If knowledge is power, then data orchestration is the path to power.
So what is Data Workflow Orchestration?
Data workflow orchestration is an automated process in which software programmatically combines and organizes data from multiple storage locations and distributed systems to prepare that data for analysis.
It can help your business handle the onslaught of data that’s bogging down your data analysis and bust through the silos preventing you from getting a full picture of what your data is telling you—all with the goal of supporting data-driven decisions to accelerate your business’ growth and profitability.
Meaningful Benefits of Orchestration
Data workflow orchestration offers meaningful benefits to any size business that’s interconnected to multiple data sources. It can make previously inaccessible information available quickly and on demand to fuel the work of marketing, sales, product development, finance, operations, your C-suite and more. And, for some businesses, it can even be a competitive differentiator, enabling you to provide clients with holistic insights or data.
If data is the new oil, as British data scientist Clive Humby famously asserted in 2006, then data workflow orchestration is the refining process that coverts crude data into actionable information—saving businesses time and money, and, most importantly, enabling smarter, data-driven decisions to accelerate growth and profitability.
At Softjourn, we’re exploring data workflow orchestration and have created a proof of concept with the goal of helping our clients effectively manage the crush of data they currently handle and the tsunami of data that’s surely in their future, and break down the data silos that obscure what their data is telling them.
Although we’re early into our exploration of data workflow orchestration, we believe it will become a core component of a modern data stack because it simplifies interconnected workloads and mitigates duplicative tasks and processes. In this article, we share what we’ve learned about data workflow orchestration, including an explanation of what it is and how it works, why it’s needed and how businesses can benefit from turning raw data into a business intelligence asset. We’ve also included an overview of our data workflow orchestration proof of concept project.
Data Workflow Orchestration and How It Works
There are many definitions of data workflow orchestration, so we analyzed a number and created our own: Data workflow orchestration is an automated process in which software programmatically combines and organizes data from multiple storage locations and distributed systems to prepare that data for analysis.
Data workflow orchestration is a relatively new concept, introduced as an engineering term only in 2017. It grew out of data automation, but whereas data automation involves managing a single task, data workflow orchestration manages a collection of automated tasks, reflecting the increased volume and complexity of data. In the past “cron jobs” orchestrated data, but now developers can use data workflow orchestration frameworks to programmatically author, schedule and monitor data pipelines.
Three Key Steps in Data Workflow Orchestration
- Organize, collect and prepare data. The first step is all about cleaning up the data mess. You need to know where your data exists, so your data workflow orchestration tools can access it. This includes legacy systems, data warehouses or lakes, and cloud-based systems. It also includes identifying new incoming data.
- Transform or unify the data. This step involves converting all the data you’ve identified into a standard format. It also may include cleanup activities, such as deduping, documenting and reporting on data.
- Activate the data. The last step is the most exciting because it involves making available the standardized data (typically on demand) to the analytic tools and applications your company uses to drive intelligent decision-making.
Data workflow orchestration works by defining basic tasks within a data system and running a direct acyclic graph or DAG that organizes relevant tasks to reveal their relationships to one another, including their dependencies. Automation through code defines the structure of the tasks in terms of linear “Of-Then-Else” workflows, such as sequence, time between tasks or conditional task execution.
It’s important to recognize that data workflow orchestration isn’t another storage system. Rather, the software that executes data workflow orchestration connects storage systems together so data analysis tools can access the storage systems as needed.
If you’re looking for concrete examples of the types of processes data workflow orchestration can power, Astasia Myers in her blog, Data orchestration—A primer, identifies several:
- Cleansing, organizing and publishing data into a data warehouse
- Computing business metrics
- Applying rules to target and engage users through email campaigns
- Maintaining data infrastructure like database scrapes
- Running a TensorFlow task to train a machine-learning model
Now that we’ve established what data workflow orchestration is, how it works and some examples of how it can be deployed, let’s look at why it’s needed.
Data Creep and Demon Data Silos
The top drivers for data workflow orchestration are the volume of data businesses now deal with and the historic problem of siloed data, i.e., data trapped “in a single location, organization or application without an easy or clear way to access and use it.”
Look for data to double between 2021 and 2024.
Seventy-four zettabytes of data will be created, copied, captured or consumed worldwide in 2021, according to statistics aggregator and analysis firm Statistica, a number that’s expected to double by 2024.
Volume as a driver is self-explanatory. The volume of data businesses handle is expanding exponentially. The more data you have, the greater the challenge to organize and transform it to extract meaningful business intelligence. And one thing we know: The volume of data businesses handle today is a fraction of what they’ll be expected to handle in the future.
Data silos limit your ability to get the most value from your data.
“Your organization needs to deploy high-performance computation solutions that can draw from your collected data without tripping over opaque systems and unavailable information.”
But data silos present an even bigger and potentially more costly problem. In his excellent Harvard Business Review article on the cost of using data, Edd Wilder-James, asserts that 80% of the work involved in using data is acquiring and preparing it. He goes on to say that data silos can drive up that work effort beyond 80%. And, chillingly, Wilder-James adds that data silos often make initiatives impossible, because these “isolated islands of data … make it prohibitively costly to extract data and put it to other uses.” He cites four reasons for data silos—structural, political, growth and vendor lock-in. If you’re interested in learning more about how data silos arise, please read his full article.
Data scattered in silos is, at best, underutilized data, but at worst it can lead to less than optimal or even wrong decisions because decision-makers don’t have a full view of what their data is telling them.
Let’s move on to the benefits of data workflow orchestration.
Data Workflow Orchestration as a Core Component of the Modern Banking Stack
By providing solutions for businesses to get their arms around big data and eliminate data silos, data workflow orchestration paves the way for complex businesses to run more smoothly, which is the compelling reason it will become a core component of the modern banking stack.
