Big data refers to data groups which are too big or complex such that the old school soft wares that were initially being used in processing them have become inadequate or unable to handle them precisely. The software applications are unable to handle such data because of a number of factors. The factors consist of capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating, and information privacy. It can also defined as the use of predicative analytics, user traits analytics, or superior data analytics techniques that help in obtaining information from any amount of data. Various experts face numerous challenges while handling huge sets of data in vital areas such as internet search, finance, urban informatics, and business informatics.
It has always been difficult for people to understand how the huge data sets come about. But the cheap and large number of devices that easily sense information such as the mobile phones, aerial, software logs, cameras, microphones, radio-frequency identification readers, and wireless sensor networks (Lohr, 2012). The advancements in technology has seen the average technological data per-capita capacity double for a period of roughly three years (LaValle, 2011). The drastic increment in data poses a threat to many ventures as to who should be held accountable for the big data utilization and control. This therefore affects all the sectors of the organization.
The initial data processing systems usually have problems handling huge amounts of data. It thus calls for use of sophisticated data analysis systems which implies that the enterprises have to invest more into the devices. However, it would be unfair to paint the big data as an issue for the various systems. It does have numerous importance in the various organizations to which it is being collected.
Big data was developed as a mean of curbing the issue of data sets growing so large and complex that the primary devices are no longer in position to fully process the data fully. By collecting the small sets of data from the numerous data sources makes it a very key weapon for different enterprises to bank on when making decisions about various entities of their firm. It also helps in quickening exposition of the insights and behaviors than the initial equipment’s.
The technology that is fostering the growth of the big data is great. They consist of a software ecosystem that has been made to allow the query and statistical analysis of huge and semi-structured information. The technology is defined as being Hadoop, which implies it has high ability and flexibility to hold a constantly increasing complex data which has opened new chances for obtaining value and business insights from promising huge amounts of firms of organizational internal data.
Big data has also made it possible to enhance the internal data with equally wide range of semi-structured data obtained from external sources such as the public sources and social media bodies. This has been known to fully enrich the potential value of the data obtained. The collaboration and processing of the internal and external data could not be possible till the birth of the Big Data reign.
The data being processed can vary from structured to fully processed data. The Big Data has been known to have a lot of metadata interlinked, which could be harnessed as data. The data obtained from the organizations databases and web channels could widen the amount of data available for analysis. The Hadoop technology that have been created from the primary levels to high level which enables it to process big and complex data (Minelli, 2013). This ability to analyze huge data sets is the primary characteristic of the Big data. The strength of the Hadoop system is its ability to linearly scale with the heightening data complexity. However, this also makes it to be close to being invaluable equipment in any Big Data use. Therefore, any Amount of data that out powers the previous traditional data systems and demands the use of Hadoop technology, is known as Big Data.
Big Data has found use in the sentiment analytics sector. Credit has to be given to the social media sites for opening new sites as well as chances for the organizations to link up with the customers. However, that amount of communications can be beyond the ability of the initial systems to hold. Sentiment analytics can be defined as the study of how customer’s views on a given point of interest to the company in question (Fieldman, 2007). Thus, sentiment analytics has come in to assist in the reading of the data acquired by summarizing the comments about a given product or a given company brand. It has also helped in data acquisition from the chats which helps in availing useful business insights.
For the sentiment analytics to be successful. The bank or organization has to check on the buyers talk about marketing success, which gives information that helps the firms to change their techniques to fully serve the needs of the consumers hence helps in the development of a competitive advantage. Secondly, it must fully determine the key buyers to enhance the virtual marketing, which helps the organizations in fulfilling its objectives by adopting a perfect plan for their business (Wu, 2014). Lastly, it should also assess the customer’s reviews about the products and services offered. Big data technologies can be used in identifying the most important customer thoughts that can be utilize by the management in improving their services and products.
