Is Big Data in trouble?

Two development firms in Big data and Analytics space; DATA -Tableau and Hortonworks saw a critical time when their data released missed the forecast by 0.05$, and dropped their stock by five percent.  This is what going frequently with companies and no one has any clue on what is going on with BI business Intelligent and Hadoop space. Should companies run from BI and Big data space before it completely collapses?

Instead of focusing on the sensational headlines; the investors and technology corporate leaders should also focus on the missed forecasts as they leave some clue on some important analysis and trends which will help them to grow.

While the companies need to look at their results in the context of the industry as a whole; which will show the exact results as per the worldwide analysis. As per the Gartner’s analysis for worldwide dollar-valued IT; it says that IT spending has grown in 2016 at a flat percent of 0.0. However, 35% of growth is fairly incomparable by this benchmark, and if we look at Hortonworks’ results for this gone quarter: then the total revenue grew by 46% year over a year.

This means, the Investors’ expectations are growing high and even they are tough to manage. To manage this kind of issues the industry observers and technology buyers should standardize the performance of both organizations against the rest of the industry before they make a knowledgeable conclusion.

As per recent survey —Teradata also reported revenue and its business shrink by 4% Year over a year. Leaving other things remaining equal, the analysis says that Hortonworks could generate more revenue than Teradata by 2020.

Let’s look on some of the data analysis pitfalls you should avoid before you are sucked in.

Confirmation Preference

If you have a proposed explanation in your mind; but you are only looking for the data patterns that support it and ignore all data points that reject It. Then let us see what will happen.

First, analyze the results of that particular patterns performed well and find the conversion rate on the landing page. This will help you to really perform high than the average you think. By doing or following such analysis you can use that as the sole data point to prove your explanation. While, completely ignoring the fact of those leads will qualify or the traffic to the landing page will be sub-par.

There is again a thumb rule which is very important to remember, you should never approach data exploration with a precise conclusion in mind; as most of the professional data analysis methods are built in a way that you can try them before you actually go and reject your proposed explanation without proving it or to reject it to the void.

Correlation Vs Cause

Combining the cause of a fact with correlation somewhat will not show any action. While, when one action causes another, then they are most certainly correlated. However, just because two things occur together doesn’t mean that one caused the other, even when it seems to make some sense.

You might find a high positive association between high website traffic and high revenue; however, it doesn’t mean that high website traffic will be the only cause for high revenue. There might be an indirect or a common cause to both that may help to generate high revenue more likely to occur when high website traffic occurs.

For example, if you find a high association between the number of leads and number of opportunities from a classic B2B data quest, then you might gather a high volume of leads with a high number of opportunities.

Here are some more things that you need to watch when doing data analysis:

• Do not compare unrelated data sets or data points and conclude relationships or similarities.
• Analyze incomplete or “poor” data sets and make proper decisions based on the final analysis of that data.
• The act of grouping data points collectively and treating them as one. Which means, looking at various visits to your website and creating unique visits and total visits as one and inflating the actual number of visitors and converting it to the best conversion rate.
• Do not analyze the data sets without considering other data points that might be critical for the analysis.
• Do not ignore any simple mistakes and oversights which may happen anytime.