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Higher education affects almost everyone – as students, parents, part-timers and citizens or as candidates of scientific, medical, and technological research. A higher college education is coming ever closer to being considered so basic that, it is too important to be left to the differential competitive forces of the current survival of marketplace.
Today higher education is caught up in indulge political issues that are increasingly relying on it to solve economic problems as well as social problems. Colleges are expanding their missions and by providing these educational services while reducing revenue from tuition and to avoid pursuit of other revenue sources when they involve relationships with the corporate world.
And the richer schools are pushed to spend down endowments that are being deemed.
Today, a new generation of Big Data analytics is taking over manufacturing and providing a totally new era to the value of research and trend analysis. Data is all grown up, with new multidimensional capabilities and broader horizons.
Data has long been the essential lifeblood of manufacturing, driving efficiency improvements, reductions in waste and incremental profit gains. Data is no longer being used for reporting past activities, it’s helping manufacturers predict future events, understand them extended value chain and enhance the customer experience they deliver.
The regulatory framework within which financial institutions and insurance firms operate require their interaction with customers to be tracked, recorded, stored in Customer Relationship Management (CRM) databases, and then data mine the information in a way that increases customer relations, average revenue per unit (ARPU) and decrease churn rate.
According to, churn has an equal or greater impact on Customer Lifetime Value (CLTV) when compared to one of the most regarded Key Performance Indicator (KPI’s) such as Average Revenue Per User (ARPU). As one of the biggest destructors of enterprise value, it has become one of the top issues for the banking industry. Customers churn prediction is aimed at determining customers who are at risk of leaving, and whether such customers are worth retaining.
Major challenge in the education industry
Digitalization reduce the importance of the top-down fact of spreading of standardized knowledge
Students learn more than they do in traditional courses. Retention rates are higher with online learning. Online learning requires less of a time investment. More frequent assessments can reduce distractions. eLearning is the greener option. Empowers Better Decision-Making. Students’ Results. Career Prediction. The Mapping Concept. Enhance the Learning Experience.
1. Creating new knowledge
2. Disseminating (reasonably well established/understood) knowledge
3. Mentoring (in smaller group settings like PhD programs)
The world of online learning is constantly evolving, and it is hard to predict where it is all going, with digitalization set to affect different disciplines in different ways. Oral class lectures are prepared in advance, providing such content online frees up class time for more discussion. The increased emphasis on interactive learning and discussion as one of the primary advantages of digitalization, since for me it’s the more interesting part of learning.
The education industry is complex and diverse. It combines a dominant public-sector of state universities and community colleges that educate most of all students, a private sector of non-profit schools that encompass some of the world’s most elite research universities, elite liberal arts colleges, and many hundred less-selective schools, many religiously oriented. Regardless of ownership form, the schools comprising the higher education industry are in competition. They compete for students, sometimes as part of their educational mission, sometimes simply as revenue sources, for individual’s donations, for governmental research grants, for corporate research support, for star athletes and even for star academics.
The methods colleges use to compete in all these realms are understandable, once it is recognized that every college is some combination of a socially conscious provider of educational services, a business searching for revenues and cost-cutting methods. The framework that underlies the schools provide teaching and basic research, even when they are unprofitable for the individual schools, and finance the emission activities through conventional business-like revenue-generating activities.
In higher education there is an unusual combination of ownership forms, even the education industry is by no means unique, mixed ownership has also long existed in other industries. The mixture leads us to ask how public and non-profit schools differ from their for-profit counterparts. we expect to find major ways in which the public and non-profit education system essentially do not differ from private for-profit schools, and we do find that. But we also expect to find ways in which they differ greatly from private firms, and we do.
WHAT ARE THE MAIN ADVANTAGES OF DIGITALIZATION ON HIGHER EDUCATION?
You can compare online education to a textbook, textbooks in which people will also be asking you questions. Unlike a textbook, online learning provides more tutoring and guidance with the updated resource. Digitalization opens higher education to people who wouldn’t be able to afford or access it, such as people living in remote locations.
