Data Analytics and Business Intelligence
min read

What Is Big Data Analytics And Why do I Need It For My Business?

Explore big data analytics, its tools, technologies, and its top application to help your business.
Pinakin Ariwala
Pinakin Ariwala
Updated on Sep 30
Data Analytics and Business Intelligence
min read
What Is Big Data Analytics And Why do I Need It For My Business?
Explore big data analytics, its tools, technologies, and its top application to help your business.
image
Pinakin Ariwala
Updated on Oct 14
Table of contents
Big data analytics – Technologies and Tools
Top 10 sectors using big data analytics
8 Ways you can grow your business with data science
Conclusion

Big data is primarily defined by the volume of a data set. Big data sets are generally huge – measuring tens of terabytes – and sometimes crossing the threshold of petabytes. The term big data was preceded by very large databases (VLDBs) which were managed using database management systems (DBMS). Today, big data falls under three categories of data sets – structured, unstructured and semi-structured.

Structured data sets comprise of data which can be used in its original form to derive results. Examples include relational data such as employee salary records. Most modern computers and applications are programmed to generate structured data in preset formats to make it easier to process.

Unstructured data sets, on the other hand, are without proper formatting and alignment. Examples include human texts, Google search result outputs, etc. These random collections of data sets require more processing power and time for conversion into structured data sets so that they can help in deriving tangible results.Semi-Structured data sets are a combination of both structured and unstructured data. These data sets might have a proper structure and yet lack defining elements for sorting and processing. Examples include RFID and XML data.

Semi-Structured data sets are a combination of both structured and unstructured data. These data sets might have a proper structure and yet lack defining elements for sorting and processing. Examples include RFID and XML data.

Big data processing requires a particular setup of physical and virtual machines to derive results. The processing is done simultaneously to achieve results as quickly as possible. These days big data processing techniques also include Cloud Computing and Artificial Intelligence. These technologies help in reducing manual inputs and oversight by automating many processes and tasks.

The evolving nature of big data has made it difficult to give it a commonly accepted definition. Data sets are consigned the big data status based on technologies and tools required for their processing.

Big data analytics – Technologies and Tools

Big data analytics is the process of extracting useful information by analysing different types of big data sets. Big data analytics is used to discover hidden patterns, market trends and consumer preferences, for the benefit of organizational decision making. There are several steps and technologies involved in big data analytics.

Data Acquisition

Data acquisition has two components: identification and collection of big data. Identification of big data is done by analyzing the two natural formats of data – born digital and born analogue.

Born Digital Data

It is the information which has been captured through a digital medium, e.g. a computer or smartphone app, etc. This type of data has an ever expanding range since systems keep on collecting different kinds of information from users. Born digital data is traceable and can provide both personal and demographic business insights. Examples include Cookies, Web Analytics and GPS tracking.

Born Analogue Data

When information is in the form of pictures, videos and other such formats which relate to physical elements of our world, it is termed as analogue data. This data requires conversion into digital format by using sensors, such as cameras, voice recording, digital assistants, etc. The increasing reach of technology has also raised the rate at which traditionally analogue data is being converted or captured through digital mediums.

The second step in the data acquisition process is collection and storage of data sets identified as big data. Since the archaic DBMS techniques were inadequate for managing big data, a new method is used for collecting and storing big data. The process is called MAD – magnetic, agile and deep. Since, managing big data requires a significant amount of processing and storage capacity, creating such systems is out-of-reach for most entities which rely on big data analytics. Thus, the most common solutions for big data processing today are based on two principles – distributed storage and Massive Parallel Processing a.k.a. MPP. Most of the high-end Hadoop platforms and specialty appliances use MPP configurations in their system.

Non-relational Databases

The databases that store these massive data sets have also evolved in how and where the data is stored. JavaScript Object Notation or JSON is the preferred protocol for saving big data nowadays. Using JSON, the tasks can be written in the application layer and allow better cross-platform functionalities. Thus enabling, agile development of scalable and flexible data solutions for the devs. Many companies are using it as a replacement of XML as a way of transmitting structured data between the server and web application.