Big data technology has enabled the marketing bodies to fully examine the strength of advertisement through social media. It has also helped in the minimization of the high expense that the firm’s had to invest in the social media control. The use of the Big Data Technology in analyzing of the social media sites provides the banking systems the necessary information about the marketing campaign, customer choices and preferences, as well as their complaints.
Sentiment analytics should be done over a long period of time so that enough information about the various trends in the marketing plan. The Big data technologies help in the reading and evaluation of the big amount of comments that are extracted from the social media. The sources of information range from the social networks, blogs, and review sites. From the data acquired, the firm can understand it key customers thus they can successfully devise perfect ways of passing their information to the customer which will give them a competitive advantage over other firms.
The Big Data technology are not only precise but also time consuming as compared to the previous systems. The initial systems were also expensive and inaccurate as only they had a limit to the number of sample sets. Thus, most institutions using the sentiment analytics equipment’s which utilize the big data from the conversations in the social media sites and logs, which helps in improving the products and service provided. As such, most banks have been able to come up with working insights from real-time social media evaluation. This has been achieved by the banking firm’s coming up with mobile banking app, which hinders of underage people doing transactions, thus inviting negative comments from the parents and the teens as they could not receive cash from their parents (Swan, 2013). This negative feedback has helped them in improving the app to allow access by teens of age between 16 and 17 years of age.
The second Big Data use is in the customer 360 plan. The customer 360 scheme aims at full comprehension of the customer as means of achieving a competitive advantage over others. The 360 degree view of the consumer depends on the past and intermediate customer traits in forecasting forthcoming customer trends as well as their next course of action. It also uses the customer’s transactions and habits in developing the lifestyle profile and discovering new thoughts. It uses the consumer attributes to develop a complete and holistic view of the customer.
By fully understanding the customer, the banks can develop actionable insights which help in bettering the marketing campaigns, targeted sales, and enhanced customer services. Proper mastering of consumer profile enables them to send transfigured messages that help in the backing up of brand and target customers. Secondly, the 360 view can be attained by fully understanding how the customers perceive the different products and services they are being offered. The Big Data analytics firms can then determine how much buyers are involved with a given product, which helps in the analysis of the demand of the products. Lastly, it can be attained by keenly checking on the situations when the consumer is about to move out. The use of Big Data in the analysis of the consumers does not only show the historical review but also it is a vital tool in predicting the future course of actions and trends of the buyers. The data acquired can also be used in avoiding trouble before it comes to happen.
If the bank, for instan8ce, has full picture of its service consumer it would use the information provided by the customer in avoiding the fraud cases in their activities. It will also enhance the customer’s engagement in the business activities offered by the bank or any organization. The banks may also use the consumer’s record of banking events to help them improve their credit card activations plans which may be attained through launching incentives that carry personalized information to each of their esteemed customers.
It can be attained through classification of algorithms to assist in the filtering the content the consumers require such as life styles preferences, or life stages event for student consumer that is healthy in enabling the manager to selectively add information obtain form the blogs, and reviews. By fully understanding the factors which may lead to loss of a customer will help the firms to re- assess their marketing strategies such as the costing practices that will help them retain the consumer hence retaining their profits. The departure of a customer can be detect by use of random forest or decision tree approach whose algorithms and logs is able to support the wide range of fields and data types that may cause the buyer to consider leaving. The second way of determining the customer churn is through the use of survival analysis, which is important in comparing different customer segments across the various time spans. The customer profile and transaction data which can be used in the analysis of customers’ use of the product. The information can also be used as traits signs of potential churn. The firms may also use information from te social sites to determine if their consumers are about to quit.
The Big Data can also be used in customer segmentation, which means the subdivision of the buyers into natural segments that have same traits or features. The understanding of the classes is essential in the determination of the needs and wants which help in the development of the sales and a marketing strategy. The Big data technologies come in handy in the development of precise segments. It also helps in the determination of new customer bases which enhances the banks or firms business opportunities.
