Big Data, Cloud & DevOps

Software project management is the practice of planning and executing software projects. Its concepts need to be understood by every team member to ensure a smooth project flow. There are different methodologies that can be mainly divided into structured and flexible approaches. The most common approach, which gained a lot of popularity in recent years, is called “Agile”. This is a flexible approach based on delivering requirements iteratively and incrementally throughout the project life cycle. This post, will give you a gentle introduction to agile and non-agile project management approaches with the focus on the Scrum Methodology. 

As enterprises look to bring artificial intelligence into the core of their business, CIOs face an increasingly complex set of challenges around making the vision a reality. Historically responsible for driving technology change, they are now increasingly being called on to drive cultural change across the organization, a critical step on the path to transformation. Where do you start? Data literacy. Data is at the very heart of AI. The more your organization leverages AI to improve business workflows and processes, the more data-oriented it must become. 

While the human factor puts the “H” in HR and will continue to do so, metrics and a human resource dashboard are game changers pushing the most progressive brands around the world to take HR decisions that seem unconventional but prove to be wildly effective. Human resource data isn’t just about hours logged or vacations booked. If managed right, this information is poised to tell very compelling stories addressing C-suite HR concerns like productivity improvement, retention boosts, and leadership development. Here are three actionable ways in which the HR data management process can be changed for the better.

Looking ahead to 2019, businesses should use what they’ve learned from the past year to understand and take action towards improvement, starting with their central command – the database. With data being produced, analyzed and stored at exponential rates (thanks to the growing technological ecosystem), the database is perhaps the most crucial element in overcoming – and preventing – similar outages and data breaches in the coming year. There are several things likely to occur in 2019. Here’s what database managers and developers can expect to see happen in the coming months.

Despite having more data, it’s difficult to extract value from it in a timely fashion. If you want to be fast and agile with your data, you need a strategy built on enabling you to do that. Your data strategy needs to include more than just raw processing power. This is where data warehousing comes in – offering unified, governed, large-scale support for analytics. When it comes to your data warehouse, you need a way to get it moving quickly – and automation can help.

When working in healthcare, a lot of the relevant information for making accurate predictions and recommendations is only available in free-text clinical notes. Much of this data is trapped in free-text documents in unstructured form. This data is needed in order to make healthcare decisions. Hence, it is important to be able to extract data in the best possible way such that the information obtained can be analyzed and used. State-of-the-art NLP algorithms can extract clinical data from text using deep learning techniques such as healthcare-specific word embeddings, named entity recognition models, and entity resolution models.

Most IT departments use an average of nine different tools to monitor their environment. Very rarely do these apps even talk to each other. Managing multiple monitoring tools is not only cumbersome, it’s incredibly time-consuming. The IT team is too busy reacting to beeps and alerts to focus on its core mission of supporting the business. For IT to play a more strategic role and provide measurable value, a better approach is needed, one that’s based on the business impact of the events being monitored.

There are plenty of great ideas and techniques in the data space: from analytics to machine learning to data-driven decision making to improving data quality. One might expect to see companies trumpeting sustained revenue growth, permanent reductions in cost structures, dramatic improvements in customer satisfaction, and other benefits.  Except for very few, this hasn’t happened. It takes a lot to succeed with data. As the figure below depicts, a company must perform solid work on five components, each reasonably aligned with the other four. Missing any of these elements compromises the total effort.

Pointers have been in and out of data models. From the advent of the rotating disk drive in the 60s and until around 1990, pointers were all over the place together with “hierarchies”, which were early versions of aggregates of co-located data. But relational and SQL made them go away, only to reappear around year 2000 as parts of Graph Databases. Here is the fascinating journey of the history of pointers in data models.

Statistical learning is a framework for understanding data based on statistics, which can be classified as supervised or unsupervised. Supervised statistical learning involves building a statistical model for predicting, or estimating, an output based on one or more inputs, while in unsupervised statistical learning, there are inputs but no supervising output; but we can learn relationships and structure from such data. One of the simple way to understand statistical learning is to determine association between predictors) & response and developing a accurate model that can predict response variable on basis of predictor variables.

 Ready to learn Data Science? Browse courses like Data Science Training and Certification developed by industry thought leaders and Experfy in Harvard Innovation Lab.  “The reality is, everyone of you today…you are a software company, you are a digital first company, you are building applications that are core…and at that core are digital products” — Satya Nadella,