelerator after the foundation is mature.

4. How can we involve management and finance departments in this agenda?

Translate the project into EBITDA impact: show what 2%, 3%, or 5% savings in annual purchase volume represents, and highlight that the savings in purchases go directly to the bottom line, unlike additional revenue.

5. Is it possible to measure ROI consistently?

Yes. Based on indicators such as cost reduction per category, shorter purchasing cycles, decreased emergency purchases, and supply errors, it's possible to build an ROI calculator that supports investment prioritization and proves that your company is, in fact, transforming data into profit and productivity.

CH | Astrein — Pioneer in Master Data Management in Latin America.

Leadership that combines experience and innovation to transform data into reliable decisions. Master Data Management