1- What is big data analytics?

Put simply, big data analytics refers to the process of extracting valuable information by analyzing different kinds of big datasets. It’s used to uncover hidden patterns, consumer preferences, market trends etc in order to help organizations in decision making.

There’s a massive amount of data available today and there’s an urgent need to capture, analyze and preserve that data for getting actionable insights out of it. By looking at the data available to a business, it can determine different ways to make good strides to attain positive results.

Today, every company, from small businesses to giant multinationals, has become dependent on data. Now, just think for a moment, what if you could be the person businesses turn to before making any business decisions? This is exactly the place that future big data analytics will hold for you.

2- Why should you learn big data analytics?

If you’re still not convinced enough by the above example, here’re the reasons you should try to become a part of the future big data analytics.

2.1- HUGE JOB OPPORTUNITIES

As organizations begin to realize they cannot make use of big data in terms of capturing, interpreting and using that data, they’ve started to look for professionals who’re capable of doing so. Just have a look at any major job portal and you’ll find that there’re lots of job postings by companies looking for data analysts. This number will eventually continue to increase as data will become more abundant and the number of professionals with skillsets needed for the job will remain low. So, now is the time to get prepared to become a part of future big data analytics.

2.2- DATA ANALYTICS IS AND WILL BE A PRIORITY FOR TOP COMPANIES

To remain competitive in the business landscape, top companies are looking to implement data analytics to explore new market opportunities for their products and services. Today, a huge percentage of major companies consider data analytics as a crucial component of their business performance and a key approach to rise above the competition and this will become even more important with competition increasing over time. It means today’s aspiring big data professionals will be able to become an inherent part of future big data analytics.

2.3- GREAT SALARY ASPECTS

Across the globe, the demand for big data analytics skill is steadily going up with a massive deficit on the supply side. Despite big data analytics considered as a hot job, there’s a large number of unfilled jobs because of the acute paucity of required skills. The difference between demand and supply is only expected to increase. As a result, wages for professionals with data analytics skills are boosting and companies are ready to offer fattier pay packets for the right people. In some countries, data analytics professionals are getting substantially higher compared to their peers in other IT-based professions. This monetary benefit can surely be considered as a great reason to become a big data analytics professional.

2.4- BIG DATA ANALYTICS IS INCREASINGLY GETTING ADOPTED BY ORGANIZATIONS

New technologies in the field are making it easier to perform sophisticated data analytics tasks on diverse and massive datasets. A lot of professionals are using advanced data analytics techniques and tools to perform tasks like data mining, predictive analytics, among others. With big data analytics offering businesses an edge over the competition, companies are implementing a diverse range of analytics tools increasingly. Today, it’s almost impossible to find a top brand that doesn’t take help of at least some form of data analytics. In light of the increasing adoption rate of data analytics, it can be said that the landscape of future big data analytics will hold a good place for skilled professionals.

2.5- YOU’LL BE A PART OF THE CORE DECISION MAKING

For the majority of the companies, big data analytics is a major competitive resource. There’s no doubt that analytics will become even more important in the near future as competition will keep on increasing. This is mainly because there’s a massive amount of data which is not being used and only rudimentary analytics is getting done. It’s an undeniable fact that data analytics is and will be playing a crucial role in decision making, regardless of the volume of an organization. Not being able to be a part of the decision-making process is something that generates dissatisfaction for a significant number of employees. As a big data analytics professional, you’ll be a crucial part of business decisions and strategies, catering to a major purpose within the company.

2.6- YOU’LL HAVE A DIVERSE RANGE OF JOB TITLES TO TAKE YOUR PICK FROM

As a data analytics professional, you’ll have a wide range of job titles as well as domains from which you can choose according to your preference. Since data analytics is used in different fields, lots of job titles like big data engineer, big data analytics architect, big data analyst, big data solution architect, analytics associate, big data analytics business consultant, metrics and analytics specialist etc will be available to you. Also, an array of top organizations like Microsoft, IBM, Oracle, ITrend, Opera are utilizing big data analytics and thus huge job opportunities with them are possible.

2.7- YOU’LL BE ABLE TO BECOME A FREELANCE CONSULTANT

A vast majority of today’s workforce keeps on looking for ways to diversify their income sources and ways through which they can maintain a perfect work-life balance. Data analytics professionals being able to offer valuable insights about major areas hold the perfect position to become a consultant or freelancer for some of the top companies. So, you don’t need to be tied to a single company. Instead, you’ll be able to work with multiple organizations who’ll depend on your insights when making crucial business decisions.

3- Key skills you should focus on to become a part of future big data analytics

To become successful in the future big data analytics landscape, you need to have the ability to derive useful information from big data. There’re different approaches to learn the key skills needed to become a data analytics professional like self-learning, learning from tutorials etc but we’d suggest you take a course in order to learn from instructors with real-world experience. Let’s have a look at the skills.

3.1- PROGRAMMING

A big data analytics professional needs to have a solid understanding of coding because a lot of customization is needed to handle the unstructured data. Some of the most used languages in the field include Python, R, Java, SQL, Hive, MATLAB, Scala, among others.

