When we think of mining, it sounds manual, tedious, and unfruitful — after all, hacking away at rock walls for hours on end hoping to find gold sounds like a lot of work for a very small reward.   

Data Mining, however, is quite the opposite — without doing much work at all, you can reap rewarding results. That’s because we have modern solutions which do it for us. 

These softwares can sift through terabytes of data within minutes, giving us valuable insights on patterns, journeys, and relationships in the data.   So, let’s dive into what Data Mining is, how we do it, and what its examples look like.

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Master Data Management, also called MDM, is a technology-enabled discipline describing how businesses utilize IT tools and solutions to govern, manage, use, and share their data. As businesses scale and grow in size, the number of applications and systems used across the board will also grow. These additional databases cause a fracture in information as they create multiple versions of the same data, and there’s no way of pinpointing which version is outdated, incorrect, or incomplete. To resolve this problem, MDM ensures the uniformity, accuracy, stewardship, semantic consistency, and accountability of the shared Master Data assets. In short, MDM enables all the employees in a business to stay aligned.  So let’s take a deep dive into what Master Data is, and how it fits into Master Data Management.

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Data Management is a set of practices that organize, refine, and store your data. These practices are broken down into 7 key types. Their collective goal is to produce data that is accurate, consistent, accessible, usable, and secured, rendering it conducive for data analytics and reporting down the line.

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Deep Learning has substantially impacted the way technology is leveraged across various sectors and industries.  

Chatbots have become integral to more and more service industries every day. Deep Neural Networks have rendered this possible, allowing Deep Learning to decipher, understand, and address the intent of the customer on the other end.

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AI technologies have gone as far as giving computers the ability to observe data, recognize patterns, predict future outcomes, and take action. Deep Learning is one of the key derivatives of AI – and with its smart-computing and self-learning abilities, it’s paving the way for the next digital revolution across a myriad of industries. 

So, what exactly is Deep Learning? Deep Learning is a complex form of Artificial Intelligence that enables machines to process data and learn logical conclusions in a way that mimics the thought process of a human.

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Machine Learning in itself is very complex and can be broken down differently depending on its variations. Generally, it is divided into categories according to their purpose. These are:

· Supervised Learning

· Unsupervised Learning

· Reinforcement Learning

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Machine Learning is a branch of Artificial Intelligence that teaches computers to complete tasks that previously could have only been done by a human being. To give you a rough idea, take a look at self-driving cars, real-time speech translation, and traffic forecasting – these are all applications of artificial intelligence and its capabilities.  

Firstly, it’s important to understand that Machine Learning is a core sub-area of Artificial Intelligence – it is a method of data analysis that automates analytical model building. In simple words, it is the process of teaching a computer system to accurately make predictions based on data it is given. The objective is to get the system to automatically learn from the data, identify patterns, make decisions, and adjust actions accordingly with minimal human assistance.  

With the increase in readily available data, cheap and powerful computational processing, and affordable data storage, the demand for and importance of Machine Learning has also rocketed. As a technology, its automated processes help analyze huge sums of data, ultimately easing the tasks of data scientists. It has changed the way of prepping, extracting, and interpreting data by replacing the traditional statistical procedures with adding automation sets of generic methods.  

By producing accurate results and analyses through the development of efficient algorithms and data-driven models with real-time processing, Machine Learning has enabled computer science, statistics, as well as other emerging applications, to propel forward in their respective industries.

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Struggling To Become A Data-Driven Organisation?

20-40% of all health sector resources are wasted.

GE estimated that a 1% efficiency gain could lead to more than $63 billion in global health care savings.

With hospitals facing unprecedented cost pressures, tracking and optimising patient flow, treatment, and equipment use is becoming increasingly difficult.

How could hospitals overcome their primary challenge of efficiently treating as many patients as they can while simultaneously improving the quality of care?

This is where data analytics comes in handy.

In 2011, a study by MIT Sloan School of Management found that businesses engaged in data-driven decision-making “have output and productivity that is 5-6% higher than what would be expected”.

The beauty about the state of business intelligence and data analytics in 2020 is that technology makes it possible to measure, test, and improve almost everything within your business operations from your ad campaigns to real manufacturing costs, customer relationships, talent, and more. The right analytics tool can help you keep track of critical data and allow you to respond in real-time.

But here’s the thing.

If you’re struggling to make data analytics work for your organisation, and it feels like you’re fishing for insight in an overwhelming sea of data - you’re not alone.

A recent survey of large enterprises found that less than 40% manage data as a business asset and only 31% considered their organisation data-driven.

It doesn’t matter how much data you have. Without taking a strategic approach to learning from it and employing the right tools, you may squander time, attention and money only to find that you haven’t used your data to achieve anything great. Here are some steps you can take to help you cover your bases, gain clarity and work towards becoming a data-driven company.

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Late one evening you are conversing with a friend or a partner about the latest gadget, a holiday destination or the latest TV series that you’ve been meaning to binge-watch. Your smart devices are probably on the table or tucked away somewhere in your bag or pocket. The next day, you open your smartphone and it is filled with ads that are related to the topics you were conversing last night. How is this possible? Is your phone really listening to you?

The simple answer is no! Your phone is not listening to you. The ads targeted and reached you though effective use of data analytics.

Data analytics has become a very popular term across multiple industries, from automotive to logistics, ecommerce to healthcare. All of which have used data analytics to their advantage and excelled. For instance, one of the biggest ecommerce giants, Amazon, uses data to improve its business and the whole ecommerce experience. Among the myriad industries utilising data analytics, ecommerce stands out as one of the front runners, having been completely transformed by it. Useful insights from the customers are gathered and integrated to help businesses make better decisions. Useful insights include the customer’s web behavior, any events that have occurred in their lives, details on what led to the purchase, amongst others.

Data has revolutionized the ecommerce industry and will continue to do so in the future. With all the data collected, businesses are able to personalize product recommendations specifically to each individual, having leverage over predictive forecasting provides insight into the customers’ behavior along with their shopping patterns, helps improve customers experiences, and prevents fraud among many other benefits.

Data analytics has completely transformed ecommerce, and it is still evolving rapidly. Experts are constantly trying to look for fresh and innovative ways to leverage the data in hand. Thus, data analytics technologies are driving the phenomenal growth of the global ecommerce industry.

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As a data solutions platform, Cubeware’s digital warehousing ecosystem let’s you view and interpret data, leading to valuable insights that can transform your business.  

A Cubeware solution gives you instant access to all of your valuable data in a safe and secure ecosystem, with complete visibility of your information. The powerful insights gained allow for informed, calculated decision making, and can be used to transform your business.  

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