Data Analytics Q + A

At the current time, automated reports are not available. As above, the report builder functionality allows users to create their own report and save it for future speed and convenience.

Please provide report samples

As above, report builder functionality enables users to create custom reports and save them.

Actionable Intelligence Report (AIR), available in the platform.

Managed Services are available on demand. RobobAI analysts and data scientists will analyse your comprehensive AP data and identify target areas for you to unlock value. Services typically include:

Comprehensive Custom Spend Data Analysis (beyond platform)

Data Extraction & Wrangling

Data Enrichment

Data Harmonisation

Custom Taxonomy Mapping

We can add the filters which are present in the data schema, we can also add new bespoke charts. These are custom modifications and incur additional time and cost.

Custom dashboards cannot be created automatically. Custom dashboards can be requested via the project team will incur additional time and cost.

The RobobAI platform only uses 3^rd party data for Modern Slavery, Sanctions, Adverse Media, Indigenous Suppliers and Sustainability dashboards. In these situations, the end client does not interface directly with the 3^rd party. We have automated APIs which connect RobobAI to the 3^rd party and fetch alerts to then visualise them throughout the relevant RobobAI dashboards.

As a standard, RobobAI use UNSPSC, the United Nations product and service classification system. We can also use alternative and custom classification systems, at additional

effort. Please contact us to discuss further.

Classification on the Fly (COTF) enables you to customise classification naming and see the effect of the change take place immediately across your dashboards.

The supplier summary is an analysis of suppliers and their spend patterns. The primary objective of this dashboard is to provide card opportunity in the data, which can be easily adopted to modify the buying channel to achieve the potential savings which is calculated by the platform.

Classification on the Fly (COTF) enables you to customise classification naming and see the effect of the change take place immediately across your dashboards.

Total spend value summation present in the supplier data.

Count of Suppliers Distinct count of supplier in the supplier data.

Suppliers being paid by card Suppliers where current payment type is Cards.

Spend with Commercial Card accepting Suppliers Spend where “Supplier Payment Enablement Type” = “INCONTROL” and “COMMERCIAL”

Suppliers Accepting Commercial Cards Distinct count of suppliers where “Supplier Payment Enablement Type” = “INCONTROL” and “COMMERCIAL”

Opportunity sizing is the potential total benefit if we convert the spend from non-card to cards. This is calculated with below formula.

Opportunity Sizing = Rebate amount + Operation cost avoidance amount + Net WACC Impact.

Net WACC Impact = WACC of Cards + WACC of Actuals.

With the help of sliders, users can modify the variables Rebate %, Operational efforts in days and WACC %. These sliders will automatically change the total opportunity size real time.

Current payment method is the buying channel or payment method used by the customer in existing data.

Invoices spend band are various thresholds of number of ticket sizes of invoices present in respective buckets. Ticket size, here, is calculated as Spend divided by number of invoices.

Suppliers spend band is the threshold of spend aggregated at a supplier level.

It is indicating if the given supplier has accepted in the any commercial cards in the past with “Yes” or “No”.

This is number of times a supplier is invoiced in a year or a data frame, in case of Data Schema 1 this is calculated based on average ticket size.

Payment term is a contracted or agreed number of days as credit to customer to pay the invoiced amount to the suppliers. This is a very important variable, as this will have an impact on the WACC of both the customer and supplier.

Card acceptance recency shows the latest time frame of the suppliers using a commercial card.

A woman-owned business is a specific designation used by American government agencies and industry associations to set aside special programs to encourage and empower female business owners. Most definitions of this term involve a practical look at the legal and ownership structure, as well as the issue of control of the day-to-day operations of a business.

Minority Business Enterprise (MBE) is an American designation for businesses which are at least 51% owned, operated and controlled on a daily basis by one or more (in combination) American citizens of the following ethnic minority and/or gender (e.g., woman-owned) and/or military veteran classifications.

Small businesses are types of corporations, partnerships, or sole proprietorships which have a small number of employees and/or less annual revenue than a regular-sized business or corporation. Businesses are defined as "small" in terms of being able to apply for government support and qualify for preferential tax policy.

A nonprofit organization (NPO) or non-profit organisation, also known as a non-business entity, or nonprofit institution, is a legal entity organised and operated for a collective, public, or social benefit, in contrary with an entity that operates as a business aiming to generate a profit for its owners. A nonprofit is subject to the non-distribution constraint: any revenues that exceed expenses must be committed to the organisation's purpose, not taken by private parties.

All the horizontal bar charts and pie charts works as a clickable filter. Users can click on the filter icon (+ Add Filter) on top left-hand side to select the different dimensions to apply the filter across the relevant dashboards.

Any user from a customer organisation that needs access, please reach out to RobobAI support team for assistance on support@robobai.com.

Yes, this can be done with automatic process if the data file adheres to RobobAI’s designated format. For details, please reach out to RobobAI support on support@robobai.com.

Data analytics is the process of examining, cleaning, transforming, and interpreting data to discover meaningful insights, patterns, and trends that can inform decision making.

There are three main types of data analytics: descriptive analytics (what happened), diagnostic analytics (why it happened), and predictive analytics (what might happen).

Data mining is a subset of data analytics that focuses on discovering patterns and relationships in data, often using machine learning and statistical techniques.

ETL stands for Extract, Transform, Load. It's a process in data analytics where data is extracted from various sources, transformed into a suitable format, and loaded into a data warehouse for analysis.

Data cleaning is essential to remove errors, inconsistencies, and outliers from data, ensuring that analysis is based on accurate and reliable information.

A data warehouse is a centralised repository that stores data from different sources and makes it accessible for analysis and reporting.

Data visualization is the use of charts, graphs, and other visual tools to represent data in a way that makes it easier to understand and analyze.

A dashboard is a visual representation of key data metrics and KPIs, typically displayed in real-time, to provide an overview of an organization's performance.

Machine learning is a subset of data analytics that involves the use of algorithms to enable systems to learn and make predictions or decisions without explicit programming.

NLP is a field of data analytics that focuses on the interaction between computers and human language, enabling computers to understand, interpret, and generate human language.

Data privacy refers to the protection of individuals' personal information and ensuring that data is handled in compliance with privacy regulations and ethical standards.

Data sampling is the process of selecting a subset of data from a larger dataset to draw inferences and make analysis more manageable.

Data normalization is the process of scaling and standardising data to ensure that it falls within a consistent range, making it easier to compare and analyze.

Data wrangling is the process of cleaning, transforming, and organising raw data into a structured and usable format for analysis and decision-making.

Data wrangling involves cleaning, transforming, and organising raw data to prepare it for analysis, while data normalisation is a specific technique used to rescale and standardise the values within a dataset to a common scale, ensuring fair comparisons between different features or variables.

Data mining is the process of discovering hidden patterns and relationships in data to extract valuable information.

Data governance refers to the management and control of data assets within an organisation, including data quality, security, and compliance. For more details about RobobAI’s information security click here.