How data preparation automation accelerates time to insights?

The amount of data generated and consumed are just massive. The advancement of technology makes sure that the amount of data that we deal with will only keep increasing in the upcoming years, says Yash Mehta, an IoT and Big Data Science specialist.

Closer to the end of this decade, the total amount of data will reach up to 572 Zettabytes which is almost 10 times more than the amount of data present at the moment. Eventually, managing and organising data will become a very complex task for organisations and the process of collecting valuable information from the gathered data consumes huge amounts of time.

Gaining real-time insights and staying ahead in the market from the rest of the competitors and the resultant pressure to work faster simultaneously is one of the major challenges faced by organisations today. Doing everything manually is not impossible but there are many challenges in doing all the tasks manually. Hence automation has become the only way for organisations to gain valuable information and streamline the data transformation process. According to a data fabric trends report, it has been found that the data automation market size will reach $4.2 billion (€3.56 billion) in 2026. 

Strategic data automation

There is a common misconception among people when they come across the concept of automation that automating business processes means replacing manpower with technology. It is important to understand that automation does not replace humans in workspaces, instead, they help them in doing their tasks seamlessly and efficiently. There is literally no technology as efficient as the human brain to analyse complex data sets.

While most of the repetitive and monotonous business operations can be automated, it requires the implementation of business logic and rules to be applied within the code that is manually coded. Interpreting and making the right decisions for business requires human intelligence and it always will. Automating tasks like cleaning up and preparing data will provide a lot of time for conducting various complex data analyses. 

Despite the expertise of the developers, the increasing need for automation will make it impossible to keep with the growing amounts of data and gather expedient insights from data. Manual coding to implement the necessary logic into automation will be very challenging when it has to be done with huge amounts of data in a very limited amount of time. Discovering new ways for data preparation and business automation will help in achieving faster time to insights.

At present, there are various data preparation tools available in the market that offers trusted, current and time-based insights. These tools encrypt the data, making it more safe and secure. For instance, K2View’s data preparation tool captures all attributes for a business entity such as customer orders and details. Additionally, collecting, processing and pipelining data by a business entity ensures data integrity while offering quick, easy and consistent access to required data. There are various other tools like Alteryx, Cambridge Semantics and Datameer.

The need for automating the data transformation process

Apart from automating repetitive and monotonous tasks and offering the organisations more time to work on other aspects of data processing and analysing, automation offers various other benefits as follows.

Maintain data records – Automating data transformation methods will allow firms to organise new data sets effectively. This, in turn, will help in maintaining the overall data sets and making them available whenever needed.

Focus on main priorities – The role of the business intelligence (BI) team is to not only deliver timely and important insights. They have to work on innovative initiatives that are highly essential for the business. As mentioned earlier, automation tasks will provide them sufficient amount of time to work on business’ vital aspects.

Better decision making – Automation enables fast access to more complete and accurate information. This will help the management teams to make strategic and swift business decisions.

Cost-effective business processes – Time is an essential factor for any business. Automating the data transformation process and other data-related tasks paves the way for reducing costs and consuming resources more efficiently while providing better results.

Ways to automate workflow

Usage of a built-in scheduler and third-party scheduler

ELT product comes with a built-in scheduler. This eliminates the need to rely on third-party applications or other platforms to launch the product. ELT tools also enable managing tasks centrally which makes it easier to maintain and manage the tasks. Another benefit of using ELT tools is dependency management. Here, a parent job can be used to trigger child jobs. Dependency management helps in categorising tasks and makes management easier. Many platforms allow executing APIs. The API calls can be scheduled in a preferred way using the operating system’s built-in scheduler.

Many third-party tools can perform ELT tasks. Using these ELT tools will provide functionalities to integrate with legacy systems within the development environment. But to use third-party ELT tools, additional costs have to be paid for the services and resources used to implement a product.

Cloud service provider services 

Companies are rapidly shifting towards cloud technologies. It has been found that 94% of the enterprises have already shifted towards adopting the cloud. Apart from storing and managing data, CSPs offer many other services that assist in automation. Such as using messaging services to trigger a task. 

Yash Mehta

Any custom tasks or production tasks that support messaging can listen to the incoming messages in a job queue and initiate a job based on the contents of the message. Despite the capabilities and the features of the product, the general working concept remains the same. AWS SQS, Microsoft Azure Queue Storage are some of the examples of such messaging services.

Apart from the above-mentioned messaging services CSPs can also provide serverless functions to help with automation. The serverless functionality can be used to trigger the jobs automatically. The advantage of using serverless functions is that the company only has to pay for the services when the functionality is being used. AWS Lambda functions and Google Cloud functions are examples of serverless cloud services.

With the help of Artificial Intelligence and Machine Learning, automation will become much easier and efficient. This, in turn, will enable organisations to prepare the data and gain more insights effectively as the technology evolves. But to adopt these technologies, organisations must have an open mind to accept and embrace the changes that come with adopting these technologies. 

The author is Yash Mehta, an IoT and Big Data Science specialist.

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