Category: Industry Insider

Whether you own a business or have a startup, you will always have a need for a data analyst who can analyse the data related to your work and guide you about the performance of your company or Business. This process of inspecting, transforming and modelling data in order to retrieve some helpful information out of it is known as data analysis. Data analysis is a general form analysis whereas Predictive Analysis is a specialized form of analysis which is used to predict the future outcomes of Businesses. It involves the use of both new and historical data to predict future activities and trends. By predictive analysis, you will not know what will happen in the future but you can know what may happen based on the current scenarios.

The Importance of Predictive Analysis

Predictive Analysis is used by several organisations to solve several difficult problems and to discover new opportunities. Some of the common uses of predictive analysis are as follows

• Fraud Detection

Cybersecurity becomes a major concern with the growing technology. With the help of predictive analysis, online behaviours of an individual can be analysed with real-time detection which helps in the detection of abnormal actions which may indicate vulnerabilities and frauds.

• Marketing campaign optimization

Predictive analysis is used to determine the response of potential customers on the marketing campaign and helps Businesses in preparing a proper marketing strategy in order to promote and cross-sell their products or services. It helps Business in attracting new customers and growing their business.

• Risk Reduction

Predictive Analysis helps Businesses and organisations reduce the risk by analysing the previous patterns. A customer’s risk is calculated by assigning him a credit score which is completely based on the previous activities. The credit score shows creditworthiness of the customer which can be used to reduce risk while making any transaction. Insurance claims and collections are some of the risk related uses of Predictive Analysis.

• Improved Services and Operations

Predictive Analysis allows organizations in the management of resources and to work more efficiently. Predictive models are used by several companies to forecast inventory and serve customers more efficiently. Hotels use it to predict the number of guests they can accommodate on a particular night which enables them to generate more revenue. Similarly, Airlines use predictive analysis to set the prices of tickets.

The Working of Predictive Analysis:

Predictive Analysis makes use of existing data to predict what may happen in the future based on several different techniques. Predictive models are created by feeding the existing data. These models provide results that represent the probability of activities that may happen in the future based on the set of input data. There are two types of Predictive model. A classification model helps you in predicting class membership and a regression model helps you predict a number.

To start with data analysis, the first step is to define the problem you need to solve which includes the answers to questions like what do you need to predict based on the past? What do you want to understand? and several other questions that describe the problem. Once the problem is defined, you need data from the past and current situations in order to build a model for prediction. The data must be taken from different places and sources which may include transactional system, third party information, weblogs and much more. After the collection of data, the data must be prepared for predictive modelling which requires someone who can understand both the data and the Business.

After the preparation of data for predictive analysis, the process of Predictive model building begins. There are several easy to use software available which makes it easier for several people to prepare a predictive model to understand and predict future outcomes. Though there is much software out there, it is still important to have a data analyst in order to help you refine the data model and an IT specialist who has specialization in intelligence course as he can help you deploy the model. Predictive modelling is a team approach and hence you will need a good team of people who can handle the data and deploy the model along with the understanding of the Business.

The most widely used predictive techniques are decision trees, neural networks and regression.

Decision Trees:

Decision trees are basically a classification model that help you partition data into subsets on the basis of input variables categories. A decision is similar to a tree, where the branches represent a number of alternatives and the leaves represent a classification or a decision. The Decision Tree model analyzes data in order to find out one variable that will split up the data into different logical groups. The decision tree is one of the most widely used and popular classification models because of its ease in understanding and interpreting.

Regression:

The most popular method in statistics is Regression. It allows you to analyse and estimate relationships among the variables. This predictive model is intended for continuous data which is assumed to follow a normal distribution. The Regression model finds key patterns in large data sets which in most cases is used to determine the impact of specific factors like price, on the movement of an asset. In the regression model if you need to predict a variable, say it as response variable then the prediction can be done in three ways. Linear regression makes use of one independent variable to predict the outcome of a response variable and Multiple regression makes use of Multiple different variables to predict response variable outcome. The regression model also helps you predict unknown variables of a discrete variable with the help of known values of other variables. This is known as logistics regression. In binary logistics, the response variable will have only two values i.e 0 and 1 at the same time, multiple logistics regression allows the variable to have several different values.

