Big Data industry & its Impact on other industries

For what started only as a collective measure to score maximum results, is now the reason behind the boom development and progress of various other industries. Yes, in today’s article, we shall be speaking about Big Data and its direct/indirect impact on numerous other industries in town.

Originally designed and developed for companies to extract information from datasets by processing and analyzing them using appropriate methodologies. This is done to replace age-old, traditional methods that are highly inefficient in today’s time, and are highly time-consuming too. This article is to understand how far we’ve progressed with machine learning in this field, among numerous others.

According to a report issued by Market to Market in the last few months of the year 2020, the big data industry is currently estimated at 138.9 billion globally. What more? The number is bound to compound annually at a staggering 10.6 percent, and yield further results worth 229.4 billion by the end of the 2025 calendar year. The value of this industry is majorly due to highly-sought-after nature it has displayed over the last few years. Given how data is god in almost all possible fields worldwide, the obvious in-trend nature of the collection and analyzing data mechanism doesn’t come as a surprise to many. Numerous fields have adapted big data services to get business booming insights into respective (by it annual or quarterly) sales and dictate strategic initiative, retails, education, healthcare, supply chain management, professional services, manufacturing, government service, and more.

Let us try and demonstrate each one of these individual industries with appropriate examples, and gain insight as to what goes on behind the scenes for them.

Big Data Examples

Retail Industry: Retail industries mainly focus on strategies directly linked with user satisfaction points. Be it pricing strategies, forecast demand abilities, it has all covered. The organizations of this industry mostly cover themselves with online tools wherein they can directly pay for a monthly/annual payment, or develop their very own ML datasets and algorithms for gaining personalized insights into the matter.

Healthcare Industry: While this industry also adopts basic retail enhancing technologies for future endeavors, it invests majorly in other sophisticated software and technology. This kind of technology is mostly done to diagnose diseases, prevent them with equal focus, predict pandemic/epidemic outbreaks, and provide medicine through the evidence-based analysis of previous patients.

Education Industry: We as a society, have definitely come a long way since the use of traditional schooling methods, and thus is the need of the hour that we are equally invested in developing digitalized study material and content for our students. After all, where else are they supposed to learn, if not in schools and colleges. Big data thus helps in refining course content, generate dynamic study materials, suggest potential career paths, and guide them too. All these measures are calculated and predicted by using the dataset of each and every student accumulated over the years—right from the kindergarten days of his/her life.

Manufacturing Industry: Much like what one would expect, big data is highly efficient in enhancing the efficiency of the machines that they use and gather highly critical reports regarding the performance of the products they produce and generate. These reports are further used to identify points of lagging wherein officials may come up with solutions to overcome problems of varying degrees.

Supply Chain Management Industry: The entire chain cashes in only on efficient consumer-supply chains so that people get smoother ordering systems. Apart from making ordering systems more efficient and less time-consuming, the ML aims at optimizing transportation routes, improve maintenance processes for vehicles, and a keep a regular check on sales aspects of the game too. The ML-based enhancement algorithms are a major win-win for both, consumers and producers.

Professional Services Industry: Much like the former categories, this industry too is directly linked to consumer satisfaction and involves physical (or stimulated physical) contact between the two. It doesn’t follow a distributed procedure wherein consumer satisfaction is conveyed through steps. It is thus only very evident that big data be used appropriately. From predicting customer demands, optimizing pricing strategies, automating transaction-based importing, and tracking product processes over time, the future is definitely bright for this industry.

Government Industry: The need to adopt sophisticated technology goes without saying. Particularly when the population is increasing at alarming rates, and the whole world is taken aback by the unfortunate Corona Virus pandemic. Be it efficient public services (transport, medical, etc.), identifying unsolicited trading activities, or even improved emergency services is definitely needful at this point in time.

How ML helps in capitalizing on big data:

Big data is highly important in gaining greater and fruitful insights into strategic initiatives and objectives. This is further amplified by implementing decision-making algorithms to recognize patterns existing between big data, and appropriately process this information too. ML basically helps even the uneducated to understand the in’s and out’s of the industry or the respective company. The steps to understanding further details regarding the business can accordingly be automated. Manual control can also be established, if preferred.

The ML services adopted by various industries thus take many-many steps forward, especially at a time where the economy of leading nations is at a hit. As predicted ad reported earlier, the big data concept will be a game-changer in the future—especially at a time where we are hopeful of witnessing flying cars, self-driving vehicles, and improved home assistants offered by the biggest technology giants to have ever walked this earth. With ML in the picture, we are looking at advanced algorithms that aim at self-improving themselves over time. The whole “self-cleansing” concept further mounts to increased production rates at reduced manufacturing costs.

Examples ML + big data application:

Some examples of successful big data and machine learning collaborations.

Market Research: Understanding the market when in business is the most important aspect of any successful organization. Be it understanding potential audience, trending products/consumer wishes, having a good knowledge of what to come up with, where to come up with, and how to come up with a service. product is highly essential.

Segmentation of target audience: Understanding your audience is definitely the right way to go. While many might aim at directly aiming at catering to large crowds, it is highly essential that businesses first establish a loyal audience base who will buy products/services without any hesitation. Further, as the business grows and starts infecting people of other age ranges, the management may take appropriate steps to add them to the camp as well. Well, after discussions of course.

User behavior prediction: The product you put out on the field may attract positive/negative/never mind reviews from your audience. So, to pull in reviews that will allow a further boost in the sales, it is highly important that we enter the zone all prepared. Predicting possible reactions beforehand can work in favor of all individual companies, and other parties involved, as they will make informed decisions when it comes to the post-production time of the product/service.

Chatbots: Chatbots, like many, must be aware here, are mini assistant programs that work on a set of pre-designed questions and answer to the moderated ones as well. These are created to assign a full-time virtual assistant that relentlessly works on providing answers to all sorts of user queries. These are highly beneficial for newbies, or advanced service producers that offer not-so-easy-to-comprehend features. This further goes on to create a sense of trust between the producer and the customer. Next, a loyal and trustworthy audience base is also maintained.

Fraud detection: Every system comes with s own sets of problems like bugs, user-interface issues, or worse, network/security issues. With security issues in the picture, there is always a high chance of losing out on critical data to malicious hackers/programmers. Thus, with ML coming to the rescue, important steps are taken to understand potential loopholes in the system and accordingly strive to get it rectified.

Predictive analysis: Predictive analysis, as mentioned earlier, aims at predicting sales, user reaction etc. and provide detailed analysis into the matter. By generating a report that already predicts what to expect, the organizations may choose to cater to appropriate audiences and produce mind-boggling results and further enhance quarterly/annual sales/profits.

Streaming recommendations or other social networks: More often than not, audiences find themselves in a tough spot wherein it is next to impossible to work on a desired network. So, this is exactly where ML jumps in. According to a select criterion fed into its algorithm, it provides alternatives to streaming platforms, or suggest other social networks. This gives the user plenty of options to consider and choose from in the future.

Implementing ML into big data:

To implement machine learning into your big data, it is important that you install a machine learning system into your digital workspace. Next, to gain ideas and insights on how to proceed further, you may refer to numerous documentation available on the internet, refer to highly engaging videos on platforms like YouTube, or even accustom yourself to providers like Algorithmia to jot down important points and work accordingly. Now this is how you are supposed to begin. (inserts laughing emojis).