Defining Key Components of Data Transformation

Discover how data cleaning, normalization, and integration impact the success of your data transformation efforts.

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Organizations rely on consumer, business, and competitor data in today's data-driven business climate to stay competitive, get insightful knowledge, and make well-informed decisions. Data transformation becomes increasingly important in this situation.

As companies use and collect data from multiple sources, compatibility becomes a concern. Sorting through reams of information efficiently is a growing challenge, which can impact analysis and competitive advantages.

Organizations must consolidate, clean, and restructure data into a usable format for analysis. A successful transformation process enhances data quality, insights, accessibility, and security to support better decision-making.

What Is Data Transformation?

The data transformation process cleans, converts, and structures the data coming from different sources and changes the various formats into unified and usable information for people and computers. Essentially, it alters raw data that’s in one format, so it matches the format of the destination system.

So why is having transformed data crucial for organizational success?

Data teams and analysts are aware of the challenges, risks, and concerns of managing data. As technology evolves, data complexity and volume grow proportionally. Storing original data is no longer enough. To get maximum value, it must be transformed to cater to an organization’s dynamic requests and needs. That’s why practicing data transformation strategies is essential.

Picture your company’s data as a complex jigsaw puzzle, where each piece represents a distinct data source, format, or structure. The goal is to seamlessly fit these pieces together to better understand the data’s meaning, support decision-making, stimulate innovation, and bolster business objectives. The importance of data transformation can’t be overstated. It involves striking the perfect balance between flexibility, scalability, data security, and compliance.

Enterprises can transform disparate source data and convert it to match the destination system’s format at two points in the data pipeline. Those with on-site data warehouses typically rely on the extract, transform, and load (ETL) process, with data transformation occurring during the transform step.

Most organizations employ cloud-based data warehouses due to their ability to scale computing and storage resources rapidly. With this immense scalability, cloud-based organizations can skip the traditional ETL process. Instead, they can adopt, extract, load, and transform (ELT) process. With ELT, transformations convert data as it’s uploaded in its raw form. This makes it possible to manage and complete all data transformation manually, via automation, or by combining the two.

Exploring the Benefits of Data

Data plays a critical role in every facet of business operations. From customer information and sales data to internal processes, best practices, and competitive insights, knowing how to read and analyze data can help enterprises improve efficiency and increase revenue.

Improved Data Quality

One of the most significant benefits of data conversion is enhancing data quality. There are risks and costs associated with bad data. Experts estimate organizations lose an average of $12.9 million each year due to low-quality data. On top of that, poor data can lead to bad decisions, lost productivity, and non-compliance, which may result in additional costs, including reputational damage and missed business opportunities. So, maintaining accurate, consistent, and reliable data helps reduce potential quality issues, missing data, and inconsistent or redundant information.

On the flip side, high-quality data can bring better business outcomes. For example, with better data, it becomes easier to identify key performance indicators (KPIs) for data analytics projects to measure operational capabilities. Accurate data helps highlight areas of opportunity and potential red flags like bottlenecks or workflow breakdowns. When stakeholders acquire enhanced business intelligence from good data, they have more confidence in it, leading to better decision-making. Moreover, data transformation can address key stakeholders’ fears and concerns. A robust data transformation strategy helps mitigate cybersecurity risks, maintain data compliance standards, and optimize technology investments.

Enhanced Insights

Data conversion is pivotal in extracting valuable insights from organizational data. With the myriad of data sources, formats, and data values that technology leaders deal with daily, it’s challenging to derive meaningful insights without transforming the data as a first step.

Converting data into a unified format results in more efficient analytics and reporting, allowing organizations to capture, diagnose, and improve internal operations and processes.

These enhanced insights empower better-informed decisions, optimize processes, and identify areas for growth and improvement. It also reduces system downtime, prevents vendor lock-in, and streamlines cloud-based infrastructure management. The data transformation process can minimize data quality issues, facilitate efficient migrations, improve data centers, and help manage unplanned obsolescence.

Improved Accessibility and Security

Data conversion plays a crucial role in improving data accessibility and sensitive data security. When source data is properly formatted and validated, it helps protect applications from potential issues, including duplicates, indexing, and incompatibility problems. The data transformation process helps enterprises facilitate usable data between various input sources, systems, and applications, which helps increase the overall efficiency of business processes, improving real-time data-driven decision-making.

