Data Consolidation – Everything You Need to Know 

In this jungle we call ‘market’, businesses are competing with one another to get a larger portion of the pie. We’re introduced to new products and services every day. Behind these products usually comes a lot of research, testing, and promotion efforts.

To stay relevant and draft the best course of action, businesses need to rely heavily on data. Data that are generated by many platforms and tools and is usually stored in many different places. So, as data is becoming more spread out and difficult to manage, we need to find ways to handle them more easily. Data consolidation is here to help us do exactly that.

What is data consolidation in layman’s terms

The definition of data consolidation refers to the process of taking data from multiple sources (within or outside your organization) and combining them all together in a sensible manner.

Often, data consolidation is mistakenly referred to as data integration. Although you do have to perform data integration in order to consolidate data, data consolidation is not only that.

Data integration is the process of migrating data from one digital place to another. This could be a data warehouse, a database, or even between spreadsheets.

Data consolidation, on the other hand, aims to integrate and combine data from multiple sources in a single place in order to provide a unified view and enable analytics.

Why is data consolidation important for your project?

Data consolidation is maybe one of the most important processes within a data-driven organization. Various systems, multiple databases, plenty of departments, and dozens of 3rd party vendors contain data about your business and customers. 

While this data provides value to the respective parties, only when you combine all of the information together and analyze it, you can make the most out of it. Only then will you be able to have a better view of what’s going on and how everything is connected together.

Data in different departments mean that besides their origin, their formats and purposes might be different. This diversity makes it difficult not only to combine the data but also to analyze it and eliminate any errors or duplicates.

When the amount of data being generated is increasing rapidly, the quality and accuracy of the information are at stake. Data consolidation aims to eliminate discrepancies before data is used for reporting or analysis, saving you time and increasing the value you’re getting out of your data.

Your business decisions are only as good as your data supporting them and without data consolidation, you lack the ability to see the full picture.

What is the use of data consolidation

Data consolidation aims to break information silos and make data available to more people and stakeholders. Without data consolidation, finance data would stay only in the finance team, marketing data would be used only by the marketing department, and so on.

Data consolidation is a pretty useful technique as it allows you to combine different data together. For example, if you used Microsoft Excel in the past, there is a great chance that you have already performed data consolidation. If you have combined data from multiple worksheets together, then you have performed data consolidation. 

Two simple yet actionable data consolidation examples 

While the theory and definition might have been a bit lengthy, it’s important to understand all the aspects of a data consolidation project. Now that you have a brief understanding of the complexity, let’s have a look at two quick data consolidation examples.

Consolidate data in Microsoft Excel

The most simple way of consolidating data can be achieved through Microsoft Excel. Let’s say you have sales data for a set of products for 3 different quarters (Q1, Q2 and Q3):

Let’s say you want to consolidate all of these quarters in order to see the revenue generated in the first 3 quarters of the year. You can go to Data and click Consolidate.

You can select the appropriate ranges and the labels to be the first row and column:

Voila! Your data are now consolidated in a single sheet showing the sum of products and revenue generated:

This is the simplest way to perform data consolidation as no external databases were used or any custom solution. This example is just to show that there is a great chance that you have already used data consolidation in your everyday projects.

Consolidate data from multiple data sources 

Before moving to the next sections, in this example, we will see how you can use Coupler.io and the data stitching feature to consolidate data from various sources into the same destination file.

Coupler.io is a data automation and analytics platform that allows you to manage your data on a professional level. With an ETL tool designed by Coupler.io, you can integrate data from multiple sources into one place. For more advanced cases of data management, you can benefit from data expert service which can help with data automation, visualization, and other sophisticated tasks.

Let’s say you want to combine your marketing data from your Facebook Ads campaigns with your LinkedIn Ads campaigns. Using the data stitching feature you can connect directly to Facebook and Linkedin and extract your daily marketing investments and stitch them together in Google Sheets. Here is what it looks like:

• Sign up to Coupler.io and create a new importer. Select Facebook Ads as your source and Google Sheets as your destination.

• Connect your Facebook Ads account, then select Campaigns as the data entity and your ad accounts of interest.

• Click Add one more source, then select LinkedIn Ads as a source application. Select Campaigns as the data entity and add a Start Date and the IDs of your ad account

Once you’ve configured your source apps, proceed to the destination setup – where the consolidated data will be loaded.

• Connect your Google account, then select a spreadsheet and a sheet to import the merged campaign data from Facebook Ads and LinkedIn ads.

To automate data consolidation, set up a schedule for automatic data refresh.

