If it’s true that data has all the answers, how can we harness this data in an effective and efficient way?
We're no longer at the beginning of a data revolution. Even major global organizations like the United Nations are looking into using data to achieve their goals.
Across all industries, verticals, and locations, we're definitely getting better at collecting large amounts of data. But when it comes to collation and interpretation, we aren’t always efficient at extracting meaning from the data we've obtained.
As organizations strive to make data-driven decisions or build data-driven applications, there's a crucial step in the process that’s still missing.
In order for data to be fully used in an efficient way, we need data orchestration.
What Is Data Orchestration?
As a company grows, data silos begin to emerge due to changing technology (like cloud adoption) as well as the sheer amount of data that is collected. Data orchestration tools are able to take data from multiple silos, organize that data, and then present it in a standardized way. This allows it to be used by a range of different applications and use cases.
Think of an orchestra. Myriad instruments — each with different sounds and features — come together to form one harmonious piece of music.
This concept can similarly apply to data, which may be housed on legacy systems or across different frameworks and environments. Data orchestration brings it all together and enables data teams to focus on writing algorithms and building out features, rather than on how to access data efficiently.
Does your organization need to implement data orchestration? Many do, but if you're still not sure, start looking out for these seven clear signs.
1. Your Data Lives in Too Many Locations
If you’re currently housing your data onsite within your own premises, you probably already know that it comes with significant upfront costs, especially when you start to scale. It's also highly dependent on human resources and equipment, which can both be costly.
Managing data sets in the cloud has its own set of challenges, especially if you're using multiple databases (like Snowflake, BigQuery, and Redshift) alongside onsite databases. If you’re using change data capture techniques to keep both the multiple environments in sync, you'll need to maintain meticulous management of the process.
And don't forget about the business teams that can over rely on tools like Excel, Airtable, or Google Sheets to store and manage their data. These tools further the difficulty of creating a single source of truth for data in your organization.
Implementing data orchestration will ensure that your data stays centralized and in sync during the migration process and beyond.
2. You Struggle with Inefficiencies
Still on a legacy data pipeline? You’re probably struggling with inefficiencies, as legacy data orchestration systems tend to be inflexible, slow, hard to debug, and difficult to scale.
Imagine trying to access business-essential data but having to wait long periods of time for data to be extracted, transformed, combined, validated, and loaded before you can even start to conduct analysis. There are opportunity costs associated with these delays — not to mention the additional costs required to maintain and manage these legacy systems.
Even if you move parts of your data to the cloud, how do you ensure that data is extracted from all these different sources and processed efficiently? If you guessed data orchestration, you’re right.
3. You Over-Rely on “Hacked Together” Scheduling Systems
Perhaps you’ve already set up workflows to extract, transform, and deliver data according to specified schedules. These can be difficult to construct, and even when they do work, they have failures embedded into their design.
One of the major issues is that the entire design is built on scheduling assumptions. If any of the processes take longer than initially assumed (due to data volume or CPU capacity), the delay might cause disruption to the entire workflow.
On top of that, if errors occur in the upstream tasks, downstream processes may not be able to take them into account. There is no way for the different parts of the process to communicate with each other in real time. This is something that data orchestration would be able to solve.
4. You Spend Too Much Time Collecting and Processing Data
Imagine trying to find relevant data but not knowing where to look. This situation gets worse if you have multiple teams — some of which may have no knowledge of the data systems — all looking to obtain data. Someone who has the know-how would have to run all these queries.
This leads to a backlog, and when the correct data is obtained for the relevant person, it could then have to be manually transformed before any kind of analysis can take place. Data orchestration would ease the burden of the person in charge of querying data, while ensuring that the right teams have access to the right data.
5. You Need to Reduce Database Costs
Data bloat has been an issue for many years. With the vast amount of data now being collected, it is inevitable that some of it will be duplicated or useless. Holding onto this data would increase the costs related to storage as well as the time spent to scrub and clean the data.
Introducing data orchestration processes will prevent you from gathering duplicate data in the first place and improve your ability to segment, link, and route data. Not only will you avoid data bloat, but you can gain quick access to meaningful data.
6. You Discover Corrupt Data Too Late
You may have set up alerts for corrupt data. But if your data is stored in multiple warehouses, lakes, and silos, alerts can be overwhelming (if they’re coming from all these different sources) or take too long to arrive (if you’re pulling data into a central location before looking for corruptions).
In a world that’s becoming increasingly dependent on data, an issue like this could have major consequences for your business. Sidestepping this issue entirely is simple with data orchestration.
7. You Struggle to Keep Up with Compliance Requirements
As data has grown in volume and importance, governments have started looking into privacy and security issues. Some have already developed regulations to deal with these issues.
If a company does not have good control over its data, it can be a challenge to comply with regulations. Data orchestration enables data teams to maintain central control over data systems and processes.
Data Orchestration Will Be an Imperative
Every day, more and more companies are moving to the cloud or implementing hybrid data systems. Whether you’re fully on the cloud already or maintain a hybrid data system, scaling will result in data growth.
That's just a way of life now for almost every organization.
In order to stay on top of it all, the need for data orchestration is inevitable. Whether it’s sooner or later for your business, whenever the time comes, Shipyard can help.
If you’re experiencing any of these signs, perhaps it’s time for you to look into data orchestration tools today. Start by signing up for our free Developer Plan, which immediately allows you to launch, monitor, and share your workflows in a matter of minutes.
