Data is among the most valuable assets for modern organizations, enabling leaders to form effective business strategies backed by provable information. But the efficacy of data-backed strategies is entirely dependent on the quality of the information they’re built from. With research indicating poor data quality may cost companies as much as $12.9 million per year.
Information pertaining to internal business processes can prove invaluable when looking to optimize and enhance essential tasks. The analysis of this data helps companies better understand where avoidable productivity and efficiency losses occur. This in turn enables teams to make informed decisions capable of measurably improving wider operations.
However, if raw data is not properly formatted and organized, it becomes difficult to produce actionable insights. For example, if the systems used to collect data (your CRM, web analytics, ERP, etc.) and those used to view it are not properly integrated, resulting insights may be filled with errors or left completely unreadable. This underlines the importance of data transformation and smart data analytics, processes designed to ensure internal data integrity and accuracy. To achieve that, your data must undergo transformations. Let’s take a look at some of the techniques you can apply.
What is data transformation?
Data transformation describes the process of converting collected data from one format to another for the purpose of analytics. Collected data will be evaluated and manipulated into a more suitable format, value or structure aligned with business needs.
Extract-Transform-Load (ETL)
Most common data transformation methods exist within a framework known as ETL, Extract Transform Load. Under this framework, data from multiple sources is collected and combined in a central location known as a data warehouse.
The business will set unique rules for cleaning and organizing raw data to ensure it’s stored in a suitable format for later analysis, visualization and storage.
ETL processes are particularly important for organizations that collect large amounts of both structured and unstructured data from multiple sources. Under a bespoke ETL framework, all collected data will be loaded into a data warehouse, then cleansed and transformed to be used in reports. This organized data can then be fed into business applications as needed.
The ETL process helps businesses to uncover actionable insights from multiple information points, and generate meaningful reports through the combined analysis of multiple sources.
For example, Customer Relationship Management (CRM) data can be analyzed alongside paid ads to compare your marketing campaigns’ performance in terms of customer/revenue generation. Reports in this case can help teams to optimize their efforts for lead generation.
Another example may be the combined analysis of eCommerce and inventory systems data to gain a clearer understanding of current market demand. You can do an ABC inventory analysis by stitching your eCommerce sales and inventory data over specific periods to determine how much inventory is needed per product category. Such reports can help businesses to avoid under/over-stocking.
Four types of data transformation techniques
Depending on the type of data and its final purpose, different types of data transformations will be needed to enrich and optimize key information:
Constructive data transformation
It is the creation of new data attributes or the enhancement of existing ones to improve the quality of data. For example, if a sales report contains quantity and price information but no total sales value, a constructive transformation would calculate this field from existing data and add it into the report.
Destructive data transformation
Basically, it is the removal of irrelevant data and duplicates to produce more accurate results. For example, if a customer contact information database has multiple copies of the same contact details, a destructive transformation may be used to automatically remove duplicate entries.
Aesthetic data transformation
It is the process of converting data into a more presentable format for human interpretation. For example, if a dataset includes unformatted numerical data in a column “Revenue”, we could remove decimals and add the currency to better readability.
Structural data transformation
It is the effort to modify the overarching structure and organization of collected data to optimize useful information for use in machine learning models. For example, changing naming conventions or removing numerical values in raw data to ensure information is structured in keeping with the parameters of specific AI tools.
Sub-categories of data transformation techniques
Within these four wider categories, multiple more specific types of data transformations can be pursued to enhance the quality and accessibility of important information.
Here are the ten of the most common types of data transformations you will have to deal with:
- Data aggregation – Combining two or more attributes into a single object to gain new insights, e.g. merging daily productivity figures to produce accurate monthly metrics.
- Data separation – Dividing data values into smaller parts for granular analysis, often used to support the training, testing and optimization of machine learning algorithms.
- Generalization – Reducing the complexity of data by using identified patterns to replace low-level attributes with high-level concepts better suited for wider analysis.
- Structural transformation – The overall structure of data is altered to support wider analytics processes, typically by combining and normalizing multiple data sources.
- Data deduplication – The removal of duplicate entries from target datasets, involving an analysis of all incoming data to ensure duplicate entries are removed at source.
- Data cleansing – The removal of incomplete, corrupt or inaccurate data from a target database to automatically eliminate errors and inconsistencies.
- Data validation – Ensures all incoming data meets predefined criteria and standards before being used in later tasks, helping teams maintain data accuracy and reliability.
- Data derivation – The creation of new data attributes based on existing information, typically by using mathematical and/or logical functions to reformat collected data.
- Data filtering – The removal of unwanted data based on a predefined set of criteria, all data of a certain type or featuring certain attributes will be removed automatically.
- Data integration – The merging of data collected from multiple sources into a single storage or analytical system, used to ensure a holistic view of all important datasets.
Taking your data transformation a step further with advanced techniques
The main purpose of all data transformation efforts is to add value to collected information by restructuring, refining or recontextualizing data in line with unique business objectives.
While the basic data transformation efforts covered above do support such tasks, if you collect large amounts of complex or unstructured data, you will benefit from advanced techniques.
Attribute construction
Attribute construction is the process of creating new attributes or modifying existing ones. Mathematical transformations, such as aggregating data based on square root values, can be applied to datasets to obtain new insights from existing features. Thus helping to highlight underlying patterns in raw data.
Attribute construction techniques are usually used to help stakeholders identify relationships between different data points that are not obvious when looking at the raw data by itself.
For example, a business may have access to data pertaining to individual transactions that have been processed over a set period of time. From this data, stakeholders can construct a new attribute to determine total sales over that period. An insight that wasn’t previously available.
Conditional Logic Statements (CDL)
This type of data transformation enables businesses to categorize datasets based on unique conditions.
For example, instead of just collecting physical security data related to how many times an entry system has been accessed, data can be further structured to account for the time and date of events. CDLs essentially add extra layers of context to a business’ stored data.
Normalization
Data normalization ensures all values in a target dataset are organized and formatted on the same scale. This is particularly important when working with any data based on numerical values.
Especially if it’s intended to be used to inform machine learning models. Essentially, normalization ensures machine learning algorithms understand how to read collected data.
Discretization
Discretization is a data transformation technique used to convert continuous data into a form more suitable for analysis. This is achieved by categorizing data into discrete intervals that are easier to understand and analyze contextually.
For example, numerical values might be categorized in intervals from 0-10, 11-20 and so on to ensure the data is properly organized.
Data smoothing
Data smoothing involves removing outliers from data to best highlight patterns in collected information. This type of data transformation is particularly beneficial when formatting data for use in machine learning models. By smoothing the information to develop an obvious and easily readable pattern, machine learning tools can more accurately analyze presented data.
Harness data transformation tools to gain more value from your data
Collected data is only useful if insights and patterns can be reliably identified. Data transformations help businesses of all sizes make more efficient and effective use of key datasets by converting raw data into formats that can be used for a wider variety of tasks.
Modern businesses have access to a multitude of platforms that can be used to streamline the collection, analysis and transformation of data. From custom ETL software developed by external professionals, to open-source applications and cloud-based SaaS platforms catering to the needs of SMEs.
To begin harnessing the power of data transformation tools, you must define your business’ priorities and identify areas in which data may not be being used to its full potential. By comparing these aspects to an available budget, identify which types of transformation tools will be best-suited to your needs.
Whether that’s a pre-made SaaS platform like ClicData, or a bespoke ETL solution, harnessing the power of data transformations can help businesses gain higher-quality insights and more usability from their data.