The advent of the big data age had companies scrambling to not only collect more data but automate processes to help manage the workload. Businesses were learning in real-time – through trial and error – how to collect, store, clean, and analyze heaps of information.

More than a decade later, tech advancements haven’t slowed down. There are more tools to use, more skills to learn, and, according to scientists, about 295 billion gigabytes of data in the world. How do businesses extract precise insights that can guide their marketing strategy and boost ROI? In other words, how do we put all this data to good use – and prove it?

Needless to say, this process was challenging enough, and many marketers, organizations, and data scientists are still wondering whether their efforts to utilize data are making a real impact.

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But as the dust settles, many businesses have landed on their feet, wiser and better equipped to thrive. Now that we’ve gotten a handle on data automation 101, we’re beginning to see an even greater level of advancement on the horizon – and that’s data orchestration.

What Is Data Orchestration?

In a nutshell, orchestrating data works just like orchestrating a symphony – all instruments must be in tune, in rhythm, and working in unison with all other parts. When this falls into place, being a data-driven business is much easier. But when data processes are not in harmony, it can lead to all sorts of kinks throughout your data pipeline.

From preparing data to analyzing, drawing conclusions, and taking action, your data may travel through various applications and departments. So what happens when the right synthesis doesn’t take place?

Disorganized Data (The Problem)

One of the biggest issues that can arise from a lack of data orchestration is unusable data. Whether it’s poor quality, inaccurate, or not in the correct format to use, this is the dreaded ‘dirty data’ problem that thwarts many companies. The impacts of disorganized data are surprisingly weighty. According to an Experian report, companies from around the world feel that an average of 26% of their data is dirty.

 

Another problem arises when data history can’t be tracked.

The provenance of data products generated by complex transformations, such as data orchestration workflows, can be extremely valuable to digital businesses. From it, one can determine the quality of the data based on its source, provide attribution of data sources, and track back sources of errors and iterations. Data provenance is also essential to organizations that need to drill down to the source of data in a data warehouse, track the creation of intellectual property and provide an audit trail for regulatory purposes.” – Chris Scalgione

Part of eliminating data silos is eliminating disparate tools that are difficult to use in tandem. Ideally, teams will have access to the same data and know how to use the same platforms to manage it holistically.

Cohesive Data Management (The Solution)

So what can a business do if it’s wading through the swamps of a data disaster? Data orchestration means carefully mastering each interaction with data from start to finish.

Let’s start at the beginning with data collection. Customers are interacting with your brand at many touchpoints – advertisements, websites, social channels, and, perhaps, in person. Each of these data sources can provide a wealth of insight – if you’re equipped to collect it in real-time.

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Next, data integration. Coming from various sources and in various formats, your data needs to be merged in one place. Once data is profiled, you can decide how it needs to change to create one uniform data set. This step is integral for accurate interpretation later on.

From here, data-enrichment processes can increase the quality of data, readying it for analysis and, eventually, decision-making. Enrichment allows for a better understanding of customers and what they respond to over time.

The tools and platforms you choose play a pivotal role in ensuring each of these processes is carried out efficiently. Currently, many data platforms have one or two strengths they specialize in, but can’t orchestrate the whole process. For this reason, many organizations must manually examine their data pipeline to find weak spots and refine the procedures.

The Future Is Orchestrated

In the end, automation is only as valuable as the tools you use to carry it out. High data quality and careful coordination must come first. The places we mine data and the uses we find for it are multiplying, along with data privacy regulations. Thus, it’s more important than ever to have a sophisticated data management system that leaves nothing to chance.

In the next ten years, more businesses will begin their trek toward a data-driven future. It’s through this transformation that companies leading the pack will be able to scale comfortably and make truly data-informed decisions.