Category: Infrastructure Managed Services

As per Gartner, “Through 2020, public cloud infrastructure as a service (IaaS) workloads will suffer at least 60% fewer security incidents than those in traditional data centers.”

Cloud Security primarily has been divided into two major parts – Security of the Cloud and Security in the Cloud.

While Security of the Cloud refers to Infrastructure Level Security, Security in the Cloud refers to the Application Level Security.

Here are 4 must-haves for a customer to have on their Checklist while ensuring Cloud Security:

1. User Tracking

The Identity and Access Management helps ensure which people can have access to what data and from what devices. There are different degree of access also that can be given to different users like view only, edit, etc.

A unique username for every user helps map every activity that has been carried out and can be tracked through which username.

This gives one complete control over who can do what, puts limits on what they can do & tracks every action of the user.

2. Traffic Monitoring & Management

Monitoring activities of each and every IP accessing the application to ensure any sort of malicious activity happening is monitored and taken care of in time. The monitoring is done through monitoring IP.

While monitoring these IPs, the ones that are not under suspicion are Whitelisted while the ones that are suspected of malicious activity are blocked.

For example – For a particular website, if there is any IP with any suspected malicious activity, it shall be blocked while the ones which are clean are Whitelisted and can access the website at a given point in time.

3. Threat Intelligence

Threats in this context can be at multiple levels and in multiple forms right from Infrastructure to the Application level. Monitoring at every level helps with alerts for threats and ensuring action is taken at the right time.

4. Risk Mitigation

Mitigation of DDoS, SQL Injections and other malicious activities through Web App Firewalls, etc. This can be done and tracked in real time through dashboards to ensure one has every minute detail of the what traffic is coming on the application, where it is originating from, updated policies etc. to ensure proper security measures are taken and any malware attack is avoided.

Think you need an expert to manage your Security piece?  Get in touch with us at cloud.sales@teamcomputers.com or +91 8860716613.

So what is Enterprise Mobility? Enterprise mobility, to put it simply, is an approach to work in which employees can do their jobs from anywhere using a variety of devices and applications.

The term commonly refers to the use of mobile devices, such as smartphones and tablets, for business purposes. But it also covers the mobility of corporate data and of workers themselves. As an example of enterprise mobility, an employee may upload a presentation from their desktop PC to a cloud storage service, and then access it from a personal Apple iPad to show at a client site. It’s that simple.
Now, let’s look at the four major reasons that might help you understand that important can enterprise mobility to be for you;

1. Increasing number of mobile workers

According to a survey, by 2020, 60% of your workforce will be mobile for work.

If you’re surprised at this number, reflect on the following facts:

• From 2010 to 2017, internet usage on desktop and laptop devices stayed fairly flat while mobile usage increased by 700 percent.
• Smartphone ownership has grown from about 30 percent in 2011 to nearly 85 percent in 2017.
• The majority of millennial smartphone users, 87 percent, say their phone never leaves their side, day or night.
• Thirty-seven percent of workers telecommute regularly.
• More than half, we’re talking 68 percent, of business emails, are now opened on smartphones.

Imagine the productivity rise if they could perform all their routine tasks with the same comfort as in office with full access to desktops and applications. According to a recent survey, 40% of businesses are working on improving their mobility strategy for individual productivity gains.

The millennial workforce demands flexibility to work remotely. Organizations that have understood this have policies such as work from home or work anytime anywhere and are building processes and systems to support this.

2. Real-time Management and Analysis

Enterprise mobility enables executives to access current and historical data literally anytime and capture data on the go to make better decisions when required. Transparent enterprise management and reporting platform can further reduce dependency on the IT teams for problem-solving.

At Team Computers. Employees now use an in-house mobile app as against biometric cards to punch in. This app further serves as a one-stop shop to apply for leave, report incidents & almost anything on the go from their mobile app. Pretty cool. right?

3. Provide an improved customer experience

71% of the companies moving towards business mobility are developing and deploying customer-facing apps focused on improving the customer experience.

What is the benefit these companies expect to achieve? The survey revealed that customers who execute business mobility plans have an average 150% return on investments (ROI) – the largest being the ability to bring new revenue streams online more quickly.

