What is Data Orchestration?
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What is Data Orchestration?
Data management is essential to a well-run organisation. Decision makers want the right data at the right time. Yet many organisations have pools of data siloed in disparate systems and sources. Data orchestration allows them to change that and create an environment where data can flow efficiently across an organisation. In this article on data orchestration, we will explain what data orchestration is, why it is important, what to look out for and why you may want to utilise some powerful tools to help you.
What is data orchestration?
Data orchestration is the process of identifying and gathering siloed data from different systems and sources across an organisation into a centralised data repository where it can be organised into a consistent and usable format.
This is important to ensure that there is an efficient flow of data between tools and systems, enabling organisations to operate with complete, accurate, and up-to-date information. Data orchestration is about ensuring that the right data is in the right place at the right time. In this way, data orchestration enables organisations to gather siloes of data and combine them to create rich insights to support agile decision-making and drive business value.
How data orchestration works
Data orchestration occurs in three distinct phases: organisation, transformation, and activation.
- Data organisation – The first step is to identify where data enters the organisation and where it is stored. Data can enter an organisation through a variety of sources, such as, for example, your CRM or social media feeds, and it will be stored in different tools and systems across the organisation. There are a wide range of places that store data. These are called data repositories and can include legacy systems, cloud-based tools, data warehouses, data lakes, data marts and data cubes. The task during data organisation is to collect and organise the data from each of these different sources to transform it into the correct format for its target destination.
- Data transformation - Data comes in various formats. It can be structured (in a consistent, pre-defined format), unstructured (consisting of different data types in their native formats), or semi-structured i.e., with elements of both. And there may also be inconsistencies with for example, naming conventions, where one system might collect and store a date as 01 January 2024, a different system might use January 01 2024, and another might store it as 01012024. The aim of this phase is to convert the data into a consistent, structured format that, from thereon, will be the organisation’s standard data format. Once this has been achieved, it can be activated.
- Data activation – This is when cleaned, consolidated data can be sent to various tools for immediate use. The data may be used to update a business intelligence dashboard for analytics and business intelligence tools such as Microsoft BI or Tableau in a CRM system or customer support platforms, such as chatbot or live chat. The options are endless. What’s important is that the data is structured in a consistent, pre-defined format throughout that organisation.
Reasons to use data orchestration
There are several reasons why data orchestration is important. Data orchestration can eliminate your data silos, ensure that your data workflows are automated, and create better quality data to make data analysis easier and drive faster data insights whilst freeing data across an organisation and addressing the importance of data governance and compliance. Let’s look at each of these benefits in more detail now.
Eliminating data silos – Resolving siloed data can be more complex than you might imagine. Data siloes often grow organically with an organisation as it scales. Resolving data siloes is rarely straightforward. Data orchestration enables the organisation to centralise the data and operate without siloes or manual migration.
Automating data workflows – Data has become such a large part of our lives that every organisation is effectively a data company. With an increasing number of data pipelines, managing data manually is no longer a realistic option. Which is where automation kicks in. Automated data workflows enable data to be usable more quickly, meaning that specialists such as data engineers can be left to focus on high-value tasks for the organisation.
Improved data analysis
One of the biggest benefits of data orchestration is that it replaces inaccessible, inconsistent data with well-structured data that is organised and usable in real-time. This enables data analysts to deliver business-critical insights quickly without the data being stuck in bottlenecks or requiring manual intervention.
Faster time-to-insights – One direct result of improved data analysis is that data bottlenecks and the need for manual data preparation are removed, enabling analysts to extract and activate data in real time.
Unlocking data across business domains – The challenge with data silos is that the data is generally out of bounds or tricky to access for outsiders. This means that centralised teams, or those with wider remits, are unlikely to be able to access data that is effectively siloed. Data orchestration breaks down those barriers, giving your data team free rein and, therefore, greater visibility of all the data across the entire organisation.
Improved data governance – Data governance is difficult when data is held in disparate parts of an organisation, or when they are uncertain exactly what data is held where. It opens the organisation up to vulnerabilities, such as offering insufficient protection to sensitive or personally identifiable information. The process of data orchestration aids data governance because it centralises various disparate data sources and provides full transparency over how all data is managed.