Neural Networks:

The more sophisticated model of the predictive model is Neural Networks. The Neural Networks model is capable of modelling relationships that are extremely complex. It is one of the most powerful and flexible models which makes it a powerful predictive model as well. With the amount of data being collected, it is becoming much more difficult to handle nonlinear relationships. Neural networks are capable of handling such data and that’s what makes it a powerful predictive model. Neural Networks are often used for the confirmation of the findings from other simple techniques like regression and decision trees. It makes use of some artificial intelligence and pattern recognition in order to model the parameters graphically. When there is no mathematical formula to relate the inputs to outputs, Neural network works fine. In the prediction model, the prediction is given more importance rather than the explanation as there is a lot of data to train the model.

How Predictive Analysis and Models are applied to Business and Enterprises?

Typically, Predictive Models and predictive analysis are used in order to forecast future possibilities. Prior knowledge of future activities and trends can help businesses grow significantly and serve customers in the right way. The historical facts and current data are analysed by the predictive model which helps the Business to understand their customers in a better way. It also allows you to choose the right product by looking at the trends. Choosing a business partner can involve risk but with the help of predictive analysis, you can identify the opportunities and potential risk involved with the Business. There are a number of techniques used by Predictive models which involves statistical modelling, data mining, machine learning and much more.

A vast number of data is collected by Businesses which is used by predictive analysis in combination with the historical data and customer insights. A number of companies now offer solutions to predictive analysis and data mining. The software for predictive analysis can be deployed on-premises or over the cloud based on the type of business or enterprise.

The software for predictive analysis uses the variables that can be measured and analyzed in order to predict the future activities and behaviour of individuals, machinery or other entities. The Predictive model is capable of assessing reliability at an acceptable level by combining multiple variables. Several advanced algorithms and different methodologies are used by the software which includes logistics regression models, decision trees and time series analysis. With the emergence of big data systems, predictive analysis has sprung up in prominence. The opportunities for data mining have also increased as several enterprises are now collecting huge amounts of data through various sources. The capabilities of predictive analytics have been expanded with the increase in development and commercialization of ML tools by IT vendors.

The insurance companies, marketing companies and financial services are adopting predictable analytics since early days to leverage the use of online service providers and large search engines. Industries such as healthcare, retail and manufacturing are also commonly using Predictive Analysis. The main objective to use predictive analytics in Business is to improve targeting audience online for advertisements, analysing the shopping pattern and identifying potential risks for the Business.

DevOps is an up and coming concept quite relevant in the world of technologies. Many companies are shifting their operations to DevOps, through which they are saving a lot of time and money. DevOps is a set of modern-day technological practices in which the functions of IT and software development are combined to create a more robust application. The main aim of using DevOps is to reduce the life cycle of system development operations. On top of that, it also provides a continuous and streamlined delivery of software that is world-class and very efficient.

 

These essential DevOps services make this application so popular, and hence most of the companies are installing one of them to make regular jobs easy and efficient. When the two vital operations are integrated, errors are significantly reduced, hence the delivery of error-free and high-quality software.

The DevOps company is quickly moving into automation to create reliable applications and integrate tedious tasks that need to be error-free. A company’s overall output margin increases exponentially when DevOps is installed within the company operations.

Benefits of DevOps

Apart from the points mentioned above, DevOps comes with various other benefits. All these benefits have accelerated why more and more companies are shifting to use DevOps.

Speed

DevOps has opened a new and revolutionary method of increasing the speed of even the everyday operations within an organization. It reduces the backend time significantly and hence increases the software development process. Product testing is not only a vital task but is also very time-consuming. Since DevOps uses automation, product testing is done within hours and the necessary modifications. Alongside the system, human intervention also reduces backend time and errors.

Reliable Services

Any software is first judged based on its quality. When DevOps is assigned the task to develop a particular software, quality and reliability are the two guaranteed terms in addition. DevOps thoroughly uses SDLC to make software testing a crucial task and double-check its reliability.

Secured System

Security of the system is a vital issue related to any IT operations. DevOps supports a high line of integrity and data security when developing a particular software. DevOps eliminates the problem of compromising data explicitly. DevSecOps is a dedicated element added to any software developed by DevOps that increases its overall security terms. All the controlling operations are responsible for data security under the component of DevSecOps.

