Why Data Transformation is Vital to Your Business’s Future
Data transformation underpins your business’s entire ability to use all that data you’re storing.
We’re not being over dramatic here – it really is one of the most important aspects of your data strategy. Without it, you can’t take your analytics or reporting processes beyond your primary systems. To put it another way, without data transformation, data stays put at a local level, so your analytics stay at that level as a result.
If you want to incorporate multiple sources of data into your analytics and identify trends and opportunities you might otherwise miss, being able to move data around your organization is essential. That’s where data analytics comes in.
As it’s so central to your data processes, we thought we’d take a look at what data transformation is, its role in the analytics process and why it holds the key to your business’s long term success.
Data Transformation Fundamentals: What Do I Need to Know?
To give you a top-level, jargon-free definition, data transformation is the process of converting data from one format or structure into another format or structure. It’s a vital part of successful data integration and the gateway to implementing advanced analytics across your business.
Each of your internal databases is based around a model with a specific schema. If you want to move data across your organization, or combine it with other data for analytical and reporting purposes, you need to transform your data to make this possible.
What does this look like in practical terms? Consider the examples below:
- You convert a Microsoft Word document to a PDF document
- You convert a CSV file containing customer data to an XML file, or vice-versa
- You convert recorded speech to typed text using a dictation tool
All of these are examples of data transformation processes your business carries out frequently, as part of its day-to-day operations. It’s when you apply this principle of data transformation at a massive scale, however, that things start to get really exciting.
Data Transformation in Big Data Analytics
We collect and store more data than ever before. When used together, this unlocks powerful insights that are impossible to observe with the naked eye.
Unfortunately, much of the data we collect is siloed into separate systems. This makes it impossible to combine, aggregate and analyze this data together, and limits the value you get from holding it. It’s by transforming this data into the right format, and by aggregating it together that you remove these barriers and start to benefit from big data analytics.
When you transform data, you can do more than simply convert it from one format into another. With the help of data transformation tools, you can include the following as part of the process:
- Data validation: ensuring that data is valid within its own constraints (for example, country matching address or dates being in valid format)
- Data cleansing: removal of corrupt, duplicate or defunct data
- Data aggregation: gathering data and presenting it in a summarized format
- Data harmonization: making sure data is in a consistent format, such as making sure all units are metric rather than imperial
- Data enrichment: combining data from different sources to improve quality
You can also start to combine previously siloed datasets to identify trends and patterns (in customer behavior or financial reporting, for example) that would previously have gone unnoticed. That’s where you start to see some major strategic advantages you can use to improve your services, predict trends in your industry and increase revenue significantly as a result.
Data Transformation Offers Major Strategic Gains Right Now
In the short term, data transformation unlocks gains on your competition. In the long term, as adoption increases, it will become essential for your survival.
Businesses of sizes and industries are waking up to the possibilities data analytics offers.
According to a recent survey of senior IT professionals, 50% of businesses are now widely using data warehousing and BI tools. This number is only set to grow in the future.
Whilst that might sound intimidating, there’s still huge potential to stand out here. Of that 50%, most are still stuck on getting the basics right. Only around 10% of firms are using more advanced methods, such as big data analytics and machine learning.
In other words, if you go beyond the basics you’re an early adopter. You can use this big data analytics to make major gains on competitors right now, but this opportunity will wane as adoption widens. Stepping up now will increase your stability in the future; waiting too long may result in playing an expensive game of catch-up for the next decade.
What are the Benefits of Big Data Analytics?
Data transformation is a vital step in cleaning, amalgamating and analyzing data for strategic purposes. But what strategic purposes are those? How does data transformation impact your ability to compete with other businesses, or the state of your bottom line?
Ultimately, it’s all to do with building on basic, descriptive analytics, which shows you what’s happening in your business at a given point, to predictive analytics, which uses large datasets to identify patterns you can use to predict future situations.
Here’s what that looks like in practice:
Better Customer Acquisition and Retention
By amalgamating sales and marketing data (for example social media engagement, keyword analytics, initial purchase spend) you can build up a detailed picture of exactly what your customer base wants.
If you use the data generated by each purchase or interaction, you can observe patterns in customer behavior that you can use to increase the amount they spend with you. Amazon’s recommended purchases bar is a great example – it doesn’t just use past purchase data, but combines this with previous customer browsing data, data from other customers’ searches and more to create a highly effective digital upselling experience.
More Focused Campaigns
A sophisticated analysis of customer trends also means you can adjust your marketing campaigns to hit the spot you know customers will respond to.
Combining search data, social media trends, customer purchase data and point-of-sale transactions identifies trends that can drive highly targeted ad campaigns at specific customer segments. Because they’re based on swathes of observable data, these campaigns offer huge ROI potential.
Easier Risk Identification
Risk management is everything for growing businesses. You can’t grow without taking some risks – what’s important is the ability to examine and evaluate those risks for potential reward vs potential damage.