“But there are other advantages too. Digitalization makes it possible for some people to pursue higher education with balance to their lives. Individuals who are already working in an industry, with heavy workloads and important responsibilities, may find that online learning makes it easier to combine their professional and social commitments. The major advantage of online learning is that the possibility with online learning to access your sessions when you want and learn at your own pace. It all very much depends on individual learning styles and professional goals.” The beauty is in finding the right balance between online and in-class learning, the best way to make progress is to experiment with different combinations. If we wait for it to be perfect before we get involved, then we will never get involved, which would be a pity. It’s a matter of familiarizing oneself with digital tools and platforms and using trial-and-error to figure out what works and what doesn’t.
From a practical point of view, staff and institutions must learn data management and analysis tools. On the technical side, there are challenges to extract data from different sources, on different platforms and from different vendors that were not designed to work with one another. From the Political point of view, issues of privacy and personal data protection associated with big data used for educational purposes is a challenge.
Big data is used quite significantly in higher education. For example, the universities with over 26000 students, has deployed a Learning and Management System that tracks among other things, when a student logs onto the system, how much time is spent on different pages in the system, as well as the overall progress of a student over time.
In a different use case of the use of big data in education, it is also used to measure teacher’s effectiveness to ensure a good experience for both students and teachers. Teacher’s performance can be fine-tuned and measured against student numbers, subject matter, student demographics, student aspirations, behavioural classification and several other variables.
It’s like a compass, pointing the way for manufacturing growth. Data can come from external sources, internal sources, or formed by machine-to-machine interaction. Together, these sources can provide manufacturers with the data that they need to know about their customers, products, processes, people, and equipment.
External sources Manufacturers can get data from external sources, such as user groups, social media, focus groups, or surveys to build customer data. Third-party surveys, portals, and call centres adds an impartial layer to the data that is less threatening to the customer. The promise of the condition of being anonymous can also generate higher response rates. This fact-scraping can be used to build accurate profiles of customers and prospects, including subjective, like colour and design preferences, common buying triggers, or evaluation criteria.
Internal sources Manufacturers can turn to their own systems for data capture and analysis. A modern, integrated ERP system can provide information on products, processes, people at all levels to the departments in the organization. Data collected through an ERP system offers benefits, such as:
? Real-time reporting with actual accuracy.
? A common database that provides one version.
? The ability to approach down into details for historical Depth.
? Relational data with context and relevance.
Machine-to-machine Now smart sensors and Internet of Things (IoT) is well collector of data directly from machines and equipment, and send it on to an ERP system, EAM system, or other enterprise applications.
Built-in, low-cost sensors can detect a wide range of attributes, including location, weight, temperature, vibration, flow rate, humidity, and balance. These conditions can then be monitored in order to identify
and predict performance issues that require service, repair, or replacement. This allows manufacturers to
get early warning of impending issues, and hopefully intervene before there’s a catastrophic interruption to
Data for manufacturers-In this, where manufacturers are capturing and using manufacturing data, now their primary focus is no longer on reporting on past events data is now being used to predict trends and anticipate needs. In this way, Big Data is acting as the gateway to the future. Anticipating consumer trends, stocking necessary inventory, and maintaining adequate resources to meet customer orders have been high priorities for manufactures for decades. As speed of delivery and just-in-time inventory (JIT) strategies gained importance, the ability to accurately forecast needs also grew. Manufacturers learned sometimes the
hard way the importance of choosing the right influencing factors or the right combination of factors. When attempting to predict the future, one data source is seldom enough. Today, predictive analytics has become a valuable science and tool for manufacturers. It turns data collected from numerous sources into a blueprint for
future actions. Modern business intelligence solutions can project trends with a high degree of accuracy. As in any data initiative, though, the output is only as good as the input. Manufacturers must take care to choose reliable data sources and to continue to refine which influencing factors provide the best signposts for future activities.
Predictive capabilities offer many benefits to manufacturers, including:
? Staffing readiness When manufacturers have a reliable forecast of product sales, departments throughout the organization can plan personnel staffing accordingly, hiring personnel as needed and allowing adequate time for team training.
? Raw resources in stock Procurement teams can use accurate predictive forecasts to better plan just-in-time inventory levels of raw materials, preventing delays due to stock outs.
