Getting data to work accurately is one of the main priorities most companies have across different industries. Nowadays, inaccurate data means real-world implications that could lead the business to big problems – for example, in finance, inaccurate data can mean violation of regulations and compliance, in healthcare could mean making a mistake when caring for a patient.
 
So data accuracy is not just something to be considered inside data quality, it’s a key characteristic that makes data something reliable to be read, analyzed and used on the most important decision-making inside companies.  

But ensuring data quality is something that was already a concern even before data science became mainstream and most technologies used today were not invented yet – every report that employees had to present and deliver had to contain only accurate and quality information. 
 
Now, with machine learning and massive internal datasets, data quality has only become more crucial and essential to the good functioning of the business. Data analytics also depends 100% on accurate data, and all the data added to the datasets must have value and be accurate from the source. So all those trends make accuracy and enterprise data processing and management more critical than ever.
 
Enterprise data processing is another hot topic for data accuracy, as it is the concept of the ability of a company to easily integrate and retrieve data with precision and accuracy, from both internal and external sources. This processing should ensure that all the information is consistent, accurate and transparent when passing it from one application to another.
 
This concept became critical when companies saw themselves needing to integrate, process and manage stored data across multiple segments and different tools, which could lead to data conflicts, inconsistencies and quality issues, making the data not trustable. The goal was to make data something all users could trust and ensure that everyone had the same information.
 
There are a few factors that should be considered when building a good action plan to create and sustain data quality and accuracy from the beginning. The goal is to start creating high-quality data in the first place.
 

What Is Data Accuracy?

Data accuracy means that the data will be error-free and can be used by the company as a good and reliable source of information. It’s the first and most critical aspect of data quality, as it can impact directly on business decisions. 
 
From planning, forecasting, budgeting, selling, producing and all other key components of a company, data will have a major influence on which direction each area is going, in order to achieve the business goals. One inaccurate, irrelevant, incorrect or incomplete information inside the company’s datasets can result in disruptions, lack of efficiency, astronomic costs and even make the business break.
 

The Main Causes of Data Inaccuracy.

Data inaccuracy does have common causes that can be investigated and solved to make your data work correctly.

• Bad data entries: inaccuracy can be the number one outcome of bad data entry practices. Your company should have data governance, or any kind of regulation, to see if the data entering the datasets are in the right format and style. 
• Unregulated data accessibility: when a dataset is accessed by multiple departments at the same time, it’s easy to create inconsistencies and inaccuracies, from misspellings to crucial modifications to the information. So all data changes must be regulated to avoid that one user will create inaccuracy to others.
• Data quality not being a priority: as mentioned above, data quality is now the main priority for most companies, as they take all base for decision-making from there, but when data quality is not treated with the importance it deserves, it’s easier to make all data duplicate, inaccurate and unreliable. There’s no point in investing in the cloud, expensive systems and huge technologies if the data being treated in those tools is not accurate. Data quality should not be talked about when a problem appears, it should be part of the business routine.

 

4 Factors to Consider to Have Accurate Data

Accurate data standards and factors can differ depending on the data itself and on its nature of it. For your company to deliver high-quality data that can be trusted by all departments, each data storage and dataset created should be managed from the beginning to the end. 
 
By only managing the final data, all the efforts required to ensure data quality will not be enough – as the errors are only found at the end – and it may take too much time and have a high cost to fix.
 
But, if your company is aware of what it needs to have the data working accurately, each dataset will deliver information with quality. The four factors to consider are to get your data working with quality are:

• Rigorous Control of Incoming Data and New Data Entries

As mentioned above, one of the main reasons for inaccurate data comes from poor data received from the sources – especially the outside sources collected by third-party tools that are out of the control of the departments or users. But as the accuracy of the data coming cannot be guaranteed, your company can create a strict data quality control task that will examine the data from the source.
 
This task should be able to check the data format, the patterns found, if the data is consistent and complete, whether the values are okay and if any abnormalities can be found.
 
Some companies already went ahead and created automated data quality alerts and jobs, in a way that any new data entries will be managed as soon as they arrive at their datasets according to their standards and KPI’s. 
 
No new data should be stored in your datasets without this quality and accuracy analysis. The control of this data can be done as you wish and with the tools your company has available, but a simple dashboard with the records and real-time monitoring will be more than enough.

• Robust Data Governance to Avoid Duplicate Entries

When data is coming from the same source, but there are different teams and departments making use of it for multiple purposes, it’s easy to duplicate the information and create duplicated data. And after it’s created, everything will go out of sync and the teams will all be looking at different results, which will lead to inaccurate analysis and wrong decisions can be taken.
 
The best and easiest way to avoid this is by having a robust data governance pipeline that will have the models, the assets, business rules and data architecture as rules. A good program that will define the owners of the datasets, that can later conduct auditions regularly to ensure that everything is in the right place, are also needed.
 
Transparency and clear communication are also solutions to improve efficiency and reduce the risks of data quality issues.

• Data Lineage and Data Integration Processes in Place

When the data is being tracked from the source to ensure that it is accurate and a robust governance pipeline is also being deployed to avoid duplicates from happening after the data is stored on the datasets, any issues identified on the data should not be complex or time-consuming to find and fix.
 
Having a data lineage and data integration processes in place will help your company find the problems without any complexity and the causes can be fixed at the root of it. Those two concepts will enable clear traceability and documentation of each dataset from the beginning. So, with just a few clicks, the full view of the data lifecycle can be analyzed.
 
Although data lineage and data integration are not difficult to implement, making the entire data flow more transparent with easier traceability can take a little time, but it’s definitely worth the effort and will save a lot of time and costs when data issues happen.

• Teams Focused on Data Accuracy

Some companies have one or two teams with critical roles built just to focus on data accuracy and quality.
 
Having people look intentionally on quality assurance of software and programs to ensure that any changes or updates didn’t affect the accuracy of the data. Besides quality assurance, it’s important to have another team focused on quality and accuracy control. This team should know all the business requirements and standards to act on any abnormalities detected. 
 
They must have all the tools needed to identify and fix anything that can be considered a problem or any outlier entry that should not belong in the dataset. This team will focus on the production environment to identify the issues before the users and departments can find them. 
 
The four factors above should all be working together to make the data work accurately. Having only a team to fix accuracy and quality issues is not enough, at the same time that only controlling the data when they arrive from their sources is also not sufficient. All four factors are a combination of efforts to ensure accuracy and success for any business.