Previously, a fair amount of risk management came down to strategic decision makers relying on their own experiences to figure this out. Enhancing this knowledge with more objective insights from big data analytics allows a greater degree of accuracy. Data transformation builds the capacity for data-driven decision making that enables your business to anticipate which risks will offer the biggest rewards.
A Smoother Supply Chain
Previously, businesses usually thought of their supply chain as a cost center. Now, with consumers demanding better service, flexible returns and faster and cheaper shipping options, your supply chain can become a major strategic advantage.
Being able to transform data into real-time supply chain analytics allows you to keep on top of supply chain issues whilst anticipating future opportunities. For example, you might use predictive analytics to identify the amount of stock needed for the holiday season whilst using advanced modeling to figure out the optimum number of drivers to hire to meet demand.
Compliance
Regardless of industry, you’ll likely have to comply with GDPR regulations (or similar, depending on your geographical location and that of the customers you want to attract). These govern how you store, process and use customer data.
On top of that, there are numerous industry-specific data governance compliance regulations you need to hit, or else risk hefty fines and serious reputational damage. For example, US healthcare organizations need to follow patient data guidelines outlined by HIPAA or risk fines of up to $50,000 (or more, in the most extreme cases).
And, as consumer concern over online data privacy grows, expect more regulation in this area, not less. The ability to keep up with evolving compliance regs will be essential in your organization’s long-term success.
Data transformation is essential here because it can automate processes that would be both time-consuming and error-prone if done manually. Automated data pipelines can redact sensitive or remove sensitive information before data enters the warehouse, or anonymize large datasets for analytics purposes.
This both reduces risk of data breach and reduces the amount of time your team spends on manual data processes. It’s more efficient, less risky and will likely become essential as data laws and governance becomes more complex.
Getting Technical: The Role of Data Transformation in BI Tools
We know what data transformation is. We know the advantages it unlocks with regards to big data analytics. But where exactly does it fit in turning raw data into something useful?
This is where data repositories come in. Data repositories are huge, centralized data stores that take in data from across your business and process it for a variety of uses. There are different types of data stores, each of which work a little differently. For access to larger datasets, you should be aware of:
- Data warehouse: a centralized repository of integrated data for analytics and reporting purposes.
- Data mart: a smaller, often departmental scale data warehouse which can be used as a standalone tool or to access department-specific information.
- Data lake: a centralized repository that allows you to store both unstructured and structured data at any scale.
Data lakes are great for storing huge swathes of data, particularly if you need to keep it in its original state. If you’re a data science heavy organization, or you need a big supply of data in different forms to develop machine learning models, a data lake is probably your best option.
On the other hand, data warehouses are designed to transform raw data from across your business into usable analytics reports for your employees. If your main purpose for your data repository is to drive strategic decision making via enhanced reporting, a data warehouse is where it’s at.
ELT or ETL: What Works Best For You?
If you’re using a centralized data repository like a data lake or a data warehouse, there are two options for your data transformation process:
- You can transform your data before you load it into your data repository (known as an Exact Transform Load or ‘ETL’ process)
- You can transform your data after you load it into your data repository (known as an Exact Load Transform or ‘ELT’ process)
In other words, you can either transform your data before you load it into your data repository or let it sit there in its original form. If you choose the latter option, your data will be transformed when users make a specific query.
Generally speaking, ETL pipelines are a great option for data warehouses because data warehouses use a relational database structure. Your data will need to fit this structure to enter the data warehouse, so transforming it before loading is essential.
Meanwhile, ELT tends to be a better fit for data lakes, which are bigger and pool both unstructured and structured data. Transforming all this data would slow things down considerably, so it’s better to only transform the data after an inquiry has been made.
ETL vs ELT: What to Use, When
In many cases, both ETL and ELT pipelines will do the job – modern, cloud-based data warehouses can work with both types of pipelines to create analytics reports for your workforce.
It is important to be aware of when this choice will make a difference, however! So…
Use an ETL pipeline if:
- Time is a real issue: ETL pipelines are easier to build and, as a more established technology, will be easy to source experts in
- You have compliance requirements: ETL pipelines can redact or remove sensitive information before data arrives in your warehouse, making it easier to hit GDPR, HIPAA and CCPA requirements amongst others. It also reduces risk of sensitive data being leaked in the event of a hack
Use an ELT pipeline if:
- You want to store unstructured data: ELT can pass unstructured data into your system, whereas ETL pipelines need to structure it first
- You’re working with massive datasets: ELT can quickly process lots of data, whereas aggregation in ETL pipelines becomes more complex as datasets get bigger
Looking for Data Transformation Experts?
At Tivix, we have a global network of data consultants, engineers and scientists that would love to help with your data transformation needs.
Whether you need data integration and maintenance services or want to build custom data pipelines (or even a full data warehouse) from scratch, we have you covered. With over a decade of experience, expertise in a wide range of data transformation tools, and the ability to scale a team immediately, we’re confident we can make your data project a success.
