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The Four Steps to Conquer Data Consolidation and Orchestration

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Insights are everything and conceptualizing the rapid change in technology as well as social patterns due to increasing connectivity and smart automation, are what the fourth industrial revolution is all about. It is how technology is changing the way we do things, but, the problem companies face is not the lack of data, they have tons of data, it’s the ability to create a unified data product out of it. 

‘We are surrounded by data – but starved for insights’’

Jay Baer

Accurate insights, however, can only come when quality data is derived and it is here where we can begin to understand the importance of what is data consolidation, what is data orchestration as well as the challenges we face in the attempt to become more data-centric.

Traditional industries need to go forward with data technologies, and to do so, begins with the consolidation of data from multiple silos into a centralized warehouse from which value can be derived. Legacy processes are rampant across the industry and issues arise due to the fact that most of them are working independently and not as synchronized data machines. Ideologies tend to differ between headquarters and the satellite units themselves, and this is where the main challenges come to fruition. With different sectors all working with different data systems, the result is different levels of data quality, different data representations & different production units.

Similar to Excel sheets teeming with needed manual processes, without using updated data consolidation tools and methods all processes are slowed down and their operational accuracy cannot be guaranteed.  The focus of Industry 4.0 is a long-term improvement in production and the number one constraint is attempting to overcome and update these differing processes without halting the entire company from working. 

The most effective way to upgrade legacy processes.


  1. Machine metrics for efficiency and predictive failure analysis
  2. Production information consolidation for better efficiency, quality, and optimization
  3. A regulatory need for harnessing integrated data

What is Data Consolidation?

Data consolidation is the collecting and joining of data from multiple sources into one destination to be utilized and analyzed – this is where all processes start. 

The root challenge when consolidating data is the sheer scale of operations. Today, these all-encompassing operations result in data being delivered with its own unique features and different rates of updating making data consolidation costly and difficult to standardize. 

In our world of big data, where every organization is now “data-driven”, or becoming data-driven, being able to manage and orchestrate your data becomes a real challenge. 

Regardless of which industry you are in, or the type of data being collected – the data is irrelevant unless it is understood and that is where the process of data pipelines in their entirety comes into play. 

What is Data Orchestration?

Data orchestration begins with defining the business questions, what dashboards and reports are required internally? Once this is determined, then the most suitable orchestration method to guarantee the timely and accurate delivery of the answers can be chosen. 

However, with everyone rushing to get to the end goal, random methods utilizing different systems and processes are considered and implemented leading to solution limitations and costly processes that are difficult to integrate together. This results in teams drowning under the number of solutions they now need to maintain.

Aggregating data into a cumulative representation is the next issue to overcome and this is accomplished with proper data normalization. Data normalization is the process of organizing data, ensuring it appears in a similar fashion across all required records and fields. A common approach to normalizing data sources is to do so at the source, but, this is not practical. The recommended approach is the usage of normalization tables to translate and align the different sources into a unified representation. At Datorios, we allow normalization with pre-joins, as part of the pipeline, dramatically reducing resources and warehouse costs.

What is Data Quality?

Data quality is the cleaning, validating, and adjusting of data by ensuring it is all in alignment so that it may be used for the right decision-making. When the wrong data is used for decision making, wrong decisions are made and this leads to money lost, if not even bigger issues.  It is one of the most important but also one of the hardest things to do with your data. Different sources may have different ways to represent the same things and a good example of this is with time stamp data. Every data supplier or data source can use different time formats and time zones that can easily lead to the wrong aggregation of data. 

For example counting the wrong events for a specific hour, an example that leads to wrong results and is the epitome of bad quality data. Another example of increasing data quality is ridding it of duplications which can have a devastating effect on the data and insights it reveals. Duplications need to be handled before things get out of hand, they are costly and can result in dirty data. By using Datorios, companies are now able to deal with duplicates on the fly, near the data source, and without the costly load & extraction of data to databases. 

