All businesses are data businesses
What ways can we use data for business?
“All companies are data businesses now,” according to Forrester.
Our evolving, quickly transforming world makes it difficult to give anything a final or definitive categorisation. Yet it’s all the more essential, with data more and more driving our ultra-connected lifestyles.
Given that data is ever more present in our lives, it’s essential to understand how we might use it effectively and responsively. Here are four of the key data types driving insights in our world today.
It’s data that holds huge volumes of information, often being created by machines with sizing ranging from terabytes to zettabytes. It’s a quantity of data not able to be fitted into a standard relational database and not able to be processed at low-latency.
With the right data strategy in place, one of the expanding ways to use big data is to understand customer behaviour and boost acquisition by delivering what the customer wants from your organisation.
It’s everything that comes out of the systems, technologies and infrastructure that power modern business. Be it from your car GPS on your way to work, email correspondence and access of applications – all produce data, that can then be used to sensor security walls and troubleshoot business problems.
Companies can draw on past machine data to understand the causes of faults or failures, for example. This will help them understand the patterns and predict future causes.
It’s freely available to access, use and rights to republish, but it’s only useful when it’s shared in standardised ways that can be understood. Using open data might be a challenge to businesses that have built their processes on a need-to-know basis, but it has significant innovation value.
One of the main benefits of open source data is that it’s cost-effective. Open source has lower upfront costs, does not require licencing and is free to use.
It’s immediately available data that instantly links the source and accessor, and it’s being accelerated with trends like edge computing and 5G. It’s been one of the most impactful data trends with communications, accelerating knowledge and insight beyond what’s been perceived before.
All decision-makers are equipped with quick, easy access to ad-hoc reports and analysis with their own real-time dashboards. That’s whether it’s on their phone or computer, or they’re in the office or at a meeting.
All data has a structure of some kind – otherwise, we wouldn’t be able to see it – but the legibility of this structure can vary. Data management and processing techniques are evolving too, and in the not-too-distant future we’ll be able to understand insights and explore our world in-more depth and more instantly than ever before.
So the question is, do you just want to store your data, or do you want to put it to real work creating actual business value?
Data, AI, BI & ML
Artificial Intelligence and Machine Learning are the terms of computer science. Artificial Intelligence : The word Artificial Intelligence comprises of two words “Artificial” and “Intelligence”. Artificial refers to something which is made by human or non natural thing and Intelligence means ability to understand or think. There is a misconception that Artificial Intelligence is a system, but it is not a system. AI is implemented in the system. There can be so many definition of AI, one definition can be “It is the study of how to train the computers so that computers can do things which at present human can do better.” Therefore it is an intelligence where we want to add all the capabilities to machine that human contain. Machine Learning : Machine Learning is the learning in which machine can learn by its own without being explicitly programmed. It is an application of AI that provide system the ability to automatically learn and improve from experience. Here we can generate a program by integrating input and output of that program. One of the simple definition of the Machine Learning is “Machine Learning is said to learn from experience E w.r.t some class of task T and a performance measure P if learners performance at the task in the class as measured by P improves with experiences.”