What is Data Transformation?
Data transformation is the process of changing data from one particular format or arrangement to another one. Data transformation is essential to activities such as data integration and data management.
Data transformation can fall under a number of different categories of activities. You might cleanse data by removing nulls or duplicate data, converting data types, enriching the data, or performing aggregations, depending on the necessity of your project.
Data Transformation Process
Usually, the process of data transformation involves two stages.
Stage one involves:
- Perform data discovery - identify the sources and types of data you are working with.
- Determine the structure of data transformations that need to occur.
- Data map to figure out how to map individual fields, as well as modify, join, filter and aggregate them.
The second stage involves:
- Extract the data from the original source. The range of sources can and will vary, and may include structured sources (databases or streaming sources), or log files from customers using your web applications.
- Perform the transformations by aggregating the data, converting the format to your needs, editing text strings and joining rows and/or columns.
- Send the data to the target store, which may be a database or a data warehouse - like improvado - that can handle the data.
Why transform data?
Data transformation serves many purposes. Businesses typically need to transform data so that they can compare it with another data set. This allows them to make informed decisions that are based on a number of different sources, not just one singular source.
In marketing terms, data transformation allows you to compare the data from multiple campaigns, allowing you to make data-driven decisions about how to best market your product.
How is data transformed?
There are a couple ways to transform the data:
- Scripting. Use scripts like SQL or Python to extract and transform data. This is a manual process.
- On-premise ETL tools. ETL (Extract, Transform, Load) tools take a lot of the pain out of scripting the transformation because they automate the whole process. These tools are hosted on your company site and often require extensive expertise and/or cost a lot.
- Cloud-based ETL tools. These ETL tools are hosted in the cloud, which allows you to use the vendor’s infrastructure and expertise.
Data Transformation Challenges
Data transformation is often time-consuming, costly, and slow.
For this reason, its best to use an ETL solution that can expedite the process.
Data Transformation: A Step-by-Step Breakdown
There are four major steps that go into the data transformation process. The exact nature of your data transformation process will, of course, vary, but hopefully these provide a map that can guide you through it.
Step 1: Data Interpretation
Answer this question: What kind of data do you currently have, and what do you need to transform it to?
Identifying the goals of your data transformation at the beginning of your process is crucial. Otherwise, it’s easy to get lost in the numbers - you may not wind up with anything useful in the end.
What is your target format? What format(s) is your data in right now? How are you going to get from point A to point B?
These are the questions you need to answer in the data interpretation stage.
A great way to format your data interpretation is by using a process called dimensional modeling, which results in two types of target tables for transformed data:
- Dimension tables: These give you the “who, what, where, when, why and how,” context for your data. Dimension tables have been called “the soul of the data warehouse, because the contain the entry points and descriptive labels that enable the system to be leveraged for business analysis.”
- Fact tables: These store the results of the events being measured, and answer the question “how many” (from the dimension table). Fact table types include periodic snapshot (summary of events over a regular interval of time), transaction (recording of events), and accumulating snapshot (captures the execution of a process, the steps of which could occur at irregular intervals, within a single record).
Executing a dimensional model will provide direction for the rest of your data transformation process, so make sure you take a careful, thoughtful approach with this step.
Step 2: Pre-Translation Data Quality Check
Once you know what kinds of data formats you are working with and what your goals are for the transformation, you can run a quality check on the data. This will allow you to identify any potential problem areas of your data set, like corrupt values or missing data points.
This is an important step, because any issues with your data set will mess up your process later on. Make sure you comb through the set carefully before moving on.
Step 3: Data Translation
Now that the quality of your source data has been accounted for, you can begin the process of actually translating the data. As mentioned above, data translation involves taking each part of your source data and replacing it with data that meets the formatting requirements of your target data format.
For example, you might be transforming an old HTML file that was written in an outdated version of HTML into HTML5 - the most recent standard of HTML. Part of this transformation process would involve replacing HTML tags that are no longer usable, like <dir> with a list tag that is supported by modern HTML, like <ul>.
You can do these transformations manually, using scripting, or using an ETL tool.
As you can see, data translation isn’t just replacing individual pieces of data - it is restructuring the overall file.
Step Four: Post-Translation Data Quality Check
Now that you have transformed the data, it is crucial that you make sure that the quality of the data was maintained throughout the transformation process. Look for inconsistencies, missing information, or other errors that may have been introduced during the data translation process. Even if your data was 100% quality prior to the transformation process, it is likely that errors were introduced throughout the process, so make sure you have accounted for those.
