It’s no secret that the online market has become highly competitive and oversaturated in the last few years. Simply offering something unique isn’t always enough for companies to stand out in such an environment. In order to remain both competitive and relevant in the market, companies must be able to predict market trends and changes in consumer behavior so that they can adapt quickly and efficiently.
This is where big data analysis comes into play. By gathering big data and using techniques like data labeling, for example, companies can sort through vast quantities of data that is roaming freely online and extract useful information and insights from it that can help them in their future endeavors. That usually means leveraging a data-driven approach to crafting marketing strategies, product features, new services and many other things that can help companies grow and develop further.
Unfortunately, gathering, analyzing and extracting information from big data isn’t as easy as it may sound. Companies, therefore, must invest in proper tools and employees, as well as proper technology that can help them out. Otherwise, you may be looking at completely wrong set of information that will lead your business astray. Experts predict that by 2025 there will be around 181 zettabytes of data roaming around and that’s not something anyone can process overnight. So with that in mind, let’s have a look at how exactly do companies analyze big data successfully.
Using AI to gather and analyze big data
Artificial Intelligence (AI) is a powerful tool for gathering and analyzing big data. The fact of the matter is that AI technology gained its fame and glory it enjoys today by first proving itself in analyzing big data and extracting useful information from it.
The thing about AI is that it does the same things a human would only at a much, much faster rate than any human ever could. While AI is busy mining data and sorting it, data scientists are trying to make sense of the information AI has gathered. Therefore, this collaboration between human and machine is of the utmost importance for successful data extraction and analysis.
How machine learning helps with big data analytics?
As mentioned before, big data is simply too massive for companies to process such quantities of information on their own. No matter how many data scientists, analytic professionals and other skilled staff members a company may have, they wouldn’t be able to process data fast enough or accurately enough for a company to capitalize on the information gathered.
So, of course, they use the big guns, which in this case means artificial intelligence and it’s machine learning capabilities. In essence, AI is taught how to process data and data labeling is used to teach machine learning what to look for. With each data set mined, AI becomes smarter and more efficient in sorting through data, providing companies with exactly what they need.
Not everything stored in big data is useful or needed. Only information and insights that can help companies gain a competitive advantage in the market. That’s why big data analysis is necessary as you still have to comb through all the data out there.
Using predictive models to extract information from big data
Predictive models are a powerful tool for extracting information from big data. They can be used to identify patterns in large datasets, uncover hidden relationships between variables and make predictions about future market trends or shifts in the market upon which companies can capitalize on. Predictive models can, therefore, also be used to detect anomalies in data, such as outliers or unusual trends.
By leveraging the power of predictive analytics, businesses can gain valuable insights into their customers’ behavior and preferences, enabling them to make more informed decisions. AI’s machine learning capabilities alongside statistical algorithms are used to make such predictions with the desired precision companies need in order to plan their next move.
How data is cleansed to improve the quality of information extracted from it?
Data cleansing is the process of improving the quality of data by removing or correcting inaccurate, incomplete or irrelevant parts. As mentioned before, as large as big data may be, most of it is junk not worth analyzing.
So whatever data is collected and stored, it must be cleansed beforehand so that useful insights can be extracted from it. Companies use data quality software and scrubbing tools to rid data of any useless information and variables. In other words, data cleansing basically means turning raw data into meaningful information that companies can actually use to their advantage.