Data Orchestration, short words, but might seem big to understand. However, no worries. Because Calculate Data is determined to make your understanding easy. So, without rambling on off-topic words, let’s see what Data Orchestration is, its purpose, parts of the data orchestration strategy and what importance it plays for companies.
What is Data Orchestration?
Data orchestration is an act of automating the process of combining, cleaning, and organizing data from multiple sources and then directing it to downriver services so different in-house teams can put it to use. Data orchestration is a way to streamline and automate data-driven practices and jobs which otherwise take extensive manual work, complicated code, and a myriad of worksheets.
What is the purpose of Data Orchestration?
Let’s see the meaning and origin of orchestration first to strengthen our understanding of data orchestration. Orchestration comes from an orchestra where a group of musicians using unique stringed instruments create a piece of music in perfect harmony. Similarly, data orchestration brings together data from different sources in one place and combines them in a perfect blend, just like different instruments blend different notes and creates harmony. Data from diverse sources maintains its uniqueness and identity but that blend makes it accessible for analysis and other purposes like strengthening sales and marketing.
Automation already known by its name automates manual tasks to increase efficiency and output with minimal human interference. In a similar fashion, data orchestration shares a similarity to automation as well. It manages and coordinates the various automated tasks that then work together in a streamlined process.
So, the sole purpose of data orchestration is to manage, coordinate and automate the data-related tasks to create a streamlined process so to make your data useful and accessible for analysis and other objectives for the growth of businesses.
What are the parts of a modern data orchestration strategy?
The core parts of a modern data orchestration strategy are
- Propensity Scoring
- Automated Bookings
Deduplication is a method of removing redundant data from a dataset which is important for the health of a data system. Data orchestration involves both batch and real-time duplicate preclusion creating a limitation that guards all of your systems against bad data. For example, deduping i.e., deduplicating new leads to counteract duplicates from creating disaster among your sales and marketing teams.
Normalization is the standardization of incoming data i.e., consistent formatting of incoming data from various platforms like web forms, list imports, manual input, etc. Normalization creates cleaner and structured data either by performing it in batches or in real-time. For example, normalizing New York to NY.
Segmenting is creating a group out of your data. Where groups are based on the qualification of certain criteria i.e., by tracking certain behaviour or profile characteristics. For example, segmenting data into distinct sales territories, scores, company size, industry and buyer personas etc. for easy territory planning & assignment.
It is a process of detecting and merging identical data records. For example, matching Leads-to-Accounts by displaying leads data with the standpoint of prospect accounts and thus running account-based marketing.
It is a process of updating data on the records through data accessible from sources like third-party data vendors. For instance, enriching vital data points on incoming leads or job titles, or company revenue and sizing to make sure that the marketing teams have the required and accurate information.
Propensity scoring is a means to predict outcomes or likelihood. For example, using any composition of data points to classify scoring models for leads and accounts. Based on the scoring model, higher scores mean there is a high likelihood of a Lead qualifying for an opportunity. Also, lead and account scores advise and help the sales and marketing teams to focus on the right prospects.
It is a mission-critical process to forward or move the data to the appropriate place, at the right time. For example, routing leads, contact and accounts and other CRM items automatically, to the right time and place.
Automated booking meetings
Booking meetings spontaneously to transform incompetent leads into qualified leads and thus initiating dialogues and accelerating the revenue engine
Why do companies need data orchestration?
Data orchestration is the best possibility for companies running multiple data systems to avoid any massive migrations or extra storage locations for data, which can end up in another data silo. So, we can see data orchestration is a way to enhance organizational competency. Isn’t it? For us, it seems very cliché to say that. Because data orchestration does more than this. Let’s see why the organization needs data orchestration.
- Cost Reduction
- Data privacy laws compliance
- Ensuring Data Governance
- Removing Data bottlenecks
Data Orchestration plays a role in Cost Reduction
Cost reduction and maximizing profit have always been every company’s goals. So, what data orchestration offers to them is to reduce the cost of paying teams who process data manually by just simply sorting data from different warehouses, preparing, transforming and enriching data and syncing data meanwhile. All this much more rapidly and flawlessly as compared to paying teams. Then why not to channelize the savings into other intuitive plans to bring advances in businesses instead of spending on wage bills?
Data privacy laws compliance
Failure to determine the ethical gathering of data and not being able to properly organize the datasets can run your business into serious problems. For example, there can be chances that data collection might cease and previous data might get deleted from data systems. So, the concern of a company on meeting its responsibilities arises. Especially, if a company has no idea about where the specific data is coming from and is storing in the first place.
So, data orchestration gives insight into where specific data is obtained and where all datasets are stored. Data orchestration functions to organize the data sets that organization collects and helps them to remain in compliance with data privacy laws like the GDPR and CCPA.
Ensuring data governance
Governance means directing and controlling an organization. And data governance is regulating, supervising, controlling, and evaluating data use in an organization by setting up data usage standards and policies. And for an organization, the requirement of data governance in clear terms makes it very challenging.
So, here what the data orchestration tool does is enforce a governance plan on the organization’s data strategy framework and data pipeline (stretched across multiple platforms) by integrating all the platforms. Data orchestration ensures that data collection aligns with the plan by quarantining and verifying the data sources that do not match the plan. This greatly enhances the trust in data for the implementation of data analytics.
Removing data bottlenecks
A data bottleneck is simply a broken point in your data pipeline which bring the flow of data to halt in your organization and is usually due to a rundown in data handling capacity and heavy traffic request. For example, spending too much time on data gathering for data analysis and then further processing it.
Thus, implementing data orchestration means automating data sorting, preparation, and organization, and transforming complex data into workable formats. This considerably lessens the time spent on data gathering and preparation by teams who are doing it manually. Thus, it simply allows you to dig into data analysis quickly and easily.
What are the challenges of Data Orchestration?
- Data orchestration tool Compatibility Issues due to heterogeneous architecture
Data orchestration involves merging and standardizing data from multiple sources. And make the merged and standardized data compatible with certain data processing or analysis tools. But this story isn’t that simple.
Different data sources include not only different database platforms but also complete cloud configurations i.e., public, private or hybrid, and infrastructures such as SaaS, PaaS, IaaS and many others too. And not to forget that every platform has unique or limited features and competencies. This implies the usage of data orchestration tools multiple times, to manage data handling across these various platforms. Thus, these processes only to bring compatibility for a specific data processing or analysis tool can be tedious and time-consuming.
- Inefficient Real-Time Data Awareness
There is inefficient streamlined data awareness i.e., referring to the prominence of data storage. The fact that there is an enormous amount of data demanding proper organization and classification is ever-growing and is going to expand with every single passing day.
Putting it to an end, data orchestration is all about increasing the usefulness of data. It organizes the data, reduces human error and costs and in a meantime, ensures data usage compliance with data privacy laws and maintains data governance in accordance with the company’s terms.
Most companies are leaving fragmented and siloed data with only heaping up manual administration. So, for such companies, data orchestration plays a prime role in relieving such challenges of manual administration. It allows companies to dig deep down into their data and enhance their sales and marketing growth.