Big Data segmentation allows the follow up of how the customers are utilizing their favorite products that opens the banks’ ability to realize the group which have been disadvantaged or advantaged. It also leads to the optimization of the marketing messages for the different classes which fully agree with the customers. This is achieved by designing targeted marketing designs.
The firms may also be used in developing loyalty programs depending on the group’s traits. The banks are able to give highly personalized cash back offers with the different groups such as the food stores, retail, or travel firms groups which greatly enhances the card loyalty and card usage.
The firms can achieve the classifications through the optimization of the price plan. The decision can be enhanced by the knowledge about the group. Big data classification gives more room for the examination of the classes and their price willingness, hence helps in the development of the improved pricing scheme for the given customer base. It can also help in relationships creation with the esteemed consumers. By employing the use of Big Data technologies, the firm may determine the most promising market segments and they must be given higher preference to help them realize the customer fulfilment. The classification also helps in the realization of the personal traits of the good business people thus it enhances the banks decision to focus a given group of customers with high potential for giving higher returns. Targeting the correct groups with right options has been discovered to possess high chances of marketing effectiveness. With Big Data it is able to review the various aspects that are far reaching that the classical segmentation of the consumers basing on age and marital conditions to foresee the consumer segments depending on their lifestyles, life stage, and vital events. All these allows the company to realize high levels of success in their newly stratified groups. The information extracted from the Big Data technological systems provides a marketing body with a chance to come up with highly personalized marketing programs that fully serves all the needs of its customers.
The classification calls for the use of clustering algorithms to determine the key patterns within the customer care data and human awareness that connect the patterns with worldly consumer traits. The customer information can be utilized in the cluster evaluation. Precise returns are realized only when data is obtained only from a given type of customer group. It has also been realized that transaction data has widely been used by the banks and other companies to stratify its customers perfectly. The transaction data also helps in the creation of a loyalty program for a given demographic group.
Most companies have employed the big data to maximize their pricing tactics. The firms have achieved this by using the data acquired from their services and products sales to make their decision about the specific pricing technic they should use. For the banks, they can come up with perfect pricing for their customers by examining the flexibility of their pricing system for each group. It is worth noting that currently the competitive market, the banks have to get a way of determining the most profitable groups. The bank will then pay more attention to them. The use for big data in segmentation can be also useful in identification of loyal groups too. The banks can achieve this by using personalized advertisement and attractive offers.
Big data has also been used in the determination of the next best offer, which allows the firm to raise its up-sell and cross-sell opportunities for forecasting the needs of the consumers. It is attained by critically assessing the customer’s market base and determining the trends that exist between the products to help predict the future consumptions. This has helped in fostering loyalty in the customer by providing them with extraordinary products that live up to their standards hence it will lead to firm customer associations. It will also heighten the interest the customers have to the services and products the bank offers.
Secondly, it may be used in assessing the product propensity, which is being used by many banks to increase their revenues by providing customers with products and services that thy real are in need of. Lastly, this ensures that products are built in such a way that they increase their revenues. A customer’s attraction towards a given product has always been dated back to historical data that underscored the purchasing trends. This propensity can be realized by using Big Data classification algorithms such as linear regression and decision trees. To add on, bundling of products and services offered depending on how best they suit each other helps the bank to use it as a competitive advantage against it competitors. Most banks use this technique in increasing the cross- and up-selling in a more classic way that will differentiate the bank from its competitors hence giving then a competitive advantage over them thus more profits.
Big data can also by the banks in the development of channel journey. Due to the high number of calls, chats, or meetings with the banks it is usually difficult for the firm to follow up their customers steps. Big data assist by giving a holistic picture of the customer’s full movements in the channels. This trends can be harnessed in the sales to determine the underperforming sector. The knowledge extracted can be utilize by the banks in maximizing their funnel conversations, improving their preciseness and evaluating the marketing results across all the channels.
From the discussions above, it is clear that data is the backbone of the Big Data. However, the algorithms form the core part of changing data into insights and even creating value from it. Both data and algorithms are vital in the changing of Big data into reality and then finally into business insights.
