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Advantages of Data Transformation and Why you Should Care

As entrepreneurs and small business owners, we rely heavily on readily-available data as well as the information we generate on a day-to-day basis to help us run our businesses more efficiently.

The abundance of resources that exist in today's digital-first environment, provides small business owners and entrepreneurs around the world with limitless opportunities for growth.

How does one avail themselves of the information collected to gain an edge in your space? How do you take what you know about your customers, products, and overall business and convert said intel into actionable information? Data transformation.

To put it quite simply, data transformation as a term refers to the actions we take - or must take- to convert data within our organizations into usable information. Transforming your data accordingly often, depending on your business needs, takes various forms.

The rule of thumb is to focus solely on the chunks of information (within your raw data ) you definitely need to accomplish your business goals. That being said, let's take a look at some of the benefits of data transformation.

Wax On Wax Off

Transforming data is included in every known method of data processing. Before converting them, data analysts shape the data into formats that are compatible with the analytics system of the business or a client.

The first process of data transformation is the extraction of said data from a source to other platforms. From here, data scientists map out and translate the data into relational databases.

Unnecessary data points are omitted after this process as columns and rows begin to take shape. Analysts can now summarize raw data by transforming them into business insights. For example, hourly online customer purchase data is converted into online sales per day.

After more data is converted and put into the system from different sources, it is sorted, computed, or omitted. Data sets are refined based on business needs and models. Sensitive information that could be at risk is anonymized and encrypted based on encryption laws and requirements for each jurisdiction.

Keeping things simple

The internet has a massive cloud of data that is rapidly increasing in volume with every passing minute. Given that and the rise of big data and consumer analytics, it can be hard to untangle these cobwebs and arrive at the insight and statistics that are relevant to your business.

Most of this data remains unused for business analytics and intelligence. But data transformation makes things easier for you to keep track, manage campaign insights, and convert different data formats—even those in large quantities—to get the most out of your business data.

Here's to working smarter

Data management platforms use the transformation process to standardize data. It gives you the liberty to access and organize these data sets based on their importance. Organized data helps you see the bigger picture of your business needs. It also aligns insights with your goals and visions. After this, data is stored in a source location.

Since it is more accessible and segmented, it will be more efficient and fast to view or compare consumer insights, market analysis, or client documents to other factors and variables.

Data transformation enables you to utilize the information that’s available in your hands to improve every aspect of your business. It is the tool behind the efficiency and data-driven success of different companies that recently migrated online. It’s the best utilization tool to keep your business intelligence at the top of your game.

Quality assurance

Big chunks of data don’t mean you can crunch them all into leads and relevant business insights. Organizations and data warehouses usually use the ETL or extract, transform, and load process—where transformation is in the middle of the metadata processing.

In this process, data transformation helps you segregate bad data from those relevant to your company. The process serves as the quality control that reduces potential risks like null values, duplicates, incompatible formats, and incorrect indexing.



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