3.2- FRAMEWORKS

Familiarity and a good understanding of frameworks like Hadoop, Apache Spark, Apache Storm are needed to become a part of future big data analytics. All these technologies would help you in big data processing to a great extent.

3.3- DATA WAREHOUSING

Adequate knowledge of data warehousing is a must to become a good data analytics professional. You’ll be expected to possess a good understanding of working with database systems like Oracle, MySQL, HDFS, NoSQL, Cassandra, among others.

3.4- STATISTICS

While you’ve to have a robust understanding of the technologies used in the field, good knowledge of statistics is also a must for working with big data. Statistics is the building block of data analytics and expertise in core concepts like random variables, probability distribution etc is extremely important if you want to hold a strong position in the future big data analytics landscape.

3.5- STRONG BUSINESS ACUMEN

One of the most crucial skills to become a big data analytics professional is a solid understanding of the business domain. In fact, one of the key reasons behind the huge demand of big data analysts is that it’s highly difficult to find someone with adequate knowledge in statistics, technical skills, and business landscape. There’re professionals who’re expert programmers but don’t have the needed business acumen, and thus may not be the ideal fit for future big data analytics domain.

Final takeaway

The advent of IoT together with the developments in the AI field has simplified implementation of big data analytics to the degree that even small and medium scale businesses can benefit from them. And since almost every sector from banking and securities, education, healthcare to consumer trade, manufacturing, and energy is directly or indirectly making use of data analytics, the importance of it increases even further. As we’re moving toward a more connected future, big data analytics is going to play a major role in the future. With technologies around the world becoming more interoperable and synchronous, data will become the most important avenue that connects them together. So, it can be said that this is the ideal time to start developing the skills and become a master of them to hold a good place in the future big data analytics landscape.

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To learn more about big data, click here and read our another article.00

The post Will You Be A Part Of Future Big Data Analytics? first appeared on Magnimind Academy.

Modern businesses understand the power of big data, but they also understand that it can be even more powerful when merged with intelligent automation. And this is exactly where the power of machine learning comes into the picture. Machine learning systems help businesses in a multitude of ways including managing, analyzing, and using the captured data far more strategically than ever before.

In simple terms, machine learning is a set of technologies which empower connected computers and machines to learn, develop, and improve based on their own learning through various methods. These days, all the large corporations, giant tech organizations, and data scientists are foreseeing that big data is going to make a tremendous difference in the machine learning landscape.

Inherently, machine learning is an advanced subset of artificial intelligence that learns new things from databases on its own in a programmed manner. It’s based on the idea that says machines can learn from data, find out useful patterns, and become capable of making decisions without much human intervention.

While machine learning has been around for decades, nowadays it has become possible to automatically and quickly produce models which can analyze more complex, bigger datasets and deliver more accurate results quickly – even on a massive scale. And by creating these kinds of models, a business stands a better chance of finding profitable opportunities out.

Machine learning doesn’t involve any prior assumptions. Once they’re provided with the required data, machine learning algorithms can process that data and identify patterns. Then those patterns can be used on new datasets. Generally, this technology is applied to high-dimensional datasets. It means the more data you can provide, the more accurate your predictions will be. And this is exactly where the power of big data comes in.

As the industry and sciences are experiencing a phenomenal rise in data generation, this scenario has presented a great opportunity for machine learning and big data to come together and create machine learning techniques which have the ability to manage modern data types by attaining computational and statistical intelligence for navigation of massive amounts of information with no or minimal human intervention.

Machines learn from extensive calculations performed over datasets, meaning the more the data, the more effective the learning. With the emergence of big data together with the advancements in computing technologies, machine learning has already evolved from that of the past. With the steadily increasing proliferation of big data analysis into machine learning, machines and devices will get smarter and should be able to perform in a more advanced manner. This will eventually lead to improvement and advancement in machine learning solutions.

The post How Does Machine Learning Benefit From Big Data? first appeared on Magnimind Academy.

While the concept of big data has been around for a significant number of years, everything has started to change with the emergence of big data analytics. This process allows businesses to perform analytical procedures efficiently and quickly, giving them a competitive advantage over competitors. Here’re some of the most prominent real-world examples of how big data analytics is being used.

1- Healthcare industry

The entire healthcare industry is getting transformed with the help of big data analytics. The ability to provide hyper-personalized patient treatment, improve the quality of life of the patients, as well as, discover medical breakthroughs – all have been impacted by big data analytics. In this industry, big data analytics isn’t performed with the focus of finding new product opportunities or increasing profits. Instead, it’s all about applying and analyzing big data to offer a better patient-centric approach. For instance, healthcare providers are analyzing historical big data to analyze and identify certain risk factors in patients, which is extremely useful for early detection of diseases, enabling both the patients and doctors to take action sooner.

2- Retail industry

Probably the maximum implementation of big data analytics can be observed in the retail industry. As the industry has gone digital, the customers have also started to expect a better and seamless experience. With the help of big data analytics, retail companies have become in a position to understand their customers more and thus, to provide a variety of personalized services. From creating product recommendations based on a customer’s past searches to demand forecasting to performing crisis control – everything is being taken care of through big data analytics.