SWOT Analysis of Big Data in Banking
Strengths of Big Data in Banking
There is a wide scope of advantages that comes with use of Big Data technological analytics in any business enterprise perfectly. The Big data is just too good to be shut down by any system considering the pace of technologically advancement that have been realized in the business sector which are revolutionizing the various business plans that had been put forward by the various management bodies. It has just led to the banking systems realizing a leap in their profit margins, corporate social responsibility practices, and the customer care services.
To begin with, Big Data has enhanced the communications with the customers. With the buyers commenting, reviewing about the services they get from a given firm it thus poses a challenge to the firm because they have to spend more in order to gain more from the social media platforms. But, all credit has to be given to the Big Data technologies that allows the firm to profile the heightening amount of data being acquired from the social sites. For instance, in some banks when a consumer enters the Big Data equipment will allow the bank officer to verify his or her profile in real-time, which helps in predicting the given products and services he might consume. There for it ease the banker’s course of advice to the client. This usually leads to customer satisfaction which is an advantage to the bank as they will receive more customers due to the good picture the well served client will send to the public. Big data also has a vital role in enhancing the digital and physical shopping spheres by giving the vital information about the various needs the customer have as it can be read from their conversations in the social media platforms.
Secondly, Big Data has greatly helped the banking systems to re-shape their products. By use of big data technological analytics such as sentiment analytics the bank is able to fully understand how the market sees their products and services, which is very healthy as it helps them in changing the structure of their products so that they fit into a given marketing system. By use of Big Data technology to fully evaluate the unstructured social media conversations allows the bank to understand the comments that are left by the buyers. The information acquired can also be used in the classification of the consumers into the rightful geographical or demographic groups. Big Data also allows big changes in the computer assisted systems to check the minutes variations in the banking processes such as how the material affect costs which later enhances the times and performance. It also helps in improving the efficiency of the banking production process at last.
It also helps the banking system in performing accurate risks that accompany the execution of daily activities of banking. As it has always been known that proper risk analysis plays a major role in the realization of success in the banking processes (Spiess, 2014). With big data analytics it possible for the social and economic factors to be critically analyzed owing to the large amount of information that is being obtained. By using the predictive analytics, Big Data has made it possible for the banking bodies to assess and analyze information from the magazines and the social media news that from time to time enhances the very important growths and developments in the banking systems. Ranging from the spontaneous analysis of the suppliers and buyers, a lot of information may be acquired that will help in improving the banking activities, all thanks to the use of Big Data analytics.
To add on, big data analytics play a very important role in safeguarding the firm’s data. By use of big data tools it is possible to keep an eye on all the organizations environment. This allows the firm to fully interpret the dangers properly. It also enables the bank to note potentially crucial information which is unsecured thus the bank will have to develop ways of safeguarding it as by the regulatory requirements. The Big Data analytics can also help flagging up any condition where the sixteen numbers are used as potentially credit card security by properly storing and communicating and investigation properly.
The Big Data analytics lead to creation of new revenue streams.by the use of Big Data to analyze the consumption scale of the market the banks get an insights that may lead to them coming up with desirable plans to realize growth in their systems. They can also sale their new ideas to more advanced firms’ and earn more income from it.
The big data can also be used in customizing the banks website in real time. Big data analytics allows the banks to mark the content website in real time to fully satisfy their customers basing on a number of factors such as customers sex, nationality, or location of the website. With all the comments about the website, the bank can re-structure it so as to fully serve the peoples interests. A good website has always been known to have numerous advantages to the banking system as it enhances their advertisement strategies.
The Big data has helped in minimizing the production and maintenance costs. Initially firms would predict the wearing out of a given machinery slowly with time and then do parts replacements and repair, which was tedious and time consuming (Kitchin, 2014). The Big data tools have simplified it by removing the cases of such tedious and expensive incurrences. The huge amount of data being reached by their systems can detect any failing grid devices and also forecast at what time they will give out. Thus, it leads to a more cost-effective repair plan for the devices and has always been known to have a minimal time consumption, owing to the quick tracing of the faults in the devices.
