Let’s be brutally honest — proper use of data can determine your business's success or failure.
But raw data is like a harvested crop - you can’t really consume it. Take any crop — it needs to be collected, stored, transported and processed to form the wide range of foods that we eat every day. The wheat crop, for example, can be baked into various foods such as cakes, pizza, or fancy Italian ciabatta, depending on what your end customer wants to consume.
The same thing goes for your data. You collect, store, and transfer data between systems; but you also need to transform your data into a format that your team can easily consume.
Product teams might want to analyze product usage per customer segment, while growth teams might want to look at customer acquisition dashboards to quantify marketing impact.
If you are part of a business team, you need to be able to transform raw data into useful information.
To do all of this you need data transformation.
What is data transformation?
Data transformation is the process of transforming your data into a new format and/or structure that is easier to integrate, analyze, or draw insights from.
For example, a sales manager might aggregate their order table to calculate average orders, and count the number of transactions per state. Their colleague in marketing might transform the same data in a totally different format, one that will suit their needs better. The VP of customer success might rely on a data engineer to transform the shape of their customer data just so he can import it into another system.
How to transform data?
The transformation of data involves a two-step process: identifying and understanding your data and choosing the best methods and tools to transform your data.
Data transformation usually begins with understanding data inputs and their formats.
You can have your data stored in many different places like CRMs, local CSV files, web application data, warehouses like Redshift or from customer management systems like Hubspot or Zendesk. The first thing you need to do is successfully identify and study these sources.
Then, these various data sources, often disparate, will limit the format in which you can export your data. You need to understand what your options are and how you can merge your data for further analysis.
Finally, you’ll need to map your data and determine which transforms are needed to get to the structure you want.
After you’ve studied and understand your data sources, you can proceed to the second stage. At this stage, you should plan how you want to implement the data transformations and what methods you will use to do that.
Ultimately you can:
- Do it manually - by building a lot of custom routines, python scripts or Excel formulas,
- Rely on your data team - wait on them to build these custom requests
- Do it automatically - by using data automation tools that lets you build pipelines and achieve goals without coding or writing SQL queries.
- Export it to outsourced teams - and pray that they will deliver
Which one you chose depends on many factors like your data volume, business needs, or available resources. Usually, it is better to implement bigger (and more complex) projects in-house with the help of your data team, and outsource the repetitive tasks to an off-the-shelf solution.
Types of data transformations
To get a better understanding of the process, we’ve outlined a list of common data transformations that are used by business teams. You can obviously automate all of these with automated data tools, but sometimes you might choose to perform them by hand.
Let’s say that you are a customer service analyst at a telecom company. You have a daunting task in front of you—you need to analyze the sales potential of a particular customer segment. The challenge is that your sales data resides in one CRM platform and your customer data in Salesforce. Both of these platforms have limited options for exporting data. This means that you have to do a lot of data transformations so you can merge the two datasets and run the analysis.
In this case, you might recall that the vertical or horizontal join might do the trick for you. These data transformations will combine your dataset based on a related column.
Using SQL, you can vertically join your tables in multiple ways:
- Inner join: return records that only have matching values in both tables
- Left join: return all records front he left table, and matching values in both tables
- Right join: return all records from the right table, and matching values in both tables
- Full outer join: return all records when there is a match in either left or right table
Or you can use a software that will let you combine these two datasets, visually.
Data aggregation lets you search, group, and summarize your data in different formats.
Let’s assume that you were selling inflatable water slides in the year of 2020, and you want to analyze which state sold the most water slides. However, the original table that you received from Shopify gives you transaction data -- the table is messy, and you can’t really use it for further analysis. In order to achieve your goal, you can aggregate your original orders data by state and year and then run an average operation on the sales column.
You can use a data automation software for that, or you can learn how to do it with SQL.
In simplest terms, data filtering allows you to select the part of your data that is needed for further analysis. For example, let’s get in the ecommerce analyst’s shoes. Their manager is asking for a report of the best performing locations in the month of December. Best performing means that orders were above 5000.
