Collaborative Technologies, Big Data Analytics, And Classification: A Discussion

Introduction to Collaborative Technologies

Collaborative technologies consist of software and networks that have been installed by an organization to enable their knowledge workers to share and interact in a way the different skills of each individual are utilized towards a common goal and outcome. These technologies are vital to an organization whose employees have a primary job of creating, distributing and application of knowledge. 

The technologies enable planning, and coordination within and outside department. In other cases, they help in communication with external stakeholder like customers. The top decision makers of an organization use the knowledge shared and created through the help of the technologies to make decisions in the best interest of the enterprise (Davenport, T.H., 2005,26).

Email is an example of a collaborative technology that is used for business and personal communication. Communication is said to be the lifeblood of any organization, therefore the email becomes an indispensable tool to workers communicating with colleagues. Employees follow up with clients’ transactions through the email. The email is effective because it is instant, email messages can be used as proof of communication in future and it is cost effective.

Lotus Notes is a very important technology to teams that are involved in intellectual work rather than physical work. The notes taking and sharing tool is used in universities and large research departments in the different industries. The information that is captured and accumulated over time is shared among the teams through a wide area network of the organization (Neilson, R.E., 1997, 11). The tool has feature that enables users to search other users in the network; the capacity to reach all users regardless of their rank in the organization, promote a horizontal knowledge sharing which is preferable compared to the top-down approach of learning.

Collaboration in the software development field has been achieved through use of collaborative technologies. The Git is one of the platforms that has enabled software engineers to co-ordinate the activities of programmers working on the same work. The Git is capable of allowing engineers to write code without un-doing the work done by others. It makes it possible to control the different versions of the software created.

The term big data is used to in reference to intricate data sets that are collected from business transactions of the organization and public data collected by external platforms. The complex data could be from private sector or the public sector. The big data is believed to have concealed valuable insights that can utilized in the decision making process at all levels of the organization.  To extract the valuable information from the raw data, it has to be cleaned, preprocessed and analyzed.

Collaborative Technologies in Business Communication

On the other hand, big data analytics is the process where the mathematical models, research methods and technology are used to capture, store, preprocess, analyze and communicate the final results of the process. The raw data on itself is not useful to the organizations unless it is acted upon. Each stage of the process poses the unique challenges that are countered by applying latest technology and innovation, for example artificial intelligence for prediction and distributed databases for storing terabytes of data (Chen, H., Chiang, R.H. and Storey, V.C., 2012,1166).

Market research firms have adopted social media analytics carryout a big part of their research. The firms are contracted by organizations to do profiling of the target public by analyzing data found in their social media profiles. Variables like age and income levels of a person are key in determining the likelihood of a person buying a certain product. Such data can be identified through social media. The data also helps in driving conversations between the organization and relevant publics.

Bureaucrats in Government have employed data science to run data analytics that are helpful in designing government programs. For instance, planning campaigns require a good grasp of the public opinion about various issues in the society; big data analysis can reveal some of the best strategy to use when implementing such a campaign.

Data collected through different government agency can be useful in spotting trends and relationships that could be difficult to identify with traditional analytical tools. For example, a policy on the security of a county can be crafted using the insights extracted from a huge amount of crime statistics that are collected for a long period of time. Therefore big data analytics is very useful in the initial stages of drafting a policy.

In the financial sector, the bankers and analysts have used analytics for risk management, security price prediction and even in product development. A new breed of mathematicians and physicist called the quants develop models that analyze a large amount of data and try to spot arbitrage opportunities in the securities markets. Some of the data is pulled off from social media platforms while the other is got from hundreds of securities exchange in the world. The new developments and innovations in the big data analytics have enabled these professionals to carry out their business (Mitra, G. and Mitra, L. eds. 2011, 34).

