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Top 14 Areas for Data Analytics Application

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In today’s world, the amount of data made available is on the increase with many businesses and companies being able to compile information across their respective industries. Of course, this gives them an advantage over their competitors to identify which areas in their services or products they need to improve on, where sales might have increased or decreased and where there might be a loophole in the market.

This has shown how important the use of data analytics is across several organizations. A researcher once claimed that advanced analytics tools have helped get deeper insights and discovery which will challenge assumptions made in business. Also, business analysts and users get more information and significant potential in creating business value and competitive advantage.

One very important benefit is that the use of data helps companies save so much money, develop better marketing strategies, improve the efficiency in procurement, support the growth of business and differentiate themselves from other competitors in the industry. There are several other areas where the application of data is known to be useful apart from companies alone.

Areas where Data Analytics Applications have been employed:

Below are the various areas where data analytics applications have been employed:

1.) Policing/Security

Several cities all over the world have employed predictive analysis in predicting areas that would likely witness a surge in crime with the use of geographical data and historical data. This has seemed to work in major cities such as Chicago, London, Los Angeles, etc. Although, it is not possible to make arrests for every crime committed but the availability of data has made it possible to have police officers within such areas at a certain time of the day which has led to a drop in crime rate.

This shows that this kind of data analytics application will make us have safer cities without police putting their lives at risk.

2.) Transportation

A few years back at the London Olympics, there was a need for handling over 18 million journeys made by fans in the city of London and fortunately, it were sorted out.

How was this feat achieved? The TFL and train operators made use of data analytics to ensure the large numbers of journeys went smoothly. They were able to input data from events that took place and forecasted a number of persons that were going to travel; transport was being run efficiently and effectively so that athletes and spectators can be transported to and from the respective stadiums.

3.) Fraud and Risk Detection

This has been known as one of the initial applications of data science which was extracted from the discipline of Finance. So many organizations had very bad experiences with debt and were so fed up with it. Since they already had data that was collected during the time their customers applied for loans, they applied data science which eventually rescued them from the losses they had incurred. This led to banks learning to divide and conquer data from their customers’ profiles, recent expenditure and other significant information that were made available to them. This made it easy for them to analyze and infer if there was any probability of customers defaulting.

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4.) Manage Risk

In the insurance industry, risk management is the major focus. What most people aren’t aware of is that when insuring a person, the risk involved is not obtained based on mere information but data that has been analyzed statistically before a decision is made. Data analytics gives insurance companies information on claims data, actuarial data and risk data covering all important decision that the company needs to take. Evaluation is done by an underwriter before an individual insured then the appropriate insurance is set.

These days, analytical software is used for detecting the various forms of fraudulent claims. Risky claims are detected by red flag indicators which can be examined. It is very essential to bring such claims to the attention of administrators, due to the manner at which automation is improving claims processing efficiency.

5.) Delivery Logistics

Well, data science and analytics have no limited applications. There are several logistic companies working all over the world such as UPS, DHL, FedEx, etc. that make use of data for improving their efficiency in operations. From data analytics applications, these companies have found the most suitable routes for shipping, the best delivery time, most suitable means of transport to select so as to gain cost efficiency and many others. Also, data generated by these companies through the use of GPS gives them enough opportunities to take advantage of data analytics and data science.

6.) Web Provision

There is this general belief that “Smart Cities” have fast internet speed provided either by their government or companies present there, therefore declaring them smart. Well, just because people can access Facebook or YouTube at the speed of lightning does not necessarily make a city smart.

Although there may be the presence of fast internet but this is just one thing; it needs to be present in the appropriate place and accessed by the right people as well. The key component of this is being able to shift bandwidth at the right time and location. This can only be achieved by the use of data.

The main assumption is that commercial and financial areas should have the highest bandwidth during weekdays while residential areas should get such on weekends. The real truth is that this situation is more complex than it looks and this can only be solved by data analytics application. For example, if a particular community wants to get the attention of web development companies and high-tech industries and make them establish there, a higher bandwidth would be required; only data analytics could get this done effectively.

