Business is still as risky as it was ever before with high stakes and massive investment on the line. However, something in the last decade has changed. Making business decisions is no longer similar to trying to hit a target in the dark. Big Data and analytics have provided sudden visibility to the business owners. Based on the newfound information that data analytics imparts, making more informed decisions have become possible.
As data-driven decisions and its success are gaining popularity, new methods and approaches are coming to light to use data for recommendations, predictions, and associations.
As exciting as it is, one thing often goes ignored by the data managers and often leads to damaging choices via data analytics. This often ignored and overlooked a detail, is data integrity.
The quality of data is the foundation which balances the future of a business. If this foundation is cracked, chipped, and missing pieces, the entire structure is likely to collapse, now or later, but it will fall.
Gathering data alone should not be the priority, but gathering quality data should be given precedence over all.
That leads to the question, what exactly is data integrity and how can a company ensure its accuracy with RPA?
Let’s find out:
What is Data Integrity?
There are certain attributes that differentiate quality data with an unruly one. As long as the data in a business owner all these features, there’s no doubt left of its integrity.
Following are some of the most important boxes every data should tick off, in order to maintain integrity:
Data consistency refers to getting the same results no matter how many times you search for it and regardless of the different sources you accessed it from.
This can only be achieved when all the data is collected in a similar way, keyed in the system in the same format, and exactly at the same location. The source of inputting the data could be from any part of the world, but the data should be stored in a database that is specifically designated for it.
A complete data would be the one where all the standard and expected data is collected. For instance, a supply business expects to know that the exact quantity of every item in their warehouses in all locations. An incomplete data would be one where the number of items in some locations is in the database whereas others are missing.
There’s a gap in the data even though the standardized system expected it to be there.
In order to make data-driven decisions, businesses need to have unique data in their database, not just generic ones. A generic data can be accessed by anyone, but it is unique data that gives companies an edge over their competitors.
Not all data is good data, especially if it is no good for the business. Before establishing a data collecting system, business should decide on the exact data they require to get a better insight into their targeted clients. A random collection of data would just end up wasting time. For instance, if a supplier wishes to know the product demand in the market for stocking up or the quality of a product they have recently started supplying, they need to collect data from their business clients, asking them specifically regarding the feedback they received on the product.
In addition to the above-mentioned attributes, there are many others, such as timelessness, reliability, validity, and accuracy of data that guarantees data integrity.
Now the question is, how do we ensure data integrity?
RPA and Data Accuracy
Robotic Process Automation tools are more popularly known for taking over repetitive tasks and back office processes.
Most business deploys it as a part of their digital workforce and reducing downtime of manual tasks. There are very few businesses that deploy RPA for any purpose other than taking over monotonous tasks.
Whatever the reason for deployment though, RPA brings along with it an added benefit, management of data and ensuring its integrity.
Here’s how automation ensures the integrity of companies’ data:
Inputting data is a repetitive task and it often involves numbers. The boredom, the tiredness, and frustration can lead to mistakes and errors. No matter how carefully an employee does it, there are always errors.
Replacing these employees with RPA guarantees that it is error-free. Even if RPA continues to work for 24 hours straight, it will still make sure the data is correct and free of errors.
Synched and Updated
One of the main reasons why data is often inconsistent when humans input it is because they cannot sync and update the database every second someone adds more data into the system.
RPA solves this problem in a heartbeat. Even when data is added sporadically, such as in case of inventory management where goods are constantly on the move, RPA updates the database as soon as there are changes in real-time. Not just that, but it also updates the system across all channels.
When, at any time of the day, the business or their clients want an update, RPA can provide it within seconds. With automation, businesses can always access the latest, most updated data for analyzation.
When a constant error is occurring in data collection, it is impossible to trace it back to the real culprit. As a result, the mistakes and errors keep on repeating and subpar quality of data keeps on being recorded.
RPA is a blessing in disguise for businesses that deals with complex data management and needs to ensure that data is 100% accurate and error free. In this case, tracing the problems in the data collection system and correcting it is no problem at all. RPA provides logs and record of all the changes it has undergone. It’s easy to look at it, and see the defects in the data collection process and correct it.
RPA picks up where humans slack, and gives them a chance to work on something that keeps them energized. Data collection, however, is not one of those things. Bigger the businesses get, more impossible it becomes to maintain data on such large scale and maintain their integrity as well. There is no choice anymore and RPA is a necessity when it comes to maintaining the highest quality of data.
Omnisys Solutions has a well-developed practice in RPA, which supports both data analytics initiatives as well as carry out automation driven initiatives. With a focus on business value, Omnisys leverages RPA to deliver tangible dollar benefits to its customers.