How to Design a Data Transformation Pathway that Benefits your Business from Day One

Data has become one of the fundamental pillars upon which business decisions make, with its guidance steering organizations to more effective choices and successful strategies. The total value of big data is continually increasing, with the total big data market expected to cross $229.4 billion by 2025. With figures like these, it’s hard to deny the importance that data is now attributed within the world of business.

Yet, most businesses still can’t gather insights from big data, with the methods they use to collect and process data being outdated or ineffective. With the majority of data falling through the cracks or being incorrectly formatted, it’s no wonder that bad data costs the USA around $3.1 trillion dollars each year, with more and more businesses failing to accommodate for the increasingly complicated tide of data their organization produces. 

If you want to make the most of the data that you collect, your business needs to ensure it has a stable data transformation pathway, helping to convert disparate fields into concrete data for analysis.

In this article, we’ll be moving through this process, demonstrating how you can create a data transformation system that benefits your business from day one. Let’s get right into it. 

What is data transformation?

Simply put, data transformation is the process of changing how data is presented in terms of the values that are displayed, the structure of the data, or the format it holds. When on-site storage was the most popular form of data warehousing, data would move through an ETL pathway (Extract, Transform, Load).

However, now that the majority of businesses have moved to cloud data solutions, the data transformation process is slightly different. Now it follows an ELT system (Extract, Load, Transform), which is more compatible with this online service. 

When we discuss data transformation, there are actually four central ways in which data can be transformed. Each of these is employed for distinct data sets, helping to create a homogenous set for analysis. These are:

  • Constructive – Raw data is added to or replicated, constructing a new figure out of the original to help with processing and aligning with other already constructed figures. 
  • Destructive – Faulty data is deleted, incorrect fields are removed, and unimportant details are deleted.
  • Aesthetic – Raw data is often unformatted and messy to look at. Aesthetic transformation is about turning raw data into a format that is easy to analyze; it is standardized. 
  • Structural – The broadest category of the four, this is a transformation that includes moving, combining, renaming, or modifying data, so that it fits into the data set.

With the power of modern cloud data warehouses, the majority of these processes are automatic. If you are manually querying your data, then it could often run incredibly slowly when compared to using a DWaaS solution. Check out Firebolt’s tutorial for managing complex SQL queries if you’re looking for a helping hand in this area. 

Why bother transforming the data?

For those that don’t frequently deal with the backend of data analysis, you may be wondering why it’s so important to transform data in the first place. There are three main reasons to transform data before analyzing it:

  • Ease of Use – There is nothing easy about comparing data sets that have different measurements, different systems, and different methodologies behind them. By transforming your data into a homogenized system, it becomes incredibly easy to analyze, making insights easier to come across.
  • Correct Format – By moving through the transformational process, you make sure that data is correctly formatted, ensuring that there are no bad fields or pointless entries. With this, less time will be spent combing through the data, and more time will be spent on the productive analysis of what you’ve indexed. 
  • Movement – Data in regulated sets is much easier to move than data that is spread over disparate files. Due to this, if you need to move your data around from different platforms, having a stable infrastructure will allow you to save a great deal of time and, therefore, resources. 

Once data is transformed, your business will have a much easier time when conducting analysis. With this, data-driven decisions will all come more naturally, helping your organization to make effective strategies for its success. 

How do I make the data transformation pathway as easy as possible?

If you want to make sure that your business can use the data it acquires from day one, then you need to make the transformation pathway as painless as possible. To do this, we recommend that you implement these three core ideas:

  • Define your strategy ahead of time
  • Start with the end product, work backward 
  • Quality assurance throughout

Let’s break these down further.

Define your strategy ahead of time

Without a data strategy, it’s hard to know where you should begin with the mountains of unprocessed data that your business will come across. Due to this, time is wasted with decision-making about what should be processed first and why. This matter is further complicated if you have a team with conflicting opinions, leading to even more delays in the transformation process.

To get around this, you should outline your data strategy to everyone in the company as early as possible. With a clear pathway that you want your employees to follow, you’ll be able to speed up the process and cut out any time taken for delegation and discussion. With a strategy that everyone understands, you’ll be well on your way to processing data as quickly as possible.

The faster you get data through the transformation process, the quicker it will become available for analysis and data-driven decision-making. With this, every department in your business is set to benefit from the power of well-formatted and constructed data. 

Start with the end product, work backward

A continual part of the data transformation pathway process that slows businesses down is the fact that they’re not sure which parts of the data to prioritize. Considering the millions of GBs of data that a company will encounter on any given day, it’s important to know exactly what you’re looking for when starting this process.

To find this out, start with the end product that you imagine. If you think the most valuable data for your business would be a comparison between your use of the supply chain and the industry average, then seek that data first. By starting with focusing on the data that is most important to you and that will let you find the insight you need to boost your success, you’ll have a much more streamlined pathway for data transformation to follow.

Prioritization based on the end product that you hope to receive from your data, instead of just running all the data you find through the process, will help to radically improve the process. Always start at where you want to end up, and then work backward.

Quality Assurance Throughout

In any business-based operation, a continual level of quality assurance is the key to keeping up your reputation and delivering an excellent service. This is no different when it comes to data analysis, with quality assurance ensuring that nothing falls through the cracks.

By turning to cloud data warehouse solutions, you’ll be able to ensure a high level of quality when it comes to the data that is transformed and entered into your system. If you make sure that only quality data reaches your pools, then you’ll be able to reduce the time it’ll take your engineers to create an analysis of that data.

By creating quality assurance checks early on in the process, the only data that will move through the process is information that you actually need for your employees to do their jobs more effectively. With this, you’ll speed up the process and create a higher-quality data pool to draw upon.

Final Thoughts

Without processing your data, it remains in an almost unusable format, leading to delays in analysis, wasted time and hours on formatting, and a less effective pool to draw upon for decision making. By outlining your data transformation strategy, working backward from where you want to end up, and implementing data quality checks, you’ll help to speed up the process.

As data is vital to how modern businesses operate and make decisions, the data transformation process is vital to feeding into these elements. If you focus on improving your transformation pathway, you’ll be well on your way towards a more effective use of information technology within your organization.

Leading to increased conversions, boosted decision making, and more effective use of company data, the transformation pathway cannot be overlooked.