7 Minute Read

A Guide to Data Consolidation: Tips, Techniques and Benefits

The ability to capture more data represents a large opportunity for businesses to drive growth due to the insights it can provide. Read on to discover some of the ways it can assist you today.

It’s a simple fact that businesses today have access to a goldmine of data. Driven by process advancements and new technology that impacts virtually every area of a business, it is now possible to capture more data than ever before.

This represents a huge opportunity. Data unlocks powerful insights, allowing you to make smarter decisions and predict outcomes with a higher degree of certainty than ever before. As a result, data has become a vital tool for the world’s leading organisations — Linkedin Chief Executive, Jeff Weiner, once admitted that “data really powers everything that we do.”1

Due to the powerful insights it can unleash, businesses are increasingly looking to analyse data sets as a collective and find relationships between them as opposed to looking at them in isolation. However, growing volumes of data spread over different systems make leveraging it to achieve desirable outcomes difficult, and 95% of businesses say that their inability to understand data is holding them back.2

That’s where data consolidation can help.

In this article, we’re going to look at the concept of data consolidation, common data consolidation techniques and the benefits they can provide. Let’s get started. 

What is data consolidation?

In the simplest terms, data consolidation is the process of gathering and combining data from multiple sources into a single location. It’s a relatively new concept that has risen to prominence in recent years due to the sheer amount of data now available to businesses. 

For example, the average business will collect data from a wide range of business systems and platforms. This includes, but is not limited to:

  • Customer relationship management (CRM) software
  • HR systems
  • Product databases
  • Sales-related data
  • Content management systems (CMS) 
  • First-party website app-related data
  • Third-party sales data, including sales data from affiliate sites
  • Manually created data, such as Excel sheets, CSV files, and PDFs

Data consolidation is all about taking all of this data and moving it into a single location like a data centre. This process allows you to access and work with data from a single point of access, facilitating the process of turning raw data into actionable insights that can ultimately drive more effective decision-making and long-term success.

Data consolidation techniques

Before delving into the benefits of data consolidation and the best practices businesses should look to implement, it’s first crucial to understand the various techniques available. Remember, there are a number of different processes that can be utilised in order to consolidate data.

The approach an organisation chooses will affect its overall strategy and the tools required to make it a reality. In this section, we’re going to focus on three of the most widely used data consolidation techniques.

Extract, Transform, Load (ETL) 

As the name suggests, ETL is a data transformation process that consists of three parts: 

  1. Extract: The first step is to extract the data from a source system. The data is then consolidated in a single format in preparation for the next step.
  2. Transform: Certain processes are then applied to the data in preparation for the final stage. These may include cleansing to remove inconsistencies and errors, sorting to organise the data by type, and removing duplicates to ensure redundant data is discarded.
  3. Load: Finally, the transformed data is loaded into a target system – for example, a data centre or database – where it can be analysed and monitored more easily. 

It’s also important to be aware of the fact that there are two ways that you can perform the ETL process — real-time ETL and batch processing. Let’s take a look at the differences:

  • Real-time ETL: As the name suggests, real-time ETL transfers data into the target system as it is captured. It does this using a process called change data capture (CDC), which recognises changes in the data source. 
  • Batch processing: Batch processing transfers data in bulk. The data is collected and stored during a certain window of time, and then transformed in one batch to the target system. This is ideal for high-volume, repetitive datasets. 

Data virtualisation

Data virtualisation is a process that sees an organisation’s data integrated from across numerous different sources without replicating or moving it. This approach makes it possible for an application to retrieve and manipulate data without requiring technical details about the data, such as how it is formatted at source, or where it is physically located.

Essentially, data virtualisation allows data to be stored in different data models and integrated virtually. This provides users with a consolidated view of their organisation’s data from which they can look to glean valuable insights.

Unlike the ETL process, data stays in its original source following data visualisation. As a result, the data can still be retrieved by front-end technology such as applications, dashboards, and portals for future use.

Data warehousing

Data warehousing involves consolidating data from multiple sources into a centralised location, known as a data warehouse. This data can then be used for ad hoc queries, business intelligence, analytics, and to uncover various critical insights. 

Over time, data warehouses build a vast amount of historical data, and as such come to act as a single source of truth for a business. Having all of a business’s data located in one place makes it much easier to identify trends and subsequently create strategies for enhancing business operations and optimising outcomes. 

