Power BI Automation: Drive Data Process for Tailored Outcomes

Power BI Automation: Drive Data Process for Tailored Outcomes

There is no doubt data automation is crucial for business sustainability. Statistics from the International Data Center (IDC) reveal the global data sphere will expand to 163 zettabytes, equivalent to 163 trillion gigabytes, by 2025. Such vast amounts of data will definitely pose a challenge to a business trying to make sense of disparate data. Data automation is a critical process to help achieve efficient data analytics and derive meaningful insights to drive faster and better decision-making. Automation saves time, increases productivity, speeds decisions, and drives efficiency while reducing costs. This blog looks at how Power BI Automation helps drive data processes for tailored outcomes.

What Are Automated Data Systems?

Data automation refers to the process of uploading, handling, and processing data using automated tools in place of manual processes. It employs unattended processes and self-updating procedures to carry out administrative tasks in a database. The automation of databases and procedures helps minimize errors on deployments, enhance reliability and improve the speeds of implementing changes.

What Is the Purpose of Automated Data Processing?

Data automation provides excellent incentives for a business. It is a cost-effective and productive solution for companies and businesses seeking to improve work efficiency and save costs. Besides, automation is also beneficial to your workforce, who are freed from monotonous and tedious tasks, so they focus on more challenging and highly stimulating activities. In a nutshell, the following are some of the ways automation revolutionizes business processes:

  • Reduces processing time: processing large amounts of data originating from disparate sources is a daunting and time-consuming task when done manually. Data extracted from diverse sources must be standardized and validated before they are loaded into a unified system. Automation helps save lots of time during this process by eliminating manual intervention translating to reduced resource utilization, time savings, and enhanced data reliability.

  • Improved performance and scalability: data automation results in better automation and scalability of the data environment. Automated data integration tools allow for simultaneous loading of data and CDC without the need for significant expertise. When data analysis demands little or no human input, data experts will do more complex analytics faster and more efficiently.

  • Cost savings: automated data analytics doesn’t only save time; it saves costs too for a business. When using manual data analysis, employee time tends to be more costly than when employing automated tools that execute analytics quickly and more efficiently.

  • Better allocation of resources: data automation leads to better allocation of time and talent. As mentioned earlier, once you take away the mundane and monotonous tasks from the hands of your employees, data scientists will focus on generating fresh insights that support data-driven decisions. Data automation also allows data experts to work with highly accurate, complete, and up-to-date data to broaden the scope of their analysis and conclusions.

  • Improved customer experience: automation helps streamline and simplify the business process, directly impacting how customers interact with a brand. Good CX means managing customer expectations consistently across all channels, including websites, SMS, chatbots, customer service center, and more. Businesses can leverage automation to engage customers better and improve CX through better and more efficient customer communications, generation of reviews and actionable customer feedback, and creation of relevant, targeted ads to improve revenue.

What Are the Disadvantages of Data Automation?

Although data automation brings about massive positive changes in organizations, there are a few shortcomings which should not be overlooked:

Failure: The absence of a human perspective in data analysis of big data may cause the process to fail ultimately. Using automation tools to draw conclusions from billions of data might prove effective to a degree. However, big data automation could promote over-dependence on automation which causes experts to ignore quantifiable values like experience and intuition.

Erroneous conclusions: Although automation can significantly improve big data analysis, flawed reasoning could lead to erroneous conclusions. Automating big data may limit possibilities and strangle innovation by restricting human behavior to artificial data sets.

Fraud and manipulation: Automating big data could lead to fraud and manipulation. Programmers with hidden agendas could introduce biases deep into algorithms on behalf of vested interests.

How to Get Started with Data Automation

It is essential that sales, customers, and inventory data be automated. However, if you think another type of data needs automation, you can include it in your automation strategy. The first step in your automation journey is to come up with a checklist to help decide the ideal data for automation. The following is a general checklist:

  • Data that require frequent updates

  • High volume data

  • Data originating from heterogeneous sources

  • Data that require manipulation before uploading/processing

Generally, consider automating any data set requiring frequent updating, transformation, or manipulation. Any data with a significant size should also be automated.

Data Automation Strategy

Data process automation strategy must align with your company objectives. The following are the steps to help put your automation strategy to work:

Classify data: the first step is to categorize the data based on ease of access and priority. Check your source system inventory and develop a list of sources you can access.

Outline transformation: your next step is to identify and outline the transformation crucial to converting source data to the required size. For example, converting complicated acronyms into full-text names. It is essential that you identify the right transformation to guarantee desired results and avoid erroneous data sets.

Develop and test the ETL process: select the ideal ETL tool based on individual data automation requirements. Ensure the tool has all the crucial features you need for processing or updating data and conserving quality.

Schedule data for update: The last step involves scheduling your data for timely updates. During this stage, leverage an ETL tool that has process automation features such as workflow automation.

Power BI Automation: The Future of Data Automation Process

When it comes to data automation processes, it is important that you use the right tools to achieve your objectives. PBRS for Power BI enables you to schedule, automate and deliver data reports generated by Microsoft Power BI and get them into the hands of people who need them in real-time. PBRS enhances efficiency while saving time and cost by allowing you to automate the distribution and delivery of SSRS & Power BI Reports & Dashboards. It can automatically schedule and run Power BI reports and send them in diverse standard formats such as Excel, Word, or PDF. If you have further questions, don’t hesitate to contact us.

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