Data is critical to the success and day-to-day operations of every enterprise today. The term “Big Data” is not just limited to being a buzzword, but is also an accurate description of the data challenges faced by large and small enterprise solutions. These facts stress the ever-growing challenges faced by having a lot of data:
• It is assumed that the digital universe of data will grow by a factor of 10 between 2015 and 2020!
• It is expected that the number of mobile phone users will reach 6.1 billion by 2020, which means more data!
• Less than 0.5% of data is ever analysed or used!
This points to the fact that the variety and the volume of data is increasing astonishingly. However, the startling statistic that just 0.5% of data is ever used speaks volumes about the unnecessary amount of data that the world is going to have in the years that follow.
This is where the science of data transformation comes to play. Data transformation, also known as ETL (Extract/ Transform/ Load) is the process via which data is converted from one format or structure to another.
HOW IS DATA TRANSFORMATION ACHIEVED?
Typically, the process of data transformation involves two stages:
- Discover data by identifying the sources and the types of data
- Determine and discover the data that needs transformation
- Determine how to map, join, modify, filter and aggregate individual fields of data
- Extract the data from the source. A source can be a database, streaming source, log files or structured sources
- Transform the data. Transforming might include aggregating the data, editing text strings, joining columns and rows or converting the data from one format to another
- Send the data to its destination. The destination can be a database or a data warehouse that handles the data in the structured or unstructured form
Data transformation can be done by:
Data transformation can be achieved by using Python or SQL scripts to extract data and transform it.
Cloud-based ETL tools
The ETL (Extract, Transform, Load) tools hosted on the cloud are the tools which leverage the expertise and infrastructure of the vendor. Denizon offers a cloud-based ETL tool that comes to your rescue for all your data transformation needs.
On-premise ETL tools
Deploying on-premise ETL tools can also take away the pain of data transformation by automating the process. However, these tools are deployed in your company’s site and require extensive infrastructure costs.
WHY TRANSFORM DATA?
It is established that the world has a lot of data, and a minimal amount of it is put into use. But the question here is, why do you need to transform data? There might be many reasons why:
When you are moving your application as a whole into the cloud, or a new data store in the cloud, you will need to change the type of data that you have.
If you want to aggregate and compare data from different sources, then data transformation is the way to do that.
TIf you want to add information to your data or enrich it, then data transformation is useful in such a scenario. Data transformation helps when you want to perform lookups, add timestamps or add geolocation data to your existing data.
If you have some form of streaming or unstructured data that you want to analyse along with some structured data that you have, then you can perform data transformation to achieve that.
WHAT ARE THE CHALLENGES OF DATA TRANSFORMATION?
HOW CAN WE HELP?
Denizon helps you manage your data transformation needs. We offer a cloud-based ETL solution that can ease your data transformation processes.
Key features include:
HOW DO WE TRANSFORM YOUR DATA?
We follow the general four steps to transform your data. The exact nature of transformation differs on the needs of your company, but the steps below are the standard methods.
STEP 1: INTERPRET THE DATA
The first step in the process is to interpret your existing data correctly. We help you determine the kind of data you currently have, and the final output format that you want to achieve.
Data interpretation can be a difficult thing to achieve as the mere extension of a file cannot determine the kind of data inside. Applications generally determine the kind of data based on the extension applied to it. But the problem here is that users can add the desired extension to a file, and it does not always mean that the file has that kind of content.
Hence, accurate data interpretation requires tools that can peer deep inside the structure of a file and its database to determine what is really inside.
Another step in the data interpretation process is to determine the target format. The target format is how your data will be after the transformation process is complete. We help you determine the target format by understanding the system that will receive your transformed data.
STEP 2: CHECK THE DATA
Once we help you determine the final transformation format, we help you run quality checks on your data. A quality check helps you determine problems like missing text and corrupt values in the database. This is the point where your data needs a bath. Carrying out data quality checks, translating, and cleansing data early in the transformation process helps you ensure that the bad or corrupted data does not end up posing problems in your transformation process later.
STEP 3: TRANSLATE THE DATA
After maximising the quality of your source data, you can begin the actual data transformation process. This process involves taking each part of your source data and replacing it with data that fits the requirements of the final format that you require.
Data translation however is not just limited to replacing individual data pieces with another piece but also involves restructuring the overall file for it to be usable finally. For example, a CSV file separated by commas might need to be converted to an XML file to organise information with the help of tags.
STEP 4: POST TRANSLATION QUALITY CHECK
After the translation is complete, we help you ensure the translated data is maximally useful or not. This post-translation data quality check process will show the inconsistencies, the missing information and other errors that might have been introduced in the data transformation process.
Even if your data was error-free during the translation process, there is still a chance that problems have been introduced along the way. Hence, the post-translation quality check process is extremely important to ensure that the final data is actually usable.
WHAT BEST PRACTICES DO WE ADOPT FOR DATA TRANSFORMATION?
Denizon follow the best data transformation practices in order to make your data transformation process easier, quicker and efficient.
Start with the end in mind
We are faced with an ocean of data to process, but we don’t just jump into the bolts and nuts of data transformation. We engage business users, understand business processes, develop insights, and design the target format of the data before we start working on it. We start with “dimensional modelling” which
- engages users early in the process,
- scopes the data transformation effort,
- provides a target for the data transformation effort, and
- provides a start schema relation of facts and dimensions, so users find it easier to grasp.
Another best practice that we adopt for data transformation is data profiling. Data profiling helps us understand the state of the raw data and the amount of work that needs to be put in before making the data is ready for analysis. Basically, we get know the ins and outs of your data before we start transforming it.
Another good practice we follow for data transformation is a data bath. After obtaining the insights from data profiling, it is easier to understand the amount of data transformation work to be carried out on the data. If your data has a large frequency of missing values or junk data, then you might need to give your data a bath and include these missing values. We help you clean data early in the transformation process, thus making it easier to ensure that bad data does not end up in the final output.
Build dimensions then facts
Dimensions help put context to the data. For example, dates, products, and customers are dimensions, while the sales results are facts. Putting dimensions around facts makes data meaningful. Sales data would not be beneficial if it was not linked to any dimension. Hence, building dimensions before facts are what makes the data transformation process easier and mapable.
Record audit and data quality metrics
We record audit and data quality metrics in the process of data transformation to capture the number of records loaded at each step of the transformation process and the time at which the steps happened.
Capturing data quality test results and including them in the audit records provides the ability to reconstruct the lineage of the data. It enables analysts to work backward and have reliable answers about the history of where the data comes from.
Engage the user community
Another best practice that we adopt for the data transformation process is to engage the user community continuously. The measure of data transformation is the extent to which the target user community accepts and uses the transformed data. The transformed data undergoes extensive acceptance testing, and we fix the defects found by business users. We engage customers and users and maximise the usability of the final data.
Data transformation is a huge, bulky, time-consuming process that requires expertise. Outsourcing this process helps you and your organisation focus on critical business decisions instead. With the best practices mentioned above, combined with our expertise, we help you transform your data with ease.