Why Is Data Quality Important In A World Of Fake News?
Introduction
In our present world, data acquisition on almost anything is possible and so much easier than any time before. The continuous advancement of Information Technology, backed by the ever-expanding communication channels has left us with a closely connected world where the minutest piece of information can go viral within seconds, regardless of whether it is authentic or not; and this has brought with it both gains and pains.
The constant influx of data from various sources into data hubs and the internet makes it next to impossible for data to be completely error-free. This lack of total control makes it necessary that the primary goal should be for data to conform to the standards set for it. Because these days, just about anyone can generate and share data, the danger of putting out fake or “bad” data is on the increase, making it essential to check, and double-check work data, especially when its origin is questionable.
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Data quality, according to experts, refers to data being in a state where it satisfies the requirements of its intended use. It can be safe then to say that the term “quality” in this case is subjective; different institutions are left to judge what quality is, as it concerns data required for their work. This premise holds then that the definition of data quality is relative; a non-profit organization’s view of data quality will not be the same as that of a purely profit-driven company.
In other words, different institutions know they have “good” data when they can use it to communicate effectively with their publics, determine what their needs are and develop useful ways to serve these needs.
However, despite being relative, all definitions and perceptions of data quality agree that for data to be qualified as “good,” it must be:
• Accurate: Accuracy is the hallmark of data quality, especially, in the area of curbing fake news. Collected or generated data must be accurate and must accurately represent the intended meaning. Any data that lacks accuracy leads to misinformation, misrepresentation, and falsehood. Working with “bad” or “dirty” data does significant damage to the reputation of an organization, putting it in a questionable stance.
• Relevant: Good data must be applicable for its intended use; else it is useless. Relevance is very crucial in deciding data quality.
• Complete: An incomplete data leaves open the error of being misunderstood, and presenting oneself or organization as inefficient.
• Comprehensive: Data makes more difference if it is easily understood. A detailed data makes work easier and useful in achieving whatever goal data is intended for.
• Timely: Data must be received at the expected time; this is to ensure the efficient use of data for work. Having real-time data allows for the making of competent and well-informed decisions.
Challenges of Data Quality
In this age where fake news (caused either through the use of “bad” data or intentionally to malign) abounds, it is pertinent that time is given to run a check on what you don’t know, double-check what you know, and even that which think you know. Always run the necessary checks to ensure that you are working with the right data, also make conscious efforts to constantly improve available data, considering the ephemeral nature of data.
Data quality is plagued by several challenges; only a few are highlighted here:
• Too much data: Having too much data can sometimes be a bad thing; especially in a situation where a good portion of data is not usable. This challenge will see you wasting productive time digging through so much “bad” data, trying to pick out the few good ones.
• Inconsistency: This is especially unavoidable if you are dealing with multiple data sources. A good pointer to inconsistency is data duplication. Once consistency becomes an issue, it is clear that data quality is lacking.
• Poorly-defined data: This is an issue with data management. Having data placed into wrong categories will bring confusion in working with data. Such data is incomprehensible hence, useless for work
.• Obsolete data: The transitory nature of data makes it easy for data to degrade fast. Working with obsolete data increases the chances of errors and fake news.
• Poor data representation: Once data is misrepresented, it cannot be used for work. Poorly represented data is bad data; using it for work is counter-productive.
Bad data can cost you more than revenue loss; your reputation is put at risk, and once damaged, reputation can hardly be repaired, no matter the re-branding and “face-lift” efforts you may make. This huge cost of bad data makes giving every necessary attention, resources and time to ensuring that you are working with high-quality data, worth it.
To ensure data quality, specific standards must be established, and adhered to; some of these standards are:
• Available data must be profiled to determine accuracy, relevance, and completeness
• Data quality must be continuously managed to ensure speed and the making of informed decisions. This is very crucial for businesses that are serious about attaining success.
• Data quality must be improved continuously to ensure that the best data is always available for work. This is important for profit-driven businesses, as it will give them a significant advantage in improving profitability; and for the non-profit organizations, in helping them achieve their mission.
Role of Data Quality
Data quality plays a significant role in different settings; regardless of whether it is a non-profit or profit-driven setting, working with quality data is crucial. For example; “good” data generated from a company’s record past reviews, maintenance, etc. done in the past can be assessed to make better business decisions in the present or for the near future, provided that its “quality” remains. These better business decisions are capable of improving the company’s practices to increase revenue and achieve all-round business success.
Having accurate, relevant and valid data is, without doubt, an integral component towards ensuring that a company does not lose its integrity and reputation or have to deal with any of the consequences that can accompany “bad” data.
In terms of business success, data quality proves to be a highly useful driving force, because decisions are not made based on mere intuition or best guesses, but credible and reliable facts. Errors which could emanate as a result of sentiments or prejudices are reduced to the barest minimum; only facts are considered and analyzed.
