Enterprises don't feel abnormal now to put their critical applications, data in cloud.
Tags :AIbusiness informationdata analysisMLraw data
The IT world is always chasing new phrases and new tech jargon, fancy new ideas and new ways to apply new tech concepts. Every technology innovation is better than a previous one, and helps in solving current business problem effectively. Every new innovation attracts both – the presenters and recipient. Both of them spend time and resources to understand the entire process before getting into business value, TCO analysis, and transformation journeys etc.
Cloud is the new normal
There was a time when Cloud had been just a buzz word and IT professionals were trying to understand concerns associated with it. There were a lot of security related myths initially, lot of eye brows were raised when enterprises had to move their in premises workloads to Cloud. Cloud as a solution stood tall and proved its value, helped answer questions on security effectively and efficiently. But those days are long gone and today, ‘Cloud is the new normal’. Enterprises don’t feel abnormal now to put their critical applications, data in cloud. Board room discussions are now revolving around Multi Cloud strategy.
So now what’s next?
As advancements kept on happening, the large-scale compute power, ability to crunch peta bytes of data in quick time and development in ability to solve business problems with algorithmic approach brought two new fancy tech terms – Machine Learning (ML) and Artificial Intelligence (AI). Today, AI is a hot topic of discussion in boardrooms, as professionals seek to improve profitability, monetise value of data, and capture new market share. Every enterprise would wish to use these technologies to gain competitive edge, better their products and services and quickest possible go to market decisions.
While everyone is trying to understanding ML and AI, we see enterprises still struggling to see business value out of these two. Even people confuse and interchangeably use ML, AI, data analysis terminologies. While decision makers are going through workshops, presentations and brain storming sessions, there is a large chunk of enterprises not even aware the size of disintegrated data islands exist in their own enterprises. This heap of data they are generating, maintaining and spending money for is even not used for analytics to take any decisions.
Many times enterprises are unable to derive the value of data due to lack of knowledge, lack of information and wrong notions of how it can work. Many times , it is even considered not possible that two dispersed, isolated, inherently different data sources like databases, excels, op premise and in cloud etc., can talk to each other and produce information.
Extracting Business Information from Raw Data
Let’s draw a parallel between oil and raw data and between petrol and business information. There are many solutions that can help convert ‘Oil‘ (Raw Data) into meaningful and more valuable ‘Petrol‘ (Business Information). CXO’s will see lots of value in this petrol and would love to see this Crude oil producing Petrol.
Enterprises can go for business analysis / data visualization tools or solutions like Microsoft Power BI, AWS Quick sight or Tabluea, etc. Interestingly, most of these tools follow ‘pay-per-usage’ model and can be subscribed on a monthly basis. So, enterprises need not to invest in them perpetually before they even see the value in them. Application of these tools / solutions can not only bring value out of Data but an entire new way of looking at collaborative effort to achieve more in less. Suddenly Data starts speaking, for example, Data of Financial system and applications start adding value to CRM, CRM Data adds value to purchase department, two diverse entities and departments start new ways and means to work in tighter processes. Adds a lot of value to decision makers to see inter related, inter dependent departments with their own and isolated data ponds producing meaningful information on an integrated, orchestrated, intuitive platform for effective live decision making.
It is very interesting journey to see the hidden value of data. These tools help in finding new meaning to data and help decision makers take an informed decision. The ability to intuitively analyse data helps to comprehend it quickly. Various statistical models, which otherwise look impossible to use for different and huge volume of data, suddenly become easy to use and experiment with. These tools bring lots of value and use cases, especially when they collaborate and can help in predictive analysis for business, which any decision make would love to have.
By no means the Data visualization tools are alternatives to MI / AL, but surely can bring a lot of value and direction to decision makers to decide the priorities, business models and areas of business focus where ML/ AI can be used for business advantages more effectively.
So in a nutshell, even before one thinks about ML and AI, one to ask ourselves and other business stakeholders, have we made an attempt to convert our Oil (Data) into Information (Petrol)?
(The article has been authored by Heramb Thuse,Head Cloud Solutions & Pre-sales, Crayon Software)