Click to learn more about author Daniel Pullen.

Nowadays, we are surrounded by data: We produce a lot of personal data and work with a significant amount of data. When it comes to the business environment, data is crucial for effective decision-making, which makes it a highly valuable resource. But it needs to be well managed in order to lead to insightful conclusions.

Since the pandemic has only accelerated the adoption of technology throughout industries, companies produce even more data because of the rapid shift to digitization.

As data volume and complexity are steadily increasing, the importance of Data Quality is also higher. For example, data scientists may spend 80 percent of their time each day cleaning the unprepared data, so they have less time to develop other important business insights.

In order to benefit from the full potential of data, in 2021 organizations will have to reshape their Data Management strategies and invest in technology that can simplify processes, in order to reveal valuable insights and make strategic decisions.

How? If data scientists are currently performing many manual tasks, such as accessing, preparing and managing data, automation is the solution to simplify all these processes.

According to the Research Hub’s 2021 Trends in Data Management report, by 2022, 80 percent of the mundane Data Management tasks will be automated through augmented Data Management (ADM), allowing data scientists to focus on the development of valuable insights.

ADM is the application of artificial intelligence and machine learning in the automation of manual tasks in Data Management processes. Data Management automation will not only lead to a higher level of productivity and efficiency, but also cost savings.

According to Gartner, automation will have reduced Data Management manual tasks by 45% by the end of 2020, as executives learn how Data Management tools save time and money.

One of the most important tools in the Data Management automation process is robotic process automation (RPA), which can automate tasks like data extraction, data cleaning, backup, storage, and identifying misconstructions. Basically, RPA for Data Management refers to software robots that can perform manual, rule-based, and repetitive tasks, enabling humans to focus on more important tasks.

Let’s see exactly how RPA works when it comes to Data Management.

Use Cases for Data Management Automation

1. In the Manufacturing Industry

Companies from the manufacturing industry rely on high production volume, so robotic process automation can successfully contribute to accurate reports that can be used for strategic business, leading to cost saving and increased efficiency.

Also, RPA can be used in inventory administration to automate emails, monitor inventory levels, or digitize paperwork. Last but not least, software robots can extract the necessary data in order to automatically generate invoices.

2. In the Banking Industry

Banks and other financial institutions have to keep track of all the personal information of their customers, like names or addresses, that can change a lot over time.

So, instead of extracting and processing manually all of the data, RPA can do this a lot faster and, what’s more, with an extremely low risk of committing errors. This also translates to higher customer satisfaction overall: Nobody likes to stand in long queues at the bank.

In this industry, robotic process automationcan also be used to produce detailed predictions and reports about customers’ payment behavior.

3. In the Health Care Sector

When it comes to this industry, Data Management is crucial, as it impacts the health and lives of people. Health care workers need accurate data, and fast, in order to make the best decision for their patients.

Let’s take a closer look at how RPA could impact Data Management in health care:

• Patient registration: This is a very time-consuming process, so software robots can extract the identification data from IDs in just a few seconds, with zero risk of errors, allowing workers to handle more important tasks.
• Appointment scheduling: Instead of using call centers with human employees, RPA can streamline patient appointments according to various criteria such as diagnosis, location, doctor availability, and so on.
• Processing insurance claims: As it involves a lot of paperwork, claim management could be successfully automated through RPA, speeding up the processing of the documents.
• Medical data recording and generating reports: Software robots can enable doctors to deliver more accurate diagnosis and the right treatments for their patients. 

4. In the Public Sector

Public authorities are responsible for the administration of many crucial processes for the smooth course of society and economy. This requires a lot of data, especially document handling and validation. Implementing RPAcan lead to operational cost savings and a higher level of efficiency and productivity.

It can also improve citizen satisfaction, if we think about reducing the paperwork needed when it comes to tax payments.

5. In the Fast-Moving Consumer Goods (FMCG) Industry

The vast majority of tasks in the FMCG industry can be automated for better operational processes. Data Management automation here refers to software robots that can be used for:

• Product categorization: Extracting data to offer a better view of market shares.
• Product delivery and returns: These processes require a lot of manual tasks, so RPA can accelerate them.
• Create an inventory: Robotic process automation can check sales data and report when there are too many or too few products.

For example, we helped an Australian FMCG food company that automated delivery confirmation in ERP. The implementation time was one week, with the following outcomes:

• 90% of effort automated
• 100% process uptime and completion
• Instant ROI as process was sidelined
• 95% reduction in processing time
• Zero error rate
• Up-to-date delivery status, prompt resolution of non-delivery, and faster remittance of open invoices

If you’re curious to find out more, you can read the case study here.

6. In the HR Industry

Employee Data Management is crucial for HR departments, and it generates a lot of paperwork that needs to be managed and stored – from hiring contracts, payroll, and other documents.

Instead of performing them manually, RPA can handle this a lot faster, reducing the risk of errors and, therefore, enabling HR specialists to focus on shaping strategies to improve employee satisfaction.

Conclusion

Technologies such as robotic process automation and artificial intelligence are already influencing our lives in an unprecedented way, from the way we live, work, and even to the way we spend our free time.

Data Management automation will become an increasingly valuable practice for any business and industry. It will contribute to overcoming the challenges of handling data – from accessing and storing to preparing or analyzing it – aspects that can be a significant barrier to success in any company if they aren’t optimized.