Introduction
“Big Data makes us smarter, no more wise “. – Tim Leberecht.
The term 'Big Data’ was introduced in the decade of 1940. Companies everywhere have made relentless efforts to explore its potential. Global tech giants have vastly increased their spending on leveraging big data technologies. This trend quickly replicated among the major players in the industry.
Due, according to a forecast issued by the research firm (IDC), Big data technology and services will grow at a CAGR of 23 percent up 2019. Annual big data spend will reach 48.600 million dollars in 2019.
This is how big data services are accepted around the world!!
Big Data has a nut “ray of hope” companies and has allowed them to make use of data of any size and volume. The bits of data collected by means of our mobile phones, GPS, sensor devices are no longer useless. All data collected is collected and processed to obtain useful information about us (customers).
Amid the growing benefits of Big Data, people don't see the things that “hypocrisy” do. This was also surprising to me. But I soon realized that Big Data always complements business intuition, but it can never replace it.
In this post, I present my research of the last 7 days. My crazy curiosity led me here. I just couldn't digest the fact that big data has everything it takes for a company to be successful.. Big Data es “capable” of many things. But ‘incapable’ what's more.
Note: My thoughts are not exhaustive, but an attempt to frame. Feel free to add your perspectives in the comment section below..
Black Friday data hack – Missing 9 days – Win awesome prizes
Exercise ‘tiny’ on ‘big data’
This exercise will prepare us for the future. We must know the things to come. Therefore, if you are reading this, I invite you to try to solve this question. You just need to write (even though I have already shared the solution):
"5 things Big Data can do" and "5 things Big Data can never do".
As an example, if I conclude using a logic that X is not feasible using any technological platform with Big Data. I will just remove all X-related business problems. Do you understand?
Below is my list. If you do not agree with any of the items on my list, Justify it! I will love modifying this list over time.. Comencemos con una breve nota sobre mi ideología sobre el uso de la intuición empresarial y la analyticsAnalytics refers to the process of collecting, Measure and analyze data to gain valuable insights that facilitate decision-making. In various fields, like business, Health and sport, Analytics Can Identify Patterns and Trends, Optimize processes and improve results. The use of advanced tools and statistical techniques is essential to transform data into applicable and strategic knowledge.... business.
Rule 80:20
The rule says
“The 80% of time is spent creating stories from previous data and the 20% of time is spent connecting those stories with today's business”.
Explanation: I believe that no analytical knowledge is useful until it is in sync with business intuition. Agree ? At the same time, wat the same time, the The data-driven component has grown up exponentially. Companies are now awash in data. But, Would that really make a difference? ¡No!
Companies must realize that a correct combination of successful business analysis over the required business intuition is in a ratio of 80:20.
If we can construct a story using analyzes that describe the past to predict future expectations the 80% weather, we must invest the 20% of time thinking about how this information is useful for business. We must think of ways that can change our future and achieve our broader business goals. This needs a strong business understanding and a solid understanding of business rules.
The component of 20% in this rule it is not replaceable. That is why, Human intervention is needed to fix this 20% and possibly no machine can make up for it. Not even artificial intelligence. Because humans think in an indefinite way that leads to imagination. Imagination is what I think no machine can contribute. My list is inspired by this rule.
5 things that Big Data 'CAN’ do
- Diagnostic analysis : We do it every day. Machines are excellent at this. Once an event occurs, we are interested in looking for its causes. As an example, suppose there is a sandstorm in Desert A. Tenemos todos los parametersThe "parameters" are variables or criteria that are used to define, measure or evaluate a phenomenon or system. In various fields such as statistics, Computer Science and Scientific Research, Parameters are critical to establishing norms and standards that guide data analysis and interpretation. Their proper selection and handling are crucial to obtain accurate and relevant results in any study or project.... en diferentes regiones del Desierto A: Temperature, Pressure, Camels, Roads, # Autos, etc. If we can relate the parameters to the sandstorm in that area, if we know some causal relationships, we can possibly avoid sandstorms. Imagine how powerful big data is!!
- Predictive analytics : We do this often. Predictive analytics is in our DNA! As an example, we have a chain of hotels all over the world. Now we need to find which of these hotels will not reach their target sales.. And if we know, we can focus our efforts on these hotels. This becomes a classic hurdle in predictive analytics.
- Find link between items / unknown events : I love this part of the analysis. Let's say the number of sales workers is literally unrelated to sales. Then you can possibly reduce the number of sales workers if that doesn't result in any other losses.
- Prescriptive analysis : This is the future of analytics. Let's say we are trying to predict a terrorist attack at a popular destination and a feasible strategy for moving people safely.. To make this prediction, must make a series of predictions, which may involve predicting the number of tourists, later the number of tourists in that location, subsequently predict the area that may be affected by an explosion, etc., etc.
- Monitoring an event as it occurs : Most people in the industry work on event tracking. As an example, you need to monitor the solution of a campaign and find segments that responded more and less. These analytics become crucial to running a business.
5 things that Big Data ‘CANNOT’ do
- Predicting a definitive future : We can achieve 90 higher grades in terms of accuracy using sophisticated machine learning tools. Despite this, never meet the 100% precision. If we could do that, could have told you exactly who to turn to and achieved a response rate of 100% at all times. But, Unfortunately, It will never happen!
- Imputación de nueva Data SourceA "Data Source" refers to any place or medium where information can be obtained. These sources can be both primary and, such as surveys and experiments, as secondary, as databases, academic articles or statistical reports. The right choice of a data source is crucial to ensure the validity and reliability of information in research and analysis.... : Imputation takes most of the time in any analysis. And I think this is where you bring your imagination and business understanding.. Possibly, one of the most boring pieces of your analysis that you will never get rid of.
- Find a creative solution to a business obstacle : Imagination is one thing that will always be a patent of the human race. No machine can find a creative solution to an obstacle. This is because even AI is coded by humans and imagination is never learned through algorithms..
- Finding a solution to a not so well defined obstacle : The biggest challenge in analytics is shaping an analytics roadblock from a business roadblock. If you can do this right, you're on the right track to becoming an analytics superstar. This paper is something that machines can never take away from you. As an example, your business problem is managing attrition. Until now, unless you define the responders, time goes by, etc. No predictive algorithm can help you.
- Data management / Simplify your data for a new data source : With increasing data, data management becomes increasingly difficult. We are moving forward with different types of data structures for different types of data. As an example, graphical data may be suitable for network analysis, but they are useless for the campaign data. This information is again that the machine cannot analyze.
Final notes
I think this post will reach its true potential if people start trying the exercise in this post.. Try to think of a more holistic view where you can see what the machine can never do. As an example, my starting point was the rule 80:20 that the machine cannot bring imagination. This starting point helped me to think about which are the pieces that need imagination in the analysis procedure.
What's your to-do list / not to do? Did you like this post? Write your comments in the box below.