Wrapping Up

The vital benefits DevOps offers are more than luring for companies to get a hand on it. With every passing day, more and more companies are considering shifting to DevOps for various reasons discussed above.

THE DO’S AND DON’TS OF WEB HOSTING

Web Hosting is completely not a geeky part to be precise. You definitely can learn web hosting in days! Period.

It is as simple as creating an account on social media. Getting started on the right foot is very important. And that can be done by choosing a suitable hosting plan

Whether you are a business owner or a blogger, you will need to make your presence felt to the larger part of the internet audience. According to Netcraft’s September 2020 Web Server Survey, currently, there are about a billion sites available on the internet. If you are in the race to join in for the long run, then you need to have a plan of action ready!

When you have your website in your hand, your next step should be to make it available on the world wide web.

As a beginner, you might come across a lot of questions. This article will help you guide your way through them and by the end of it, web hosting will be a cakewalk!

I have compiled here are a few terms and questions that you might come across when you embark on this journey of hosting your site.

Some Common Web Hosting Terms

It is very important to know the lingo before you start working on your website. If you are new to web hosting, then it is important for you to go through these terms:

1. Web Server:

A web server is server software, or hardware designed to run this software, that can fulfill the client requests on the World Wide Web.

In simpler terms, it stores the web content and makes it available to the user who searches for it.

The web hosting service providers give the facility of servers on which you can host your website.

1. DNS

Domain Name System (DNS) translates the alphabetic name of the website into a numeric IP address that the server is able to respond to. It is a site naming system in the world of computers.

So, when someone types the domain name, it will be connected to the DNS server via the IP address and it will point it to the web hosting server.

1. CMS

A Content Management System (CMS) can help you create the website making it easy to manage, that too without using coding!

It is a user-friendly application that you will need to install on a hosting server, and it will make website management very simple. WordPressis the commonly used CMS that is very popular among users.

P.S: According to WordPress blog, 38% of the web is built on WordPress

FAQs on Web Hosting:

1. What does web hosting mean?

When you run an application, you need resources for it to run like a storage space and access. Similarly, putting up your website files on a server is known as web hosting. The server’s role is to make it accessible to people using the internet. The web servers receive the requests that come in from the users and then redirect them to the website that they are looking for.

Popularly known websites like Netflix and Paypal also use third-party service providers for their site hosting. Only tech giants like Google, Amazon, and Microsoft host their services on their platform.

This is because of the abundance of financial and technical resources. When you want to make your site accessible to the people, you will be charged according to the type of service you choose for your website hosting.

1. How to get started?

The two basic things that you need to get started are:

(i) Domain name

(ii) Web hosting service provider

The domain name must be unique and available to be assigned to your site. It will be the identity by which the users will be able to find you on the internet.

You will need to purchase a domain name from the domain name registrars to get a licensed name for your site.

Choose a site friendly hosting plan from a service provider like Bluehost, Hostgator, etc.

1. What are the types of hosting?

Web hosting is of various kinds, but the major ones that you need to know are Shared and Dedicated hosting.

Shared hosting is suitable for small businesses and bloggers as it is easy on the pocket and thus a convenient way to start. This hosting is cheaper as it allows multiple sites to share the same server. The downside of shared hosting is that it might make your site loading speed slow on busy days.

In the case of dedicated hosting, the complete server is dedicated to the website. Managing this might require technical skills as these sites are generally the ones receiving a lot of traffic.

1. How much will it cost?

The cost of hosting a site depends on a lot of factors. To begin with, you should go for a shared hosting plan and then expand your budget once your sites start generating revenue.

Initially, the website may demand a lot of funding by installing extensions, templates, etc, therefore you should plan your hosting budget accordingly. You can browse several hosting plans and choose the one that suits you the best. Generally, the cost comes between $2- $5 per month when you talk about shared hosting and $80 per month for dedicated hosting.

Time to get started!

Having the basic idea before hosting the website will give you clarity about the plan that will be more suitable for you. Hosting services are designed in such a way that it is beneficial for the site owners in all ways possible. So, start with the best hosting service for your site and start making your digital footprint on the internet.

Data is one of the best sources of growth for businesses around the world. Data Analytics give precious insights into the daily operations of the companies. Analysis of data helps in coming up with an efficient way of working. It predicts market trends and risks simultaneously.