Compliance with data privacy laws and regulations – Data privacy laws like the GDPR provide strict guidelines around data collection, use, and storage. It also offers consumers the opportunity to opt in and out of data collection or request that your company delete all their personal data. To do this, the organisation must know what data is stored, who can access it and how it is being managed. This is only possible if the organisation has an understanding of their entire data circle and a robust data management plan.
Removing data bottlenecks – Bottlenecks are an ongoing challenge that can be fully resolved with data orchestration. Without it, you are reliant on manual processes taking place across multiple storage systems in an organisation that inevitably has multiple data requests and priorities. This means that there is a time-lag between when teams need the data and when they receive the data, which can make the data insights outdated by the time that they are analysed. Data orchestration eliminates this kind of stop-start process because your data would be delivered to downstream tools for activation in a structured, consistent format.
Common challenges of Data Orchestration
Data orchestration isn’t easy. There are some common pitfalls. Let’s take a look at some of these now.
Data silos - A data silos is when a repository of data is closed off and used exclusively by one area of that organisation. Data silos are common in organisations of all sizes. As tech stacks evolve and customer ownership is split between multiple teams, it is easy for data to become siloed between different tools and systems. Breaking down these silos and standardising the data can be complicated, but it is essential in the process of the data orchestration.
Data quality – Data quality is always a concern when consolidating data from disparate sources. Siloed data creates an easy environment for data inaccuracies. Different teams may have adopted different naming conventions for the similar data sets, leading to duplicates. For these reasons, Data cleaning is an essential part of the data transformation process.
Compatibility – It is important to ensure that the solutions you are choosing are capable of integrating with every data repository within your organisation. Without full compatibility, you will create gaps in your technical infrastructure and be unable to realise the system’s potential.
Data integration - Connecting different tools and systems can be an arduous process if done manually, however in most cases it can be done using automated systems with pre-built integrations for data warehouses, marketing automation and business intelligence tools.
Popular data orchestration tools
In a world where organisations use data to drive decisions, data management has never been so important. In fact, in most sectors it can provide a distinct competitive advantage. Done well, and the right data is automatically available throughout the organisation, without manual intervention when the decision maker needs it.
Let’s take a look at the different types of data orchestration tools that are available on the market:
- Data integration tools – These tools do two important jobs here. The first is to move data from one system to another. The second is to ensure that the data is accurate and consistent. Data integration tools often use Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) processes to perform these tasks.
- Data pipeline management tools – These tools are designed to support the flow of data throughout the entire process, from data ingestion to data processing and data storage. It is important that these tools have excellent scheduling and monitoring functionality so that the data is processed and moved through the pipeline efficiently.
- Data scheduling and workflow management tools – As the name suggests, these tools support the scheduling and execution of different data processing tasks. Here it is important that the user can define workflows and dependencies between different tasks whilst also monitoring and managing the progress of each workflow.
- Data governance and metadata management tools – Data governance is the process of ensuring that data across an organisation is secure, accurate and usable. It is there to unlock the potential of your data. These tools are designed to manage and oversee important areas such as data lineage, data quality, and data catalogs.
FAQs
What is a data repository?
A data repository is a place where data can be stored. It may be in the form of one or more large database systems, responsible for collecting, managing, and storing specific data sets for data analysis, sharing, and reporting. Data repositories are accessed by authorised users to retrieve data by using query and search tools, aiding research or decision-making. Often referred to as a data library or data archive, they are particularly useful when combined with data from different sources such as databases, apps, and external systems, to provide a complete perspective or unified view.
What are data silos?
A data silo is a repository of data that is closed off and isolated from the rest of an organisation so that it can be used exclusively by one area of that organisation. It’s easy for organisations to end up with data silos if they don’t have a well-planned data management strategy. Think of grain stored in a farm silo, closed off from outside elements and controlled by the farmer. The grain is stored for a specific and deliberate purpose on an area of land that suits the farmer’s needs. That’s the same for data silos which often occur naturally in large organisations where a separate business function is operating independently, with their own IT objectives, priorities, and resources.
What is a data management strategy?
A data management strategy is an organisation’s long-term plan for how it will use data to achieve its goals. Taking into account future growth or diversification plans, a well-constructed data management strategy will be reviewed and revised periodically to ensure that data is used efficiently and effectively across the entire organisation for now and in the future.
What format can data come in?
Data can come in various formats. It can be structured, unstructured, or semi-structured. Structured data is stored in a predefined, highly specific format. Unstructured data is composed of different data types that are each stored in their native formats. Semi-structured data has elements of both.
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