Data is the backbone of the digital world. Every day, online users generate billions of digital touchpoints, each of which includes small but valuable nuggets of data. Any given digital interaction is small by itself but stitched together, they comprise a massive amount of information. By 2025, researchers predict that humanity will have generated 175 zettabytes of data.

Humanity’s collective data isn’t your problem to solve–but your brand’s data is, and it’s a safe bet it will continue growing at a rapid pace. Data drives decisions in every business department, and the increasing reliance on AI and machine learning means fresh and accurate data is more important than ever.

But before you can process and analyze data, you need to merge it into a single system. This process, known as data ingestion, involves collecting data from disparate sources, cleaning and standardizing it, and finally storing it. While it’s necessary, the process can be fraught with tedious and frustrating challenges.

Data ingestion doesn’t have to be painful, though. Let’s look at some of the potential friction points and how to avoid them for a simple and streamlined data ingestion solution.

A critical component of any brand’s analytics

In a high-velocity digital world, a robust customer journey analytics tool is essential to continually improve CX. Fresh, relevant, cohesive, and accurate data is the linchpin of effective analytics, which is often a stumbling block. Just because data exists somewhere in your organization, doesn’t mean it’s available to the right people. An Arm Treasure Data survey found that 47% of marketers cited siloed and inaccessible data as their biggest challenge.

Whether your organization uses data warehouses, data lakes, CDPs, or other storage solutions, you need to collect everything in one, unified place. There are two main strategies for data ingestion:

• Batch processing: In batched scenarios, the data ingestion system moves large chunks of data at periodic scheduled intervals. The frequency can be anywhere from every minute to every day (or more for some applications) and depends on the time sensitivity of the data. 
• Streaming or real-time processing: Unlike batching, streaming data ingestion is a real-time process. As soon as a user action creates data, the system extracts, processes, stores, and makes it available to downstream analytics systems. While streaming data ingestion takes more resources, it’s invaluable for analytics applications that require split-second decision-making.

Data ingestion is essential, but a challenge

Robust analytics drive revenue and data is the lifeblood of any analytics toolset. Unfortunately, collecting all of that data isn’t always simple. Data streams come in a variety of shapes and sizes, sometimes from unreliable sources, and engineering teams have to make it all work seamlessly.

Data ingestion can go wrong in a variety of ways, but these are some of the most common issues:

• Complexity: Having a lot of data doesn’t necessarily mean you can effectively utilize it. Brands need to standardize and normalize the data to suit your company’s needs, and when each department has a different tool (or two), the process becomes more challenging than simply plugging in a feed and mapping field names.

• Speed: Every aspect of data ingestion from mapping sources and standardizing to debugging errors takes time. Multiply that process based on the thousands of data sources a large company might have, and it places an immense time burden on engineering and data science teams.

• Compliance: In a privacy-first digital world, compliance with regulations like GDPR and CCPA isn’t a luxury, it’s a necessity. However, this can add challenges to the data ingestion process, as you need to make sure every stream and source you’re using is compliant before it hits your system.

• Reliability: With the staggering amount of new data emerging every day, technical hiccups can lead to devastating losses of information. Data ingestion relies on uninterrupted network connectivity and reliable storage uptime, but it needs to be robust enough to work around and recover from errors.

• Cost: You need somewhere to store the vast amount of data your brand generates, and while storage space gets cheaper over time, it still adds up. Add in software infrastructure, licensing fees, and a team of data scientists to manage the process, and the total cost can spiral quickly.
  
  

Simplifying & streamlining data ingestion

Given how important data is to every facet of a brand’s operations, a robust data ingestion strategy is critical. Not only do you need to stitch together massive amounts of disparate data, but you also need to ensure you’re not missing or losing information. Research indicates that as much as 55% of data in many companies is “dark data,” meaning it’s untapped and underutilized. Imagine the insights lost to the abyss of dark data.

The best way to prevent those issues is to make sure you’re ingesting all of your brand’s data. Make the process easier for your engineering team and facilitate more rapid and accurate insights in the following ways:

• Automation: Data has grown too much in both scope and complexity to rely on human intervention. Tools to automate the consumption of schema, metadata, and structural boundaries can significantly reduce the administrative burden of data ingestion.

• AI: Data standardization and normalization are essential steps to successful ingestion, but can be painfully tedious. However, AI and machine learning algorithms are well-suited to handle those tasks. Deploying AI to deal with cleansing data for ingestion can massively reduce the time and effort engineering teams spend on integrating new data sources.

• Enable self-service options: Empowering people with the resources to do their job is a key component of maintaining high organizational velocity. By giving marketing and product teams the ability to ingest new data sources, you can avoid forcing them to wait on IT support, and free up your engineering team to deal with more pressing concerns.

• Rigorous data regulation: Once you’ve gone through the trouble of cleaning and standardizing your data, you want to keep it that way. Robust data governance policies will prevent issues like data bias from creeping in as you integrate new sources and streams.

• Invest in customer intelligence: Between data warehouses, CRMs, CDPs, DMPs, and other platforms, most companies have a complex and fractured data ecosystem. Leveraging a customer intelligence platform, like Scuba Analytics, can stitch together all of your brand’s data and give users a 360-degree view of your customers’ lifecycle.

Data ingestion is easy with Scuba Analytics

Ingesting all of your brand’s data is critical to organizational success. Given the complexity of disparate sources, the rapid pace at which data accumulates, and the importance of regulatory compliance, it can be a tough task.

Scuba Analytics simplifies the data ingestion process and makes it easier for you to aggregate your brand’s datastreams and get data into your users’ hands with these key features:

• Compatibility with AWS, Azure, or your private cloud—regardless of that platform, your company retains control and ownership of its data.
• A quick data ingestion setup process that will allow your team to start visualizing customer journeys in a matter of hours.
• The ability to rapidly scale to meet the needs of brands generating tens of billions of data points per day.
• Built-in compliance with GDPR, along with SOC 2 Type 2 certification, ISO 27018 certification, and Privacy-Shield certification.
• Robust self-serve capabilities, allowing teams to perform data ingestion with minimal support from engineering.
• An SRE team that’s on-call 24/7 to support your company’s data infrastructure needs.

Interested in learning more about how Scuba can help make data ingestion easier? Explore our demo today or talk to a Scuba expert.