Data is the new ace in the business world. Many companies realized how practical data analysis is, and how significantly it affects the prospect of a business. The data transformation process makes data more organized. Both humans and computers will understand the transformed data, which will lead to better decision-making.

Some of the advantages of data are:

Decision- making

Data is the solid foundation behind the decision-making of many companies. It provides evidence and insights, which directly helps in the decision-making.

In many well-known companies, cutting-edge analytics process large amounts of external and internal data. The data is quickly analyzed to get the answers to the problem that a company is facing. The data produces valuable answers and insights to the business problem.

For example, if a team of people can not decipher the reason behind the fall of the profit of one of the products, then they can reach out to the solution of data analysis. The data will give them the answer like- due to overpricing of the product compared to other businesses, the product incurred the loss. So, insights like this are helpful. But one thing is Companies should standardize the access of data. Like the IT cell or data-analysts should not only access it, all the departments of the company should have the access so that they can make better decisions with it.

Data Personalized Content

The art of knowing what a customer or a buyer wants way before they even know that themselves means that a business can make their campaigns related to the market more personalized therefore making it better. A company can approach the process of data analysis to understand their customer and make a marketing approach that will attract a specific group of audience that they need for their business.

Some big companies have created a device. That will collect data on customer interaction and what they want to buy.

Data helps a company to analyze the effects of new products or services. And how they will help in the profit margin of the business.

Many theme parks use a device to interpret data on visitors’ behavior. So that they can create a more magical experience for people.

Development of product and services

Data analytics help enterprises understand their current business progress efficiently. It offers well-designed statistics that predict the future and provide knowledge on the recent market trends.

Data transformation services help in product analysis.

Product analysis is essential for companies because it gives a thorough knowledge of the performance of the product.

From discovering the best part of the product to why customers buy it, people can get every detail with data analysis. It backs up in the process of predictive analysis. Like if a company designs a product. How it will perform in the future market can be explored through the method of data analysis.

Another thing called data metrics is used by big banks too. The data metrics help to understand the client’s needs better. The banks use transactional data to spot whether a customer has paid twice for a financial service.

For example, if a person purchases travel insurance that is already there with a bank account. Then through data metrics, the bank understands the problem and informs that to the customer.

The process increases customer satisfaction and loyalty.

Cost reduction

Data is not only about the improvement of business or customer satisfaction. It is also about how a company can increase revenue and make additional profit.

Many companies use data to redefine the traditional methods.

Data also gathers information on a customer and their process of spending their money. This results in companies understanding how their business is performing compared to their competitors.

With data analysis, a business can come up with innovative ways of increasing its income.

Similarly, companies can make use of the GPS data from our smartphones through routing. It saves extra money for the company. GPS is not new though its new advanced features help businesses in saving both money and time.

Summing-up

As we saw, whatever the problem is, data is the solution for that.

With the usage of data transformation tools, any company can earn a large sum of profit. It offers enterprises the confidence with which they can defeat their rivals in the best way.

Everything is possible with data. Starting from targeting audiences, making strategies to improvise methods that are good for the business.

There is no limit to the value that data offers to organizations.

A company that will opt for data analysis will always enjoy its competitive advantage at the most.

Every company uses data analytics as a tool to get insights into their customer. Analytics became popular in the e-commerce sector for the benefits it provides to companies. That is why many big companies heavily invest in data analytics and insights to know more about their buyers.

As the trend of Artificial intelligence and machine learning, is expanding day by day, the method we use analytics also keeps on growing and changing with us.

Earlier, the businesses only focused on gathering descriptive data about the customers and the products. But now, the companies started to believe in both descriptive and predictive analytics to be more precise about a customer’s behavior.

So what exactly are descriptive and predictive analytics and prescriptive analysis?

Descriptive data analysis is the analysis of the data that provides thorough information about the things related to your company. It gathers information on stuff like sales reports of a month, marketing rates, web hit numbers. One of the main purposes of this analysis is to track the progress of a project initiated by a company.

On the other side, Predictive analytics is the data that offers information on the future progress of a company. It predicts what will happen in the future in your business. It understands more complex AI processes and machine learning. This factor helps predictive analytics to understand the algorithm of how well a product will perform and who will buy it. It also suggests effective marketing strategies that can gain profit for a product.

