Big Data, Cloud & DevOps

What are the common mistakes made in building ML products? The goal of the note is to provide someone with limited ML understanding a general sense of the common pitfalls so that you can have a conversation with your data scientists and engineers about these. Many companies wanted to use ML and had built up ‘smart software’ strategies but didn’t have any data. You cannot use machine learning if you have no data. You can apply ML on small data sets too, but you have to be very careful so that the model is not affected by outliers and that you are not relying on overcomplicated models.

Data-driven culture is about setting the foundation for the habits and processes around the use of data. Data-driven companies establish processes and operations to make it easy for employees to acquire the required information, but are also transparent about data access restrictions and governance methods. So, why is it important to build a data-driven culture in your organization? The data can only take an organization so far. The real drivers are the people and hence building the culture around data is important. An organization can work upon to build data-driven culture.

There’s a lot of data, and with more on the way, lots of data scientists are needed to make use of it all. Data science is only growing more critically important as our technology advances. It will be key in all businesses in the future and there looks to be a massive shortage of data pros, a job that pays well and is fun. It helps us tell important stories. I don’t need to produce an infographic to make this tale any more compelling. Data science deserves a spot in every STEM education curriculum.

What is data? How to define data from different viewpoints? What are tools in Data Technology & what to use when? How to apply Data Governance & build Data Strategy? And finally, how every aspect mentioned above fits together in business & technology ecosystem? Data at the fingertips of almost every professional can be truly transformational. So building Data-Driven Culture is the most challenging yet the most rewarding aspect. And to create a Data-Driven Culture, first and foremost thing is to make every employee, every professional data literate.

Cloud services now provide the capability to both store and compute vast amounts of data. As both public and private clouds have quickly become a business necessity with the explosion in the availability of data in recent years, the evolution of Datacenter depends on a well-designed cloud-based architecture that is capable of flawless delivery, and must include five major processes – Visualize, Consolidate,  Integrate, Automate, Federate. Indeed, cloud computing is widely leveraged across a variety of problem domains ranging from movie recommendation systems to unraveling the mysteries of the universe. 

One of the main problems with organizations attempting digital transformation is an embedded complexity in their processes. This complexity has usually arisen from being product-focused rather than customer-focused. While tackling the process innovation, it is not something that should be delayed. With two-speed IT, one now has to introduce a whole new IT model for the agile development, which includes more new processes, instead of striving for simplicity. The short-term goal of IT business units should be to move to the agile philosophy, which is a milestone on the roadmap to continuous delivery and implementing DevOps.

What is data science? Why it is important? What is the difference between Artificial Intelligence, Data Science, and Machine Learning and Deep Learning? Data Science is an amalgamation of many other fields like mathematics, technology and domain. It has its own concepts, process and tools. It’s really tough to know each and everything related to the subject unless you have really worked on complex data science problems in the industry for a couple of years. You can learn the data science concepts like types of learning and when to use which kind of learning algorithms?

Fraud analytics can identify the current behaviour and help in fraud detection whereas applying this knowledge in a model of predictive analytics can help in fraud prevention. Since tasks like data extraction and pre-processing are of paramount importance, we would need data scientists who possess not only a technical knowhow but more importantly patience, perseverance, critical thinking, and domain understanding. In here, the imputation for missing values may not be required but reported for certain attributes. Even when required, it may not be as easy and straightforward as in the different problem statements, especially when a few indicators are about to raise a red flag. 

Data analysis helps to make sense of our data otherwise they will remain a pile of unwieldy information; perhaps a pile of figures. This is essential because analytics assist humans in making decisions. Therefore, conducting the analysis to produce the best results for the decisions to be made is an important part of the process, as is appropriately presenting the results.  Its an internal organisational function performed by Data Analysts that is more than merely presenting numbers and figures to management. It requires a much more in-depth approach to recording, analysing and dissecting data, and presenting the findings in an easily-digestible format.

The increasing security threat surface is a major challenge for businesses, particularly those functioning in the BFSI sector. There has been a lot of news of frauds as well as hacking from this sector, which is giving Chief Information Security Officers sleepless nights. Digital deployments bring with them increased vulnerabilities. The attack surface has increased because of the extensions within enterprise. Frequency and sophistication of cyber threats are continuously growing. In the evolving threat landscape, digital transformation technologies such as Artificial Intelligence and Machine Learning hold a great deal of hope.

Business Intelligence (BI) has been a foundational element of enterprise computing for over thirty years. you may also have heard of Operational Intelligence (OI). BI is a highly evolved form of decision support software whereas OI is an emerging next-generation form of digital automation. And that is a very big difference indeed.  Using BI to mine Systems of Record data looks to improve “human-in-the-loop” business processes by arming decision-makers with a higher quality of insights. By contrast, using OI to mine machine data logs seeks to improve “human-above-the-loop” processes