In-memory Database Systems

These database storage systems are designed to overcome one of the major hurdles in the way of big data processing – the time taken by traditional databases to access and process information. IMDB systems store the data in the RAM of big data servers, therefore, drastically reducing the storage I/O gap. Apache Spark is an example of IMDB systems. VoltDB, NuoDB and IBM solidDB are some more examples of the same.

Hybrid Data Storage and Processing Systems – Apache Hadoop

Apache Hadoop is a hybrid data storage and processing system which provides scalability and speed at reasonable costs for mid and small-scale businesses. It uses a Hadoop Distributed File System (HDFS) for storing large files across multiple systems known as cluster nodes. Hadoop has a replication mechanism to ensure smooth operation even during instances of individual node failures. Hadoop uses Google’s MapReduce parallel programming as its core. The name originates from ‘Mapping’ and ‘Reduction’ of functional programming languages in its algorithm for big data processing. MapReduce works on the premise of increasing the number of functional nodes over increasing processing power of individual nodes. Moreover, Hadoop can be run using readily available hardware which has sped up its development and popularity, significantly.

Data Mining

It is a recent concept which is based on contextual analysing of big data sets to discover the relationship between separate data items. The objective is to use a single data set for different purposes by different users. Data mining can be used for reducing costs and increasing revenues.

Top 10 sectors using big data analytics

Big data is finding usage in almost all industries today. Here is a list of the top segments using big data to give you an idea of its application and scope.

what is big data analytics and why i need it for my business?
  1. Banking and Securities : For monitoring financial markets through network activity monitors and natural language processors to reduce fraudulent transactions. Exchange Commissions or Trading Commissions are using big data analytics to ensure that no illegal trading happens by monitoring the stock market.
  2. Communications and Media: For real-time reportage of events around the globe on several platforms (mobile, web and TV), simultaneously. Music industry, a segment of media, is using big data to keep an eye on the latest trends which are ultimately used by autotuning softwares to generate catchy tunes.
  3. Sports: To understand the patterns of viewership of different events in specific regions and also monitor the performance of individual players and teams by analysis. Sporting events like Cricket world cup, FIFA world cup and Wimbledon make special use of big data analytics.
  4. Healthcare: To collect public health data for faster responses to individual health problems and identify the global spread of new virus strains such as Ebola. Health Ministries of different countries incorporate big data analytic tools to make proper use of data collected after Census and surveys.
  5. Education: To update and upgrade prescribed literature for a variety of fields which are witnessing rapid development. Universities across the world are using it to monitor and track the performance of their students and faculties and map the interest of students in different subjects via attendance.
  6. Manufacturing: To increase productivity by using big data to enhance supply chain management. Manufacturing companies use these analytical tools to ensure that are allocating the resources of production in an optimum manner which yields the maximum benefit.
  7. Insurance: For everything from developing new products to handling claims through predictive analytics. Insurance companies use business big data to keep a track of the scheme of policy which is the most in demand and is generating the most revenue.
  8. Consumer Trade: To predict and manage staffing and inventory requirements. Consumer trading companies are using it to grow their trade by providing loyalty cards and keeping a track of them.
  9. Transportation: For better route planning, traffic monitoring and management, and logistics. This is mainly incorporated by governments to avoid congestion of traffic in a single place.
  10. Energy: By introducing smart meters to reduce electrical leakages and help users to manage their energy usage. Load dispatch centers are using big data analysis to monitor the load patterns and discern the differences between the trends of energy consumption based on different parameters and as a way to incorporate daylight savings.

8 Ways you can grow your business with data science

Today, the advent of Internet of Things and the development of AI technology has simplified implementation of big data solutions to the degree that even medium to small scale businesses are benefiting from it. And since the top 10 list comprises of sectors which are directly or indirectly associated with various businesses, the imperative of this technology increases even further. Using big data analytics, businesses can take informed decisions and better their operational efficiency in a number of ways.

What-Is-Big-Data-Analytics-And-Why-do-I-Need-It-For-My-Business_2

E.g. Using big data analytics, businesses can take informed decisions and better their operational efficiency in a number of ways. E.g.

