Data,Information,Insights,Actionable Insights

Journey of Data

The most important element in driving a company's operations efficiently and successfully is making the correct business decisions, which originate from meaningful or actionable insights rather than transactional data. There are four steps in the data journey that lead to actionable insights :

  • Data is the raw and unprocessed facts in the form of numbers and text, can be quantitative (measured) or qualitative (observed)
  • Information is the processed, structured and human-friendly presented form of data
  • Insights is generated by analyzing information and highlight conclusions
  • Actionable Insights is the set of business insights to take the action immediately for a meaningful outcomes

Raw data, or unprocessed facts like numbers, letters, symbols, and pictures, are stored in a database. Once processed, structured, and aggregated, data becomes information. The information is processed and turned into reports, dashboards, and data visualizations. Finally, when a set of actions is chosen, insights become actionable insights.

Data Driven Gap Analysis

Data Driven Gap Analysis

Data is everywhere. Global datasphere will increase from 45 zettabytes (1 zettabyte = 1 trillion GB) in 2019 to 175 zettabytes (1 zettabyte = 1 trillion GB) in 2025. In this environment, businesses are striving to become more data-driven, sustainable, and automated.

  • According to Forrester, 74% of companies aspire to be “data-driven,” but just 29% are effective in doing so.
  • Data-driven implies based on data, analytics, or insights.
  • It needs a growth mentality, constant testing, predictive modeling, and feeding prediction mistakes.
  • Actionable analytics need high-quality data and competent analysts who are thinking forward.

Prescriptive, Forward-looking, Findings, Recommendations, and Predictions are characteristics of data-driven analytics that always ask “why” or “how” instead of “what” regarding the facts or information. It makes strategic choices through data analysis. It enables businesses to adopt more customer-centric practices.

Spend Data Analytics Process

Data Analytics Process

An organization's most essential and difficult data-driven task is to turn transactional data into actionable insight that leads to excellent business decisions and outcomes. The detailed data transformation stages or processes are:

  • Data Extraction : Collection of an organization’s data from multiple sources
  • Data Consolidation : Putting all extracted data of different format into an integrated destination
  • Data Profiling : Examining, analyzing and creating useful initial summaries of source data.
  • Data Transformation : Also called as "data enrichment" comprises data cleaning, data clustering, and data classification or categorization into numerous buckets of information called categories or taxonomies..
  • Data Loading : Insertion of converted data into an operational data store, data mart, data lake, or data warehouse
  • Data Insights : Using reports, dashboards, and data visualizations to analyze data and draw conclusions
  • Actionable Data Insights : It may impact choices and drive the company effectively with a meaningful result

An organization's raw transactional data is transformed into actionable information through extracting, transforming, and loading (ETL). For example, collecting data from many data sources, integrating data from these sources with different headers, cleaning data using efficient cleansing logic, grouping and classification for single or multiple data attributes, discovering the appropriate insight to act, etc. To solve the difficulties, experienced analysts, sophisticated technology, and smart choices are required.

Spend Data Cleansing,SupplierClustering,Spend Data Classification,Spend Data Enrichment

3C’s steps of Data Enrichment

A business need data-driven insights.  It arises from proper data synthesis during ETL/ELT transformation. So, during the phase of data extraction to data insight, data transformation or data enrichment is essential. It has three main components, commonly known as the 3C's of Data enrichment. These are:

  • Data cleaning is the process of finding and removing redundant, erroneous, corrupted, or missing data from a dataset.
  • Data Clustering is a method for dividing data into subsets or clusters based on inter- and intra-similarity.
  • Data classification is the process of categorizing cleaned or grouped data for comparable products or services.

Data cleansing is required for the other two procedures. So we're down to the finest transactional data. It therefore improves data quality and productivity. However, data grouping and classification improve descriptive and predictive quality. Supplier data clustering methods include hierarchical, density-based, and fuzzy match. Data is categorized using a standard hierarchical taxonomy or framework like UNSPSC, NAICS, SIC, or the customer's own authorized taxonomy.

Fully Automated, in-Built Data Enrichment & Visualization

In2In global provides an online user application platform, to execute their own transactional data

Automated Data Transformation
Registered users can use automated data transformation application like data cleansing, supplier clustering & data classification; inside the application to enrich their dataset.
Data Insight on-premises
Ability to show in-built data visualization or insights on enriched dataset like Pre-enrichment (data profiling) and Post-enrichment dashboard for better business decision.
Leverage trust & Data Security
In-built data transformation application leverage users, to protect their dataset from data theft. In2In global assures a full data security & trust on client dataset or information.


