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If there is data coming from different sources, whether it is the CRM, social media feeds or behavioral event data. And this data is likely stored in various different tools and systems across the technology stack (such as legacy systems, cloud-based tools, and data warehouse o lake).
The first step in data orchestration is to collect and organize data from all these different sources and ensure that it is formatted correctly for the target destination. Which brings us to: transformation.
The data is available in several different formats. It may be structured, unstructured, or semi-structured, or the same event may have a different naming convention between two internal teams. For example, one system might collect and store the date as April 21, 2022, and another might store it in the numeric format, 20220421.
To make sense of all this data, companies often need to transform it into a standard format. Data orchestration can help reduce the burden of manually reconciling all this data and applying transformations based on your organization's data governance policies and monitoring plan.
A crucial part of data orchestration is making data available for activation. This happens when clean, consolidated data is sent to downstream tools for immediate use (for example, creating a campaign audience or updating a business intelligence dashboard).
Data orchestration is essentially the undoing of siled data and fragmented systems. Alluxio appreciates that data technology undergoes major changes every 3-8 years. This means that a 21 year old company may have gone through 7 different data management systems since inception.
Data orchestration also helps you comply with data privacy laws, remove data bottlenecks, and enforce data governance – just three (among many) good reasons to implement it.
Data privacy laws, such as the GDPR and CCPA, have strict guidelines for data collection, use and storage. Part of compliance is giving consumers the option to opt out of data collection or to request that your company delete all of their personal data. If you don't have a good handle on where your data is stored and who accesses it, it may be difficult to meet this demand.
Since the GDPR was enacted, we have seen millions of erasure requests. It is essential to have a solid understanding of the entire life cycle of data collected to make sure nothing escapes.
Bottlenecks are an ongoing challenge without Data Orchestration. Let's say you're a company with multiple storage systems that you need to query for information. The person responsible for querying these systems is likely to have a lot of requests to sift through, meaning there can be a delay between teams that they need of the data and those who there receive effectively, which in turn can make the information obsolete.
In a well-orchestrated environment, this type of start-and-stop would be eliminated. Your data will already be delivered to downstream tools for activation (and that data will be standardized, meaning you can have confidence in its quality).
Data governance is difficult when data is distributed across multiple systems. Companies do not have a complete view of the data lifecycle and uncertainty about what data is stored (e.g where) creates vulnerabilities, such as not adequately protecting personally identifiable information.
Data Orchestration helps remedy this problem by offering greater transparency into how data is managed. This allows companies to proactively block bad data before it reaches databases or impact reporting and set permissions for data access.
There are several challenges that can arise when trying to implement Data Orchestration. Here are the most common ones to be aware of and how to avoid them.
Data silos are a common, if not harmful, occurrence among businesses. As technology stacks evolve and different teams own different aspects of the customer experience, it's all too easy for data to become siled across different tools and systems. But the result is an incomplete understanding of company performance, from blind spots in the customer journey to mistrust in the accuracy of analytics and reporting.
Businesses will always have data flowing from multiple touchpoints into various different tools. But breaking down silos is essential if these companies want to get value from their data.
In recent years, some trends have emerged regarding how companies manage the flow and activation of their data. An example of this is real-time data processing, which is when data is processed within milliseconds of generation. Real-time data has become crucial across all industries, playing a key role inIoT (for example, proximity sensors in cars), healthcare, supply chain management, fraud detection, and near-instant personalization. Particularly with advances in machine learning and artificial intelligence, real-time data allows algorithms andartificial intelligence to learn at a faster pace.
Another trend has been the shift to technologies based on cloud. While some companies have moved entirely to cloud, others may continue to have a mix of on-premise systems and cloud-based solutions.
Then, there's the evolution of how software has been built and deployed, which impacts how data orchestration will be performed.
– Not incorporating data cleansing and validation
– Not testing workflows to ensure smooth and optimized processes
– Delayed responses to issues such as data inconsistencies, server errors, bottlenecks
– Not having clear documentation in place regarding data mapping, data lineage and a monitoring plan
To measure the ROI of data orchestration:
– Understand basic performance
– Have a clear set of goals, KPIs and objectives in mind for data orchestration
– Calculate the total cost of the technology used, along with time and internal resources
– Measure important metrics such as time saved, processing speed and data availability, etc.
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