Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree. ...Full Bio
Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree.
Python Opens The Door For Computer Programming
How Big Data is changing the Business Landscape
The amount of data being collected globally is increasing exponentially. This proves that we are currently in the age of Big Data. Simultaneously, many more related fields like Data Science, Data Intelligence, Artificial Intelligence, Machine Learning, and Deep Learning have also seen a lot of growth and are revolutionizing businesses and industries across the world and making the competitive world. To remain the part of this competitive market organizations lookout for the skilled data scientists and data professionals.
Big data promises to bring disruption as its revolution works its way through all large and small organizations. Following are the ways that big data is changing the business landscape:
Better Business Intelligence:
Business intelligence is a collection of data tools that is used in evaluating a business. It goes in the hand of big data. Before the rise of big data, business intelligence was narrow. Big data has given birth to business intelligence. Many companies are gearing up by hiring business intelligence experts because they help to take a company to the best level.
The business which generates data can apply business intelligence. Nowadays, it's very hard to find a business that is not generating any data at all. This means that any business can get benefit from business intelligence.
More Targeted Marketing:
Big data analysis isn't always hundred percent correct but this high accuracy permits companies to target marketing to recognize the customer needs. This analysis helps business to know that what products their customers might need in future. While current data analysis techniques it is not quite at the level to make these kinds of predictions regularly, they are converging to the next level as well.
Proactive Customer Service:
Businesses can identify accurately what their customers want before the customer even needs to voice their anxiety. This kind of proactive customer service will alter business that demands to differentiate them based on better customer service. Customer support would have a good idea what the call was about and deliver knowledgeable customer service. Further big data analysis could allow customer support to proactively contact customers on accounts where predictive analysis determines that the customer might have a future issue.
Industrial engineers are engineers of effectiveness. They know that the process can't be made more efficient without data. Big data is delivering rich data about every product and process. This rich data is telling a story that smart businesses are attending nowadays. Engineers are examining big data and looking for ways to make processes run more competently. Big data analysis works well with Theory of Constraints. Constraints are easier to recognize and once recognized, it's easier to identify how if the constraint is the most binding constraint. When this constraint is discovered and removed, the business can see vast increase in performance.
Big data has control to provide the information needed to reduce business costs. Companies are now using this technology to find trends and predict future events. Organizers can determine when to produce and how much to produce. They can determine how much inventory should be in hand, because it's very expensive to carry inventory. Big data analysis also helps in predicting that when sales will occur and same also helps in predicting when production needs to occur.
Hence, Big Data is obviously changing the business landscape and it something to embrace that we want to help our business to achieve more. It won't be long before those businesses that haven't embraced big data will find themselves left behind.