The volume of data is exponentially increasing. Data is collected from just about anything you can think of. From the weather, camera feeds, to wearable devices so many people utilize, data is captured in numerous ways. Businesses also capture data: data on their customers, sales, marketing and so much more. This data is often stored in the systems that generated it (i.e. Google Ads). But, with data stored in these systems, it is siloed from other systems, users, and decision makers. So how can organizations capitalize on generated data and gain actionable insights from it? Data centralization.
When implementing your solution, there are four key aspects of data centralization:
- Data warehousing
- Data transformation
- Data orchestration
While each of these components is its own process, the combination of the processes is what empowers data centralization as a solution. To see what tools Untitled recommends for each of these components, check out our Modern Data Stack Tools page. Let’s break down the meaning of each component and how they contribute in enabling a data centralization solution.
Before an organization can centralize data, it has to have data. For many companies, this data exists within the business systems used on a daily basis. While many business applications have some level of reporting capabilities built in, users are confined to what each system allows them to access. On top of that, users are unable to use the data in one system and enrich this information with data from another system.
ETL, or extract-transform-load, enables companies to obtain data from these business systems and move the data to a destination that will allow them to access all of it in the same place. ETL is often referred to as data ingestion, meaning that data is extracted (ingested) from a source and moved to a destination. There are two primary options when it comes to ingesting your business data: custom data pipelines and off-the-shelf ETL tooling.
Custom Data Pipelines
A custom data pipeline is an ETL process that is developed and managed by an engineer, team of engineers, or a larger data team as a whole. These pipelines are configured to connect to an application programming interface (API) which exposes the data the engineer(s) are seeking to obtain. Once data has been obtained from the API, the data may move into a temporary staging area where it is transformed into a format that can be loaded into the chosen destination. Custom data pipelines require the technical expertise of writing code as well as continuous monitoring and management.
Off-the-Shelf ETL Tools
Off-the-shelf ETL tools are cloud based data integration applications that allow for users to connect to data sources and sync data from source to destination in a matter of minutes. Using off-the-shelf ETL tools requires little technical expertise and enables you to pass off the development work of building data pipelines. Off-the-shelf ETL tools are limited to connecting only the data sources the tool provider has chosen to support. Untitled recommends Fivetran when it comes to off-the-shelf ETL tools.
Determining whether a custom data pipeline or an off-the-shelf ETL tool is the right solution for your data centralization project depends on various factors. The first thing to consider is if there are non-standard or obscure data sources that are being connected. If so, it is likely that an off-the-shelf ETL tool is not going to support a connection like this, thus restricting your selection to a custom data pipeline. However, when creating a custom data pipeline, you need to ensure the source you are connecting has an API available for the data you want to obtain, otherwise you will not be able to get the data you want.
The second thing to consider when selecting which type of ETL solution to choose is the technical ability of the end users interacting with the solution. If you have a technical team, they likely have the technical competence to manage a custom data pipeline, while a non technical marketing manager could handle an off-the-shelf tool by themselves.
Finally, it is important to understand the value of each solution. While custom data pipelines may not charge you a usage fee, they do require time and effort from your data engineering team, as well as the continued maintenance of the pipelines. An off-the-shelf solution doesn’t require any additional data management tools and is simplistic to set up, but will cost you a fee for the amount of data you are moving.
2. Data Warehousing
Once data has been obtained from business systems, it has to be stored somewhere that will allow other tools and users to interact with it. This is what a data warehouse is for. A data warehouse is a repository of all your business data that has been integrated through the ETL process. By storing all of your data in the same location, this data can be analyzed to find deep insight for your business.
When it comes to data warehouses, there are multiple different types that can be selected. However, when it comes to data centralization, Untitled believes the best option is to leverage a cloud data warehouse.
A cloud data warehouse is the best option for data warehousing for several reasons. First, there is no maintenance involved. This eliminates the need for having database administrators, or a massive team of data engineers on staff to manage your database instance. Secondly, scalability can happen almost instantly. If you find that you need more capacity or performance, cloud data warehouses can be quickly scaled to meet those needs. Finally, cloud data warehouses are optimized for complex analytical processes, making them the ideal solution for those who need to leverage data visualization tools like Sisense.
3. Data Transformation
Once data has been stored in a data warehouse, some may argue that the data has been centralized at that point. At Untitled, we believe that a data centralization project isn’t complete until your data can be utilized. After all, just as parts and pieces are organized in a physical manufacturing warehouse to support the production of goods, transforming your data to make it usable is also part of centralizing your data.
Data transformation has two primary purposes: cleaning and preparing data, and modeling the data that has been prepared. When data is loaded into the data warehouse, Untitled seeks to keep intact a property of data called immutability. This means the underlying raw data that has been loaded into the warehouse is never modified. Raw data should be kept in its raw state so there is a quality standard to fall back onto. In order to utilize this data, transformation processes should be created to form a degree of separation from raw data by creating a staging layer. This staging layer should contain a one to one relationship with the raw data tables that are needed in data processes.
Within this staging layer, data will be cleaned and prepared as well. Field names may be renamed to provide more context into what is contained in columns. Data types may be changed to allow more flexibility or rigidity. Additional columns may be added to provide more context and enrich the models they are associated with. This paves the way to enabling powerful data models to be built for any business use case.
By building a level of separation from raw data, data processes and users can now interact with staging layer data with the knowledge that any modification to the staging layer will not impact the quality of the raw data stored. But data transformation shouldn’t stop there. Once a staging layer has been created, an additional layer can be created that models the data in the staging layer into meaningful models designed for each business use case. This new layer can be separated into models intended for different parts of the business. This separation can be referred to as marts.
To summarize, when data is loaded into a warehouse, data being stored is in its raw state. A level of separation should be created so that raw data remains immutable: the staging layer. Data in the staging layer can then be leveraged by impactful data modules used for business use cases: the marts layer. By transforming your data this way, raw data quality is enforced, and data models can be created that never modify the raw data empowering those models.
4. Data Orchestration
While data orchestration is a vital component of data centralization, it doesn’t physically handle data per se. Instead, it acts as the conductor of the data centralization process, telling the different processes what to do and when. This process allows for the automation of an entire data centralization process to perform in a cohesive manner.
Within the ETL process, data orchestration determines how often a data pipeline attempts to obtain data from a source and when it should push data to a destination. Within the data warehouse process, orchestration ensures that the necessary resources are available to handle any data processing requests. Finally, within the data transformation process, models can be orchestrated to only be built after new data has been loaded into the warehouse. This means that data models won’t contain stale data. Not only that, but orchestrators allow for processes to create a directed acyclic graph (DAG), which ensures that all dependencies are handled before dependent processes run.
How Can Untitled Help?
Leveraging the Four Key Aspects of Data Centralization
Untitled believes that data centralization is a major component of driving massive value for any business. By not having your business data in a centralized environment, your business is missing out on the ability to leverage deep, actionable insight. Untitled rapidly deploys modern data stacks to help businesses take advantage of their data. If you’re not taking advantage of your business’s data, or you don’t know where to start, let Untitled help!