Imagine a weapon so strong it can repel and redirect kinetic energy from bullets.

In the Marvel universe, that weapon would be vibranium, the metallic ore that powers Captain America's shield and The Black Panther's suit.

Vibranium is also the reason Wakanda is the Marvel world's most technologically advanced country.

Good data is like vibranium. Why? It's well-sourced and verifiable. Good data can effectively transform a struggling business into a successful one.

In the global marketplace, good data powers dynamic business analysis, the kind that promotes business agility. Essentially, good data boosts supply chain efficiency and customer satisfaction.

The opposite, bad data, is what keeps CTOs, BI professionals, and developers up at night. Bad data is suspect data, data that's missing or flawed. It's also very dangerous. In fact, bad data can lead to misguided decision-making and lost profits. According to Gartner, poor data quality costs companies millions in revenues annually.

Preparing Data for Analysis Isn't A Cake Walk

Good data is reliable, accurate, and relevant.

The insights value chain is only as strong as its weakest data link, however. According to KPMG, 71% of CEOs say they have disregarded insights from unreliable data.

So, it's no surprise that BI professionals spend 80% of their time preparing (cleaning and transforming) data before they can engage in actual analysis.

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Data Cleansing Versus Data Transformation

Raw or source data is often

  • Inconsistent, containing both relevant and irrelevant data
  • Imprecise, containing incorrectly entered information or missing values
  • Repetitive, containing duplicate data

To extract reliable insights from raw data sources, we need to "clean" and "transform" that data. At its heart, data transformation facilitates a data-driven culture, the single most important predictor of a company's success and ability to innovate.

What Is Data Transformation?

While data cleaning deletes corrupt or incorrectly formatted records, transformation converts a data source into an accessible format. Both data cleaning and transformation are part of the data preparation process.

At its heart, data transformation is about identifying the data's original format, deciding on its new format, and then implementing the necessary format changes.

Common transformation languages include DataWeave, XSLT, and Pig. The latter's SQL-like scripting language (Pig Latin) can invoke MapReduce or Apache Spark code to execute Hadoop processes.

Pig's versatility allows users to execute advanced transformations without knowing how to code Java.

With tools like these, we can easily perform accurate transformations that turn data into actionable insights. The competitive advantage gained is worth its weight in gold.

 

The Top 5 Data Transformation Best Practices

#1: Start with Data Discovery

The first step in data transformation is to identify your sources and types of data. You'll also need to determine the format of your end target data. Data discovery paves the way for data visualization, which allows users to identify connections between variables and determine if these are worthy of more analysis. By displaying data visually, we can get granular insights into how our supply chain practices directly impact our organization's bottom line.

#2: Data Mapping: You'll Need A Road Map for Transformation

"I love it when a plan comes together." Colonel John "Hannibal" Smith, The A-Team.

At its heart, data mapping is a roadmap for how you'll migrate all of your data into one system. To successfully migrate data, it's important to establish a plan for how data merging, storage, and transformation will be executed. Xplenty allows you to automate the data mapping process without writing a single line of code.

#3: Use Data Profiling and Metadata Management to Ensure Data Consistency

In this step, we examine our raw data and metadata to determine whether the data is valid. There are 3 main types of data profiling:

Structure discovery: This type of profiling allows us to identify how data is structured. For example, do the home, work, and mobile device phone number fields contain the right numbers? Or, which credit card numbers contain numerical errors? We can use pattern matching or column profiling to find valid sets of formats.

Structure discovery also facilitates range analysis to help us determine the minimum and maximum values, medians, modes, means, and standard deviations for given attributes. In structure discovery, we focus on validating the consistency of our data. The goal? To facilitate efficient query processing in the end target database. 

Content discovery: In this type of profiling, we're interested in the quality of individual pieces of data. In content discovery, we look for the meaning behind the data. Does our data contain obscure, incomplete, or invalid values? Here, we may use completeness analysis to look for errors, such as zeros, spaces, or nulls. This is an important aspect of profiling because incorrectly formatted phone records reduce the quality of customer contact lists.

Relationship discovery: This type of profiling helps us identify what associations exist between our data sources. Here, cross-table profiling helps us spot semantic and numerical differences in various column sets. This reduces duplication and helps identify associations between data value sets.

Meanwhile, cross-column profiling helps us determine the relationship between elements in the same table. Relationship discovery is all about understanding the context of data to make better decisions. For example, identifying connections between transaction types can increase upselling or cross-selling opportunities. 

Finally, metadata management provides an audit trail that allows us to gather crucial metadata about the transformation process. Types of metadata gathered may include input/output data volumes, aggregate totals, timestamps, and source-to-target mappings.

The diamond in the rough? An audit trail allows us to answer important questions about the origins of key attributes. A metadata-driven business framework promotes agility and optimizes how we meet industry standards. This, in turn, increases our brand's appeal to more customers.

