Big Data: A Stairway To Heaven Or A Highway To Hell?

 Are you eager to get big data? Slow down and learn about the issues that can emerge when designing a big data project. As a data consulting services provider, Platingnum will help you overcome these issues and harness the full value of big data.

Big data poses not only many challenges, but also severe threats. And here’s the difference between them.

If you go to a store and a big puddle falls in the path, you might choose to walk past it or climb over it. If the puddle is too big, you would ask others to assist (turn to ‘puddle consultation’). After that, experts will clear the puddle. This is a challenge: an obstruction in the path that can be resolved very quickly.

But later, you know that you need to walk through a bad neighbourhood to get to the store. Then it’d take too long to drive around so the store will close when you get here. It is a more basic topic that can trigger problems.

Obviously, these walking issues cannot be compared to big data issues, but the idea is also the same: obstacles are on the top, while difficulties are of a deeper sort. We’ve already discussed in this article what big data challenges you can face. But what are the issues?

Issue #1: Inadequate Big Data Analytics

While data scientists everywhere are doing their hardest to boost data quality and to make predictive algorithms more resilient (data-related issues immune), big data analytics is not ideal. It’s just not yet possible to fix any of the issues related to the reliability of your data.

More ≠ better

Like heavy snowfall, data is piling up at a high pace and a gigantic amount. You may think it’s good: more data means more accurate insights. But in fact, large volumes of data do not always equal large volumes of actionable insights. Often the data that you have and all the information that it provides statistically is simply not a representative sample of the data that you need to study. For example, views on Twitter vs. opinions of the general public. Let alone the prejudice of the former, it doesn’t even include the views of the whole society (for example, the elderly and the introverts often get excluded). This way, you can quickly get the wrong research findings.

Besides that, in such ‘heavy snowfalls,’ it actually becomes more difficult to locate what you need while removing data that bears no use whatsoever.

Weird Correlations

We all know that big data is useful at discovering correlations. If there are any, they’ll find all of them. But the point is that the similarities that big data often discover are not significant at all. Suppose the total number of AC/DC songs purchased in the UK has fallen over the year, and so has the UK crime rate. Could that suggest that AC/music DC’s encourages people to break the law? No, no. But big data will also show you this correlation. And this is how you can spend a lot of time, manually looking for very interesting correlations in a strange sea.

Going Around in Circles

If the text is machine-translated, say, from English to Japanese, there is a big risk that the result will be at least a little incorrect in a few areas. But if a text such as this is left alone, it’s not that bad. It is much harder when such an incorrect translation is used by another big data algorithm as a ‘source of truth.’ The results of the big data analysis would be far from sufficient if the big data tool uses the snippets of information provided by another big data algorithm as raw data. The more ‘circles’ there are, the worse the result.

Sly Users

Big data algorithms are also dependent on unique markers ‘attached’ to the item being examined. And because of this, the findings of big data analytics can be ‘falsified.’ As soon as anyone determines which markers have an impact on the result, they will change the evaluated items to meet the conditions set by the markers.  The perfect example here will be the sly students and their attempts to cheat point-scoring software.

‘Rares’ and ‘Subjectives’

Not all, by its essence, can be evaluated simply by crunching the numbers. The more arbitrary or unusual the item being analysed, the greater the risk of poor outcomes. As an example of ‘rare,’ let’s see if Google will translate a poem. The response would be, “Very bad.” Partially because poets prefer to use exquisite and eloquent words that Google has never seen before. But that doesn’t mean that these words are incomplete or should be replaced by synonyms, doesn’t it?

And as an example of the ‘subjectives,’ let’s try asking a big data analytics platform to tell us what poets are the most influential in history. There are different ways to get a response here, but odds are, it’s not going to be that accurate. And it is understandable: among several obvious empirical considerations, this issue is profoundly subjective.

Issue #2: Hasty Technical Advancements

Techni-uncertainty

As far as we can see, there are no factors that can limit the technical growth in big data. It’s going to grow more, and maybe even faster, which is precisely the problem. At such a rate, it is impossible to predict whether it would be effective to address the potential problems using the technologies that you have to choose now. Much like a smartphone, you can buy the hottest one ever, but it’ll be old in a year, not gold.

Still Under-Qualified

The shortage of trained experts in the industry is one of the oldest issues with big data. It was like that in 2014, and it’s like that in 2021. And rapid technical innovation also adds to the cause. As a result, many businesses need to retrain their own employees or work with under-qualified ‘outside’ professionals.

Issue #3: Negative Social Impact

Big data is unlikely to influence society as much as the appearance of cell phones, but it also triggers some disturbing patterns that affect all.

‘D’ for Discrimination

As mentioned above, big data analytics depends on some of the markers of the analyzed items. If the person attaching the marker seems to have a bias against the problem, the outcome will be affected. As a result, bias markers are equivalent to bias research. And it’s very upsetting for some software. If a bank credit-scoring app looks at your social networks and sees that you like rap music, you can score less and not get the much-needed loan. Essentially, this is yet another way to discriminate against others.

No More Privacy

Suppose you go to the travel agency’s website to check how much it costs to go to Greece for the summer. And, you’re going back to that critical job you’ve been doing. And when you’re surfing the web for work-related data, you’re unexpectedly starting to see endless advertisements for Greek travel packages. There’s a lot going on, doesn’t it?

Big data mechanisms are used to identify your preference in a single product or service and then make a customised bid to increase sales. And as long as those targeted deals are valid and don’t threaten your ‘internal site space,’ that’s fine.

But once you start thinking about it, how many strangers already know that you’re going to spend time in Greece this summer? And while this scenario is very innocuous, what if your current position gets in the wrong hands?

These questions remain unanswered for now.  Governments in the UK and Europe are trying to combat it, but unregulated use of our personal information still leaves little – if any – room for privacy on the Internet.

Don’t Get Overwhelmed Though

Despite all the big data issues, you’re not supposed to get worried and try to stop big data as soon as you can. Yes, it is not yet feasible to find a solution for all of them (imperfect big data analytics, privacy violation issues, hasty technological advancement). However, it is more than possible to identify and execute these workarounds with the assistance of experienced Big Data Consulting experts. This way, big data issues are only going to be in the background, as the company thrives and climbs the stairway to Heaven (on Earth).