Eventually, click Save and Run to launch your importer. And this way, you have both Facebook Ads and LinkedIn Ads data stitched together in a single Google Sheets document.

Note: Coupler.io automatically compiles the list of matching column names from your sources and distributes data under these columns. If the column names in both sources are completely different, they will be added as separate columns to the right of the consolidated data. To optimize your data consolidation experience, it’s best to combine matching data sources that have the same structure of records, for example, different campaign reports from Google Ads, or the same type of reports but from different QuickBooks or Salesforce entities.

In this example, we used the Extract-Transform-Load (ETL) technique as we are extracting data from various sources, transforming it (stitching them together) and then storing it in a single destination. The type of data consolidation is via Data Warehousing. We didn’t use any software to virtually access the data, rather we extracted the data from the sources and stored it in a destination after we transformed it. We’ll talk more about techniques and types of data consolidation below.

What are the benefits of data consolidation in business

Using data consolidation can bring a lot of value and numerous benefits to an organization. Let’s see some of them below:

• Better decisions: As mentioned, bringing all of your different data together can help you have a holistic view of your business. How do your marketing efforts impact your sales data? What are the profits if you incorporate your marketing costs? How external factors, e.g. the weather, can impact your productivity? All of these questions could be answered only if you are able to combine data from multiple sources.
  
• Productivity and efficiency: Data consolidation can help a lot with your productivity and efficiency as well. Imagine a scenario where you want to calculate the exact profits for the past quarter after you incorporate your marketing and employee costs. In this case, you would have to request data from different departments. The time and effort for this would be really high and you would need to do it once per quarter.
  
• Operating costs: Low productivity requires more work which eventually translates to higher costs. If your people are dealing with the same processes again and again, which take ages to be completed, you’re missing a great number of opportunities. Data consolidation can help automate such processes and combine data from multiple sources where everyone can have access and bring more value to their work.

Imagine having a dashboard like the above displaying all your marketing data from multiple platforms. This dashboard is only possible via data consolidation. By consulting such a dashboard you can make better decisions, improve productivity (as you will have direct access to all your marketing efforts using a single dashboard), and optimize your operating costs as your people will invest their time where it matters.

Main data consolidation challenges you can encounter

Of course, data consolidation is no easy feat. There are plenty of challenges that you’ll need to deal with depending on the complexity of your data infrastructure and your desired goal. 

Let’s see some of the main challenges you may need to consider:

• Security issues: First and foremost when you’re about to kick off a data consolidation project, you will have to take into consideration all the security aspects. As data will be traveling between systems, you will need to make sure that there is no leakage and nothing is lost along the way.
  
• Limited time: A data consolidation project is not something that can be completed in a matter of days. Before starting a data consolidation project you will have to consider whether you have enough time to invest in the project. The invested time will definitely be worth it but it’s always a challenge.
  
• Limited resources: You may or may not have a dedicated team of analysts and engineers, nevertheless, there is often only a limited set of resources that can be invested in a consolidation project. You or your people will need to invest some time into such projects, time that could otherwise be needed somewhere else. Therefore, it’s important to assess the underlying skillset of your team and decide whether new hires or external consultants are needed.
  
• Incompatible data types: As mentioned, data are siloed under different departments, databases, and platforms. All these different sources might indicate that the underlying data will have different formats. It requires a lot of expertise in order to be able to transform the data so it can be consolidated and used together.

• Multiple platforms & locations: All these different platforms that may be used to store the data create a complex data landscape. In order to consolidate the data you will have to connect to these platforms and be able to distribute the data. This can be particularly complex as different IT systems require different types of connections and management.

• Latency: One last thing to consider when you’re about to start a data consolidation project is latency. In an ideal scenario, you would have all databases up to date when you are about to consolidate everything. But most of the time that’s not true. What if you have financial reports up to the last quarter while your marketing data is close to real-time? How can you consolidate different data when everything is not up-to-date? Latency is a great challenge to consider.

These are important challenges and you might feel overwhelmed about whether you are able or ready to handle them. Coupler.io with the data analytics consulting service can provide you with the right professionals to help you make the right choices and resolve any underlying challenge you may face during your data consolidation project.

Core data consolidation techniques to use

When it comes to data consolidation techniques, thankfully, there are not many to choose from as we have enough things to consider already. There are two main approaches here:

• Extract-Transform-Load (ETL): Maybe one of the most popular techniques of data consolidation. There is a great chance that you have heard the ETL abbreviation already. ETL is the process of extracting the data from the different platforms, transforming it in order to be in the right format and loading it raw in a destination so anyone or any tool can access it.
  