At this point, data needs to be aggregated to create needed dashboards and reports for decision-making. This is probably one of the most difficult steps, but it can be made easy by implementing the aforementioned strategies. 

Four easy steps for data orchestration and consolidation:

1. Simplify and Speed Up Your Connectivity and Extraction 

The problem with having too much data is that it is only growing. The first step of a data consolidation strategy is to simplify the extraction of data sets in their entirety. With endless connectivity options, ready-made transformers can be combined with the development of AdHoc data clients to extract the right information, and automatically send it to a unified data target. This guarantees the delivery of real-time and accurate data – without headaches. Next, it is important to ensure that only the relevant production units are sending data.  

It is suggested to not approach legacy data systems as a whole, don’t try to change them in their entirety just ensure they are sending needed data when required. With the right combination of connectors and data only being sent from required production units at the right time, connectivity and extraction are simplified – easing the process and guaranteeing a smooth working machine. 

2. Implement Easy Data Cleaning & Real-time Data Normalizing

Each data source has very specific properties and issues arise when you consider that all of this needs to be synced. This makes data cleaning and data normalizing a very difficult process because you can’t stop production just to adjust all systems into the same format. The process is seemingly endless and costs too much making it not very practical. 

The best way to clean and normalize data is to use pre-built mapping functions or catalog tables that take RAW data from different units and normalizes it into a common structure. It is the process of picking the needed data sets from each source and making sure they all “speak” the same language. Cleaning your data by ridding it of duplications from the get-go saves time and money. 

No matter the language, time stamps, or format, prepare data by applying pre-made transformations to make instant data changes – many of which can occur before it is even loaded into your system.

3. Guarantee Quality and Validation

Now that all the data is aligned it needs to be checked with our unified quality criteria. To guarantee full control of the quality, it is vital to deal with data quality as closely as possible to the data source. The challenging part of the data orchestration process is deciding what to do with bad data, whether to lose it to reduce the overall data amounts, correct it so you don’t miss any bit of data, or redirect it to your organizational error control center.  Planning this step as early as possible will guarantee the validity of data sets and ensure the quality of your data as a whole. 

4. Ensure Aggregation and Understandability at Every Stage

By joining and aggregating all normalized data you can create the basic data set needed to develop your wanted answers. It is a challenging data task that can be made “easy” by applying the previous steps by reducing the overall amount of data to be processed, increasing visibility across assets, and applying the right pre-processing solutions. Implementing proper aggregation at every stage of the data orchestration process guarantees the accuracy of end-user reports and ensures that business decisions are being made on high-quality, accurate data with the most up-to-date business insights for that competitive edge everyone is hoping for. 

Answers Without Explanations are Useless 

Throughout all of these processes, visibility is of the utmost importance. Data observability allows for a better understanding of all processes and allows for data analysts to better explain their answers. Proper visibility provides a means to find errors in your pipeline greatly assisting in debugging efforts and maintenance activities.

By utilizing these principles and applying transformations before loading, organizations can use fewer products, have less overall data to analyze, increase their data visibility while enjoying reduced maintenance and debugging times, today, and as their pipelines grow.  

Utilize these four steps to conquer your data consolidation and orchestration before they get out of hand and use the Datorios Debugger to streamline your processes now to tackle the beast of data before it gets out of hand.


  • What are some common challenges with data consolidation and orchestration? The main issue when it comes to both data consolidation and orchestration is the sheer amount of data we now need to handle on a daily basis. In an attempt to overcome this, teams begin implementing random methods and utilizing different systems and processes – quickly leading to solution limitations and costly processes that are difficult to integrate together. In the attempt to quickly find suitable solutions, numerous processes are undertaken leaving teams drowning under what they now need to maintain.
  • What are examples of data consolidation? Solutions that standardize the unique features and varying delivery rates of data sets are examples of data consolidation.
  • What are the key concepts of data consolidation? The key concepts of data consolidation are simplifying and speeding up connections and extraction activities, easy cleaning and real-time data normalizing, and increased data quality.

Modified on April 17th, 2023

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