HeroPath Journey Part 2

HeroPath Journey Part 2: Transformation | Making Data Usable

Lixar I.T. Tech

Raw data can be overwhelming, inaccessible, and downright unusable. If you want meaningful insights, the information you analyze needs to be structured. This is what data transformation provides. Transforming data is the process of altering it to make it easier to understand. This alteration may be:

  • Constructive: adding, copying, and replicating data
  • Destructive: deleting fields and records
  • Aesthetic: standardizing salutations or names
  • Structural: renaming, moving, and combining columns in a database

With traditional on-premises data warehouses, transformation is part of an ETL process (extract, transform, load), which can be quite time-consuming. However, modern cloud-based data warehouses and lakes can scale compute and storage resources with latency measured in less than a minute. This means you can load raw data directly into the warehouse or lake, and transform it at a later query time (ELT), saving big on time and headaches.

The Benefits of Transforming Data

Transforming data is a vital step in gaining meaningful insights that you can use to solve business challenges and make better decisions. Transformed data is easier for both humans and computers to analyze. Simply put, transforming your data makes it usable, offering the following benefits:

  • Better-organized data is easier to understand
  • Formatted and validated data improves data quality
  • Structured data ensures compatibility between systems

Once the data has been transformed, not only will you have facilitated more effective data analytics, but you will be well on your way to data-driven decision-making that will optimize every aspect of your business and operations. 

Following transformation, data can be modeled into meaningful analytics from which you can gain valuable insights. You will also be better positioned for the future, as these analytics can be used to drive new technologies such as machine learning (ML) and artificial intelligence (AI), which rely on having a strong data analytics backbone. Build predictive models and What If simulators to forecast outcomes that were once unknowable. Automate to optimize operations. Build and test machine learning models and drive AI solutions. It all starts here.  (For more about advanced analytics, be sure to follow up on our next article, about engaging with data).

But what does it mean, exactly, to be positioned for the future? What does data transformation look like in action? Let’s explore a recent success story below, through the lens of this important step.

We recently worked with a company that requires specific weather conditions to operate effectively and safely. This means they rely heavily on weather forecasting which, when inaccurate, can lead to extremely costly delays. Having spent more than half their time delayed by weather over the last year, it became clear that their methodology for weather prediction was insufficient. They needed to do better, and recognized that stronger data science and machine learning applications were the way to do so. However, given the state of their data, transformation would have to play a big role. Lixar’s experts were up to the task.The project required data transformation for both data science and for data visualization (viz) purposes. To start, we had to ingest two sets of historical data, 1) forecasted weather; and 2) actual weather. Using ten years worth of data, collected every six hours, provided us with over 14,000 data points. Throughout the years, how that data was represented had become inconsistent, so once ingested, we had to perform structural and aesthetic transformations to map like data to like data, to get it all  into a usable state. For example, wind speed was sometimes provided in kilometers per hour, but in miles per hour at other times. Similarly, wind speed was spelled as “wind-speed” in some places, and as “wind_speed” in others. All of this needed to be aligned through data transformation before any data science or data viz could be performed. In some cases, data scientists and data viz experts even required different data formats, which led to two stages of transformation. Mainly structural for data science, and mainly aesthetic for data viz.Using an ELT process in Microsoft Azure Cloud, we leveraged best-in-class tools, such as Data Lake for storage, and Databricks to perform transformations. Afterwards, results were visualized using PowerBI. The project was a huge success. With clean, usable data at hand, the company was able to improve their prediction accuracy by 8%, which translates to major cost savings.

The Value of Expert Support

As demonstrated above, having expert support will help you ensure that the transformations performed are of value to your specific situation and goals. Having gone through a discovery session and the ingestion phase with you, experts are equipped to make better decisions about tools/components, storage, and format. Transformation can be time consuming, resource-heavy, and costly, especially when dealing with on-premise systems. But having the right support will mean saving on time and resources, and ensuring the solution suits both your budget and business needs. 

One key ingredient to an efficient and cost-effective transformation phase, is using a cloud-based data platform. Performing transformations in an on-premise warehouse after loading (ELT) can over-burden the system, slowing down other operations. Cloud-based warehouses and lakes, however, allow you to transform after loading (ELT), because the platform’s scalability means you can always meet demand. 

Building on our 20+ years of experience getting businesses data-driven and AI-ready, Lixar has developed a cloud-based modern data platform, HeroPath, that makes ingesting, transforming, and engaging with data quick, easy, and cost-effective.

Getting Started with HeroPath

HeroPath is a fully managed, cloud-based, modern data platform. Its scalability and flexibility for today and tomorrow allows you to optimize every step of your data journey, fueled by best-in-class Microsoft components, that can be customized to meet your specific business needs. Our unique approach ensures that we understand your goals and help you achieve them by: 1) ingesting the right data; 2) transforming it using the right components, and 3) allowing  you to engage with it through interactive visualizations. Moreover, our accelerated infrastructure means your pipeline can be ready in a day, rather than weeks or months.

At its core, transforming data is an artful calculation of knowing the right components to use at the right time. For advanced analytics-focused organizations, this may mean leveraging Databricks, which can be scaled up and down as required. When it comes to storage, transformation, and modeling, our experts will help you select the best-in-class tools that are best suited to your needs. This modern DevOps approach understands that transformation is a process, and having the right tools is essential to building your data-driven future.

For more information about our data solutions, please contact us at data@lixar.com. For more about the HeroPath Journey, check out our past article on Data Ingestion, and stay tuned for an upcoming article on Engaging with Data.