5 Foundational Aspects of Data Automation

Venkat Obillaneni

Cloud Data Engineer at Aezion, Inc. | + posts

Venkat Obillaneni is Aezion Cloud Data Engineer and Data Practice Lead.

Having already explored the characteristics of a modern data architecture, it only makes sense that we now dive into the critical aspects of data automation, which goes hand-in-hand with data integration and implementing a successful enterprise data management strategy.

The fact is, businesses are generating and storing an ever-increasing amount of data. On one hand, those huge data stores represent priceless insights that can drive growth. On the other hand, managing all that data often poses so many challenges that businesses end up struggling to access it quickly or consistently enough to actually use it. The technical challenges of handling data are the biggest roadblock to unlocking Business Intelligence (BI).

With data coming in so many different formats from so many sources, achieving efficient, accessible, and cost-effective analytics is no small feat. Data automation plays a big role in the process, as it helps simplify organization, sanitation, access, and reporting in multiple ways. So, let’s explore five critical aspects of understanding and using data automation correctly.

#1 Getting to Know ETL Operations

To understand data automation, you first have to know the three main components that make up a data automation tool. These are often summarized as “ETL,” which stands for Extract, Transform, and Load.
Extraction is the process of taking data from one or multiple sources.
Transformation is the process of modifying data to fit a standard structure, which might require operations like replacing state names with state abbreviations or converting all data into CSV format.
Load is the process of moving data from one system to another system.

These key elements make data automation possible by collecting your data, standardizing it, and then shipping it off to the system(s) where you need to have access to it.

#2 Developing a Data Automation Strategy

Failing to create a data automation strategy will cost your company in terms of wasted time and resources. Allowing teams to try to move forward without a strategy to guide them will surely lead them to straying away from the original plans and missing key steps, deadlines, and milestones.

As part of the development of your data automation strategy, you must identify the business areas that stand to benefit the most from automation; sort your data based on its importance and format; prioritize the processes that you plan to automate; and outline the required transformations that must take place for data automation to be effective.

#3 Defining Data Access and Ownership

Defining data access and ownership is yet another crucial aspect of data automation. In practice, there is often not a single team responsible for all data. Instead, separate groups will likely own a set of elements within your ETL process. How you divide ownership will depend on your existing teams and their responsibilities.

One of the most common approaches is centralized data access and operation, where the entire ETL process and any data automation associated with it is owned by the central IT department and likely managed by multiple teams within the department. Another option is hybrid data access and operation, where the extract and transform procedures are each owned by a department and the loading process is part of the central IT department.

Lastly, you might take the approach of completely decentralized data access and operation. In this instance, each department will be in charge of its own set of ETL processes. This is the most complex choice and you need to make sure that each department’s processes don’t contradict that of another department. In other words, investing in company-wide data standardization will prove crucial with this approach.

#4 Measuring Internal Business Results

Three of the biggest benefits a company can expect when implementing data automation is reduced processing time, improved ability to scale, and better performance. Alongside these benefits, you’ll likely experience improved cost efficiency and a myriad of other side advantages that come along with more efficient internal processes. The key is to not only set goals but put someone in charge of monitoring and measuring your progress towards them.

If one goal is to reduce processing time, that means that manual intervention should be minimized and monitored. You should also be tracking metrics like data reliability, resource utilization, and time savings. If another goal is to improve the performance of your data environment, you should be tracking the need for manual task updates, the time it takes to execute jobs, and similar data points.

#5 Ensuring End-User Results

Internal business metrics, like improved performance within your data environment, is no small accomplishment, but the impact of data automation really shines when you’re able to show measurable improvement for your end users. Data automation can support every team in your company, from customer support to accounting, and improved access to relevant data should directly improve these teams’ ability to satisfy clients.

Aside from tracking how your data automation strategy improves the customer experience, you should also keep in touch with business users to see how it is directly improving their workflows. For instance, data automation reduces human input, thereby improving data quality by reducing human error and manual integrations. This can save teams countless hours spent entering, altering, and updating information within systems, allowing them to focus on what really matters: Growing the business.

Take The Next Step

Aezion has helped countless companies plan a data automation strategy that makes the most of existing tools, talent, and goals. Contact us today to discuss how we can help your organization get on the path to efficiency and performance through a data automation solution.

Venkat Obillaneni

Cloud Data Engineer at Aezion, Inc. | + posts

Venkat Obillaneni is Aezion Cloud Data Engineer and Data Practice Lead.

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