Generally, having accurate data can help businesses in a number of ways, some of them are:
• Better Customer Relations: Accuracy and truth form the bedrock of any meaningful customer relationship. Data quality ensures accuracy in communication, which fosters trust and understanding between a business and its customers; businesses can know their customers, what their needs are, and the best way to satisfy these needs. Thus; helping them to focus every effort in serving these needs adequately, instead of investing time and attention to what does not matter to their customers. Doing this will help to create goodwill between businesses and their customer base.
• Consistency: This is more important for businesses that operate several entry points for their customers. Data quality, in this case, helps businesses to avoid duplication in communication, and a host of other challenges that can stem from working with “bad” data.
• Low Communication Cost: This relates well to consistency. Having and working with high-quality data enables businesses to send out only intended messages to the intended audience, without wasting resources on duplication.
• Sales boost: Good data can lead to a dramatic boost in lead conversion rates and closed deals.
How Can Data Quality Help In Different Business Cases?
Data quality is very crucial for businesses in understanding and staying in contact with theircustomers. The availability of data quality makes it easy for companies to acquire data on their customers; making it easier for them to create well-tailored and personalized content for their target audience to achieve set goals, and also encourage and build customer loyalty that can last over decades.
Data quality is very useful in business, as no one can build a business that can stand the test of time, on the foundations of falsehood.
Data Quality In sales: Accuracy in sales is very crucial; building and sustaining long-term revenue pipelines and generating constant revenue are not done on ignoring glaring or not-so-glaring inaccuracies, making approximations and best guesses. Business deals, or even any deal for that matter, are closed based on accurately sound information.
If you do not begin your prospecting process with accurately verified data, you might just be burning resources and wasting time in the pursuit of imaginary leads. This type of situation puts your reputation and your company’s at the risk of being un-informed and inefficient and further causing you to lose an opportunity (as well as possible future chances) at revenue growth.
If you must succeed in sales and close big deals; not just once in a while, but consistently, then you must make data quality a central part of your sales process. To ensure you are always working with high-quality data, you must restrain yourself from jumping into action at any data you receive.
It is necessary that you take specific steps to spot any fake news or questionable data, before swinging into action with prospecting. There is a need to:
• Consider your data source: Ask questions like; from where did this data emanate? Is the source reputable? Did it come from an insider? Can this source be trusted; have you gotten reliable information from this source in the past?
• Consider validity: Ask questions like; how old or fresh is this data? Is this data error-free?
• Consider credibility: Ask questions like; is this data in context with what I already know? How well does it align with what is consistent? Data quality must be in context; if data is left hanging loosely, then its authenticity should be questioned.
• Always run all necessary checks on information tips you receive about possible leads to ascertain data quality. Take advantage of every tool available to you; marketing automation systems, sales tools, etc. check, check and check yet again. Repeated checks to ensure data quality is worth it because accuracy is the foundation for building a strong customer relationship, which is central to business growth.
Data Quality in Marketing: Data quality plays a very vital and evident role in marketing. Working with high-quality data on target audience specifics like demographics and other relevant data makes it easy for businesses to rightly target their audience, focus their marketing efforts effectively, and ultimately, achieve their desired results.
The availability of “good” data helps to cut the cost of marketing and increase speed. When working with quality data, marketing messages can be well crafted to appeal to the target audience; instead of “throwing” marketing messages into the space (which will cost more) hoping that the right people will see and connect with it, messages can be personalized and sent to only the target audience, reducing cost and ensuring speed.
Data Quality in Non-profit Organizations: A non-profit organization can lose its reputation and goodwill from its audience in the absence of high-quality data.Undoubtedly, misrepresentation and misinformation can put a non-profit organization in a bad light; hampering the achievement of its mission.
Working with “bad” data can cause a non-profit organization to lose whatever credibility it once had; and this is bad for business, as a good image is the pivotal part of its growth. Every act of goodwill, moral and material support, once enjoyed from its audience will be out rightly withdrawn from any non-profit organization that is perceived as fake, for whatever reason(s) by its audience.
Conclusion
Data quality is central to any effective communication; be it in business or any other sphere of life; its importance cannot be under-played if success is the goal.
Information and Communication Technology advancement, though plausible has made data quality almost impossible; the constant influx of data in to the internet from various unverifiable sources, makes it even harder to separate what is true from what is false and what is real from what is fake.
Regardless, there are necessary measures towards ensuring that work is done with only high-quality data. The process of doing this might be strenuous, but the gains of getting it right far outweighthe pains of not.
Data quality is achievable and crucial in today’s world where “bad” and “dirty” data sell faster than the good.