#4: Execute Basic Data Transformations First

Here, we implement important transformation processes, such as:

  • Recoding gender variables
  • Converting calendar time and character set objects
  • Identifying and removing duplicate data sets
  • Classifying uncategorized text strings into fixed categories

Basic transformations are often synonymous with data "cleaning" processes, essential steps in preparing data for analysis. You'll want to:

  • Scrub for duplicates
  • Standardize data processes at the point of entry to capture only relevant, high-quality data
  • Validate the accuracy and consistency of your data
  • Create a workflow to get teams accustomed to a data-driven culture
  • Get support from key stakeholders so that the right teams receive access to the right data

#5: Perform Advanced Data Transformations to Add Relevant Features

Advanced data transformation is sometimes called feature engineering. Unlike simple data transformations, this step involves the process of adding additional features (feature extraction) to improve the predictive capabilities of machine learning algorithms.

Types Of Advanced Data Transformations

  • Filtering: Here, we can selectively isolate individual columns and rows. This type of transformation removes records from an existing dataset but retains others.
  • Aggregation: This allows us to link or fuse data from multiple sources, such as individual, household, and county records.
  • Splitting: Here, we can divide a single column into multiple columns.
  • Derivation: This describes the process of creating new data elements from existing data, by using mathematical or logical transformations. For example, household composition can be derived from marital and census data.
  • Summarization: Values are summarized and stored at multiple levels as business metrics – for example, total orders by gender, ethnic heritage, and/or socio-economic status.
  • Bucketing/Binning: This transformation is used to change a numeric series into fixed, categorical ranges, say, from {2,5,8…} to {2-5, 6-9, 10-13…}. Take, for example, the seasonal fluctuations in consumer prices. Bucketing/binning lets us isolate noisy data and look at long-term averages. The focus away from short-term volatility provides a truer picture of price trends over time.
  • Z-Score Normalization and Max-Min scaling: In scaling, we change our data ranges, but in z-score normalization, individual data features have zero-min and unit variance. So, all values will be between 0 to 1. Scaling is especially important because datasets often contain elements in varying units and ranges. This is incompatible with many machine learning algorithms that use Euclidian metric measurements.

The Top 4 Data Transformation Challenges

According to a new survey, companies are falling behind in their data-driven goals: 72% of survey participants have yet to forge an internal data culture, while 52% say they have not leveraged data and analytics to remain competitive.

So, why are companies failing?

The talent gap may be insurmountable. Depending on your infrastructure, transforming your data may require a team of experts and substantial investment in on-premise infrastructure. New tools have evolved to optimize the process of data transformation. However, the ability to wield big data technologies successfully requires both knowledge and talent.

The process of preparing and migrating data is complex and time-consuming. Data scientists and BI professionals maintain that the process of data preparation (prior to transformation) takes up more than two-thirds of their time. According to a 2017 Crowdflower report, data scientists spend 51% of their time compiling, cleaning, and organizing data. They also spend 30% of their time collecting datasets and mining data to identify patterns.

Without the proper tools, data transformation is a daunting process for the uninitiated. Ideally, data discovery and mapping must occur before transformations can commence. Without a proper roadmap, the already daunting task of data transformation is made more challenging. However, roadmap and workflow creation may be impossible without the proper tools and expertise.

Developing a sustainable, fault-tolerant data pipeline often requires consensus building. For many organizations, building an efficient data pipeline involves extensive buy-in from key stakeholders. Consensus on the data collection and transformation process must often precede the building of a pipeline. This is easier said than done.

Additionally, the pipeline must easily accommodate changes to support scalability and functionality. The path to ETL hell is broad and wide, especially if there isn't an efficient mechanism in place to support schema evolution.

Effortless Data Transformation with Xplenty      

Xplenty offers a cloud-based ETL solution that facilitates efficient data transformation.

To code or not to code, that is the question. With Xplenty, you don't need to grapple with Pig, SQL, or Java code to fix bugs. Our platform allows you to execute basic and advanced transformations with ease.

Xplenty facilitates agility. You can integrate multiple data sources and retrieve insights from your data in real time. This means you can use reliable data to optimize your algorithms and achieve business agility.

The right ETL platform can save you money on OpEx and CapEx costs. Xplenty's solution is cloud-based, so you don't need to rely on IT talent to maintain expensive infrastructure.

Xplenty provides network, system, and physical security. Our physical infrastructure utilizes AWS technology and is accredited for ISO 27001, Sarbanes-Oxley, PCI Level 1, and SOC 1 and SOC 2/SSAE 16/ISAE 3402. Additionally, our platform also complies with the dictates of international privacy laws.

 

Are you ready to experience the Xplenty difference? Contact us to learn how you can achieve seamless data transformation today.