So many technologies are a part of the collection and extraction of modern data, and they play a role across a variety of industries. Big data, for instance, is the general platform that involves collecting, storing, analyzing and procuring insights from vast quantities of data. AI and machine learning are two technologies used to sift through the data and information in smarter, more efficient ways. Mobile and IoT devices are used to collect the data from customers, users and audiences.
As you might expect, data can play an integral part of decision-making and strategy-building in industries such as healthcare, retail and marketing, manufacturing and so many more. But it is of no use, to anyone, if it is not reliable or accurate. This component or element is referred to as “data veracity” and is also one of the five V’s of data. The five V’s include volume, variety, velocity, veracity and value.
Inaccurate or invalid data can cause significant issues, the least of which is skewed insights and poor decisions or actions. Let’s say, for example, incorrect data shows that a particular diagnosis fits a patient’s symptoms. Doctors and health professionals might then launch related treatments, only to find they either don’t work or outright harm said patient. Inaccurate data in healthcare and medical fields can be detrimental to all involved, potentially leading to the death of a patient.
You can see why data veracity is so important.
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Ensuring Data Veracity
There are three major facets to data veracity:
- Is the data accurate?
- Is it precise in regards to what it’s reporting?
- Is it trusted, and does it come from a reliable source?
Any one of these facets can lead to inaccurate data, or questionable yet warranted doubt as to its reliability. If a data source cannot be trusted, how can the subsequent data be? If you can’t pinpoint the accuracy, how do you know the resulting information is true? If processes and measurements are not precise, then you can’t be sure the insights being reported are accurate.
Furthermore, ineffective veracity can negatively influence the other Vs or elements of data. If data is not accurate, its value suddenly becomes much lower if not gone entirely. Volume and variety, at least for reliable segments, also decrease.
This is why many regard veracity as one of the most important aspects of modern data. So, then, how do you ensure data veracity is of the highest standard? Where do you start?
Mitigating “Dirty Data”
Erroneous or inaccurate data is often referred to as “dirty data” or unclean. For obvious reasons, it’s important that the information flowing in — or out — remains both accurate and reliable.
To do this, you must follow a basic process to secure and understand what you have available. Step one, for example, is to know your data and data streams.
1. Know Your Data
Before you can process, clean and organize your data, you must know where it’s coming from, what it’s going to be used for and how it will apply to your business and strategies going forward. Without a proper direction, there’s no way to know just how valuable the information is, nor what parts are most pertinent to your current project(s).
Establish what your system will be doing — and, more specifically, what how you’ll leverage the data. Do this before you do anything else or even sift through the information.
2. Align Your Inputs
Data often comes in varying forms, most often through individual fields or elements with separate details. It is then parsed and added to a larger database, which advanced systems must further organize and analyze.
Think of a simple contact form on a website, for instance. Each field denotes a particular segment of data your customers or audience will be providing, from their name and location to their email address. If a customer includes the wrong information in a particular field, that dataset essentially becomes useless until you can swap the appropriate information.
It’s all about aligning your inputs, or the data coming in to match up with your overall database and the information you want. Ensure your systems are collecting and reporting the necessary inputs.
3. Vet Your Source
You will find there are numerous sources or origin points for data. You won’t always be collecting it from your customers directly. Sometimes, it will come from IoT or connected devices, and other times it will come from point-of-sale systems. It may even come from co-workers or alternate streams — such as a mobile app.
Before extracting and merging this information with your core database it’s important to both vet and identify the source, as well as the validity of said source. If you’re collecting location data, for instance, is it from a device that can be trusted and you know takes precise measurements?
Coca-Cola relies on incoming opinions and preferences from their customers to inform marketing and sales decisions. They cut right to the source directly to find the information they needed — in this case, it was their target audience. It’s important to consider how Coca-Cola is using this information in relation to the source because it makes a huge difference as to the value and accuracy of the resulting details.
4. Prioritize Data Governance
Data Governance, which you are likely familiar with, is the general management of data in an enterprise setting. It expressly outlines procedures for ensuring the availability, integrity, accuracy and security of data as it pertains to the organization. Incorporating a data governance strategy, especially across larger organizations helps keep everyone on the same page, particularly from one siloed department to another.
Data governance is itself admittedly more complex in nature, but having it in place can both verify and authenticate any data being collected, handled or stored by your teams.
Data Veracity Is of the Utmost Importance
In the end, when dealing with data, no matter the industry, veracity is incredibly vital to its value. Can the information being collected be verified and attributed appropriately? Is it useful for what you’re trying to do, and is the right information accessible?
How you answer these questions will determine the overall usefulness of your data systems and the resulting insights gathered. Remember, although the raw data is being processed to extract actionable intel, that information will be utterly useless if the core data itself is not trustworthy.
It’s up to you and your teams to establish a system that can verify, evaluate and leverage the data streams flowing into — or out of — your business.