Thanks for joining us for this three-part blog series on data orchestration. We will be covering the following topics throughout this series:
- What is data orchestration and how to get started
- The four parts of data orchestration
- Data orchestration and Extract, Load and Transform (ELT)
- Why is data orchestration necessary?
2. What is the connection between GDPR and data orchestration?
3. How can data orchestration benefit your firm?
- When do firms need data orchestration?
- The pain points eliminated with data orchestration
Please contact us if there are any other areas you would like more information on – we look forward to hearing from you.
So let’s get started:
What is data orchestration and how to get started?
Data orchestration is the process of organizing different data silos from multiple locations and sources so they are in a suitable format for data analysis tools. Once the data is in an analysis tool, the data can be examined to help make better and more informed decisions.
In the old days (pre-2010), you would have needed a developer to write custom programs to extract the sources and link them together to achieve this kind of data analysis. Now with data orchestration, all you need is software that connects storage systems, making this a more straightforward and cheaper option.
However, according to CBI’s Tech Tracker 2021 report, firms now have so much data they don’t know what to do with it! Also, according to a report by Validity, respondents believe their firms lose over 10% of annual revenue due to poor data quality.
These are shocking statistics considering how easy it is to use data orchestration methods to allow firms to gain insights and make informed decisions that drive profitability.
The four parts of data orchestration
The four parts of data orchestration are data preparation, transformation, cleansing, and synchronization.
Let’s look into each of these in a bit more detail:
Data preparation
Data preparation is checking your data to ensure it is accurate, adding in any required labels, or deciding where it needs to sit. It may also include using third-party tools to provide any data that might be missing.
Data transformation
Data transformation is determining and implementing a standard format for the data to be presented in.
Data cleansing
Data cleansing is cleaning the data, i.e., removing duplicates, incorrect data, etc.
Data synchronisation
Data synchronization is constantly updating the data between all the data sources.
Each of these four steps needs to be completed for effective data orchestration.
Data orchestration and Extract, Load and Transform (ELT)
Data orchestration and ELT work hand in hand.
ETL stands for Extract, Load, Transform and is a set of processes a firm will use to move data from the necessary sources into the database of their choice. This is a process automated by data orchestration, requiring little human intervention.
Later in the blog series, we will discuss how important data orchestration is for your firm.
Final thought
After reading this blog, you will understand what data orchestration is and the high-level steps required to start. In our next blog, we will talk about the impact of GDPR on data orchestration.
About LeadScale
At LeadScale, behind all data transferred to your CRM, a team of experts verifies every lead that comes through. What makes us the uncommon partner for your firm is that we are more than a software. We are the people behind it, making everything work perfectly for your firm to thrive.
Contact our team to see how you can make the most of your Marketing budget with LeadScale.