What is Data Orchestration & Why It’s Essential for Analysis

What is data orchestration?

Data orchestration is the process of moving siloed data from multiple storage locations into a centralized repository where it can then be combined, cleaned, and enriched for activation (e.g., generating reports in a business intelligence tool). 

Data orchestration helps automate the flow of data between tools and systems to ensure organizations are working with complete, accurate, and up-to-date information. 

The 3 steps of data orchestration

Data orchestration is a multi-step process that spans organization, transformation, and activation. Below, we go into more detail about each of these three overarching steps. 

1. Organize data from different sources

You likely have data coming in from a variety of sources, whether it be your CRM, social media feeds, or behavioral event data. And this data is likely stored in various different tools and systems across your tech stack (like legacy systems, cloud-based tools, and data warehouses or lakes). 

The first step in data orchestration is to collect and organize data from all these different sources, and ensure it’s formatted properly for its target destination. Which brings us to: transformation. 

2. Transform your data for better analysis

Data comes in various different formats. It can be structured, unstructured, or semi-structured, or the same event might have a different naming convention between two internal teams. For example, one system might collect and store the date as January 21 2020, and another might store it in the numerical format, 01212020. 

To make sense of all this data, businesses often need to transform it into a standard format. Data orchestration can help lift the burden of manually reconciling all this data and applying transformations based on your organization’s data governance policies and tracking plan. 

3. Data activation

A crucial part of data orchestration is making data available for activation. This is when cleaned, consolidated data is sent to downstream tools for immediate use (e.g., creating a campaign audience or updating a business intelligence dashboard). 

3 reasons to use data orchestration

Data orchestration is essentially the undoing of siloed data and fragmented systems. Alluxio estimates that data technology goes through major changes every 3–8 years. That means a 21-year-old company might have gone through 7 different data management systems since their inception. 

Data orchestration also helps with compliance with data privacy laws, removing data bottlenecks, and enforcing data governance – just three (out of many) good reasons to implement it. 

1. Compliance with data privacy laws

Data privacy laws like the GDPR and CCPA have strict guidelines in place around data collection, use, and storage. Part of being compliant is giving consumers the ability to opt-out of data collection, or request that your company deletes all their personal data. If you don’t have a good handle on where that data is being stored and who’s accessing it, it might be hard to meet this demand. 

Since the GDPR was enacted, we’ve seen millions of deletion requests. It’s crucial that you have a strong understanding of the entire data lifecycle to make sure nothing slips through the cracks. 

2. Removing data bottlenecks

Bottlenecks are an ongoing challenge without data orchestration. Say you’re a business with multiple storage systems that you need to query to gain insight. Chances are, the person responsible for querying those systems has a ton of requests to sift through, meaning there can be a lag between teams needing the data and actually receiving it – which in turn, can make any insights outdated. 

In a well-orchestrated environment, that sort of start-and-stop would be eliminated. Your data will already be delivered to downstream tools for activation (and that data would be standardized, meaning you can have confidence in its quality). 

3. Enforcing data governance

Data governance is difficult when data is spread across multiple systems. Businesses don’t have a complete view of the data lifecycle, and uncertainty over what data is being stored (and where) creates vulnerabilities, like not adequately protecting personally identifiable information.

Data orchestration helps remedy this by giving greater transparency over how your data is managed. This allows businesses to proactively block bad data before it reaches databases or influences reporting, and set permissions around data access. 

Common challenges with data orchestration

There are several challenges that can crop up when trying to implement data orchestration. Here are the most common to be aware of, and how to avoid them. 

Data silos

Data silos are a common, if not detrimental, occurrence among businesses. As tech stacks evolve – and different teams own different aspects of the customer experience – it’s far too easy for data to become siloed between different tools and systems. But the result is an incomplete understanding of how the business is performing, from blindspots in the customer journey to distrust in the accuracy of analytics and reporting. 

Businesses are always going to have data flowing in from multiple touchpoints into various different tools. But breaking down silos is essential if these companies want to gain value from their data. 

Data quality issues

Let’s say you’ve overcome the issue of data silos, and your data has successfully been consolidated in a central repository. It should be smooth sailing from here, right? To answer “yes,” to that question, we have to consider a crucial prerequisite: the data you’ve consolidated needs to be accurate. 

When data exists in silos, it doesn’t just lead to fragmented understandings. It creates an easy environment for inaccuracies. Different teams may have different naming conventions for the same events, leading to duplicates. This is where data cleanliness comes into play, helping to correct any inconsistencies or errors. 

Integration challenges 

Manually connecting different tools and systems can be an arduous process. Especially as tech stacks evolve, it can be difficult to keep track of all these different integrations. 

Luckily, there are ways to automate this. For instance, Twilio Segment comes with hundreds of pre-built integrations for data warehouses, marketing automation tools, BI tools (and more) – and the process only takes only a few minutes. 

How to ensure data security

Data security refers to how well you protect your business data from potential threats, ranging from cyberattacks, to unauthorized internal access, or data breaches. 

Ensuring data security is an ongoing, complex task, but here are a few important steps: 

• Have full visibility into what data you’re tracking, why you’re tracking it, where it’s stored, and who has access to it. 

• Encrypt data both at rest and in transit. 

• Educate internal employees about data security and how to avoid cyberattacks like phishing, malware, etc. 

• Get the proper accreditations and attestations (like ISO 27001, 27017) 

• Regularly conduct risk assessments and security-design reviews. 

Learn more about how Segment handles data security. 

Emerging trends in data orchestration

In recent years, a few trends have emerged in regards to how businesses manage the flow and activation of their data. One example of this is with real-time data processing, or when data is processed within milliseconds of being generated. Real-time data has become pivotal across industries, playing a key role in IoT (e.g., proximity sensors in cars), healthcare, supply chain management, fraud detection, and near-instantaneous personalization. Particularly with the advancements in machine learning and AI, real-time data allows algorithms and artificial intelligence to learn at a more rapid rate. 

Another trend has been the shift to cloud-based technologies. While some businesses have moved entirely to the cloud, others may continue to have a mix of on-prem systems and cloud-based solutions. 

Then, there is the evolution of how software has been built and deployed, which impacts how data orchestration will be done. Learn more about the move from monoliths to microservices, and then monoliths in containers here. 

Making data useful with Segment’s CDP

With Twilio Segment’s customer data platform (CDP), organizations are able to have confidence in the collection, quality, and activation of their data. Several time-consuming, manual processes are automated with Twilio Segment, like automatic data validation, enforcing standardized naming conventions, or building data pipelines. 

With hundreds of pre-built integrations, connecting to different tools and systems in your tech stack takes only a few minutes to get up and running. Even better, you can use features like Transformations to correct or customize data as it flows through Segment. And with Replay you can test new tools before you fully commit to them, avoiding vendor lock-in. 

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