Big Data 

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