The Big Data tools help the providing the banks full insights about their business. Traditionally, the firms would want to analyze big amounts of data then they would request their information technology expert to help them. This would therefore imply that a lot of time will be wasted in seeing the problem solved. However, with use of big data analytics, the experts can come up with algorithms and build up their repeatability, which will enhance quick searches. They can come up with systems and initiate interactive and changing visualization tools which allow business users to analyze, view and benefit from the data acquired.
Moreover, by using the information technology to better the customers experience in both retail and business banking always helps in the development of interest-based and fee-based revenues. Most banks are moving towards diversifying their wealth management groups to ensure that there is small danger and a promising fee-based revenue. The data acquired on a number of strategies such as differentiated services, cross-sell and up-sell plans, and growth into the revenue-control markets schemes available worldwide. All this strategies can be can be bought by the banking systems in ensuring that they acquire more income.
Weakness of the Big Data in Banking
The big data as discussed has numerous benefits but it also has its own faults. The problems range from financial constrain to the cases of insufficient number of experts to run in. The Big data requires heavy financial investment into it in order to fully launch it and get the best returns from it. The technology used in Big Data plan are very costly (Rabl, 2012). They include technology world Hadoop technology which is a more recent advancement hence acquiring it and installing it will cost the firm huge sum of money. Therefore, the use of Big Data is limited to only companies with heavy financial muscle to fully buy and install the technology program. Thus, the limit makes it an inefficient business plan that can be used by the small ventures as it will drain most of their profits into maintaining it.
Secondly, the skills required in the utilization of the Big Data technologies is very dear. It requires highly specialized information technology personnel to check on the proper utility of the facility so that the firm gains more from the project. Failure to which leads to errors in the system which leads to misleading interpretations from the data which will lead to improvising of programs which are not perfect that will even lead to customer loss which implies that there will be reduced returns.
The Big data program may face challenge of data loss since the computerized Big data analytics program can be damaged by viruses (Russom, 2011). The malicious minded people can come up with viruses that are so powerful to corrupt the whole system leading to data loss thus the company or bank may loss important details that may even lead to them shutting down as a result of accruing a lot of losses.
The other problem is that Big Data analytics requires synchronization with the social media and the banking feedback systems which implies that the firm has to invest more money by collaborating with the social media sites firms and other information bodies to allow them to use the data collected from their platforms. Such a venture is costly as the company will have to pay for the copyrights of firm before being given a go ahead.
Lastly, basing judgments on the data acquired from the Big Data portfolios can be misleading. The data can be biased due to wrong evaluation of the statistics given from the data. For instance, if poor regression curves and other measures of dispersions are determined then it will mean that the decisions made from the data analyzed about the various marketing policies, or even consumer segmentation would be incorrect and it will lead to obtaining of poor returns from the business activities (Ammu, 2013). Thus, on the same note, the Big Data analytics requires statistical experts to fully assess the data obtained before submitting to the management for further analysis before it can be used for the policy making discussions. This calls for more financial investment which cuts more into the profit margins of the firm.
The security of data obtained is usually not standardized as every person within the firm is allowed to the access the big data technologies starting from the unskilled personnel who may mishandle the equipment leading to constant repairs which are very expensive hence it continues to dig deep into the profit margins of the firm. The algorithms can also be manipulated by poor performing departments of the banking organization, to provide a wrong impression of the success which does not reflect on the activities that are on the ground. Hence it minimizes the importance of the data being kept by the organization to be used in the prediction of the future development plans of the firm. Thus, the program entails the installation of strong firewalls and other data security programs that will help in the safeguarding of the details.