But this is not the end; we have another data analysis process called prescriptive analysis. This analysis is helpful for your company because it states all the better methods you can use to enhance the profit of a project. This data analysis not only describes what will happen in the future but also provide the solution and methods that can bring out a positive outcome. Prescriptive analysis is also becoming one of the best ways of analysis in the world of data.

Why Prescriptive Analytics is important

Each of these data analysis plays an essential role in the function of the data classification process for a company. We always do not need a complex algorithm running in our data. Sometimes, we just want to know the financial progress of our business or how much traffic our business websites are attracting.

For this reason, prescriptive analytics is becoming beneficial for companies. It improves the results and optimizes the overall performance of the project.

Benefits of Prescriptive Analysis:

Big data benefits

This mode of analysis has benefits, in which it makes a company aware of the new technologies or modern techniques that improve the utilization of resources. It gives us an insight into consumer behavior very well.

Companies use this method of data analysis to gain knowledge on the best social media engagement for better opportunities. The data analysis tells a business when is the best time to post content related to their project so that it gets the traffic it needs.

For front-line workers, prescriptive analysis suggests the best-personalized option, so that the business can improve its customer experience. It also enables industrial-scale data analysis to make it easier for a company to scale its internal decision process.

Companies use Prescriptive analysis:

This process of analysis is being used efficiently by many big companies.

Data enrichment companies saw a rise in the demand for prescriptive analysis among businesses now.

Many organizations use prescriptive analysis to produce engaging content that will be relevant and valuable to the readers. The more a business constructs relevant content, the more audience it gets. On top of that it through this analysis, a company understands its audience better.

It proficiently makes accurate business decisions for the project.

Future of big data for business

The future of prescriptive analytics will simplify analytical development for automated analytics.

The process does not need any human to decide for the business. It will generate automated decision-making for the company.

For example, if an enterprise needs marketing strategies for the best email they can send to a customer, automated analytics will provide that solution for them. The concept of prescriptive analysis is growing with time, and its usage will increase in the coming years.

Conclusion

The prescriptive analysis comes with many benefits, these benefits will expand the future of the online business in the best way. It is driving the future of big data analytics. The data transformation process became smoother with prescriptive data analysis.

Also, with this, the enterprises can avoid making any wrong decisions that will lead to the loss of the business. As it is the final tier in the modern data processing system.

The logic of business decisions will get evolve with prescriptive analysis.

A company needs this analysis in its big data strategy. It will help them to make business plans faster and better.

Business intelligence is a technology-driven process. The main work of this thing is analyzing the data and, after that, delivering all the actionable information. This process usually helps the executives, managers, and workers make good and informed business decisions. In the first part of the business intelligence process, all the organizations usually collect data from any internal data analytics or insights of the IT systems and other external sources available to them.

Hereafter that, they prepare this data for the file analysis process. After this process, they usually find and run a lot of queries that are present against the data. In this process, they also create data visualizations, all types of business intelligence dashboards, and reports to make the analytics results easily available to business users. They often use this data for operational decision-making as well as well strategic planning.

What is the goal of this process?

Here the ultimate goal of this business intelligence initiative is to get or take better business decisions that will be fruitful for the company. Here in this process, they also help the business organizations to increase their income or revenue. Here they also try to improve their operational efficiency, thereby using it to gain competitive advantages over all of the people who are their rivals in the business world. To achieve this goal, the business intelligence group generally integrates a good combination of analytics, data enrichment companies, data management, and all the reporting tools. Here they also try to find various possible methodologies that they can use to manage and then analyze all the data they have.

How do this business intelligence process work

Here it is a fact that a business intelligence architecture generally needs much more than just software. Here all the Business intelligence data is generally stored in a data warehouse. These are usually built for an entire organization. Here they can also be built for all the other smaller data marts that hold the subsets of big business organizations that hold the whole business or the individual departments or any business units. In this step, the data classification process is often used.

Here a big company usually has ties or has done deals with an enterprise data warehouse. In addition to that, they also store data on the data lakes, which are based on Hadoop clusters or other big data systems. Nowadays, these systems are increasingly used as repositories or landing pads for any business intelligence and analytics data. This process is especially used for log files, sensor data, text, and all the other unstructured and semistructured data types.