For example, a large multinational company could have valuable data in different departments and regions of the organization. If the source data isn’t in a universally accessible format, insights could be overlooked. Transforming raw data into a uniform configuration makes it usable across different systems and applications, and ensures accurate, high-quality data is provided for analytics projects.

Data Transformation Strategies

Several different data transformation strategies and techniques are used to clean and structure data. But not all approaches work for all types of data. Paying attention to raw data types and the source system it comes from is essential before determining the process.

Transformation is a crucial step in various processes, such as data integration, migration, warehousing, and wrangling. Methods of data transformation can be:

  • Constructive, which adds, copies, or replicates data
  • Destructive, which may delete certain records and fields
  • Aesthetic, which standardizes certain values
  • Structural, which includes renaming, moving, and combing data

Some examples of transformation strategies include:

  • Data smoothing and cleaning: Removes the noise from data sets by identifying outliers, fixing inconsistencies, or filling in missing values to ensure data is clean.
  • Data reduction: Helps reduce extensive raw data sets into smaller groupings in a summary format to eliminate redundancies for better analysis.
  • Data integration: Merges data across sources and datasets to provide a more comprehensive view of the information.
  • Data manipulation: Adds, deletes, or modifies existing data or data sets to create new data sets.

Since businesses must manage vast amounts of unstructured data from disparate sources daily, they also need a data transformation strategy. This action plan enables data conversion, regardless of format, to be integrated, stored, analyzed, and mined for business intelligence. Transformation gives key stakeholders and decision-makers valuable insights into operational and informational functions.

Importance of Data Conversion for Businesses

Bad data costs organizations nearly $13 million per year and can undermine business objectives and create customer distrust. Data transformation processes, such as ETL and normalization, convert data into actionable insights that drive informed decisions. These decisions can ultimately help the organization stay ahead of the competition.

There are many data transformation benefits for organizations, including potential growth. By integrating data from multiple sources and formats, data transformation makes raw data compatible and easier to analyze, producing insights that support innovation and business expansion. The B2B companies that prioritize data-driven reporting experience a 15–25% increase in earnings before interest, taxes, depreciation, and amortization (EBITDA). These organizations are pulling data from internal and external sources to generate analytics and reports that identify key sales growth opportunities.

When data is transformed, it also helps remove the silos that make it difficult to share, access, and integrate within different departmental systems and operations. For example, advanced analytics and machine learning business models depend on high-quality data to deliver accurate predictions and recommendations, fueling organizational growth by optimizing processes, reducing costs, and identifying new attributes and opportunities. Companies that understand these challenges—and look to improve scalability and performance—know it’s a good idea to avoid these inefficiencies.

Ultimately, this type of data migration aims to remove multiple data formats, improve structure and quality issues, and make accessing and analyzing data more effective. As organizations face increased data security and privacy regulations while fighting against outdated legacy systems, removing potential storage and processing roadblocks is critical.

Optimizing Data Transformation with Seagate Solutions

As an expert in data transformation tools, Seagate offers a range of data storage systems and solutions tailored to the needs of technology leaders. These systems handle growing data demands while ensuring reliability, performance, and security.

Seagate solutions address the unique pain points and challenges technology leaders face. For example, Seagate Exos® CORVAULT™ is a high-density data storage solution that’s hyper-efficient and self-healing, using Autonomous Drive Regeneration (ADR) to minimize downtime and human intervention. And for those organizations looking to expand storage capabilities without additional complexity, Seagate JBOD storage systems provide flexible and cost-effective efficiency without sacrificing performance.

Seagate offers secure, scalable, cost-effective storage solutions and data transformation tools that meet stringent data privacy, security, and performance requirements. Leveraging these data transformation tools ensures that the cloud computing and data storage strategies businesses are adopting are reliable, accessible, compliant with regulatory requirements, and aligned with overall business goals.

Moving Forward with Data Transformation

Embracing data transformation is essential for organizations today. Yet, many organizations find organizing, transforming, and structuring data to be an overwhelming task. Several issues can arise during the data transformation process without the necessary expertise, potentially hindering the end results. Issues such as poor transformation that leads to migration failures and corrupt data carry significant risks.

Creating a data-driven culture within a business is possible through proper research, planning, and long-term analysis and transformation strategies. Seagate provides products and services that can automate various steps in the data management process, allowing for more efficient and accurate data management. Talk to a Seagate expert today to learn more.