• Extract-Load-Transform (ELT): This type is similar to ELT with a minor difference. In this case, you don’t have to transform the data before loading. You keep the data as is and anyone or any tool that needs to access it, they have to make the transformation themselves. This can be particularly useful when different organizations or departments need to access the data as they might require their own transformation.

Now that we’ve seen the two different techniques, we can decide on how to use them. Again, we can choose the option that fits our specific case study:

• Hand-coding: Hand-coding is the manual process where an expert (a developer or a technical analyst) builds the appropriate scripts to extract data from the various sources and consolidate them. It requires specific expertise and can be time-consuming but this approach is rather useful for smaller jobs and when time is not a big constraint.
  
• Software: As opposed to hand-coding, you can use pre-built software tools that exist in the market. Such tools can simplify the process of extracting and transforming your data and save you a lot of time and money. For example, using Coupler.io you can avoid the required expertise you need with a hand-coding approach.

Two types of data consolidation

Now that we’ve seen the different approaches/techniques for data consolidation projects, we can move on to seeing the different types as well.

There are two different types when it comes to data consolidation. What’s best for you depends on your specific use case, so let’s see their definition and use cases:

• Data Virtualization: Data virtualization refers to the process of combining data from heterogeneous sources without actually moving them. It can be a more complex approach but you can save a lot of space as you don’t need to store the same data twice. In contrast to an ETL (or ELT) process, data stays in place and can be virtually retrieved by external tools before combining them with other datasets.
  
• Data Warehousing: Data warehousing is maybe the latest trend in the field. It involves importing data from multiple sources and storing them in a centralized location. Then, you can use this location to feed data to multiple tools and maintain uniformity. There are many popular tools used as data warehouses, the most common of which are Google BigQuery, Snowflake and Amazon Redshift.

Data consolidation steps from A to Z

When you are planning on starting a data consolidation project, it’s important to take into consideration all of the above but most importantly, make sure you don’t overlook any business or technical aspect. Let’s see a list of things to consider before your project:

1. Objectives: Before starting with your project, it’s important to ask the right questions and identify your main objectives. What is the main reason you need your data consolidated? What are the specific outcomes you’re expecting? Who will eventually need to access the data?
   
2. Documentation: After you set the objectives, you will need to identify and document the existing processes. How are you currently analyzing data? How often do you do it? How much time does it take you to do it?
   
3. Logging: Then, you will need to log the existing infrastructure in order to understand what needs to be built/used. Can you identify all the sources of your data? What hardware/software are in place? Are you using any cloud-based data stores?
   
4. Development: As soon as you have everything documented and logged, you will need to plan the development of the data consolidation project. Will you build it on your own or purchase a tool? Do you have the appropriate skill set to build it? Do you require any external expertise? Will you need additional hardware? How much time will it take?
   
5. Maintenance: One important thing that many people fail to consider is maintenance. As with every other solution, data consolidation requires maintenance as it’s not a one-off project. How much maintenance will be needed? Who will do it? How often?
   
6. Evaluation: Last but not least, you will have to consider a framework to evaluate this project. Have you established any KPIs and metrics to consult? How will you know that this project was a success?

While the above is not an exhaustive checklist, there are some questions that you can answer before you start with your process. These steps can help you ensure that you’re doing the right thing and that the data consolidation project is exactly what you need.

What are the essential data consolidation best practices?

Let’s assume that you have asked the above questions to yourself and you have decided to proceed with the data consolidation project. What are some best practices that can bring success to your project? Below are 4 things to consider:

• Plan: Before starting the project, make sure to plan properly. Plan for the time, the resources and the costs so you can have enough budget and time to complete it. Without proper planning, a data consolidation project (like every other IT project) may fail at any time.
  
• Skillset: Every data consolidation project requires a different skillset. Make sure you have the right expertise (internally or externally) for each specific case study.
  
• Processes: Establish consistent and bulletproof processes. Make sure that the solution is properly tested and frequently checked because your decisions will be as good as your data.
  
• Backups: Make data backups frequently! We can’t stress enough how important this is. Without backups, if something fails you may lose important data that could damage your business or decision-making. Backups are an essential part of any data consolidation project.

Data consolidation’s role for decision-making

Based on the examples, we hope that everything we’ve talked about comes together. Data consolidation is integral to your decision making and it can be as easy or as complex as you want it to be. It may require a few hours or a lot of expertise depending on your project.

Nevertheless, we hope that reading this will help you plan your project better and make the right decisions for your organization.

You can explore this topic further and learn more about combining data from different sources.