What is Big Data Analytics: Why It Matters To You

The volume of data collection is the most important factor in defining big data. The data sets that make up big data are often huge, measuring in the tens of gigabytes range and sometimes exceeding the threshold of petabytes. Big data was first introduced by large databases (VLDBs), which were managed with database management systems (DBMS). Big data today can be divided into three types of data sets: structured, semi-structured, and unstructured.

Structured datasets are data that can be used in their original form to produce results. These data include relational data like employee salary records. Modern computers and apps are programmed to create structured data in predefined formats, making it easier to process.

On the other hand, unstructured data sets are not properly formatted and aligned. Human texts, Google search results outputs, and others are examples. These data sets are more complex and take longer to convert into structured data sets, so they can be used in generating tangible results. Semi-Structured data sets are a mixture of structured and unstructured information. These data sets may have a structure but lack the defining elements necessary for sorting or processing. RFID data and XML data are two examples.

It is difficult to define big data because of its evolving nature. Based on the technologies and tools needed to process them, data sets are relegated to big data status.

Big Data Analytics- Technologies and Tools

Analytics is the process of extracting valuable information from different types of large data sets. Big data analytics can be used to uncover hidden patterns, market trends, and consumer preferences for organizational decision-making. Big data analytics involves many technologies and steps.

Data Acquisition

Two components of data acquisition are identification and collection. Analyzing the two types of data that are naturally available to identify big data, namely digital and analog, is how you can identify big data and AI.

Born Digital Data

It refers to information that has been captured using a digital medium (e.g. a computer or smartphone app, etc. Because systems continue to collect different types of information from users, this type of data is constantly expanding. Born-digital data can be traceable and provide business insight as well as personal information. Cookies, Web Analytics, and GPS tracking are just a few examples.

Born Analogue Data

Analog data is information that is presented in pictures, videos, or other formats that relate to the physical elements of our world. These data must be converted to digital format using sensors such as cameras, voice recorders, digital assistants, and microphones. Technology's increasing accessibility has led to an increase in the speed at which analog data can be converted or captured using digital media.

The second step of the data acquisition process involves the collection and storage of big data sets. A new method was developed to collect and store big data. MAD stands for magnetic, agile, and deep. Management of big data is difficult because it requires large amounts of storage and processing power. This makes creating such systems impossible for many entities that depend on big data analytics. The two most popular solutions for big data processing are distributed storage and massively parallel processing. MPP. MPP is used in most of the top-end Hadoop platforms.

Non-relational Databases

These databases store massive data sets and have evolved in the way they are stored. JavaScript Object Notation, or JSON, is the preferred protocol to save large data. JSON allows for tasks to be written in the application layer, which allows for better cross-platform functionality. This allows for the agile development of flexible and scalable data solutions that are flexible enough to be used by developers. It is being used by many companies as a replacement for XML for the transmission of structured data between web applications and servers.

In-memory Database Systems

These database storage systems were created to address one of the biggest hurdles to big data processing: the slowness of traditional databases in accessing and processing information. IMDB systems store data in the RAM of large data servers, drastically reducing storage I/O. Apache Spark is one example of an IMDB system. There are many other examples, such as VoltDB and NuoDB. IBM solidDB is another example.

Hybrid Data Storage and Processing Systems, Apache Hadoop

Apache Hadoop, a hybrid data storage/processing system, provides low-cost scalability for small and mid-sized businesses. The Hadoop Distributed File System is used to store large files across multiple systems, known as cluster nodes. Hadoop uses a replication mechanism that ensures smooth operation, even in the event of node failure. Hadoop's core uses Google MapReduce parallel programming. Named after the 'Mapping and 'Reduction of functional programming languages in Hadoop's algorithm for big data processing, MapReduce is based on the principle that MapReduce increases the number of functional nodes rather than increasing the processing power each node. Hadoop can also be run on readily available hardware, which has greatly accelerated its development and popularity.

Data Mining

This is a new concept that uses contextual analysis of large data sets to find the relationships between different data items. It is possible to use one data set for multiple purposes by different users. Data mining can be used to reduce costs and increase revenues.

Top 5 Sectors Using Big Data Analytics

Big data is being used in nearly all industries. To give you an idea about the scope and application of big data, here is a list.

