{"id":6484,"date":"2024-06-28T16:35:48","date_gmt":"2024-06-28T21:35:48","guid":{"rendered":"https:\/\/www.stratospherenetworks.com\/blog\/?p=6484"},"modified":"2024-06-28T16:35:48","modified_gmt":"2024-06-28T21:35:48","slug":"what-is-data-transformation-and-why-does-it-matter","status":"publish","type":"post","link":"https:\/\/www.stratospherenetworks.com\/blog\/what-is-data-transformation-and-why-does-it-matter\/","title":{"rendered":"What is data transformation, and why does it matter?"},"content":{"rendered":"

\"DataIf there\u2019s one thing you can count on in life, it\u2019s the need to continually transform. Only those who adapt will survive and succeed. As Benjamin Franklin once said<\/a>, \u201cWhen you are finished changing, you\u2019re finished.\u201d<\/p>\n

Adaptation is a necessity not only for individuals and businesses but also for data. If you want to realize the full potential of your informational assets, data transformation is essential. Here\u2019s everything you should know about the process and how it lays the foundation for success in various professional pursuits, including the development of artificial intelligence<\/a> models.<\/p>\n

What is data transformation?<\/h2>\n

You\u2019ve likely heard the quote, \u201cKnowledge is power,\u201d attributed to Francis Bacon and invoked multiple times by Thomas Jefferson in his correspondence, according to the Thomas Jefferson Foundation<\/a>. By extension, the wealth of data available to most modern businesses has the potential to be extremely powerful. However, to successfully harness the power of that data, you must first optimize it, according to Hewlett Packard Enterprise<\/a> (HPE).<\/p>\n

Data transformation is the process of converting, structuring, cleaning and enriching data to prepare it for reporting, analysis, storage, and training machine learning or AI models, HPE explains. Refining raw data is imperative for businesses that want to realize its full value and drive innovation and efficiency.<\/p>\n

How to transform data<\/h2>\n

The details of data transformation depend on the specifics of the project, according to IBM<\/a>. It could be as simple as standardizing the format of dates in a spreadsheet or as complicated as consolidating input from numerous sources into a single consistent dataset. Depending on the circumstances, the process might require the oversight of individuals with high-level data science and data engineering skills.<\/p>\n

There are various ways to approach data transformation. Organizations with on-premises data warehouses commonly employ an extract, transform, load (ETL) methodology, according to Spiceworks<\/a>. Alternatively, you can opt for an extract, load, transform (ETL) strategy in which you load raw data into a warehouse and convert it after receiving queries.<\/p>\n

Data scientists can employ a range of techniques to optimize data during the transformation process, according to IBM. Depending on the project, they might rely on one or more of these procedures.<\/p>\n