An automotive giant, in partnership with Team Computers, is using Apple iPads at their dealerships. The sales person at their showrooms can easily access and share vehicle details like specifications, colors, prices, etc. instead of the traditional brochures & leaflets.

4.Process Efficiency and Reduced Costs

Furthermore, the usage of interactive maps or the recording/logging of location-specific data can help improve processes, reduce costs and deliver value faster and better to customers. Uber has created one of the best use cases here. Isn’t it?

With an enterprise mobility management solution in place to provide access to corporate systems through mobility devices, overall adoption rate and productivity from employees can be achieved.

Think you need enterprise mobility? Get in touch with us

at social@teamcomputers.com or give us a quick call at +91-9350593984

Team Computers organized In-House cricket tournament “TPL North’18” on 10th March’18 at Sports Fiesta Club, Chattarpur. 4 knockout matches were played for which the winners are – Kockpit, INS, BA, IMS.
Semis and Finals are planned for 17th March’18. We are eagerly waiting to know the winner of TPL North’18

Descriptive Analytics and Predictive Analytics have been around the corner for quite a while now. Descriptive Analytics involved looking into the past, from one minute ago to several years ago. The objective was to answer the question, “What has happened within our organization?” Similarly, Predictive Analytics involves looking at vast amounts of Historic and present Data to make predictions about the future (Forecasting). If we see descriptive analytics as the foundation of Business Intelligence and we see predictive analytics as the basis of Big Data, than we can state that prescriptive analytics will be the future of Big Data. So what exactly is Prescriptive Analytics?

Prescriptive analytics has been one of the largest Big-Data buzzwords in recent years. Being able to automatically prescribe actions in order to attain some goal would mean a huge step forward in decision support or automatic decision making for any field. According to Wikipedia, Prescriptive analytics not only anticipates what will happen and when will it happen, but also why will it happen. Further, prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option. Prescriptive analytics can continually take in new data to re-predict and re-prescribe, this automatically improving prediction accuracy and prescribing better decision options.

Despite being relatively new, prescriptive analytics promises to be extremely powerful and accurate in its predictions. Prescriptive algorithms use a huge variety of techniques, such as machine learning, artificial intelligence, and mathematical sciences, to understand the impact of future decisions and adjust actual decisions based on that outcome. One of the most well-known examples of prescriptive analytics is the Google’s new self-driving car. During every trip, it makes multiple decisions about what to do based on predictions of future outcomes. For example, when approaching an intersection, the car needs to determine whether to go left or right and, based on various future possibilities, it makes a decision. So, the car needs to anticipate what might be coming in terms of traffic, pedestrians, etc. and the effect of a possible decision before actually making that decision.

Imagine that a car seller wants to get some insight in his Business. First, he starts with some descriptive analytics and calculates the number of sales and profit he made in the past 5 years. He also defines some cohorts of interest. Then, he moves on to predictive analytics and calculates what his expected profit is for the next month, based on the current and past situation. He also calculates a more advance prediction, such as the expected number of sales if the supplier prices go up five percent. In the prescriptive analytics case, the car seller goes one step further. He wants to increase his total profit by five percent and wants to know what he should do; should he a) find a new supplier, b) stop selling low-end cars, or c) start an advertising campaign.

Prescriptive analytics are relatively complex to administer, and most companies are not yet using them in their daily course of business. When implemented correctly, they can have a large impact on how businesses make decisions. Larger companies are successfully using prescriptive analytics to optimize production, scheduling and inventory in the supply chain to make sure that they are delivering the right products at the right time and optimizing the customer experience.

Combining so many different data sets can also be used to offer doctors recommendations in the best possible treatment for a patient. Thanks to combining and analysing multiple data sets, a Health Care Center was able to improve healthcare and reduce re-admission rates by 10%, thereby saving $ 6 million annually. Also pharmaceutical organizations can benefit from prescriptive analytics by improving their drug development and reduce time-to-market for new medicines. Drugs simulations can improve medicines faster and it becomes easier to find the right patient for clinical trials based on multiple variables.

Prescriptive analytics is the future of Big Data, but it is still a long way away before it will be common language. The potential is enormous, but it also requires massive amounts of data to be able to make correct decisions. Only a handful of organizations and industries have that amount of data and data sets to make something useful out of it with prescriptive analytics. However, in 5-10 years will be as normal as Business Intelligence today.