Photo by Joshua Sortino on Unsplash
In the 21st century, running a business means managing data in the most effective and appropriate way. From software development and interface design to hardware maintenance and information technology, there are many facets of how you take care of data. What informs research? How can you best meet your customers’ needs, and how do you ensure that all departments have a good enough CRM to ensure flawless communication?
These are the questions to consider if your business is at the forefront of technology in general. Maintaining a modern business means answering questions like these and having many balls in the air. Even in the 90s business owners needed to wear many hats to ensure that their business were running smoothly, and this was before social media.
The modern data stack is not beyond comprehension, but there are certain specific items of which you may not yet be aware, especially if you do not have much of a background in software development or computer science. One extremely important facet of which you may not be aware is data reliability. Data reliability is as immediately important as the other aforementioned aspects of data development and maintenance.
What Is Data Reliability?
Data is said to be reliable if it is current, relevant, and effective. Say you have stored data in an old spreadsheet that has been shoddily maintained for years. Every time you query the sheet, it seems like the information is not quite right. The very numbers do not make sense, either being too high or being too low. Sometimes, there might be fractions where whole numbers ought to be. There should be strings where integers ought to be. Your data is unreliable, and you need to develop access to reliable data.
Data unreliability is very common among growing businesses that are transitioning from startup to a more long-term status. As businesses scale and employ new technologies, old information can fall through the cracks. The more information a business needs to manage, the more complex the technologies to maintain data must be. Singular manual errors like typos and syntax errors can destroy entire data sets or otherwise render data sets obsolete.
Regardless of your company’s size, someone is going to make a mistake. Speed is the most important part of software development, so developers cannot afford to be accurate. This is why it is important to invest in tools that guarantee the reliability of data. Procedures that prevent inaccurate or incomplete data from advancing through the ranks of your business’s data stack should already be in place.
Such a supportive investment will allow for your business to maintain confidence in the data with which to conduct research and analysis. The right data leads to fruitful research. Fruitful research leads to insights with regard to addressing customers’ needs and addressing the internal needs of each of your departments.
An important aspect of any business’s data stack, data reliability is an important module that has a major effect on business outcomes, and there are many tools and technologies your business may employ to improve reliability and maintain consistency, no matter the fiscal quarter.
What Tools Improve Data Reliability?
There are several tools you can employ against the inconsistencies inherent to long-term data collection. Two of the most critical tools most businesses employ are transform tests and alerts. A transform test is a test during which code identifies some sort of bug or issue and flags outcomes as either unwanted or significant. It is similar to debugging more generally. If the test uncovers an issue, then the faulty code will not be live in such a way as to damage the reputation of whatever company. This is because transform tests run before the transform; the actual running of the code.
Transform tests can be a good way of keeping inaccurate data from spreading across departments and making its way into the actual archives and warehouses of a business. Transform tests will allow for customers to maintain trust in your business.
Alerts are a bit more fundamental. If a business fails to monitor its data, then the worst part may be missing out on key outcomes. It is not that the business has gone downhill, but it has missed an opportunity to move uphill. Automated alerts can ensure that you are reaching your goals and genuinely advancing the effectiveness of your business.
Data alerts are the ideal manifestation of vigilance against errors in data, acting as one of the only bastions against inaccuracy that a business ultimately has. Data reliability is a constant battle for which there is never enough preparation. These alerts and transform tests are the backbone of data reliability. To employ both is to transform raw data into deidentified usable data that can inform future research.