Our recommendation for marketers that want an easy, simple data transformation process : Improvado.
Let’s take a closer look at some other data transformation tools and how they can be used.
Best Data Transformation Softwares
What is Improvado?
Improvado is an incredibly helpful data transformation tool for marketers, because it was designed by marketers, for marketers. The platform lets you gather all campaign data into a single dashboard in real time, combined with the ability to view that data in automated reports and well designed custom dashboards.
Who should use Improvado?
The tool is perfect for marketers, built specifically to focus on the marketing dilemma. Improvado provides a way to connect any marketing platform you may use. Along with this, the integrations with the platform run quite deep, pulling granular data from both the keyword and ad level and allowing marketers to see the whole picture.
Possibly one of the biggest benefits of using the improvado platform is its excellent customer support representatives. They are able to help you build out any custom dashboards and integrations however you want them. Data in improvado can also be viewed in any BI tool you use, like Tableau or Looker, as well as the platform’s dashboard.
Improvado pricing is done on a custom basis. The company can assess your business needs and give you pricing details during a call.
Improvado provides more than 80 integrations. There are plans to increase that number to 500 by the end of 2018. Custom integrations can also be built out for additional data sources that you may need.
What it is: SAP is an agile platform for data transformation that enables successful analytics, data migration and master data management (MDM) initiatives.
What it offers: SAP is a self-service data transformation tool that provides a facility of on-premise as well as on cloud deployment. It quickly transforms data into easily comprehensible and actionable information. It simplifies the way data is accessed, which makes it much more productive as well as agile.
How it’s different: With SAP user coordination and sharing is quick, simple and easy. It provides fast insights through single-click import of multiple datasets gathered from different sources. It also facilitates data curation with an interactive interface for better insights. It provides automatic data cleansing and duplication that delivers operational data sets.
Who can use it: Data analysts, analytic executives, IT leaders, data scientists, business analysts, and business owners.
Industries it caters to: Energy and natural resources, financial services, automotive industry, consumer industry, public services, and service industry.
“We used SAP for accounting and reporting, but the reports were hard to interpret. Even with very little background with these types of softwares, I was able to turn reports into easy-to-read dashboard and other graphics.”
“The tool has great features like the planning menu and the easy of use interface. It's a complete tool for Business Intelligence and analytics, with beautiful views and charts.”
What it is: It is a leading self-service tool for data preparation and analytics.
What it offers: Alteryx Analytics offers unique and easy data transformation, blending and analyzing capabilities in a single tool.
It makes use of repeatable workflow, provides deployable analytics and then shares the derived analytics to provide deeper data insights in just hours.
How it’s different: Data analysts and scientists love this platform because it enables quick and easy connection and cleansing of data directly from data warehouses, data spreadsheets, cloud applications and various other sources.
It easily integrates the data then conduct a predictive, statistical and spatial analysis without the need for writing another code. It uses a similar intuitive interface for users for this purpose. It offers scalable analytics, which can translate into your organizational success.
Who can use it: Data analysts, data scientists, analytics leaders, BI directors, IT and data management teams, C level executives, students, academics, and non-profit organizations.
Prominent Clients: AnalyticsIq Inc, Belk, BloominBrands Inc., Cardinalhealth, Cineplex, Dairy Queen.
“Alteryx works extremely well with a multitude of BI and database solutions and would be a great fit for any team trying to speed up the typical daily grind of data prep and ETL so their analytics and data team can be more agile, accurate, and automated.”
“Alteryx Designer is a very intuitive way to map out a process in a step by step visual workflow which can be then executed with the click of a button. Tedious manual tasks can be automated with ease.”
What is SAS?
SAS is one of the leading data transformation tools, allowing users to access data across many different sources. SAS Data Management can perform complex analyses and deliver information across organizations.
With SAS, activities are managed from central locations, providing users with the ability to access the tool remotely, from wherever they are, as long as they are connected to the internet. This makes it extraordinarily convenient for marketers to access the information that they need. Raw data files can be viewed in external databases, and data can be managed using various data tools, as well as data displayed in statistical graphics and reports.
“A powerful tool for big data management.”
“SAS is an amazing software that make statistical analysis. It is very accurate and gives you a lot of detailed data.”
No pricing information is available on the website. You can contact the company for a quotes, demos, and free trials.
If you're in the market for a marketing analytics platform to help you transform and aggregate all your data into one place, you'll likely want to review this list in detail. Each of the above softwares works well, and your pick should depend on your individual needs.