Opportunities of Big Data in Banking
The Big Data Technology analytic has opened a wide variety of chance for the financial services and banking. The big data has helped increasing a wide range of options and fresh thoughts in a number of functional areas in the banking and financial services provision sector.
First, the Big Data use has been used in revenue improvement and profit margins increment. This has been achieved by using it in launching new services and in the expansion of the business into a global level market with specific services (Labrinidis, 2012). The big data technological analytics such as the cross-sell and up-sell chances have been greatly advanced thereby reducing the risks and costs which lead to higher profitability.
Secondly, it has been used in the risk management sector. The Big data has been employed in the improvement of credit assessment by using non-traditional data sources. The utility of the Hadoop and the spark open technologies in the large number of parallel set computers which enhances the security thus providing a new opportunity for the security sector. To add on, big Data has been employed in fraud detection in the banking system and other financial services firms. It has been used in matching unusual events to the personalized data that has been stored in the systems, so that a decision is quickly made about any fraud attempt hence improving the security.
Thirdly, big data analytics such sentiment analytic has offered a wide range of opportunities to the bank by enhancing its chances of regulating illegal trading activity. this is attained through the identification of business misbehaviors by matching unstructured texts such as IM chats, emails, and calls for a given business activity that was being transacted. Thus, Big Data has improved the broker and trade compliance sectors by offering them a way of checking on people’s failures and malicious intention through tapping their conversations.
Moreover, big data has been used to quickly interpret large data sets for anti-money laundering reasons. The Big Data technologies have helped in the reduction of the cases of having fake positive money. It has also been used in the study of the customer’s behavior and needs. It has also been utilized in the consumer identification so that those who have the potential of creating long term profits to the firm are separated and it also helps in the segmentation so that the bank knows the type of service and products a certain group is bound to consume. Thus, it has help in the realization of customer satisfaction which can be used as a competitive advantage tool by the organization. This has been achieved through the use of the Big Data technological analytics such as the marketing and customer 360. Lastly, it has a higher opportunity for the banks to protect their reputation by constantly re-shaping their brands to serve the interests of its customers. This has been achieved by the use of the big data analytics to constantly check their web sites so that they can fully picture and assess their buyer’s sentiments towards the organization’s employees, servers, and prices. All the information will help them in the regulation of any mess by coming up with brands which fulfill the consumers quests.
Threats of Big Data in Banking
As it has always been known that data is power. Hence, traditionally, the freedom of communication was highly guarded. But since the coming of the big data technologies many firms are openly giving information out, which poses the question is it being done responsibly? If not then it would been that the people will lose the trust they had in the firm thus they would tend to revolt against their information being taken without consent which imply that the firms will have to invest more money into the media and social media so that they can access the information that is useful for their companies growth and development.
The big data technologies get threats from the civil society groups who have outline ethical framework for data usage by the banks and other firms. The framework emphasizes on the transparency, user gain, and public benefit. As such if the big data is only benefiting the firm and disadvantaging the public the civic societies will fight to see that the program is faced off despite its tremendous vitality for the bank (Tole, 2013). Thus, the use of the big data has been controlled by other groups implying that the firms has to follow all the restrictions which will limit its productivity.
The big data technologies also face restrictions from the national government which may come up with legislation which regulate the activities of the big data analytics. Thus, by doing so the value of the big data will have been reduced significantly. The firms will also have to follow the legislation even if it means doing away with the program at all. The heavy penalties that are put to control the use of data makes the organizations ways of utilizing data hence lowering its efficiency.
Big Data Algorithms
The term Algorithms refers to the process or set of rules to be strictly followed in the determination of the answers to a given set of problems in a computer. Most of the big data technologies analytics use the algorithms from the computers to acquire useful data for either conversations from the social media or the bank’s web site feedbacks which are then analyzed by the experts and then submitted to the management for the decision to be made on the best way forward. It is worth noting that for the algorithms to be useful they fully depend on their decision trees and random forests.