Getting more granular, data workflow orchestration can help businesses:
- Manage data by eliminating data fragmentation, tech integrations and the need to migrate data from one location to another to end data silos.
- Move to the cloud by addressing issues relating to cloud configuration and infrastructures.
- Save time and money by eliminating manual coding to maintain hundreds or thousands of apps and servers, including connectors between systems to link data pipelines.
- Comply with regulations and data governance and privacy law requirements by organizing data to retrieve it across silos and prove it was collected ethically.
- Reduce bottlenecks by making data quickly accessible for internal use or by business clients.
- Reduce data transformation errors by automating the data standardization process.
The cumulative result is better decision-making, driven by fast access to data that isn’t obscured by data silos or non-standardized formats, or lost because of the sheer volume to be analyzed.
Who’s It For?
The need for data workflow orchestration cuts across business verticals. And it’s not just for mega businesses. Any business interconnected to multiple data sources can benefit from employing data workflow orchestration to extract insight from its data based on a holistic view.
Smaller businesses, in fact, may be advantaged in implementing data workflow orchestration because their number of interconnected data sources isn’t as large as their bigger counterparts, and they’re likely to be better positioned to identify where to start their data workflow orchestration journey.
If your decision making or ability to comply with laws/regulations is compromised by the volume of data you’re collecting or by data silos, or if you’re unable to serve your clients fully because you can’t access their data quickly and holistically, your business is a candidate for data workflow orchestration–regardless of your business category or the size of your operation.
How to Start With Data Orchestration
Based on our initial work, we recommend a phased approach to data workflow orchestration. Look for a potential early win in your business that will serve as a strong example of data workflow orchestration benefits, paving the way for increased investment and a fuller rollout.
Edd Wilder-James, who we quoted earlier, suggests this approach to get started:
“… identify high-value opportunities. Analyze your business needs and choose a problem where data could provide a tangible benefit, perhaps in enhancing sales or preemptive incident response. Draw in the data from around the organization and invest in these use cases first.”
He further recommends moving forward with each progressive step building toward an integrated platform. And he cautions that successful data workflow orchestration requires executive support from the top, as it will affect many areas of your organization and will require cross-functional cooperation.
From the outset, it’s smart to be realistic about how quickly you can achieve full data workflow orchestration integration. It’s clear that full integration is a long-term process that must be carefully planned and executed. Quick wins are certainly possible, but it’s important to remember that the only way to eat a whale is one bite at a time.
Buy or Build?
As always, a key question is whether to build your own data workflow orchestration solution or buy it. Unless you have data engineers to spare over an extended period, we believe you’ll need to explore buying or licensing your solution. There are excellent data workflow orchestration packages available, but buying off the shelf typically means compromising on customization and features unique to your operation. Do you really want to invest in data workflow orchestration and not get a solution that meets 100% of your needs now and in the future?
Although compromise may be acceptable for some organizations, if you’re interested in achieving the greatest value from your investment and full control over the rollout—including timing and applications—we suggest a custom approach. And this is where Softjourn can help.
If this article has inspired you to learn more about data workflow orchestration and its potential to convert your data into business intelligence, we invite you to contact us. Whether you’re a current Softjourn client or a business just looking to brainstorm, we’re happy to share our thoughts—without pressure or obligation—to see if we can help you take the first bite of the whale.
Softjourn’s Data Workflow Orchestration Proof of Concept
At Softjourn, we’re great believers that the best way to become truly knowledgeable in an area is to get your hands dirty. And, for a bunch of data scientists and engineers, this means getting busy coding to create a proof of concept. This approach is a great teaching tools and helps us evaluate the feasibility of projects from different angles—with no downside or exposure. And the learnings—including the pitfalls—are invaluable.
The starting point to launching a useful proof of concept is having a strong use case. In our desire to create a POC for data workflow orchestration, we focused on a use case for automated invoicing and processing accounts receivable and payable. From our ongoing work, we’re familiar with the challenges these companies face in terms of the number of large number of data sources they’re connected with bidirectionally and the absence of standardization of the large volume of data they handle. We also know they have zero margin for error in their work.
The business requirement “musts” we established for our data workflow orchestration proof of concept solution for our hypothetical company were:
- Under the complete control of the company
- Efficient and scalable
- Able to accept invoices from many sources into the system and export invoices to the AP systems to pay hundreds of thousands of invoices [daily/weekly/monthly?]
- No manual development or manual data configuration to connect to third parties with which data would be exchanged
- Flexible workflow
To ensure our proof of concept was realistic, our simulated data was not standardized, so we needed to transform it before it entered the workflow.
By iterating our approach several times, we were able to create a data workflow orchestration solution that achieved the goals we established for our hypothetical company and demonstrated tangible value.
Please contact us to learn more about our proof of concept. And, we’re eager to learn of your needs for data workflow orchestration solutions to support your company and its goals.
Conclusion
The world of data is growing more complex every day. Your clients and customers are telling you a lot about themselves, which is great news for informing your business’s next steps. But if you’re not orchestrating your data into usable information, you’re leaving valuable context on the floor. And that is affecting your potential growth and profitability.
The hard truth is that the ability to collect data has far outstripped the ability of many businesses to use their data effectively.
Data workflow orchestration is today’s answer to transforming big data into big information. It’s the next frontier for data, and we’re looking forward to making this journey with you.
Interested in Learning More?
At Softjourn, we’ve recently completed a data workflow orchestration proof of concept, because we believe many clients can benefit by unlocking the full value of their data.
To learn more about data workflow orchestration, including our proof of concept, download our white paper, or contact us.
We’re happy to explore—without pressure or commitment—how data work orchestration can unlock the power of your businesses’ data.
