Everything Data Analytics

Before any fruit and vegetables are sold in supermarkets, they are first harvested from various farms and orchards. Then, they are sorted into their respective categories, cleaned to remove any bacteria and soil, and packaged uniformly during the preparation stage. Finally, depending on store orders, they are sent out to various locations.

In the data analytics world, raw data goes through a similar process before it can be analyzed and reported on — and this is where ETL comes in. 

There are three main steps in the ETL process — Extract, Transform, and Load.  Together, they work to prepare your raw data for analysis and reporting.

In the food industry, quality control ensures that the meat, bread, and other edible items reaching you are of the best quality. Companies use various ways to manage this, from developing fixed product formulations to standardizing manufacturing processes. By following these procedures, the quality of food is kept consistent and reliable for the consumers.

In the same way, companies also need to manage and control the quality of their data. This has become increasingly important due to the enormous amount of data that’s become available and accessible in the recent decade.

As a company’s datasets grow, it’s critical to ensure that they are consistent and accurate for analyses and insights. After all, data-driven decisions are only as good as the data itself. With datasets regarding customers, finance, operations, and sales comprising the core of most organizations, Data Quality and Data Quality Management only become more vital.

What do you do when you outgrow your reporting system?

Meet Farmer X. They’re a manufacturer and distributor of organic foods. However, over the last decade, Farmer X experienced a huge sales growth — this was not so great for their reporting system, which was all completed on cloud-based spreadsheet applications.

With the Cubeware Cockpit, Farmer X was able to complete analyses and generate reports — both planned and ad-hoc — with swift accuracy, detail, flexibility, and transparency. 

Not only did this free up Farmer X’s budget, resources, and time from manual reporting, but the employees of Farmer X were finally able to focus on bringing their visualizations to life.

What do people mean when they refer to Dark Data? And how is Dark Data different from regular data? 

Almost everything we do involves data —we’re either generating it or receiving it, whether we’re aware of it or not. And this adds up to, well, a lot of data — and to no one’s surprise, a lot of it is left unused. 

In short, Dark Data refers to the data that we often collect but do not use, especially for Business Intelligence purposes. 

Often times, Dark Data is what leads to businesses experiencing the DRIP syndrome, which stands for Data Rich, Information Poor. This happens when companies gather a lot of data but don't have the tools in place to turn it into valuable information. 

Well, we’ve got a lot to cover so let’s dive into more detail as to what Dark Data is and what its advantages and disadvantages are.

In our two previous videos, we discussed what Prescriptive Analytics is, how it works, and what its examples look like. To check them out,  

You share a lot of data with a lot of companies — these collected datasets need to be stored securely to prevent any cases of stolen, misused, deleted, or altered data.   

Many businesses today have migrated to the digital landscape, rendering it especially critical that they implement measures that protect their digital assets. After all, companies used to protect their physical assets, so now it's time they learn to do the same with their digital ones.  

During the 2012 Brazilian Grand Prix, Sebastian Vettel was in the running to win the World Championship for the 3rd time. However, during this decisive race, Vettel’s car was hit from the back early on.

If this were any other sport, the competition would have been over. However, Vettel’s analysts and engineers swooped in the moment the impact occurred. Teams of analysts spanning across continents work together in real-time on data analytics to churn out insights and solutions.

So, let’s dive into how Data Analytics was used to help Sebastian Vettel clinch his 3rd Championship.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.  

For more info on Digital Warehousing, 

Adapting to the drastic economic change, more and more companies are seeing the value of data-driven business strategies, and this has opened up an abundance of job opportunities especially in the area of data intelligence.   

With the aid of analytical tools, the person who accumulates, processes, and performs analysis of data is referred to as a Data Analyst.  

He or she is required to present data in a simple visual format, in order to help organizations and companies understand how to make better business decisions. If you are looking to equip yourself with the ability to be a data analyst, here are some of the criteria.   

HARNESS THE POWER OF DATA.  

Outsmart your competition and sharpen your edge with actionable data insights.  

Join globally recognised brands and some of the worlds most innovative companies, who use Cubeware to transform their business through data

Convert raw data into profitable business decisions. 

Your business most certainly has a mine of under-utilized data. Now imagine having all that data organized and archived in a simple presentable format, couple that with smart computing and you have everything you need to make calculated and informed business decisions.

There are four types of data analytics systems that being used in business.

