Think about all the data your company generates daily — it comes in from systems like CRM software, internet commerce, and even IoT devices. You know, this deluge of data is too much to manage. In this case, data consolidation becomes useful.
What we call “data consolidation” is just bringing all of your data into one central location and organizing it. It matters because it’s simpler to make wise choices, enhance business operations, and maintain an advantage over competitors when all of your data is in one location.
The more data your company produces, the more challenging it becomes to make sense of it all. However, you may bring order to chaos with the correct tools and tactics. Consolidation brings together data from various sources and stores it in a single, organized location. You will see the big picture of your company and find important insights hiding in plain sight.
Don’t worry if data consolidation is foreign to you; we are here to guide you. We’ll explain data consolidation, why it’s essential, and how to use it.
Data consolidation combines data from several sources — such as departments, systems, or even sites — in one spot to help you plan strategically and manage, analyze, and apply it.
Data consolidation is important, but why? Here’s the broad picture — what it can do for you:
So, what is data consolidation? It fundamentally is about establishing a “single source of truth”.
You are preparing yourself for wiser, more strategic decisions when all your data is aligned and easily available; you are also enabling data to work for you rather than the other way around.
There is no universal data consolidation type.
Depending on the kind of data you are handling and the issues you must solve, it takes on several forms: customer data consolidation, database consolidation, and CRM consolidation.
Customer data originates from email interactions, web visits, social media, and more. Customer data consolidation is meant to bring all of this different information together into one, logical structure. It helps you to draw a clearer, more whole image of your clientele.
You can anticipate their desires, better understand their preferences, and customize their interaction with your company when you have all of their information in one location.
Most of the companies handle several databases; for example, you may have a system for each department or project. In database consolidation, many databases are joined into one larger repository, so that your data access becomes simpler, less complicated, and more efficient.
Consolidation of customer relationship management (CRM) systems changes everything for businesses using multiple systems as it aggregates their data into one unified system.
Better business choices may result from your ability to see more precisely how your teams interact with clients (CRM consolidation enhances cooperation and teamwork as well).
Consolidating information from several sources does not have to seem like an impossible chore — if you can compile reliable data, your business will be able to make more informed decisions. The main steps of the data consolidation process are as follows:
Finally, build up safeguards, such as access restrictions and security measures, to protect your data and maintain regulatory compliance.
For companies trying to control and make sense of their data, consolidating information is a game-changer rather than just a backend tech chore, and here’s why:
Consolidating all of your data exposes your business to deeper insights, better outcomes, and fewer bottlenecks. Whatever the size of your company, this is a change worth doing.
As mentioned above, data consolidation is essentially about combining data from several sources into one ordered system. Think of it as creating a single home for all your data.
The good news is that there’s more than one way to get it done. Here is a list of common techniques you may use to properly consolidate your data.
Usually referred to as ETL (extract, transform, load), the whole process also includes a more recent variant known as ELT (extract, load, transform).
An ETL procedure indicates the conventional path of data consolidation.
First, data is extracted from multiple sources. Next, it’s transformed, meaning it’s cleaned up and reformatted so everything matches. At last, the data finds its home — perhaps a data lake or data warehouse.
This technique may be done mostly in two ways:
ELT offers a contemporary interpretation of data consolidation. First, much like with ETL, data is taken from all your sources. It then loads right away into a central staging area without previously being changed. In the end, once the data is in the system, it transforms to make format changes possible.
For agile companies, this is the perfect strategy. Loading raw data initially allows businesses to subsequently clean and change it, customizing the data to fit certain requirements.
One place where data from all your data sources combines for easier access, analysis, and application is a data warehouse consisting of three components:
All of the information in your company is shown in detail through data warehouse consolidation. Since everything is kept in one location, you are free from juggling multiple systems.
It also guarantees that all of your data is consistent and current, therefore facilitating the analysis of trends and evaluation of success.
A contemporary way to keep enormous volumes of data in one location is using a data lake.
What makes it unique? It maintains all of your data in its natural state and manages ordered, unorganized, or mixed data.
There’s no need to reorganize or clean up your data before adding it to a data lake — you can store it exactly as you collect it. Data lakes include anything from unstructured data like videos or social media postings to organized data like spreadsheets.
Also, structure is applied just when you’re ready to study the data, rather than arranging it beforehand.
When it comes to big data analytics and machine learning, data lakes are your best bet for discovering useful insights. Also, your company’s data lake expands as it grows, and you can add as much data in whatever format as required.
If your company uses machine learning, predictive analytics, or any technology that processes large volumes of information, this is the perfect fit.
A data lakehouse is a type of data architecture wherein a data lake and a data warehouse are combined. Such a lakehouse lets companies employ low-cost, flexible storage for structured, unstructured, and semi-structured data. It also offers data formats and data management tools, therefore enabling machine learning, business intelligence (BI), and predictive analytics.
Many of the components in a data lakehouse are known for old data lakes and data warehouse ideas, but in a manner that blends them into something fresh and more efficient for the digital environment, for example:
The primary drawback of a data lakehouse is its still-relatively-new and undeveloped technological nature. This makes it hard to say if it will function as promise, and it can take data lakehouses a while to catch up to more established large data storage options.
Although data storage consolidation offers many benefits, there are several challenges you’ll need to address as well:
Data consolidation presents serious challenges, but they are not insurmountable. Despite these challenges, remember that Visual Flow’s data engineering and consulting services may help you maximize your combined data.
Follow these easy guidelines to simplify your data consolidation projects and guarantee accurate, safe, and ready-for-use data:
This is how you will go above obstacles in data storage consolidation and provide a strong basis for wise business choices. Maintaining your data tidy, actionable, and ready to support expansion will depend much on a little investment upfront.