Corporate Training Analytics

With high demand for data scientists and the high salaries that they draw, it’s often not practical for organizations to keep them on staff.   Instead, various organizations work to ramp up their prevailing staff’s analytics skills, comprising predictive analytics. But companies need to advance with caution. Predictive analytics is particularly easy to get wrong. Here are the first few “don’ts” your team needs to learn, and their parallel cures. 1. Don’t Fall for Buzzwords — Explain Your Objective Data Science doesn’t essentially refer to any specific technology, method, or value proposition. Rather, it indicates a culture — one of smart people doing creative things to discover value in their data. It’s essential for everyone to keep this on the top of the mind when learning to work with data. Under the broad umbrella of data science sits predictive analytics, which provides the most actionable win you can draw from data. In a nutshell, predictive analytics is technolo...

Modern organizations realise the tremendous potential of machine learning and AI but at the same time are struggling to draw valuable insights from the massive amount of data they generate and save every day. Machine learning, the field of computational science centred on pattern recognition is playing a very important role in our daily lives. We can find everyday examples of machine learning in action right from suggestions offered by Amazon and Netflix, pre-approved credit card offers, saving and investment offers from your bank or for that matter Apple’s Siri, machine learning continues to make our lives simple and convenient. One thing in common among all these is the creation of predictive intelligence based on historical trends. To put in simple terms, machine learning facilitates complex problem solving by creating accurate predictions without the need for complex computer programming. Machine Learning’s strategic role in the modern organization In enterprise business...

People or organizations carefully following the trends and expert opinions in data science and predictive analysts will know that it is best to start from machine learning.   Experts often advise that it is best to take one step at a time. Start with the proverbial’ low hanging fruit’ and then move on to bigger and more complex operations as you gain relevant experience along the way. Machine-learning-as-a-service (MLaaS) platform Current trends and indications clearly point towards the value of machine-learning-as-a-service (MLaaS) platforms. Machine Learning is fast turning into a commodity thus making it well within the reach and resources of small and mid size organizations.   Leading vendors such as Microsoft, Amazon and Google provide Application Process Interfaces (APIs) and platforms to run basic ML operations without the elaborate need to invest in building complex infrastructure and hire professionals with deep knowledge and expertise in data analytics. It...

No matter what sort of business you have, your employees may become bored and stale doing the same routine tasks every single day. In order to foster an engaged workforce or set up employees for success, you may consider offering opportunities to cross-train your employees that will broaden their horizons and make them a valuable asset to the organization. Cross-training is not only helpful in improving productivity, but it also allows employees to develop new skills in a specific field that will heighten their professional development and career growth. Here are the reasons to consider cross-training employees: 1. Maintain the same productivity even when employees are absent.   There are certain things that can’t be avoided, such as family emergency or a sudden injury. But when one employee is absent for a couple of days, the other members can easily cover up for that absence if they are well-versed with that employee’s key tasks.   For short-term absences, cross-t...

In this digitalized world, the amount of data produced by large business organizations is growing at a rapid pace. Today, every large company is struggling to find ways to store, manage, utilize, and analyze the data. Furthermore, you would be astonished to know that the data produced by large business enterprises is growing at the rate of 40 to 60% per year. However, simply storing this massive amount of data won’t be useful for your business. This might be the reason why business enterprises are looking at options like building data lakes and using the latest tools and technologies that can help them in handling big data analytics challenges to a great extent. With no further ado, let’s take a quick look at some big data analytics challenges faced by large business enterprises and how to overcome them. 1. Handling voluminous data in less time Handling the data of any large organization is a challenge in itself, but when it comes to handling voluminous data in a short span ...

If recent reports are to be believed, there will be a need for an additional 346,000 data scientists in Europe by 2020. This clearly shows the skills gap in the UK in the field.   The demand for data scientists and analysts is increasing, but there is not enough talent in the market. The Annual activity report 2015 of the European Commission highlights the problem more precisely. At that time, 77% of data analyst jobs remained unfilled, and it was predicted that the problem would become worse with a 160% increase in demand by 2020. The reason behind this yawning talent gap is clear – demand has surpassed supply and there is a lack of corporate training in data analytics .   There has been an exponential growth of data sources as well as of organizations’ hunger to utilize this data and make the most of the data collected from the disparate sources. A recent study conducted by the International Data Corporation predicts that the 'digital universe' will reach 40 zettabyte...

Today business enterprises are trying to bring machine learning into their arsenal, but without the right skills they will likely fail. The number of jobs that machine learning could make redundant over the next few decades is a growing cause of concern amongst many of us.   According to a research study conducted by the PwC, 38% of jobs in the United States will be automated by the end of 2030, while in other parts of the world it would be somewhat less. In the United Kingdom, it will be 30%, and in Germany, 35%. While it can’t be denied that hundreds of thousands of jobs will be lost, as with all periods of technological advancement, we will witness the creation of new jobs. Many of these new jobs will be dedicated to developing and supervising machine learning algorithms, helping business enterprises to incorporate and implement the technology and bring in efficiencies in their business operations. To some extent this has already begun.   According to Indeed.com, a p...