3- Media and entertainment industry

The media and entertainment industry is one of the biggest users of big data analytics. As the number of users of different digital gadgets is increasing rapidly, media and entertainment companies are leveraging the power of big data analytics to a great extent. Some of the biggest benefits that are being experienced by the industry include on-demand or optimized scheduling of media streams, getting actionable insights from customer reviews, predicting the actual interests of audiences, successful targeting of the advertisements, and many more.

Final Thoughts

For any business, big data analytics is a crucial investment that can help to optimize the real-life situations where common people are involved to a great extent. Implementation of big data analytics not only helps businesses to achieve competitive advantage but also drives customer retention and reduces the cost of operation. And as technological advancements steadily continue to emerge, big data analytics will become even more important to businesses across industries.

The post What Are Real-life Examples Of The Application Of Big Data Analytics? first appeared on Magnimind Academy.

Key advantages of big data

Here’re the biggest advantages of using big data.

• Improved business processes: Probably the biggest advantage of big data is it helps businesses to gain a huge competitive advantage. Apart from being able to understand, as well as, target customers better, analyzing big data can result in the improvement and optimization of certain facets of business operations. For instance, by mining big data retailers can not only explore patterns in consumption and production but can also promote better inventory management, improve the supply chain, optimize distribution channels, among others.
• Fraud detection: This advantage of using big data comes from the implementation of machine learning technologies. It helps banks and other financial institutions to detect frauds like fraudulent purchases with credit cards often before even the cardholder gets to know about it.
• Improved customer service: One of the most common goals among big data analytics programs is improving customer service. Today’s businesses capture a huge amount of information from different sources like customer relationship management (CRM) systems, social media together with other points of customer contact. By analyzing this massive amount of information they get to know about the tastes and preferences of a user. And with the help of the big data technologies, they become able to create experiences which are more responsive, personal, and accurate than ever before.

Key disadvantages of big data

Despite the advantages of big data, it comes with some serious challenges that make its implementation difficult or risky. Here’re the biggest disadvantages.

• Privacy and security concerns: Probably the biggest disadvantage of big data is that it can make businesses a softer target for cyber attackers. Even giant businesses have experienced instances of massive data breaches. However, with the implementation of GDPR, businesses are increasingly trying to invest in processes, protocols, and infrastructure to be able to maintain big data
• Need for technical expertise: Working with big data needs a great deal of technical proficiency and that’s one of the key reasons for which big data experts and data scientists belong to the highly paid and highly coveted group in the IT landscape. Training existing staff or hiring experts to handle big data can easily increase the cost of a business considerably.

Final Takeaway

Despite the advantages and disadvantages of big data we discussed here, it just cannot be denied that data powers almost everything these days and businesses have only started to scratch the surface of the possibilities. While in the future, the complexity might be higher, but we can surely hope to see more advanced big data operations to sail through the challenges.

.  .  .

To learn more about big data, click here and read our another article.

The post What Are The Advantages And Disadvantages Of Big Data? first appeared on Magnimind Academy.

These days, lots of resources (social media platforms being the number one) have become available to companies from where they can capture massive amounts of data. Now, this captured data is used by enterprises to develop a better understanding and closer relationships with their target customers. It’s important to understand that every new customer action essentially creates a more complete picture of the customer, helping organizations achieve a more detailed understanding of their ideal customers. Therefore, it can be easily imagined why companies across the globe are striving to leverage big data. Put simply, big data comes with the potential that can redefine a business, and organizations, which succeed in analyzing big data effectively, stand a huge chance to become global leaders in the business domain.

Structures of big data

The Structure of Big Data

Big data structures can be divided into three categories – structured, unstructured, and semi-structured. Let’s have a look at them in detail.

Structured data

It’s the data which follows a pre-defined format and thus, is straightforward to analyze. It conforms to a tabular format together with relationships between different rows and columns. You can think of SQL databases as a common example. Structured data relies on how data could be stored, processed, as well as, accessed. It’s considered the most “traditional” type of data storage.

Unstructured data

Unstructured Data

This type of big data comes with unknown form and cannot be stored in traditional ways and cannot be analyzed unless it’s transformed into a structured format. You can think of multimedia content like audios, videos, images as examples of unstructured data. It’s important to understand that these days, unstructured data is growing faster than other types of big data.

Semi-structured data

It’s a type of big data that doesn’t conform with a formal structure of data models. But it comes with some kinds of organizational tags or other markers that help to separate semantic elements, as well as, enforce hierarchies of fields and records within that data. You can think of JSON documents or XML files as this type of big data. The reason behind the existence of this category is semi-structured data is significantly easier to analyze than unstructured data. A significant number of big data solutions and tools come with the ability of reading and processing XML files or JASON documents, reducing the complexity of the analyzing process.

Conclusion

While data analytics aren’t new, the emergence of big data has dramatically changed the nature of work. It’s important for businesses looking to make most out of the big data to try to adopt advanced tools and technologies to keep up with the pace at which the data is growing.

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