I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots. ...

Full Bio 
Follow on

I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots.

Why is there so much buzz around Predictive Analytics?
688 days ago

Changing Scenario of Automation over the years
689 days ago

Top 7 trending technologies in 2018
690 days ago

A Beginner's Manual to Data Science & Data Analytics
690 days ago

Artificial Intelligence: A big boon for recruitment?
691 days ago

Top 5 chatbot platforms in India

Artificial Intelligence: Real-World Applications

Levels of Big Data Maturity

5 Best Machine Learning Algorithms for Beginners

Why do customers prefer chatbots for online shopping?

Levels of Big Data Maturity

By shiwaneeg |Email | Apr 23, 2018 | 17931 Views

Big Data have been heard for some time now. The concept of Big data is continually evolving and being reconsidered, as it remains the driving force behind many ongoing waves of digital transformation, including artificial intelligence (AI), data science and the Internet of Things (IoT). 

The data which is unstructured, time sensitive or simply very large cannot be processed by relational database engines. So, this type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.

Big Data is a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data.

Data is a critical competitive factor and is becoming more and more crucial for achieving success. In theory, hardly anyone disputes this to any extent. But in practice there are some very different levels of maturity when it comes to handling data. 

This infographics, created by Knowledgent, shows five levels of Big Data maturity within an organisation. 

1. Infancy: 

The first level is the Infancy phase. This is the phase where one starts understanding Big Data and developing Proof of Concepts. 

2. Technical Adoption:

The second level is the technical adoption phase. In this phase, the company is prepared to implement the different Big Data technologies. These technologies, whether on premises or in the cloud, enables an organisation to develop new Proof of Concepts / products or Big Data services faster and better.

Once the IT department is capable of working with the Big Data technologies, and if the business understands the significance of Big Data for the organisation, an organisation is ready to enter the 3rd level of the Big Data maturity index.
3. Business Adoption: 

The third one is the Business adoption. This level leads to more in-depth analysis of structured and unstructured data available within the company, resulting in more intuitions and better decision-making.

4. Enterprise Adoption: 

The fourth level is Enterprise Adoption where there is adoption of Big Data across enterprise, resulting in integrated predictive insights into business operations. Here, Big Data analytics plays an integral part in the company's culture. 

5. Data & Analytics as a Service:

Companies that have reached level 5 of the Big Data maturity index have integrated Big Data analytics in all levels within their organization. Also, they are truly data-driven and can be seen as 'data companies' regardless of their offerings. 

These companies are significantly successful in gaining a competitive edge based on their Big Data insights. It is impressive if companies could strive for level 5 of the Big Data maturity index. These companies can result in better decision-making, better products and better service.

Source: HOB