Data Transformation Service Provider With Cost-Effective Actionable Insight

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 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.

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.

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.