Your business is more dynamic than ever. The truth is, it has to be. The pace of technological innovation is breakneck.

As a result, you must be prepared to proactively shape-shift in order to meet the market’s rapidly changing expectations and stay ahead of the competition. In that respect, change is the only constant. 

This means that in the not-too-distant future, your business will likely look completely different than it does today. Your future-state success depends on your team’s ability to move in the right direction at the right time — and navigate the turbulent waters of change gracefully. To do that, you’ll need to make decisions grounded not in gut feelings but in data. 

Centralizing all of your organization’s data (instead of just the metrics you know you need today) puts you in a powerful position. It enables you to make deep insights about your business — even when the questions you ask change in ways you couldn’t have anticipated. 

Discover the future of data centralization

The new ELT model of data centralization works to advance your business’s data insights. Check out our ELT reference architecture to learn more.

What does it mean to centralize your data?

At the most basic level, data centralization is about collecting all your business’s data in the same location. 

While this may seem like an obvious concept, data centralization isn’t the norm for most businesses. Instead, many businesses have multiple sets of data that are segmented in silos, from internal systems and digital platforms to third-party data vendors. For example, a business’s Google Ads data may be housed within their Google account, while customer click data may live in another vendor’s platform. 

When you centralize your data, you bring it out of those respective silos and store it in a way that makes it easily accessible to your entire organization. Data centralization is necessary to get a 360-degree view of your business and customers. Without a centralized set of data, you can’t relate marketing data to sales and sales to customers and products. In order for your business to move quickly, data from all customer touch points and business processes must automatically and reliably flow into a centralized location for deep, holistic analysis.

The benefits of centralizing your business’s data 

Data centralization is the first key step in taking ownership of your data so that you can use it to more flexibly surface insights. When you centralize your data, you: 

• Put all your data in one place so that it can be meaningfully analyzed. It’s nearly impossible to accurately merge and relate disparate data sets when that data is cordoned off in silos. Not only that, but the work of manually aggregating your data takes too long. By the time you’ve pulled metrics, your original questions may no longer be valid or current. 
• Claim ownership of your data and ensure that it never gets lost. Many businesses rely heavily on data vendors to capture and store data from various sources. But what if they decide to switch from one vendor to another? Or what if an existing vendor changes its methodology or goes out of business? Ideally, if you switch from one vendor to another, you want to have the ability to relate the two data sets. But you can’t do that unless you have all the raw data from both vendors. Unless your business takes the additional step of saving all the raw data from each individual source, you risk losing it. Those data gaps make it nearly impossible to draw meaningful conclusions. In that respect, centralizing your data is a little like taking out an insurance policy on your own information. It allows you to claim full ownership of your data and protect it from loss. 
• Gain the ability to manipulate your data however you want. When your data is centralized, you have the ability to manipulate it however you want. And when you centralize it using the most modern methods (more on that shortly) you clear a path for as-yet-undefined analytics that haven’t even occurred to you yet. 
• Put your entire team on the same page. Centralizing your data allows you to make all of your data accessible to your entire organization, regardless of functional units. Marketing’s data is now accessible to your sales team and vice versa. By putting all of your data in one place, you level the informational playing field. When your entire team works from the same pool of data, you can be sure that you are measuring success in the same way. 

Innovations in data centralization: ELT vs. ETL

Data centralization is the first step to fully owning and utilizing your data. But it’s important to note that there’s more than one way to accomplish this goal. The technical approach you take to centralizing your data will impact the level of flexibility you have in working with your data moving forward.

ETL: Extract, Transform, Load

Traditionally, the most common approach has been ETL, which stands for extract, transform and load. With ETL, you start by extracting all of your raw data from its various sources. Next, you figure out all the ways you want to transform the data, or analyze it according to specific metrics. For example, one transformation might be marketing spend by day. You would then load all of your transformed data into a data warehouse for storage and discard any raw data that hasn’t been identified as useful.

With ETL models, companies are forced to anticipate all the ways they want to slice and dice data on the front end. But in a rapidly shifting market that becomes much more difficult. Businesses today are changing so rapidly that it’s impossible to fully and accurately forecast the exact metrics they might need in the future.  

This traditional approach to data centralization harkens back to when data was stored in hard drives in physical storage centers. In this arrangement, data storage was so expensive that it made sense to keep and store only the data that you knew you needed. 

ELT: Extract, Load, Transform

Now that cloud computing has driven down the cost of data storage, a better approach to data centralization has emerged. This new approach is called ELT, and it flips the traditional ETL model on its head. Instead of extracting and transforming a curated selection of data before loading it into storage, ELT systems extract and load all of a business’s raw data. The data is collected and stored indefinitely without first deciding how it might be used or transformed. It can then be transformed as businesses identify new questions or measurements.  

Tools like Looker allow you to create a trusted, standardized, and centralized data model on the fly. 

The ELT approach gives businesses the ability to easily ask new questions of their data as their needs change. With the traditional ETL model, if you wanted to add a new data transformation to your stored data, your engineering team would need to build it out. In addition, if you decided you wanted to begin measuring a previously untracked dimension or metric, you may not have any historical data to work with (since only transformed data would have been preserved). With ELT, all of your data is stored indefinitely. And that data can then be transformed on demand without the assistance of an engineering team.  

The fast pace of change will almost certainly require your business to turn on a dime. Your centralized data offers the insights needed to anticipate those shifts and pivot gracefully.