You don’t have to be a data scientist to interact with big data. Quite the opposite—the world of big data has become so accessible and intertwined in our everyday lives that avoiding big data would be a more impressive accomplishment. 

Think about how you check your bank account activity. While the dashboard you check daily is quite simple to interpret and understand, behind the scenes is financial data that needs to be wrangled into place, securely managed, and updated around the clock. Purchasing a flight works similarly. As you (and many others) search for a flight, the backend system is calibrating demand, flight costs, and time of year to generate prices. You might not see the data, but behind those user-friendly interfaces is quite a whole lot of it. 

So what are these data-powered technologies called? Quite simply, data applications. Data applications cater to consumers—like bank account owners or flight shoppers—but they can also be built internally for business stakeholders and subject matter experts. 

Let’s take a closer look at what data applications are, some real-world examples, and some of the challenges of working with data applications. 

What are data applications?

Data applications are applications built on top of databases that solve a niche data problem and, by means of a visual interface, allow for multiple queries at the same time to explore and interact with that data. Data applications do not require coding knowledge in order to procure or understand the data at hand, which makes them ideal for business users or consumers. 

For example, you might build a data application for your taxi company that visualizes all incoming data from a taxi fleet in order to monitor revenue on a real-time basis. Or, take real estate companies like Zillow or Trulia, which have built user-friendly applications on top of huge volumes of housing market data for prospective buyers to search and compare. 

A data application is like a cross between a data visualization and a web application. Similar to a data visualization, data is presented in a visually-appealing manner that allows for easy consumption and interaction. However, like a web application, that data is constantly changing and updating as new data powers the application; the goal is not one analysis, but on-going data surveillance or use over time.

What are the benefits of data applications?

The benefits of developing a monetizable data application for consumers are obvious—but what about internal data applications? Why build a custom data application for your organization when there are so many out-of-the-box software options available? 

For one, a data application doesn’t have to be built entirely from scratch. Various platforms on the market allow organizations to build personalized data applications while they control back-end processes such as server infrastructure and security.

Let’s take a look at some of the other reasons that organizations are choosing to build their own data application: 

  • Can be tailored to fit specific use cases.
    Every business model and subsequent data use case is unique, and can’t always be shoehorned to fit into ready-made software. Building your own data application offers more flexibility and customization to best meet the needs of your business.
  • Cost savings.
    Building your own data application doesn’t come cheap—often, organizations invest in data engineering and development resources in order to build and manage the application. However, software can run up a hefty bill, too, and over time prove to be more expensive in the long run. Finally, with an increasing number of accessible data application tools, more and more business users are starting to involve themselves in the data app development process.
  • More efficient visualization.
    Some organizations might consider forgoing a data application altogether, simply relying on business users to manually pull in data into spreadsheets and, later, leverage visualization tools to create dashboards. This is a great tactic for one-off analytics projects, but it doesn’t make sense if business users are looking for the same updates each day—it’s quite simply not fast enough. With data applications, all of your data is immediately accessible with easy-to-understand visualizations.
  • Offers more control to those who know the data best.
    There’s a big difference between the technical expertise of a data developer or data architect vs. the domain-specific expertise that a business user might have. Sure, your data developer knows how to work with data, but she likely doesn’t know how it applies to the business. Data applications lower the technical barrier, allowing business users to leverage their domain expertise in real-time for more effective strategy.

What kinds of data applications are organizations building?

Let’s take a closer look at the business strategy of building a data application to understand how organizations apply data in order to fuel their data applications, and how these data applications are supporting specific revenue goals. Here are three examples of modern data applications.

  • Data application for manufacturing defect detection
    Imagine you’re the manager of a high-volume auto parts manufacturing company. Each hour, your company produces hundreds of engine parts—balance shafts, cylinder heads, etc. Because of the high production volume, it’s essential that you understand exactly when a systemic defect has occurred, lest it begin to multiply and lead to hundreds, if not thousands, of unusable products.
    A data application that is powered by factory data is able to quickly alert you to any possible defects coming off of the manufacturing line. Should you notice something odd, you can immediately put a stop to the line in order to further inspect the products. However, this speed would be much more difficult if analysts had to routinely wrangle and visualize data manually.
  • Data application for pricing optimization
    As pricing becomes a central lever for differentiation, organizations need to be able to respond to market changes faster than ever, which means optimizing for a continuum of decisions (such as list price or promotions) based on context (such as localization or special occasion) to serve the function of multiple business objectives (such as net revenue growth or cross-selling).
  • Data application for data aggregation
    Consulting for a large organization is challenging for many reasons, but high on the list is data. Understanding how data is collected, where to find it, and how to extract it can make or break a consultant’s findings—and, depending on the scale of data, consume a lot of their hours.
    Deloitte built a data application called Cortex that works to extract data from various places across the organization so that it can be consolidated, standardized, and prepared for analytic use. The application has become a differentiator for Deloitte and marketed as a key part of their global analytics platform.