• Banks and Securities: To monitor financial markets using network activity monitors, and natural language processors, and to reduce fraud transactions. Monitoring the stock market, Exchange Commissions and Trading Commissions use big data analytics to prevent illegal trading.
• Communications and Media: Live reporting on events across the globe via multiple platforms (mobile and web) simultaneously. The music industry is a sector of media that uses big data to monitor the latest trends and ultimately use autotuning software for catchy tunes.
• Sports: To analyze the viewership patterns for different events in a region and to monitor individual players' and team performance. Big data analytics is used to analyze sporting events such as the Cricket World Cup, FIFA World Cup, and Wimbledon.
• Healthcare: To collect data on public health to respond faster to individual health issues and track the spread of new viruses such as Ebola. Different countries have different health ministries that use big data analysis tools to make the most of data from surveys and Census.
• Education: To update and improve prescribed literature in a range of fields that are experiencing rapid development. It is being used by universities around the globe to track and monitor the performance of their faculties and students and to map the interests of students through attendance.

7 Ways You Can Grow Your Business With Data Science

The Internet of Things and AI technology have made it easier to implement big data solutions. This is true even for small businesses. The importance of this technology is further increased by the fact that the top 10 list includes sectors directly or indirectly related to various businesses. Big data analytics allows businesses to make better decisions and improve their operational efficiency.

E.g. Big data analytics allows businesses to make better decisions and improve their operational efficiency by using big data analytics. E.g.

• Using company data to identify areas for improvement.
• Using customer data such as credit information and social media streams to develop or improve products and services.

Using data science to improve your business -

• Helps managers make better decisions - Big data analytics is a trusted advisor to an organization's strategic planning. This helps staff and management to improve their analytical skills and their decision-making abilities. The upper management can then set new goals by measuring, recording, and tracking performance metrics.
• Helps to identify trends to remain competitive - As we mentioned in the post, data analytics is primarily about identifying patterns within large data sets. This is especially useful in identifying emerging market trends. These trends can be used to your advantage in introducing new products or services.
• Enhances efficiency and commitment of staff to core tasks and issues - Employees can be more productive by being aware of the advantages of data science and making them aware of how they can use it. These employees will have a better understanding of company goals and be able to take more action to address core issues at all stages. This will result in greater operational efficiency and profitability for your company.
• Identifies opportunities and takes action - Data science is about continuously looking for ways to improve organizational functioning. Data scientists can find inconsistencies and improve existing systems by identifying them. Data scientists can then drive innovation and create new products, which could open up lucrative avenues for your business.
• Promotes low-risk data-driven action plans - Small and large businesses can now take action based on quantitative, data-driven data through big data analytics. This strategy can help businesses avoid unnecessary tasks and foresee potential risks.
• Validates decisions - Analytics allows your business to make data-based decisions. However, analytics can also help you test these decisions by adding variable factors to verify flexibility and scalability. You can make positive changes to your organization's structure and function by using data science and big-data solutions.
• This tool helps you select the right audience - Big data analytics offers a key advantage in that you can use customer data to gain more insight into consumers' preferences and expectations. Companies can use tailor-made products or services to target their audience through deeper analysis of customer data.

Conclusion

The world is moving towards a connected future. Big data solutions will play an important role in automation and the development of AI technologies. Machine Learning is being used by companies like Google to deliver their services with greater precision. Big data will be the glue that connects the world's technologies as they become more interconnected and synchronous. Companies that use big data solutions must keep up with their changing nature. Those who are still reluctant to invest in big data solutions should reconsider their organizational policies. These tips will help you get the most from your big data investment.

• You can demand a value proposition for big data by investing in the right technologies to capture and store it. You don't have the data if you don't have the right technology. Data discovery tools can help you find big data that is relevant to your business.
• Use big data to innovate and improve your services and applications.
• Organize training for your entire organization to familiarize them with big data solutions.
• Collaborate with big data users in related fields to reap more benefits and lower usage costs.
• Don't be afraid to integrate big data with other enterprise infrastructures.
• When moving to a new platform for data processing, make sure you choose one that offers a support system for big data such as MapReduce or in-memory data processing.
• Create a tech strategy to manage your data. Layout a plan for processing and storing them.
• You should also plan the financials for the storage and processing of your big data.

Big data is also connecting with government and public sector agencies. This is good news for businesses around the globe as it will allow for deeper public-private partnerships in many areas.