1. Descriptive Analysis, Diagnostic Analysis, Predictive Analysis and Prescriptive Analysis. Descriptive data analysis looks at past data and allows us to see changes affecting a business

2. Diagnostic data analysis aims to determine why something happened. Once your descriptive analysis highlights change in a business, diagnostic analysis can be used to decipher the cause of that change. 

3. Predictive data analysis predicts likely future scenarios. In this type of analysis, trends are derived from past data, which are then used to predict the outcome of business decisions.  

4.Prescriptive data analysis combines the information found from the previous 3 types of data analysis and forms a plan of action for the organization to face issues or decisions.

Data driven decision making requires a thorough view of all aspects of your business. The data functions as a set of eyes for decision making executives as it provides them with both an overview of the market and details about the business.

Therefore, any errors in information could cause knock on effects that severely impact the business, leading to bad decision making and misguided strategy.

HARNESS THE POWER OF DATA.

Outsmart your competition and sharpen your edge with actionable data insights.

Join globally recognised brands and some of the worlds most innovative companies, who use Cubeware to transform their business through data.  

For more info on Utilizing Data for Better Business Decisions,

As we struggle to deal with the COVID-19 crisis, governments around the world are forced to adopt new technologies and systems in order to protect their populations. Artificial Intelligence has proved highly effective in aiding governments as diverse as South Korea and Germany. In this video we highlight how AI and machine learning are being used to fight the virus.  

At present, automotive companies are capable of collecting and analyzing an enormous amount of raw data. Advancements in data analytics and its fusion with the automotive industry has led to smarter, more connected vehicles, and drastic improvements in sales and marketing.    

IHS Automotive has estimated that an average car produces approximately 30 terabytes of data daily. The onus is then on manufacturers to effectively use the data for competitive advantage. Since so much data is produced, the automotive industry today has turned into a data-driven industry. Thus, automobile manufacturers can use data analytics software to glean new insights into their business and make better decisions, which can then be used to maintain and expand market position and profits.   

Thanks to the data-driven age we are living in, data analytics software is now able to help the automotive industry to design and build better cars today, and help make the cars of the future a reality.  

HARNESS THE POWER OF DATA.  

Outsmart your competition and sharpen your edge with actionable data insights.  Join globally recognised brands and some of the worlds most innovative companies, who use Cubeware to transform their business through data.  

In building a successful business, arguably the most valuable asset within the organization is the people. Human resource departments globally are now looking towards data analytics to provide informed and unbiased decision making. This method is considered to be more effective and objective compared to the traditional method involving human feedback and reviews.  

Thanks to the rapid advancement in artificial intelligence and machine learning, Human Resource professionals now have even more data available to help improve human resource department's efficiency   

HARNESS THE POWER OF DATA.  

Outsmart your competition and sharpen your edge with actionable data insights.  Join globally recognised brands and some of the worlds most innovative companies, who use Cubeware to transform their business through data.  

Strategic thinking and planning are crucial to growing your business, especially in trying times. With the advent of data-driven decision making, the area is rapidly evolving, and, for the past few years, businesses have increasingly turned towards data analytics to assist in decision-making. Entrepreneurs and enterprise leaders should take advantage of the shift from traditional methods to more modern ones by providing their analysts with the proper tools to uncover insights and design an up-to-date process that drives the business forward.  

Here are our essential do’s and don’ts of data analytics, to help guide your organization in transforming data into measurable results.  

Simply put, the supply chain is there to serve the demand of customers while data analytics becomes the linchpin of the demand-driven supply chain which sounds appealing, but what does it look like in practice?  

A demand-driven supply chain will anticipate and determine what will they choose regarding issues like customer preference, what are they willing to pay, and shows how social media is influencing their decisions and more. That is where data analytics tool looks into the issues such as social sentiments, it can play a big role in a demand-driven supply chain, the kind of insights you will get will inform decisions at each step along the chain to see the whole supply chain inward and outward.  

Data analytics will generate insights for you and show you the way to make a smarter, customer-centric, demand-driven supply chain. It will connect you to each step that serves your customer better, smarter, and more efficient from behind the scene.   

In the last decade, data analytics have helped revolutionise the marketing industry, allowing brands to deliver more targeted messaging and more effectively measure their return on investment (ROI). Every online transaction made, whether through banking or shopping; GPS pinpoint taken by smartphones, or website visits – all contribute to the generation of a large and varied dataset.