What are tips for building a data application?

Data applications are designed around specific kinds of data. But before that data can be fed into a data application, it must first be ingested and stored in a data platform, the underlying foundation of all data applications.

“Data platform” is a term for technologies that allow for data to be governed, accessible, and deliverable to users, other technologies, and, of course, data applications. When most people think of data platforms, they think of the top three data providers, Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), though Snowflake and Databricks, among others, are prominent players, as well.

Getting data applications right means building a strong data platform foundation. Here are some tips for ensuring that your data application is set up for success.

  • Understand the data you’re dealing with
    Document data sources, including all of the applications that you’ll be using to collect data. Make a plan for the most effective way to gather data without risk of data corruption and set standard data quality parameters. Finally, determine the volume of data that you’ll be working with on a day-to-day basis, and how often that data volume might change.
  • Choose the right platform
    Perhaps this is the most obvious tip, but it’s important if your organization is starting from square one. Choose a data platform that can support the unique needs of your data application, as well as one that you feel confident will be a strong partner as you deploy and manage the data application.
  • Remain flexible
    You have a goal in mind for your application; you understand the type of data you want to work with in order to reach that goal. And yet, it’s important to remain flexible. Keep in mind that you may find insights beyond even what you’d imagined. The advantage of a big data platform is that it consolidates all of your data, allowing for more exploration than under traditional data warehouses.
  • Leverage data mining
    Data mining is the process of analyzing data in order to establish patterns and relationships to the data required for your data application. With data mining techniques, you’ll be able to better understand where to source new data and where to invest your resources.
  • Invest in data preparation
    Data preparation is one of the most important components of your data platform to get right. The success of your data application rests upon the quality of data it uses, which means all of the many steps involved in data preparation—cleansing, standardizing, enriching, validating, to name just a few—must be done correctly. As the old adage goes, “garbage in; garbage out;” should poor quality data be used for your data application, then its insights will be poor, as well.
  • Iterate, measure, and repeat
    A data application isn’t a “set and forget” process. Instead, you should be continually evaluating if the right data is being collected, if there’re new sources that could be added, or if there is data that has become irrelevant and could be removed. Your business changes, as does the needs of your data application. Ensure that your data application is always up-to-date. And along every step of the way, measure your results. This could be done a number of different ways, such as the volume of data collected, but it depends upon the goals of your data application.

How can I get data preparation right for my data application?

Because data preparation is such an important step for your data application, let’s dive a little deeper into how to do it right.

First, let’s talk about how data preparation has traditionally been done at scale. A small IT team typically used hand-coding or ETL (extract, transform, and load) tools and processes to maintain data quality throughout the entirety of the organization, from ingestion through delivering requirements to the business. Today, however, in the same way that data applications have increased data accessibility, organizations are looking to involve more resources in the data preparation process.

Enter, modern data preparation platforms. A modern data preparation platform gives business users a means to access and prepare data while still allowing for IT oversight. For one, this is a more efficient approach—instead of a small task force chasing down issues of data quality, there are more eyes on the data—but it also leads to better curation for the end analysis. IT will still curate the best stuff, make sure it is sanctioned and re-used (this ensures a single version of truth and increases efficiency). But, with business context and ownership over the finishing steps in cleansing and data preparation, these users can ultimately decide what’s acceptable, what needs refining, and when to move on to analysis. 

And this approach reaps big benefits for data applications. A data application makes sense of data in a faster, more consistent manner for business stakeholders. But imagine if those who had the best context of the data could not only interpret that data quickly vis-à-vis data applications, but could also curate and prepare the right data for those applications? Those data applications would be more targeted, robust, and ultimately more effective.

The Designer Cloud data preparation platform for data applications

Alteryx Designer Cloud powered by Trifacta is widely recognized as the industry leader in data preparation and serves as the foundational data preparation platform for many data applications. In fact, our customers include Deloitte and The TopLineLab, mentioned in the examples above. 

Designer Cloud’s machine-learning powered platform acts as an invisible hand during the data preparation process, guiding users toward the best possible transformation. Its visual interface automatically surfaces errors, outliers, and missing data, and it allows users to quickly edit or redo any transformation. Finally, it integrates with any data application and can pull in data from anywhere within the organization. 

Learn why organizations are incorporating Designer Cloud as a key part of their data application strategy today. Schedule a free demo from our team or get started right away with Designer Cloud powered by Trifacta on the platform of your choice.