7.) Proper Spending

Another issue with Smart Cities is the large amount of money spent on little work. Small changes or landmark remodeling which one could dismiss as unnecessary projects consume so much money. Data analytics applications would target where taxpayers’ money would have a major impact on and the kind of work that would be adequate for it. The targeting of where this money should be spent would lead to the entire city’s infrastructure getting a facelift with a reduction of excess money spent.

8.) Customer Interactions

This is another one of the applications of data analytics in insurance. Insurers can determine a lot about their services by conducting regular customer surveys mainly after interacting with claim handlers. They could use this to know which of their services are good and the ones that would need improvement. Various demographics may desire diverse methods of communication like in person interactions, websites, phone or just email. Taking the analysis of customer demographics with feedback can help insurers improve on customer experience depending on customer behavior and proven insights.

A study recently carried out showed that a lack of investment in technology was the cause customer dissatisfaction of the present generation of insurance customers because they prefer using mobile and online channels, social media and other recent mediums to interact with their agents. However, the older generation still prefers the use of the telephone. To improve the overall experience of customers, it is best for insurance companies to provide a wide range of communication methods for their customers.

9.) City Planning

One big mistake being made in many places is that analytics is not considered when pursuing city planning. As a matter of fact, web traffic and marketing are still being used instead of the creation of spaces and buildings. This really causes a lot of issues to power over data due to its influence on things like building zoning and amenity creation. Models that are built will maximize the accessibility of specific areas or services while the risk of overloading significant elements of the infrastructure in the city is minimized. This implies that it creates efficiency.

We usually see buildings that are built on spots that look suitable but actually have a negative effect on other places. This is because such issues were not considered during the period of planning. Data analytics applications, as well as modeling, would make it easy to mark the outcome of erecting a structure on any spot.

10.) Healthcare

One challenge most hospitals face is coping with cost pressures in treating as many patients as possible, considering the quality of healthcare’s improvement. Machine and instrument data use has risen drastically so as to optimize and track treatment, patient flow as well as the use of equipment in hospitals. There is an estimation that a 1% efficiency gain will be achieved and would result to over $63 billion in worldwide health care services.

11.) Travel

Data analytics applications help in the optimization of traveler’s buying experience via social media and mobile/weblog data analysis. This is because customers’ preferences and desires can be obtained from this, therefore, making companies sell products from the correlation of the current sales to recent browse-to-buy conversion through customized offers and packages. Data analytics applications can also deliver personalized travel recommendations depending on the outcome from social media data.

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12.) Energy Management

We are in an era where firms make apply data analytics to energy management and cover areas like energy optimization, smart-grid management, distribution of energy and building automation for utility companies. Data analytics application here focuses mainly on monitoring and controlling of dispatch crew, network devices and make sure service outages are properly managed. Utilities get the ability to integrate as much as millions of data points within the performance of the network which allows the engineers make use of the analytics in monitoring the network.

13.) Internet/Web Search

When one mentions the word ‘search’, the first thing that comes to the mind is ‘Google’. In fact, Google to some point can be used in place of ‘search on the internet’ by saying ‘Google it’. Well, apart from Google, there are several other search engines such as Bing, Yahoo, Duckduckgo, AOL, Ask, etc. Each of these search engines is as a result of data science applications because they use algorithms to deliver the best results for any search query directed at them in just a split second. In respect to this, Google is known to process over 20 petabytes of data daily. Of course, without analytics and data science, this feat wouldn’t have been possible.

14.) Digital Advertisement

Apart from web search, there is another area where data analytics and data science serves a very important purpose – digital advertisements. From the banners displayed on several websites to the digital billboards seen in the big cities; all are controlled by data algorithms.

This shows why digital adverts get more CTR than the conventional way of advertisements. Targets depend solely on the past behavior of users.

The importance of data analytics applications cannot be overemphasized because it is used in almost all areas of life today. We can see that having data is very important before making certain decisions so as to avoid unnecessary issues.

Also, handling valuable data inefficiently could lead to several problems like different departments in an organization not understanding how to make use of it which would lead to data not used to its full potential or serving any purpose.

However, data has become more available and accessible to more people therefore no longer at the disposal of data scientists and analysts. Almost everybody within an organization can make use of data for the increase of productivity and make very important decisions. Of course, proper use of data would have a positive impact on business and even the society in general.

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