Importance of business intelligence data

Here the business intelligence data generally includes all the historical information and all types of real-time data that are generally gathered or collected from any source system. These are often generated on their own. This helps the business intelligence tools to support both strategic and all the tactical decision-making processes, which will be helpful for a company. Before they are used in any form, all the raw data collected from all the different source systems should always be integrated, consolidated, and then thoroughly cleansed by using all types of data integration and data quality management tools that are available nowadays. These are usually done to ensure that all the BI teams and business users using it are analyzing accurate and thereby consistent information.

All the steps that are involved in the BI process

Data preparation- in this step, the data sets are generally organized and then modeled for the analysis.

The analytical querying of a data

Here is the distribution of all the key performance indicators (KPIs) and all the other types of findings related to the business users.

The use of this information to help a person or a company to influence and drive business decisions.

Before, it was seen that the business intelligence tools were primarily used by all the IT professionals who ran queries for the companies. Here they produced dashboards and specific reports for all the different types of business users. Nowadays there it has been seen that their is change in this trend. Nowadays, all the business analysts, executives, and workers are using these business intelligence platforms themselves.

This has been made possible by developing all the self-service business intelligence and all the data discovery tools. 

Conclusion

It is being seen nowadays that all the Self-service business intelligence environments are helping all the business users query their data, create data visualizations. Then it also helps them design dashboards of their company on their own. This process uses all other forms of advanced analytics like data mining, predictive analytics, text mining, etc. It has also been seen in many cases nowadays. However, they have different types of advanced business intelligence projects and software, separate teams of data scientists are doing them, statisticians, etc., to get the perfect picture of a company’s situation.

Virtual onboarding is the process of integrating new employees into the workforce. Due to the global COVID19 pandemic and the subsequent lockdowns, many companies had to freeze hiring temporarily. However, now that it’s clear that remote working is the “new normal,” it’s crucial for organizations to have clear onboarding strategies for their new remote recruits.

Here are some activities and steps that recruitment managers can take to meaningfully connect their new hires with other remote workers on the team –

Clear Details

One of the unfortunate hallmarks of remote working is confusion. Many long-term employees struggle to follow instructions or understand project demands while working over the Internet. It’s normal for employees new to the organization to find remote working highly confusing for the first few weeks.

That’s why it is crucial to share clear and well-defined details about the organization, their responsibilities, etc., before their first day at the job.

• Share organizational charts clarifying important names and titles that the recruit needs to learn on the first day.
• Encourage other team members to bond with the recruits and help them memorize basic facts about the company, their responsibilities, etc.
• To ensure remote recruits don’t feel isolated, partner them with existing employees. Managers shouldn’t hand the full responsibility of onboarding to subordinates. But, they shouldn’t refrain from delegation either.
• Ask recruits about how they feel about their new workspace and culture.

Announce the Recruits

One of the most effective ways of welcoming recruits over the Internet is by making announcements. Traditional office initiations and announcements are tools that companies have always used to make recruits feel welcome. Carrying out these processes on the Internet is more rigid but not impossible.

• Compose and share messages about the recruitment on group channels (e.g., Telegram groups).
• Involve the recruits in video conference meetings. Make sure all members of the workforce are present on this call to welcome them into the company,
• Send company-wide emails sharing details about the recruits.
• Ask the recruits team-building questions in video conference meetings.

Team bonding is much more challenging when you’re working remotely. But that doesn’t make it less important.

Encourage Feedback

Whatever onboarding methods managers use to announce the recruits, they should always be open to feedback. Remote work is intrinsically isolating. The risks of miscommunication and misunderstandings are ever-present. The last thing managers need a recruit who feels unfulfilled or not included in their first week at the job.

Role-Specific Training

One or two hiring managers should oversee this part of the virtual onboarding process. Role-specific training processes typically last for a couple of weeks. During this process, hiring managers must –

• Clarify team structure and roles
• Establish clear expectations, both short and long-term
• Give the recruits access to useful job-specific tools and resources that other members of the workforce use.
• Describe typical days at work.
• Explain how KPIs are tracked in the organization.

Joining a new organization will always be intimidating. To create comfortable environments for recruits, hiring managers must follow these steps to create safe spaces for them to explore, socialize, and ask questions about their new roles.