Data Automation - Part II

In part I of this series, we have gone through methods of data capturing. In this part let's explore why and how to automate the captured data.

Data automation is considered vital for business sustainability. Entire business ecosystem is built on a foundation of data that is always changing. That data is housed across and used by many distinct systems across organization. The efficiency and effectiveness of business operations heavily rely on how easily data can be collected, updated, and passed between systems. According to the International Data Center (IDC), the global data sphere is expected to grow to 163 zettabytes by 2025. That’s equivalent to 163 trillion gigabytes, or ten times more than the amount recorded in 2016. That’s a lot to deal with, isn’t it?

This is where data automation comes to your rescue!

So, what is data automation? In a very simple language, we can say - It’s the use of technology to automate the way we collect, updates, processes, and stores data. Below are few of the reasons why data automation is good for any business.

• Smarter Operations: Getting inadequate or erroneous data is a common problem for businesses, and it can drastically slow the processes. Data automation can help to improve overall operations for effectiveness and speed, helping businesses to run faster, smoother, and with fewer delays.
• Superior Data Quality: Handling large volumes of data manually leaves open to the risk of human error, and trusting on redundant, poorly integrated technology to keep information straight puts businesses equally at risk. Data handling is better fit for technology that doesn’t make errors and never exhausts.
• Stronger Sales & Marketing Efforts: Sales and marketing teams rely on correct data to find the right possibilities and reach them through targeted campaigns. Data automation solutions can help make sure data is always correct and up to date, giving them the best chance at being productive.
• Better Customer Experiences: It’s not enough to offer a great product or service. Customers also expect to have a great experience with businesses. Data automation solutions ensure team has the right data at their fingertips to meet the needs of clients, from accounting team to customer support.
• Happier Employees: The last thing employees want is to struggle with data to do their jobs. Using data automation tools makes it easier for them to focus on the work that adds value to business. It also allows them to take on more strategic projects and build their skills.
• Cost Savings: It goes without saying that data automation solutions can drive down costs across the business. Better data corresponds to better decision-making, faster processes, optimized experiences, and mitigates compliance issues across every department in the business.

Understanding Data Automation Strategy

It’s critical to have a general Data Automation plan in place for all businesses. Having a strategy in place ahead of time can assist business in engaging the right people at the appropriate moment. Without a robust Data Automation strategy in place, business firms will drift away from the route it should be on. As a result, data process automation plan should be in line with business goals. But before diving into the technical nitty-gritty of data automation to be undertaken, it is important to determine who owns Data Automation in that business organization. Depending on the team model, different groups will own different parts of the ETL process:

Centralized: Central IT organization owns the full ETL process and all data automation.

Hybrid: This model may vary, but often the individual departments will own the extract and transform processes, and the central IT organization will own the loading process.

Decentralized: The individual departments will each own their own ETL process.

Below are some steps that can be undertaken to develop a Data Automation Strategy:

• Identification of Problems: Determine which of business primary areas could benefit from automation. Simply consider where Data Automation might be useful, i.e., how much of data operators’ time is spent doing manual work? Which components data operations are constantly failing? Make a list of all the processes that could be improved.
• Classification of Data: The initial stage in Data Automation is to sort source data into categories based on its importance and accessibility. Look through source system inventory to see which sources have access to. If we are going to use an automated data extraction tool, ensure it supports the formats that are important business.
• Prioritization of Operations: Use the amount of time consumed to estimate the importance of a process. The greater the amount of time spent on manual labor, the bigger the impact of automation on the bottom line. Make careful to factor in the time it will take to automate a procedure. Quick wins are the way to go because they keep everyone’s spirits up while demonstrating the value of automation to the business owners.
• Outlining Required Transformations: Ascertain whatever changes are required to convert the source data to the target size. It could be as simple as turning challenging acronyms into full-text names or as complicated as converting relational database data to a CSV file. Finding the necessary alterations to achieve the intended results during Data Automation is critical; otherwise, entire dataset might get corrupted.
• Execution of the Operations: The execution of data strategies is technically the most difficult component. We’ll look at how to implement three separate processes: better reporting, better engineering pipelines, and better machine-learning procedures.
• Schedule Data for Updates: The next step is to schedule data so that it gets updated on a regular basis. It is advised that you use an ETL product with process automation features such as task scheduling, workflow automation, and so on for this stage. This ensures that the process is carried out without the need for manual intervention.

We can begin implementing our automation strategy once we have a better understanding of the environment of Data Automation within the business. To get started, follow these steps:

Step 1: Identification of Data: Choose a couple of high-value datasets for which gaining access to the source systems will be simple. Determine which source systems you already have access to by looking at your source system inventory.

Step 2: Determination of Data Access: Determine how the data will be obtained. If it is going to be an SQL query, a CSV download, or something else. This stage will require the participation of the Data Custodian as they are the best resource for gaining access to a dataset’s source system.

Step 3: Selection of Tools and Platforms: Choose dependable, well-supported automation tools like Python’s NumPy, Pandas, and SciPy packages. These packages, when used in conjunction with other tools, can automate a wide range of data analytics tasks. Automated analytics solutions are also available on cloud platforms that host enterprises’ data warehouses.

Step 4: Defining Transformations and Operations: Outline any necessary transformations for the dataset. It could be something as easy as converting long acronyms to full-text names, or as sophisticated as converting a relational database to a flat CSV file. Work with the Data Steward and Data Custodian to determine which fields should be extracted and how they should be structured for publishing.

Step 5: Developing and Testing ETL Process: Select an ETL publishing tool and publish the dataset to the Open Data Portal based on the requirements stated in stages 2 and 3. Verify that the dataset was successfully loaded or modified without any issues through your procedure. Iterate, test, and develop. After you’ve prototyped an automated procedure, thoroughly test it. Automation should lower the amount of time spent on repetitive tasks. A failed or propagating error-prone automated analytics system can wind up costing more time and resources than a manual solution.

Step 6: Scheduling the Automated Work: Schedule dataset to be updated on a regular basis. Refer to the metadata fields collected as part of data inventory or dataset submission packet concerning data collection, refresh frequency, and update frequency.

Step 7: Delineate the Objectives and Test the Procedure: Since data analytics is frequently cross-functional, several teams, including marketing, operations, and human resources, may need to be involved in the planning process. Set clear goals and expectations for the automation process ahead of time to help teams collaborate and understand each other as the process progresses. Implement the automated procedure and keep track of its progress. Most automated data analytics systems include recording and reporting features, allowing them to operate with little supervision until failures or adjustments are required.

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