To begin with the decision trees are very vital in the extraction of data. This is because the decision trees have higher ability to deal with a wider range of problems. They also have the ability to handle almost all type of data ranging from arithmetical, nominal, or others. They work by subdividing the data into various sizes into smaller data sets. They aim reducing the total entropy of data within each set. The splitting of data continues until a given data size is reached. Efficiency of the data subdivision involves decision trees assessing all the components to determine the most important of them all.
Generally, decision trees are employed in segmentation of data into highly refined data sets. They are also used in exploring most valuable field for a given data set that can be harnessed in the other data algorithms.
Random Forests refers to a process which is used to enhance the efficiency of the decision trees model by developing the changing decision trees but maintaining the target. Minor changes in the decision trees serves as security check for any errors and the noise of the give decision tree itself. Every decision trees collects over the target space. The relevant field space is then given the data.
Big Data Algorithms Clustering
Segmented detection refers to the automated determination of useful trends with a given data set. The determination of the most useful data has always posed a threat to the efficient extraction of data from a huge amount of data. It helps in minimizing the noises by determining data sets that form natural clusters over a given data set (Fahad, 2014). The groups are essential in splitting up the complex problem into smaller data sets that can easily be understood by the individual who are meant to use them. After successful detection the clusters often present themselves as the unit of study. The cluster algorithms are unique as they do not have targets factors like the other types of algorithms.
It by using the Big data clustering algorithms that the banks gain the ability to find solutions as the segments detected are normally targeted to solve a particular market issue. Most commonly used for of this type of algorithm is the K-means. It is usually employed in getting groups within a given data group. It works by giving a K named segment seeds haphazardly within the given data size.
Big Data Text Analytics Algorithm
Owing to the large amount information being mined from the social media, internal and external reports, and news reports it has turned out to be difficult for the people to literally read all the information within the required set period of time. The Big data technologies text algorithms help by voluntarily reading the material and offer a summarized report. They highly bank on the probability theory and the rarity and occurrence of given words that carry the main message of the whole text under discussion. They are employed in sentiment analysis, where the required field has chances of being negative, neutral, and positive comment. The most commonly used is the Naïve Bayes algorithm which has high efficiency in the determination of the probability that given type of words belong to documents from a certain classes of people.
The Big Data Link Analysis Algorithms
The link assessment has been developed over the high need to determine the connections that exist between given variables. It is useful in determining the individual or groups of people who are linked to a given information that has been mined from either the social media, or the firm’s website feedback systems. However, link analysis algorithms fall between the direct and indirect data mining plans (Fan, 2013). It can also be used in the development of fresh variables that can be used by other techniques. This type of algorithm has found its success in the verification of the vital sources of information on the web platforms by analyzing the link (Zikopoulos, 2011). It has also been used in the determination of the most important consumers of the banks products and services from it call pattern data to hiring new subscribers from very competent networks and gaining the insights on any case of fraud. The links may be recorded as HTML encoding between the web pages or call logs on the mobile phones.
Big Data Survival Analysis Algorithms
It is usually used as danger alert mechanism by the big data technologies analytics. It is from this algorithm that the bank is able to detect the departure of a consumer or even the chances of a given buy ceasing to buy a given product. Survival analysis algorithm offers snapshots of the consumer’s products life cycle and also provides insights as to when one should start worrying about consumer relationship (McAfee, 2012). It has also been useful in providing information about the probability of a given consumer moving into another customer class, which may be used by the banks to determine the best way of serving the customers so that they all are satisfied. It also shows the probability of a give customer to reduce or increase customer contract. It also shows how the different factors affect the customer tenure quantitatively. All the information obtained from the survival analysis algorithm can be employed in the marketing process to ascertain the length of time likely to be taken by customer segments and thus helps the bank in determining the most efficient of accruing profits from the groups.
A Review of Most successful Big Data Systems
Each year, moth, week, day, and hour I always picture how the world look like if there was no Big Data technologies incorporated into the various business systems or even state firms. All I get is that nasty sluggish, and underdeveloped systems giving low yield to the various firms. But currently the fruits of big data can be visualized at any moment. It is great!
