Data is a powerful asset for businesses, regardless of industry or size. In fact, it can be argued that data is the ultimate competitive advantage. It’s what enables businesses to understand their customer and market needs, monitor performance trends and take actionable steps to reach new audiences with relevant products and services. However, while data continues to grow rapidly in value and importance as time goes on, many small businesses are struggling to keep up with its increasing volume, variety and velocity. This blog post will introduce you to data automation and its role in business intelligence and analytics. We’ll explore the benefits of implementing data automation best practices in your organization so you can meet your business goals efficiently and cost-effectively.

What is Data Automation?

Data automation is the process of implementing technologies that analyze, interpret and organize your data so you don’t have to. The data might be stored in systems like ERP, CRM, accounting software, email marketing tools, marketing automation tools, etc. Data automation tools pull information from these systems and bring it together in one place to create a central data source. The data source can then be used by BI tools to create visualizations, generate reports, and make predictions based on historical trends. Data automation is not just a single software tool or piece of technology, but an approach to managing data that allows you to scale your data analysis without adding more people or budget to the equation.

How Does Data Automation Help?

Data automation helps by taking away the repetitive, time-consuming tasks that distract you from more important business objectives. Think about it: manual data collection (i.e. collecting data via spreadsheet or a manual system like a paper filing system) is error-prone, time-consuming, and inefficient. It’s not scalable, either. As your business grows, you’ll either have to hire more people to collect data manually or find a new way to manage the data stream. But with data automation, you can scale your business with one centralized data source. Data automation tools also allow you to collect data from systems and sources (internal or external to your organization) that you previously couldn’t reach. With this additional data, you can gain a more comprehensive and accurate view of your business. You can then use this data to adjust your business strategy and make more informed decisions.

What’s the business impact of Data Automation?

Businesses that are data-driven can be more efficient. Their data is both more accessible and valuable than ever before. Teams can be more efficient if they automate repetitive tasks. By avoiding hiring additional workers, reducing overhead, and boosting employee productivity, companies can save money. Automating their backend systems can also assist businesses save money. Here are the 5 key business benefits of data automation:

Saves Time

In the corporate realm, time is precious and limited. Manually going through an abundance of information can take up a lot of the time we have. With data automation, custom software can do this job for you, allowing you to concentrate on other important tasks.

Reduces Operational Costs

Data automation can achieve the same results as manual data collection but much more quickly and economically.

Reduced human error

Data Automation software can work with large amounts of data quickly, accurately and precisely. It is able to collect, manipulate, upload and analyze data in a very efficient way.

Improved decision-making

Manually analyzed and reported data can often lead to misguided conclusions and can have a negative impact if not addressed in a timely manner. By automating the data collection process, you are presented with reliable numbers and figures which can be used to make decisions that are based on facts, and will be beneficial in the long run.

Steps to Start a Data Automation Program

The three main aspects of Data Automation are Extract, Transform, and Load, which are otherwise referred to as ETL. The Extract phase is the part where information is taken from one or multiple source systems. The Transform phase includes customizing the data into a specific format, such as a CSV file. This step might include changing abbreviations into full names, for example. Finally, Load is the process of sending the data to the destination, like an open data portal.

Developing a data automation strategy requires multiple departments working together to achieve certain milestones: 

Problem Identification

Ascertain which of your organization’s primary areas could reap advantages from automation. Just think about where Data Automation might be advantageous. Think about this: how much of your data personnel’s time is used doing hands-on work? Which parts of your data procedures are repeatedly not working correctly? Compile a compilation of all the procedures that could be enhanced.

Data Classification

At the outset of Data Automation, it is essential to divide source data into various segments depending on its significance and availability. Scrutinize your source system register to determine which sources you can gain access to. Additionally, if you plan on utilizing an automated data extraction tool, make sure it is compatible with the formats that are required for your company.

Prioritization

You can use the amount of time taken to gauge the significance of a process. The more time a task takes to complete manually, the more beneficial automation will be for the company’s profit. Consider the amount of time it will take to automate the process. It is beneficial to pursue simple and achievable goals because it keeps morale high while showing owners the advantages of automation.

Define Transformation Requirements

After that step, it is necessary to identify the alterations that will be needed in order to change the source data into the desired size. This could involve something as basic as changing abbreviations into the complete phrase or as complex as changing information from a relational database into a CSV file. Pinpointing the transformations that are necessary to get the desired outcome during Data Automation is essential; otherwise, all of the data could be ruined.

Execution

Data techniques are the most difficult to implement, technologically speaking. These processes require three things: adequate reporting, adequate data processing pipelines, and good machine-learning methods.

Schedule Data for Updates

The following step is to plan your data in a way that it is refreshed frequently. It is suggested that you select an ETL tool with features like task scheduling, process automation, and so forth for this phase. This guarantees that the activity will happen without requiring any manual interference.

Building An Automation Department – 3 Levels of Data Automation Engineers

Level 1 – Data Automation engineer

An information technology specialist whose primary role is to arrange data for analytical or operational purposes is known as a data engineer. These software professionals are generally in charge of constructing data pipelines that amalgamate data from distinct source systems. They assemble, unify, and refine data and structure it for utilization in analytics applications. Their goal is to make data available effortlessly and to optimize their organization’s vast data environment. 

The amount of data an engineer works with is contingent on the company, particularly the size. More data is the responsibility of the engineer if the company is bigger and the analytics structure more complex. Industries such as healthcare, retail and financial services have more data-intensive requirements.

Level 2: Data Automation architect

IT personnel known as data architects are assigned the task of establishing the policies, plans, patterns, and technologies related to compiling, arranging, storing and accessing data within the business. This role is sometimes confused with that of a database architect or data engineer, yet the data architect concentrates more on the big picture of business intelligence and data regulations, while the other two concentrate on the practical application of those fundamentals in the formation of individual databases.

Level 3: Data Automation team lead

Team Leaders of Data Automation Engineering construct, supervise, and direct a team of experienced Data Engineers to fabricate the total data transformation framework, data mining techniques, and ML algorithms for rendering efficacious data solutions within the primary product. They also enhance the Data Transformations module to boost the performance and user satisfaction while reducing the price of computing and the occurrence of technical issues.

How Datasearch can help build your Data Automation Function

If building a Data Automation department is part of your strategic plans, then at Datasearch Consulting we’re here to help. We’ll start with a consultation on a phased approach to hiring, and help scale the department to meet your data automation needs. We can assist in sourcing and hiring from C-Level all the way to Junior Data Automation Engineers. Visit our website, or contact us today at info@datasearchconsulting.com to see how we can help.