What is Data Orchestration & Why It’s Essential for Analysis
Over 87% of companies have low business intelligence and analytics maturity. Low data maturity prevents companies from getting the most out of their data. That shockingly high number comes from Gartner research, and unfortunately, improving your business and analytics maturity isn’t simple.
Gartner recommends fixing the problem by breaking down data silos and using data governance. It’s easy to write down those two things, but actually taking action to fix them can be a complex process.
Starting with data silos, they often naturally pop up inside companies where they can’t be easily solved. Migrating siloed data to one location is often too big of a task for any company to pull off.
Data governance is a completely separate issue, and companies that have siloed data systems find it extremely difficult to implement data governance because there are too many systems to keep track of.
If this sounds like your company, you can solve those problems through a process called data orchestration. That will help you improve your company’s data maturity.
What is data orchestration?
Data orchestration is the process of taking siloed data from multiple data storage locations, combining and organizing it, and making it available for data analysis tools. Data orchestration enables businesses to automate and streamline data-driven decision making.
The software that executes data orchestration connects your storage systems together so that your data analysis tools can easily access the necessary storage system when they need to. The platforms that handle data orchestration don’t act as another storage system. Instead, they’re a completely new piece of data technology, which gives them a distinct advantage when it comes to breaking down data silos.
The 3 steps of data orchestration
The orchestration process is done in three steps:
Your data orchestration tools first need to understand and organize both your existing data and new, incoming data. Your company might have data in your legacy systems, in cloud-based tools, and in data warehouses or data lakes. Wherever that data is, data orchestration tools need to be able to access it and understand what type of data exists and where it came from.
Data orchestration tools take data in different formats and transform it so the data is in one standard format. That makes data analysis quicker because you won’t have to spend time manually reconciling data. For example, something as simple as a date could be collected in multiple ways. One system might collect and store the date as January 21 2020. Another might collect it and store it in the numerical format 01212020. That disparity can make data analysis time-consuming or incomplete.
The most important part of orchestration is making the data available to the tools that need it. That’s called activation. Activation happens when orchestration tools send the data to the tools that your company uses to operate day-to-day. That way, the data you need is already there when you need it — data loading isn’t required.
Not only do those three steps happen simultaneously, which speeds up data analysis, but they also happen in real-time. That’s important to note because real-time orchestration allows you to analyze the most recently collected data. That’s crucial for enterprise companies that collect millions of data points per second.
Why use data orchestration?
Data orchestration is essentially the undoing of data silos so that your data isn't fragmented and can be accessed quickly. Theoretically, if a company managed their data well enough, they wouldn’t need data orchestration and could meet all their data needs independently. But managing data “well enough” is rarely practical, because of the speed at which technology changes and many companies take a big data approach that leads to 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 — leaving data scattered across 7 different systems. And you can either keep your data scattered across all of those systems, or catch up with orchestration.
Data orchestration is the best option for most companies with multiple data systems because it doesn’t require any massive migrations or extra storage locations for your data, which can sometimes leave you with just another data silo.
But that’s not the only benefit of data orchestration. It also helps with compliance with data privacy laws, removing data bottlenecks, and enforcing data governance.
1. Compliance with data privacy laws
The GDPR, the CCPA, and other data privacy laws require companies to prove that their data was collected ethically. That includes detailing when, where, and why the data was collected. If you don’t have your data organized, it’s hard to prove that you're complying with those laws.
On top of that, the GDPR gives consumers the ability to opt-out of data collection, or request that your company deletes all data you’ve previously collected from them. 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 let consumers opt out of your data collection practices. And ensuring you’ve deleted all of their data when asked? That’s even harder when you don’t know where it’s being stored in the first place.
Both opt-out and deletion requests do happen frequently. Since the GDPR was enacted, we’ve seen millions of deletion requests. It’s crucial that you have a strong understanding of where your data is, and orchestration can help you get there.
2. Removing data bottlenecks
In a traditional, unorchestrated data ecosystem, if you want to analyze your data the process can get bogged down.
You might have multiple data warehouses and other storage systems that you need to query. If you don’t have the knowledge of those systems, you’ll have to go to someone who does have the ability to run queries on your data.
Chances are, that person has a lot of other employees asking for data too. So, your queries are added to a to-do list. When that person finally gets to yours, they’ll pull the data you need and send it to you. But, you’ll now need to manually transform the data so that you can actually use it. Once that’s done, you still need to load it into a business intelligence tool or another analysis tool. When you’ve finally completed that whole data process, you can start on your analysis.
In an orchestrated environment, almost all of those steps are eliminated. Your data will be activated at the endpoint so you’ll be able to jump right into your analysis tools and get to work. The data will also be standardized, so there’s no need to manually transform it.
Some estimates say that 80% of the work involved with data analysis is simply acquiring and preparing the data. That’s where a lot of the bottlenecks come from. So, a key use of data orchestration is that it can drastically reduce the amount of time spent on those two steps because it’s able to automatically handle the heavy lifting of acquiring and preparing your data.
3. Enforcing data governance
Data governance is difficult when your data pipeline is spread across multiple data systems. Since your data orchestration tool connects all of your data systems, it’s easier for it to enforce a data governance strategy.
Remember that orchestration can organize your data in real-time. If you’ve created a tracking plan or a data strategy framework, your data orchestration tool can ensure that data collected complies with that plan. If the collected data doesn’t comply with that plan, your orchestration tool can either block those data sources, or quarantine them until you have time to understand how something slipped past your tracking plan.
Data orchestration is built for data governance, and data governance gives you better confidence in your data. That helps improve your data analytics.
Making data useful with data orchestration
Data orchestration is all about making your data more useful. Too many companies today are leaving their data fragmented and in silos. That prevents them from easily getting a full understanding of what their data is telling them.
For companies with siloed data, data orchestration exists to help them get the most out of their data.
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