By obtaining Shopify orders data, the analyst receives a twenty-column long dataset of every transaction that has happened in the past three months. To get the right subset of that data -- you guessed it -- they need to filter by the right conditions, meaning orders should be higher than 5000 and month should equal December. The filtering will scrub the data that doesn’t belong in this filtering, and he will be able to then present it in front of the manager.
You can try our Shopify integration to easily pull and filter your data!
Data cleaning or data scrubbing is the process when you delete out-of-date, inaccurate, and unnecessary values or characters to increase the accuracy of your data. The process also takes care of missing values and, in most of the cases, resolves merging errors like duplicate values.
Most of the time, this is done manually by database professionals, and as the data grows, this can become very error-prone and mundane. Statistics show that data scientists spend 80% of their time cleaning data rather than creating insights, which is not surprising.
If you're planning to automate your data transformation process, a good place to start is to automate your data cleaning tasks.
Data splitting can cover the process of partitioning existing data into two partitions or performing split operations for columns.
For example, let’s say that you just imported your product data from Airtable, and in one of the columns, you store your product categories. There can be up to four categories per product, so the category column usually contains multiple values separated by commas. Ultimately, you may want to identify which categories are performing well. In order to reach this point, one prerequisite is splitting the category column. To solve this task, you can use a transform that lets you separate the text by a given character; in this case, you would separate using the comma.
Data enrichment means combining data from third party services to enhance the data you collect. You can enhance different things, but the goal is to always make informed decisions.
Well-established enrichment processes will ensure that your data is always up to date, but, be sure to clean your data first. A good data enrichment example is when the sales team combines internal sales data to third-party ads data to analyze the performance of the sales funnel.
Data extraction transforms usually support the process of obtaining data from various databases, warehouses, or other platforms that a business may use to store data. They ensure the destination system knows how to read the extracted data from another system.
You can build custom pipelines for each use-case, or you can build automated workflows that will do that for you.
With the data sorting transforms, you are able to arrange your data in a meaningful order so you can analyze or present it more effectively. For example, you can quickly identify the best performing markets in your business by analyzing sales data at the state level then sorting the sales column in a descending order.
Finally, a whole list of transforms can format the data without changing the values. Here are couple of examples:
- Renaming columns
- Adjusting dates and time
- Converting text case (to uppercase, lowercase, titlecase)
- Removing dollar signs/ quote marks/ percent signs
Challenges with data transformation
There are four common challenges that businesses face with data transformation:
- Business professionals are dependent on technical teams to orchestrate these data transformations, and this can become a bottleneck for making decisions.
- Technical and data teams might require a lot of manpower and resources to support the ad-hoc requests they receive.
- The process can be very repetitive and error-prone
- If the process is not streamlined, then most of these scripts and Excel files are non-reusable, and they can’t be scaled to support many departments
The data transformation process should be well integrated with your systems, it should be collaborative and very easy for all teams to use. It is imperative that companies should aim to streamline the data transformation process, by implementing tools and practices that will make data accessible for both tech and business teams.
Streamlined data transformation with Intersect
To convert your raw data into valuable information, you need to do a lot of the same steps that involve many data transformations.
If your business teams are often blocked by data operations, and if your data team is overwhelmed by the amount of the ad-hoc requests they are getting --- you should start thinking about streamlining your data transformation process.
One way to streamline this process is to use an out-of-the-box solution that will free your teams from all the manual and repetitive work.
With Intersect, business professionals can easily build complex transformations and orchestrate them into collaborative data notebooks. They can integrate disconnected data by using our native integrations and can schedule their notebooks to run whenever they want to.
Many different teams use Intersect to power their data operations.
Marketing teams merge ads channels and create various reports to analyze marketing spent. Sales teams visualize their sales pipeline, and aggregate and analyze their cold email campaigns. Customer success productionize feedback forms that enable them to analyze the customer journey. Data analysts democratize data for everyone, and find time to focus on important projects.
Everyone wins with Intersect. If you want to streamline your data transformation process, let us know by booking a call with our support team.