The Human Aspect of Data Automation

This blog post originally appeared on BI Builders on November 10, 2022.

How data automation can make humans more productive and happier

Most organizations today consider data as a strategic asset as it enables decision-makers to have better information available when and where they need it. As data is collected, curated, and presented by humans for humans, how can data automation make humans more productive and happier?

The data engineer today is not fulfilling her potential

Let’s introduce the data engineer: She is a highly skilled professional with at least 3-5+ years of technical university education eager to start solving business problems using data and simplifying data preparation for the business. These individuals love a challenge, thrive when solving it, and feel fulfilled when their data is ready for use by a satisfied business user or business analyst.

She is typically working on long and constantly growing backlogs as the need for insights and data is increasing across the organization to answer questions such as: How are our projects performing? How well are we staffed? Are we using our resources in an optimal manner? Where should we focus our efforts? How are our customers’ expectations changing? She is a critical and scarce resource in the organization.

However, the process of getting access to and preparing data is long and filled with manual, repetitive steps. There is less time left for the most value-generating tasks such as talking to the people who need the data to make sure she delivers on expectations, innovating, and solving business challenges. As a result, she is not working to her full potential and the organization is not getting its full value. She might also get tired of inefficient work processes and switch to a new organization with a modern work environment where more of the manual, repetitive tasks are automated.

Data automation makes your data engineers more productive 

Data automation looks at the complete work process from raw data to delivered insights with the question in mind; what can be automated? For example, data is typically residing in disparate and siloed data sources such as legacy business applications, ERP systems, CRM, APIs, and Excel files, and must be extracted before it can be curated. Automation simplifies connecting and extracting data efficiently from the data sources. Also, data automation lets the data engineer configure complex tasks and auto-generate code, rather than having to code everything from scratch.

Data automation also addresses questions such as: How do we optimize processing? How do we automatically document every number in every dashboard we deliver? How do we respond to changing business needs? How do we migrate between development, test, and production environments? How do we automate tests? How do we balance price performance in the cloud? How do we prepare for future changes in storage and processing technologies? How do we make sure the data platform runs without downtime? How do we upgrade or change business applications without interrupting business operations? 

Gartner¹ states that data automation tools such as Xpert BI make each data engineer four times as productive. That means:

• Organizations can more quickly respond to changes in the market as the time to market for new data-driven innovation is massively reduced
• Organizations can realize valuable projects and opportunities that would otherwise not be started
• Organizations need to hire fewer data engineers as they get more value out of existing data engineers
• Organizations can focus more resources on analytics, AI, ML etc. that heavily rely on curated data

Data automation makes your data engineer happier – and creates a WIN-WIN-WIN situation for the whole organization

When using data automation, the working day for the data engineer is massively improved. Instead of spending hours on frustrating, repetitive tasks, she can focus on challenging and value-adding tasks. She can focus more on the data consumers and users, on the business processes that they are trying to optimize, yes- she can even contribute with more detailed suggestions on how to use data for the given use case.

Understanding data and how to best utilize data in various business processes is complicated and requires both technical knowledge and business understanding. So, let’s make the most of all the clever data engineering minds by letting them contribute where the value is higher and automate the repetitive tasks that kill enthusiasm. 

• WIN 1. Higher job satisfaction: Engineers get to use their minds on challenging tasks rather than repetitive and less rewarding tasks. It is also proven that workers who like their job also perform better and have a lower absence level and are more likely to stay in the job. 
• WIN 2. Faster time-to-market for data: higher efficiency through automation of data delivery enables the organization to get the value of using data faster. Additionally, it will in many cases enable the stakeholder to get the project initiated and started AND done. 
• WIN 3. Accelerate the innovation rate: A closer cooperation with IT and business will also accelerate the innovation rate on how and where to use data, hence increasing productivity and/or efficiency and/or decision accuracy at a large scale in the organization.

¹ Gartner, "Automating Data Warehouse Development," Henry Cook: https://www.gartner.com/document/3980336