“Big Data” has been on everyone’s tongue for the past few years, and for good reason. As digital devices and touchpoints expand, so does the amount of data we each create. This information can be used to help our clients and customers better understand, make more effective decisions and improve our business operations. But only if we understand everything.
By selecting the right big data sources and applications, we can give our organizations a competitive advantage. But to do this, we need to understand the definition, capabilities and effects of big data.
Big data already has extensive applications. From Netflix recommendations to healthcare monitoring, it runs all sorts of predictive models that improve our daily lives. But the more we rely on it, the more we have to question how it shapes our lives and whether we should rely so much on it. While progress is inevitable and something to be embezzled, the contribution of Big Data should not measure how many companies apply it, but rather how much it improves society as a whole.
Defining Big Data and its relationship with Artificial Intelligence (AI)
Big data is more than just big datasets. It is defined by three Vs of data management:
- Volume: Big data is often measured in terabytes.
- Variety: It can contain structurally different datasets, such as text, images, audio, etc.
- Velocity: Big data should be processed quickly because data is created at increasing speed.
As the volume, diversity and speed of information expands, it is transformed into big data and becomes too much for people to handle without assistance. So we use artificial intelligence (AI) and machine learning to help analyze it. Although the terms Big Data and AI are often used interchangeably and go hand in hand, they are actually different.
“In many cases, due to the speed, scale or complexity of the data required for observation, analysis and operation, it is no longer possible to solve every problem through human interaction or intervention. The various options can be evaluated based on the available information and then the best course of action or response can be selected based on the probability of the best outcome. ”- Ilan Sade
Simply put, big data powers the AI with fuel so that it can drive automation. But there are risks.
“But the tendency to add too much data to AI can damage the quality of AI decisions. So it’s important to take advantage of big data and analysis to prepare and measure your data for AI, but don’t shy away from adding data or complexity to your AI projects. Most AI projects, which are mainly narrow artificial intelligence projects, do not require big data to provide value. All they need is a good quality data and plenty of records. ”- Christian Ehl
Realize the business potential of big data
Properly covered, it will withstand a great deal of adverse conditions.
“Some examples include hyper-personalization of a retail experience, location sensors that help the company route shipments more efficiently, more accurate and effective counterfeit detection, and even wearable technology that provides detailed information about workers’ movements, lifts or their location. To reduce injuries and increase safety. ”- Melvin Greer
But this important competitive advantage is unnecessary because many companies struggle to find all the data and separate the signal from the noise.
According to Greer, five major challenges prevent companies from realizing the full potential of big data:
- Resources: Not only is the data scientist in short supply, the current pool also lacks diversity.
- Data Integration: Data is constantly being created and it is a challenge to collect and sort from different channels.
- Incorrect or missing data: Not all data is good or complete. Data scientists need to know how to distinguish misleading from accurate.
- Unfinished data: Clearing data is time consuming and can slow down processing. AI can help manage this.
- Truth seekers: We should not assume that data analysis will give a definite answer. “Something that leads to the possibilities of information science is correct,” Greer wrote. “It’s a subtle but important subtlety.”
It is very important to face the first challenge. The only way to solve other problems is to first create the necessary human capital and provide them with the necessary equipment.
True The promise of big data
Data is a wonderful tool, but it is not a cure. Indeed, “too much of a good thing” is a real phenomenon.
“In my many years working with many businesses, I’ve actually seen some companies that didn’t use enough data. However, these events have been compared to the number of times I’ve seen the opposite problem: Companies that rely heavily on data are harmful. The idea that information is needed to make a good decision is destructive. ”- Jacqueline Knollis
To illustrate his point, Nolis describes the role of the Coca-Cola cherry sprite. What decision inspired you? Data. People were adding cherry-flavored “shots” to Sprite in self-serving soda dispensers. So make a score for big data.
But as Knollis noted, the very similar-flavored cherry 7UP already existed এবং and had been since the 1980s. So using the soft drink islet at the local grocery store, the data team can more efficiently come up with new flavors. Lesson: Too much reliance on information can hinder commonsense decision making.
Big data applications: when and how
So how do we know when big data needs to work for our business? That decision needs to be made on a case-by-case basis according to the needs of each individual project. The following guidelines can help determine the right course:
- Consider the desired result. If it is to meet a competitor, what the competitor has already done may not be a good use of the resources invested. It is best to use their example as a guide or inspiration and save big data analysis for more complex projects.
- If disruption is the goal, big data can be applied to test new ideas and assumptions and possibly reveal other possibilities. But we need to be careful about the following: Data can kill creativity.
- If a business decision is urgent, “data is still being analyzed” is not an excuse to delay. In a PR crisis, for example, there will be no time to mine the data available for our insights or guidance. We need to rely on our existing knowledge about the crisis and our customers and take immediate action.
Of course, sometimes big data is not only useful but necessary. Some situations call for big data applications:
- To determine if a strategy is working according to plan, only information will tell the story. But before we can measure success, we must first set up our metrics and determine the business rules that determine success.
- Big data can help with the process and create models from huge amounts of data. So as a general rule, the bigger and more data-intensive the project, the bigger the data can be helpful.
Big data may be a common topic in technology today, but it’s more than just a buzzword. The potential to improve our business and our lives in the long run is real.
But that potential needs to be used purposefully and in a targeted fashion. Big data is not a wonderful drug equivalent business. We need to be aware of where our applications can help and whether they are unnecessary or harmful.
Indeed, the full promise of big data can only be realized when it is driven by thoughtful human skill.
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All posts are the author’s opinion. As such, they should not be construed as investment advice, or the opinions expressed must not reflect the views of the CFA Institute or the author’s employer.
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