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Big Data 

Large volume of data – both structured and unstructured

Uses of Big Data

Big data is a term that describes the large volume of data – both structured and unstructured; big data analytics market is set to reach $103 billion by 2023

Structured data is augmented by unstructured data, which is where things like Twitter feeds, audio files, MRI images, web pages, weblogs are put
Cost reduction through Big Data tools like Hadoop and Spark is that these offer cost advantages to businesses when it comes to storing, processing, and analyzing large amounts of data
Fraud Detection. Excellent at detecting patterns and anomalies. These abilities can give banks and credit card companies the ability to spot stolen credit cards or fraudulent purchases
Innovation. Big Data analytical tools dig deep into insights and extract information that can be transforms to new business strategies and action plans  

4 V's of Big Data

 Four popular V’s that are key characteristics of this kind of data


The main characteristic that makes data “big” is the sheer volume. Organisations collect data from a variety of sources, including business transactions, industrial equipment, videos, social media and more. 


Data comes in all types of formats – from structured, unstructured, numeric data in traditional databases to unstructured text documents, emails, videos, audios, stock ticker data and financial transactions


Velocity is the frequency of incoming data that needs to be processed. Think about how many SMS messages, Facebook status updates or credit card swipes are being sent on a particular telecom carrier every minute of every day


Veracity refers to the quality and truthfulness in the source of the data. Data that is high volume, high velocity and high variety must be processed with advanced tools (analytics and algorithms) to reveal meaningful information.

Our Expertise In Big Data

Big data, big results, not bound by industry


Collection of Big Data through modern metrics and click-through rates will allow our team to be able to guide you to personalised marketing tactics to approach your customers


Big Data has transformed and evolutionalised real time tracking, from transport to tracking parcels and communication, be able to predict and resolve issues timely


Big Data Analysis will help your business to find areas that can be breached and rescale and protect your business by singling out anomalous activity that often signifies security issues


Big Data will provide insights for you to redevelop and innovate your products and services. This would enable your business to streamline your research and manufacturing processes
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Applications of Big Data Analytics 

Advanced analytics can be applied to big data, several types of technology work together to 
help you and your business get the most value from your information

What Big Data Can Do For You

Big Data Analytics helps business use their data to transform it into new products and opportunities. This implementation eventually leads to smarter business moves and operation, more efficient processes, higher profits and happier customers
Learn More About Analytics
Significant reduction in costs
Compete with bigger businesses
Ability to innovate and create new products and services
Faster, efficient and reliable decision making
Understand customer preferences and improve results

"The main goal of a formal organisational strategy for data and analytics is typically to improve decision making with analytics in a wide realm of activities. [And] our survey results and interviews offer strong evidence that successful analytics strategies dramatically shift how decisions are made in the organisation"

From the white paper Beyond the Hype: The Hard Work Behind Analytics Success

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Businesses are using analytics to make more informed decisions and to plan ahead. It will help your business to uncover opportunities which are visible only through an analytical lens. Analytics helps companies to decipher trends, patterns and relationships within data to explain, predict and react to a market phenomenon. It helps answer the following questions:

What is happening and what will happen?
Why is it happening?
What is the best strategy to address it?

Collecting large amounts of data about multiple business functions from internal and external sources is simple and easy using today’s advanced technologies. The real challenge begins when companies struggle to infer useful insights from this data to plan for the future. Using analytics businesses can improve their processes, increase profitability, reduce operating expenses and sustain the competitive edge for the long run.

Many companies have also reported significant challenges when implementing big data analytics initiatives.

Talent Acquisition. Data scientists and big data experts are high in demand —and highly paid — workers in the IT field. The AtScale survey found that the lack of a big data skillset has been the number one big data challenge. Hiring or training staff can increase costs considerably, and the process of acquiring big data skills can take considerable time.

Setup Costs. Today’s big data tools rely on open source technology, which dramatically reduces software costs, but enterprises still face significant expenses related to staffing, hardware, maintenance and related services. It’s not uncommon for big data analytics initiatives to run significantly over budget and to take more time to deploy than IT managers had originally anticipated. Storage space to house the data, networking bandwidth to transfer it to and from analytics systems, and compute resources to perform those analytics are all expensive to purchase and maintain. Some organizations can offset this problem by using cloud-based analytics, but that usually doesn’t eliminate the infrastructure problems entirely.

Compliance. Compliance is defined simply as complying with government regulations. Much of the information included in companies’ big data stores are sensitive or personal, and that means the firm may need to ensure that they are meeting industry standards or government requirements when handling and storing the data.

In the Syncsort survey, data governance, including compliance, was the third most significant barrier to working with big data. In fact, when respondents were asked to rank big data challenges on a scale from 1 (most significant) to 5 (least significant), this disadvantage of big data got more 1s than other challenges.

Data analytics companies help the organisations with all of these. Identify and build robust data management layer, conduct various analyses to answer what, why and how of business scenarios and generate insights which help drive additional impact in the way the business is running as usual. They also help scale up and operationalise this process.

As of 2019, the infographic describes below the amount of big data generated every minute. Data analytics companies are established to enable other businesses put together this information to be further processed and used.

Businesses may not have the awareness about the data they have in terms of what opportunity the data brings with the multiple dimensions it has, how to use the raw data unless its processed and put in a structured form.

They are not equipped technically to manage, process, engineer and use data to be used for business decisions therefore relying upon data analytics companies. Many companies have reported, and a number of reports and dashboards but they do not know what insights to generate and how to act on these insights.

The culture of the company does not support data-driven decision making and it depends on heuristics. The business doesn’t have the expertise or core competency in terms of dealing with big data.

Data Analytics often refers to the techniques of data analysis. It includes algorithms, the process of data mining methods, etc. Based on these techniques, a data scientist can figure it out which method gives more efficient/quick results with fewer calculations.

Big Data as described above is a term that describes the large volume of data – both structured and unstructured – that engulfs a business on a day-to-day basis. In recent years, there has been a boom in Big Data because of the growth of social, mobile, cloud, and multi-media computing. We now have unprecedented amounts of data, and it is up to organizations to harness the data in order to extract useful, actionable insights.

Data Mining is a process that is used to extract usable data from a larger set of any raw data. It implies analysing data patterns in large batches of data using one or more software. Its key features include automatic pattern predictions based on trend and behaviour analysis, predictions based on likely outcomes, creation of decision-oriented information, focus on large data sets and databases for analysis and clustering based on finding and visually documented groups of facts not previously known.

The process using these terms altogether goes about like displayed below:

Big data → Data Mining → Data Analysis → Data Analytics → Data Science