On top of this, data warehousing provides businesses with the capacity to categorise data, facilitating improved analysis relating to a particular function or process, including:

  • Sales
  • Recruitment
  • Marketing

The benefits of data consolidation

Now we know what data consolidation is and the techniques you can use to achieve it, let’s dive deeper into the benefits it can bring once implementation is complete. These are numerous and wide-ranging and include:

  • Reduced costs: We’ll start with a big one. Data consolidation directly boosts your bottom line by reducing maintenance costs and eliminating redundant and time-consuming processes. 
  • A single source of truth: Data consolidation provides a single source of truth for all data-driven decisions. With all data standardised and processed according to the same rules, you gain a level of clarity and accuracy that would be impossible if your data were scattered across multiple platforms and systems.
  • Enhanced oversight: Consolidating data effectively allows organisations to obtain a 360-degree view of performance across categories and revenue streams whilst combining first and third-party data sources. 
  • Simplified management and maintenance: Having all your data in a single repository allows you to manage and maintain it with comparative ease. There are fewer points of failure, meaning fewer outages, less downtime, and less risk. 
  • Improved security: Data consolidation helps to reduce the attack landscape, allowing you to keep your data more secure. In the unfortunate event of an attack, data consolidation also improves disaster recovery capabilities. 
  • Support for compliance: In a world of ever-changing rules and regulations, it’s never been more important to set your data infrastructure up for success. Data consolidation makes it easier to ensure that your data collection and data management efforts meet strict compliance requirements. 
  • Rapid analysis: If your data is spread across multiple systems, it can be virtually impossible to undertake a rapid analysis. Data consolidation allows you to get a fast and accurate reading on all or any of your data. 
  • Improved productivity and flexibility: Data consolidation streamlines your IT and data infrastructure while improving data quality and accuracy. This allows you greater productivity and flexibility than ever before.

Data consolidation best practices

Despite the numerous benefits of data consolidation outlined above, it can be a time-consuming and complicated process. That’s why it’s also crucial to get it right. Otherwise, an organisation’s data-informed processes and efficiency are likely to suffer.

As a result, there are a number of best practices that businesses should look to implement in order to minimise issues and maximise the return on investment (ROI) that data consolidation can offer. The most significant of these include:

  • Effective planning: As with any endeavour, comprehensive planning is critical for success. Ensure that you have a realistic timeline and budget – and set measurable targets for success.
  • Ensure data cleanliness: It’s vital to clean data prior to its integration into data models and categories for analysis. That means aggregating data into the correct levels of granularity to make it easier to manage, maintain and subsequently digest.
  • Working towards continuity: Data consolidation isn’t just a one-off process, it’s a continuous one. That’s why it’s pivotal to build an ongoing data consolidation process that will serve the business over the long run.
  • Keep data raw: Keeping data in its original form helps to keep it accessible, prevents continual referrals back to the source system, and makes it easier for data tables to be repopulated on demand if required. 
  • Using the right tools: The right technology helps to ensure that the process of data consolidation is smooth and error-free. For example, if you are gathering data from multiple locations, you’ll need an ETL tool that can connect to them all.
  • Getting the right skills: Data consolidation is a complicated process that requires particular skills and expertise. While it’s important to understand and address any skills gaps before you undertake any large-scale changes to your data infrastructure, the process of hiring, training, and retraining staff can be expensive and time-consuming.
  • Working with outside experts: By partnering with external data experts, you’ll save the time and cost of equipping employees with the skills needed to implement data consolidation processes.

Getting started with data consolidation

The benefits of data consolidation are numerous. Perhaps the biggest challenge that stands in the way of businesses realising those benefits is a lack of expertise, with a significant skills gap in the market making it difficult for businesses to hire and retain top talent.

Unfortunately, this means that undertaking data consolidating processes in-house can be time-consuming and expensive. Furthermore, trying to implement data consolidation without the required knowledge and expertise to be successful can result in poor data, poor insights, and ultimately poor business performance.

This is why it pays to work with experts. By teaming up with an external partner, you can implement complicated and nuanced data processes with maximum efficiency and minimum hassle, saving you valuable time, money, and resources. 

At Ipsos Jarmany, we help businesses build powerful data strategies, including data consolidation. By partnering with us, you can gain access to the expertise, experience, and tools you need to harness the power of data — allowing you to make better decisions, boost efficiency, and gain a competitive advantage in your industry. If you’re ready to start the process of consolidating your data in order to harness insights and drive long-term business success, contact us today.

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