June 4, 2020

Unraveling The Common Misconceptions Surrounding AI and Big Data

Misconceptions on Big DataThere are numerous false notions regarding how Artificial Intelligence (AI) and Big Data work together, which has created a lot of confusion in the minds of people. We have decided to uncover and resolve some of the biggest misconceptions surrounding this topic with the help of the leading IT experts.

Organizations today are overwhelmed with the volume of Big Data being generated and processed, as deriving business value and insight from has become complicated. Big Data naturally results in advanced analytics; whenever mass information with the potential to enhance business processes is gathered, businesses don’t want to only scratch the surface.

It’s necessary to discover the unknown, find the root cause, and predict what’s going to happen as you need to address these issues with extreme precision. The human mind can’t do that without enlisting the help of machines, and that’s where Artificial Intelligence (AI) can emerge as a way of making sense of all that information and as a discipline that demands large data sets for performing.

It’s natural that AI and Big Data are commonly associated with one another today. An extremely strong relationship exists between AI and Big Data, in which Big Data fuels the development of AI.

However, some misconceptions persist about AI and Big Data, and these have resulted in potential confusion that IT leaders must clarify. These include the likes of:

1.    Not All Big Data Demands the Application of AI

AI helps to drive analysis, but this doesn’t mean that value gets extracted from Big Data. Advanced analytics is a concept that most organizations have taken for granted despite benefiting from it for several years. It depends on the data set size and number of different data sets you must analyze.

Even with the greatest minds in the world, it is impossible to find insightful patterns in huge datasets in an adequate amount of time. However, not all Big Data sets are varied and huge, so you don’t always need Machine Learning (ML) to gain insight from them. IT organizations can also use business intelligence, data warehousing solutions, and analytics to visualize insights after analyzing data.

2.    AI and Advanced Analytics Aren’t the Same

Most people use the term ‘Big Data’ to give a broad description of the advanced analysis of such information assets. That’s normal. However, they may think that advanced analytics and AI are interchangeable terms, which is completely wrong.

Advanced analytics and AI are closely linked, but there are several differences. For instance, AI can try out assumptions, self-learn, and enhance its analysis. The analytics can analyze data, can’t self-learn, and relies on people to set its parameters.

3.    Big Data Can Throw Off AI Models

Big Data creates the foundation for ML and AI, as the more data you acquire, the better the models are going to be in terms of accuracy and insights. However, Big Data can also introduce bias into ML and AI, when it‘s not controlled. Focus excessively on the quantity of data instead of its quality is to blame for this.

AI and ML are going to fail when people can’t control the underlying data. Collecting massive amounts of data into a data pool doesn’t offer a sufficient foundation for the success of AI and ML.

4.    People Are Vital for Combining AI and Big Data

Transparency and trust are vital when it comes to intersecting Big Data and AI because you’ll need solid data foundations to drive AI to the right insights.

You will also need solid data foundations to bring people into the big picture with data governance to take control of the data and the algorithms it requires for the bigger picture.

5.    Not All Data is Useful for AI

There’s a fine balance between having data and having the right data to provide insights whenever data is used in combination with AI. Not everyone has the same problem with AI because it can’t be used to create something out of nothing. All business leaders should be aware of this fact.

6.    Businesses Are Unaware They Are Already Combining AI and Big Data

There are software solutions with AI capabilities built into them already, which must be trained, used, and installed. These solutions will not only speed up the adoption of AI but will also help organizations deal with specific business needs. In such cases, you don’t necessarily need to understand the science of AI to reap all of the many benefits that it has to offer.

7.    Most AI Flavors Don’t Need Big Data

The philosophy of ‘Garbage in, Garbage Out’ is applicable here because you don’t need large amounts of good data to drive meaningful value from your AI efforts. However, the amount of data you need must vary because when it comes to Big Data. Large data sets of structured and unstructured data will feed some applications of AI that need such volumes for training.

You will need it to train AI, analyze information to spot patterns, and use probability for finding the answers to your questions. Not all AI requires Big Data analytics, and AI by design needs large and normalized data sets to meaningfully discern patterns and generate requisite outputs.

The volume of data needed will be driven chiefly by the complexity of the problem and the number of input features that must be evaluated before the algorithm can be used. For example, ML requires less data for training than deep learning.

by Bobby J Davidson

We love our company and we love what we do.  Check out the ‘Why Percento‘ page to learn more: Love of Technology and Business!  As the President of Percento Technologies International, I provide day-to-day leadership to the company’s senior management and I am personally involved in the strategy, business development and sales activities of the firm.

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