  • Utilizing company data to identify the need for improvement in existing policies and processes.
  • Utilizing customer data available with the company such as social media streams, credit information, and internal or external consumer research, to improve or develop new products and services.

Deploying data science for your business –

  1. Empowers management to make better decisions
    Big data analytics acts as a trusted advisor for an organization’s strategic planning. It helps your management and staff in enhancing their analytical abilities and thereby improve their overall decision-making skills. Measuring, recording and tracking performance metrics then allow the upper management to set new goals.
  2. Helps identify trends to stay competitive
    As mentioned earlier in this post, one of data analytics’ primary objectives is to determine patterns within large data sets. This is particularly useful for identifying new and emerging market trends. Once identified these trends could become the key to gaining a competitive advantage by introducing new products and services.
  3. Increases the efficiency and commitment of staff in handling core tasks and issue
    By making employees aware of benefits of using the organization’s analytics product, data science can make them more efficient at their jobs. Working with a greater insight into company goals, these employees will be able to drive more action towards core tasks and issues at every stage. Hence, improving the overall operational efficiency of your business.
  4. Identifies and acts upon opportunities
    Data science is all about constantly looking for areas of improvement in the organizational workings. By discovering inconsistencies in the organizational processes and existing analytical systems, data scientists can introduce new ways of doing things. This, in turn, can drive innovation and allow new product development, opening profitable avenues for your company.
  5. Promotes low risk data-driven action plans
    Big data analytics has made it possible for small and big businesses to take actions based on quantifiable, data-driven evidence. Such a strategy can save a business from unnecessary tasks and sometimes foreshadow risks.
  6. Validates decisions
    Apart from allowing your business to base decisions on data, analytics also helps you test these decisions by introducing variable factors, to check for flexibility and scalability. Using data science and big data solutions you can introduce favourable changes in your organizational structure and functioning.
  7. Helps in selecting target audience
    One of the key value props of big data analytics is how you can shape customer data to provide more insight into consumer preference and expectations. A deeper analysis of customer data can help companies in identifying and targeting audience with utmost precision using tailor-made products and services.
  8. Facilitates sensible recruitment of talent
    Human resource departments are constantly at work in companies to find talent that fits the prescribed criteria. Big data has made their task simpler by providing comprehensive data profiles on individuals by merging social media, corporate profiles and job search databases. Now your HR Department can process CVs much faster and recruit the right talent quickly and without compromises.

Conclusion

The world is moving towards a more connected future, and big data solutions are going to play a big part in automation and development of AI technologies. Companies like Google are already using Machine Learning processes for greater precision in delivering their services. As technologies around the globe become more synchronous and interoperable, big data will become the core that connects them together. Therefore, companies using big data solutions need to keep up with its evolving nature while those still reluctant to invest should rethink their organizational policies. There are a few pointers which can be helpful in getting the most out of your investment in big data.

  • Demand a value proposition from big data by investing in adequate technologies to capture and store data. If you do not have the data, then you do not have the benefits. Data discovery tools can help you in digging up big data which is relevant to your business.
  • Make use of big data to improve and innovate your applications and services.
  • Arrange organization-wide training to accustom your staff to big data solutions and their usage.
  • Interact and collaborate with big data users from associated fields of businesses to derive more benefits and bring down usage costs.
  • Avoid siloed big data management and stay open to integration with shared enterprise infrastructures.
  • If shifting to a new data platform, choose those which have a special support system for big data, such as in-memory processing, MapReduce, etc.
  • Develop a tech strategy for your organization’s data and lay out a plan for capturing and processing them in the long-run.
  • Plan the financials for storage and processing of your big data as well.

Moreover, big data is also resonating with government and public-sector agencies, which is a good sign for businesses all around the world as this will help deepen the public-private collaboration in a range of fields.

Pinakin Ariwala
About the author
Pinakin Ariwala


Pinakin is the VP of Data Science and Technology at Maruti Techlabs. With about two decades of experience leading diverse teams and projects, his technological competence is unmatched.

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