User enabled Automated Enrichment
5 Project / 5,000 rows per project
(Cleansing, Clustering & Classification)
Data Profiling + 25 Spend Insights
User enabled Automated Enrichment
20 Project / 25,000 rows per project
(Cleansing, Clustering & Classification)
Data Profiling + 25 Spend Insights
Automated + Consulting
20 Project / 100,000 rows per project
Advance Profiling & Cleansing
Multi-Language & Translation
Currency Conversion
Clustering improvement
40 Dynamic Spend Insights including Actionable Insights
Automated + Consulting
20 Project / 500,000 rows per project
Custom Profiling, Cleansing & Taxonomy
Multi-Language & Translation
Currency Conversion
Clustering improvement
40 Dynamic Spend Insights including Actionable Insights
Data Extraction,Data Consolidation

Data Extraction and Consolidation

The first two phases of data intake include extracting transactional and operational data from a single or many data sources and consolidating it into a single data repository as a standardized, duplicate-free data set. Then it transforms or enriches data. Data extraction conceptually or physically (from the source system).

  • Extraction of logical data may be done in two ways: fully or incrementally, with the new or changed data being tracked using timestamps..
  • Physical Extraction has two kinds. Either data is extracted directly from the source system for processing in the staging area or data is extracted from an external area (flat files, or some dump files in a particular format) that maintains a copy of the source.
  • Business executives prefer “Hand coding” by data engineers for small datasets from few data sources or “ETL Tools” for big datasets from many data sources.
  • In all instances, a Schema is required to provide a smooth, error-free data consolidation.

In addition to limited resources, data distributed across many places, information about data sources, and how to manage data security for sensitive information etc., data extraction & consolidation are time-consuming tasks. Using a good ETL tool or automated programming script that can help the process to run more efficiently, saving operational costs and improving the speed and soundness of business decisions.

Data Transformation Services

Data Transformation

Data transformation is the process of converting raw data into business insight. It enables procurement executives to make informed business choices. In order to get visibility in the form of insights or actionable insights, operational data should be analyzed and standardized. The different process steps of data transformation are:

  • Data profiling shows data uniqueness and consistency in summaries and graphs.
  • Data cleaning is the process of finding and removing redundant, erroneous, corrupted, or missing data from a dataset.
  • Data Clustering is a method for dividing data into subsets or clusters based on inter- and intra-similarity.
  • Data classification is the process of categorizing cleaned or grouped data for comparable products or services.

Data profiling determines whether data is appropriate for a “go or don't go” data enrichment decision. Professional leaders may conduct data profiling on enhanced data to see whether advanced data enrichment is needed. Or they may use data analytics/insights to make the best choices for assessing opportunities and minimizing supplier risk.

TaxonomyConsultancy Services

Taxonomy Consultancy

Transformed data should be classified into bought goods/products or business services for better insights. Organizations also have general ledger (GL) codes that finance uses but aren't always the greatest match for procurement. As a result, spend taxonomy is a hierarchical structure document that aids in the logical categorization of comparable spending items or services.

  • Taxonomy can be universal & standard like UNSPSC, SIC, NAICS or customer based.
  • The hierarchy ranges from 3 to 5 levels of categories (generally used 4 Level), from general to specific. E.g., Professional Services (L1) → Marketing Services (L2) → Advertising (L3) → Radio & TV (L4)
  • Standard taxonomies are a wonderful place to start, but big organizations prefer their own taxonomy as per their business products/services.

The difficulties in categorization are (1) too much expenditure will be categorized into generic categories (2) duplicate categories like Business Services → IT Consulting & IT → Business Services Consulting (3) large amount in “Miscellaneous Spend” won't be able to gain a clear perspective into what & where spend is going on. However, taxonomy aids in improved category administration, expenditure visibility, negotiation, saving potential, and business growth.

Data Visualization Services

Data Visualization

Having seen key expenditure data insights, procurement managers and business executives may now consider what actionable insights to ask for. Rather than just answering a question, these ideas inspire action. Actionable insights force you to reassess the issue and seek a fresh solution. Some of the key attributes of actionable Insights are:

  • Achieve significant results by influencing choices and driving the company
  • Is a game changer for clients
  • Increases consumer engagement, trust, and sales
  • It ensures that information is sent to the appropriate person or key decision makers at the right moment.
  • It drives actions and outcomes.

A company leader may use opportunity assessment findings to rethink and act towards a meaningful result. The major insights are (1) Potential Saving opportunity, (2) Spend forecasting, (3) Tail spend analysis, (4) Payment term analysis, (5) Indirect spend Analysis, (6) Supplier Diversity Analysis & (7) Contract compliance analysis.



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