To start with, big data has been known to lead to heavy government savings. For the wise governments that have employed the use of big data to realize massive fall in the state incurrences. It has led to the replacement of highly paid professionals with few data scientists who can produce equally and even more results than the people (Srinivasa, 2012). Since, the replacement will lead to reduction in the amount of money used by the government in covering all the salary of its workforce. The same leaf if passed to the medicine, teaching, security groups, will also lead to pay reduction as lowly paid groups can be used instead. Hence, the government could have saved it cash that it would have spent on the same services by low skilled personnel implying low costs as most of the work is done by the system itself.
The most fascinating application of big Data was in Singapore, where it was employed and led to reduction of the gum chewing practices among the people. The country had realized that most of the criminals were used to gum chewing in the streets. By use of Big Data the malicious individuals have been spotted. It was also used in the follow up of gum-chewing practices, and also in the apprehension of the wrong doers. By use of the Hadoop technology, judgments have been simplified as it offers accurate evidence to the jury to base its actions on.
In 2016, big data was used in finding an amicable solution for the climate change problem. This was first used in Paris, where it changed the perception of the people about climate change issue, how it as to be dealt with, and even be measured. It also portrayed climate change as a chance for proper market capitalism in overall, and big data to be more specific. The Big data technology analytics will now be used in the gathering of climate change thoughts from all the social media platforms and web sites which will then be utilized by the specialist in devising better ways of dealing with it.
Big data was also used by the Real Madrid team in reassessing their weakness that made them to reclaim their position in the Spanish premier league. The Big data was used by the Real Madrid team in 2016 to win the champions league title, the Spanish league title, and the Copa Del Rey coup. It is also with the use of big data that the same team was suspended as a result of fielding an underage player in Spanish cup game. Thus, if the Big Data is fully incorporated in all the games the teams will realize drastic changes in their results.
Moreover, recently Big Data was benched the traditional Data warehousing. It has led to introduction of the best strategies, tactical and operational decision making grounds just from the information mined from all the relevant sources. It has used the commodity hardware in distinguishing itself from the other hardware that are to now still being used by the majority of people.
Lastly, Big Data housing became a problem of great help the European Union. On the grounds that Big Data speeds up the data collection process, the EU will strategize so that it mines and stores perfectly. It does all it can to protect the data integrity. The Union has placed its Big data in both the public and private sites so that all the information obtained will be all round. To add on, big data has been used in attempt to address the world problems such as hunger, poverty, and refugee issues. This has been possible owing to the large amount of information that has been availed to the people by the Big data technologies. The data has been used by the government in planning for better ways of dealing with the pressing issues.
The use of Big Data analytics in banking has seen drastic changes in the management of the banking and financial systems. It has also helped other platforms in their decision making plans as they simplify data into easier forms to be interpreted by nearly all the people in a given organization. The sentiment analytics have been found to be the cornerstone of Big Data technologies because of their ability to extract crucial information from the social media sites, new papers, are even feedbacks given to the firm. The feedback can be harness by the management to plan best ways of utilizing the opportunities available so that they can turn to be the firm’s competitive advantage. It can also help in administering the real issues that the people face so that they can serve them right to realize customer satisfaction.
The Big data customer 360 can be used in the personalization of consumer profile which is very vital in avoiding fraud cases, or insecurity issues in the banking process. It has also been used by the banks to understand the given type of products and services that a given group of people may easily buy from the bank. On the other hand, Big Data clustering has helped in the identification of the most influential customers so that the bank can structure ways of gaining more from them. The segments also help in the identification of the needs of a particular group of people so that they are fully served which will help in improving their customer care systems.
In general, big data is very useful in any economic, social, and political entity as it traps information that is relevant to any setup. I would therefore advice that the banking, financial service, and other organization to adopt the Big Data technologies so that they can realize a leap in their production.