? Spare parts inventory An accurate understanding of the product lifecycle translates to being better prepared for necessary maintenance, including having the consumables and parts that require regular replacement in stock.
How can Big Data foster? growth opportunities?
Big Data also give the benefit of significant return on investment (ROI) and lead to manufacturing growth?
These types of questions manufacturers must answer, if they want to take advantages of Big Data’s potential.
Big Data acts as a compass, it provides a guide, but it’s not magically going to start generating greater sales and more customers. Collecting data, whether from machines through the Internet of Things (IoT) or from customers through online portals is not the end. Data must be interpreted into action. This requires an understanding of the relevance of the data and careful attention. This is also where many manufacturers fall short in their Big Data initiatives. But with careful analysis, data can be used to identify and analyse by helping manufacturers:
Identify new geographic regions to target Manufacturers can match demographics of current customers with profiles of prospects in other regions. Global expansion becomes easier when manufacturers specifically know what prospect characteristics to target.
Expand into micro-markets Manufacturers can use data to spot pools of untapped opportunity. They can identify micro-markets that are currently under-served or that need specialized make-to-order (MTO) products. Manufacturers can become trusted advisors and build a market only by being the first to reach a new market.
Tap into a customer base Data can help manufacturers identify opportunities to sell, cross-sell, and re-sell to their current customers. They can predict their customers’ needs and the performance gaps in their current products. Data from successful customers can help manufacturers reinforce their message and demonstrate the value of upsell and cross-sell products.
BANKING INDUSTRY – DATA MINING TECHNIQUES
Data Mining is an important component of every CRM framework that facilitates analysis of business problems, prepare data requirements, and build, validate and evaluate models for business problems. The data mining process and algorithms enable firms to search, discover hidden patterns and correlations among data, and to extract relevant knowledge buried in corporate data warehouses, in order to gain broader understanding of business. Data mining uses sophisticated statistical data search algorithms to find, discover hidden patterns and relationships, and extracts knowledge buried in corporate data warehouses, or information that visitors have dropped about their experience, most of which can lead to improvements in the understanding and use of the data in order to detect significant patterns and rules underlying consumer’s behaviors.
Data mining involves four tasks; classification, clustering, regression and association learning; which are classified into two types of data mining; verification-oriented (where the system verifies the user’s hypothesis) and discovery-oriented (where the system finds new rules and patterns autonomously). Data mining process compliment other data analysis techniques such as statistics, on-line analytical processing (OLAP), spreadsheets, and basic data access.
Data Mining Techniques Generally, there are two types of data mining tasks: descriptive data mining tasks that describe the general properties of the existing data, and predictive data mining tasks that attempt to do predictions based on available data. Data mining applications can use different kind of parameters to examine the data. They include association (patterns where one event is connected to another event), sequence or path analysis (patterns where one event leads to another event), classification (identification of new patterns with predefined targets) and clustering (grouping of identical or similar objects). Decision tree is a symbolic learning technique that organizes information extracted from a training dataset in a hierarchical structure composed of nodes and ramification. The tree-like output of decision tree makes it easy to understand and interpret, making it the mostly widely used data mining algorithms in many domains such as supplier selection and email user churn analysis. It is capable of building models based on numerical and categorical datasets.
Decision tree is also used for classification patterns or piecewise functions. Cluster analysis is an approach by which a set of instances (without predefined class attribute) is grouped into several clusters based on information found in the data that describes the objects and their relationships. A cluster uses a collection of data objects that are similar to one another within the same cluster and are dissimilar to the objects in another cluster. While in classification the classes are defined prior to building the model, cluster analysis divides the data based on their similarities. There are different types of clustering from different point of view.
Data mining is a process to extract knowledge from existing data. It is used as a tool in banking and finance in general to discover useful information from the operational and historical data to enable better decision-making. It is an interdisciplinary field, confluence of Statistics, Database technology, Information science, Machine learning and Visualization. It involves steps that include data selection, data integration, data transformation, data mining, pattern evaluation, knowledge presentation. Banks use data mining in various application areas like marketing, fraud detection, risk management, money laundering detection and investment banking. The patterns detected help the bank to forecast future events that can help in its decision-making processes. More and more banks are investing in data mining technologies to be more competitive.
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