Classification Results

Collaborative Technologies for Software Development

Weka is a tool with a simple to use graphical user interface that loads data of different formats and process to give a desired output depending the chosen task. The software is capable of preprocessing, filtering, classifying and clustering data. It is designed in way that a non-expert in machine learning can carryout basic data mining tasks using the software. It contains a number of algorithms that are well known in academia and the industry. The decision trees, Naïve-Bayes algorithm and neural networks are some of the methods that have been implemented in the software. Lastly, unlike many other data mining software, Weka is free and open source, which makes it easy to add other routines or modify the existing ones.

In this problem of determining if a patient is or not ailing from diabetes given a set of attributes about them is classic example of classification. The problem of classification is best-solved using machine learning tools. The J48 decision trees are used to label a patient as negative or positive based on their age, body mass index, skin thickness and the other attributes.  The decision tree is developed to have a node that represents each attribute. The attribute is categorized as yes or no outcome. The algorithm iterates through the data and finds the inputs that are similar to the target. The attributes that have a high level of similarity in respect to the target are said to have a high information gain. 

The results of the classification problem show that the tool was able to correctly classify 73% of the instances. It means given the 9 attributes of any patient the trained model can tell us if the patient has or does not have diabetes. The different types of error measures show how far the model was from getting the correct classification or labeling. The confusion matrix shows that 407 patients were correctly labeled as negative and 108 of them were wrongly labeled as positive. On the other hand, 160 of them were labeled as right as positive.

In summary, the tool provides an easy and attractive way of solving different data mining problems; be it clustering or classification. The algorithms implemented are well researched and give high accuracy provided the input data is clean

The online information board presents the information in a number of ways, namely: a list containing the different types of diseases available for analysis, a map that show the geographical location of the patients, a graph that displays the relationship between two key statistics, a table with the different age groups affected by the respective disease and a ruler-like graphic that shows the average number of the staying length.

Introduction to Big Data Analytics

The board utilizes visual objects like the map to help the users visualize the location of the patients. The map assists the users to compare the prevalence of the respective disease across a number of regions. For example, the region on the map with many signs means many patients live in that place.

The information is presented in both a quantitative and qualitative way. The line in the graph showing the hospitalization rate and length of stay is a qualitative way to demonstrate the negative relationship between the two variables. The national average, hospitalization rates and length of stay are quantitative information.

Generally, the information board presents the medical analysis indicators in a neat and comprehensive way. The use of graphics is commendable, since research has shown that drawings, graphs and visual objects makes the information more palatable to the general public that do not have expert knowledge on the subject (Galitz, W.O., 2007,20). In addition to the use of visual objects, the dashboard uses different colors that make the whole board attractive to the eye. The menu are presented in a format that is easy to navigate without getting lost.

In spite of the good features, the board can be improved by adding a bit of user interaction with the board. For example, the designer could add a feature that allows the users to plot the data by select- drag and- drop methods. That kind of interaction will enable the users to visualize the data in a number of ways, for example histograms and scatter plots .Web boards designed with interactivity features enhance user experience and engagement (Nielsen, J., 1999, 15).

Lastly the dashboard could add a variety of multi-variable analysis, where the user can see the relationships between different diseases. The list of diseases could be accessed through a search bar that will enable the designer to increase the number of diseases in that list. A dashboard with a wide, relevant and enough information will serve a range of users who will in turn enhance the credibility and reputation of the information board (Reeves, L.M., Lai, 2004,16).  

References

Dave, B. and Koskela, L., 2009. Collaborative knowledge management—A construction case study. Automation in construction, 18(7), pp.894-902.

Neilson, R.E., 1997. Collaborative technologies and organizational learning. Igi Global.

Davenport, T.H., 2005. Thinking for a living: how to get better performances and results from knowledge workers. Harvard Business Press.

Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4), pp.1165-1188.

Mitra, G. and Mitra, L. eds., 2011. The handbook of news analytics in finance (Vol. 596). John Wiley & Sons.

Nielsen, J., 1999. Designing web usability: The practice of simplicity. New Riders Publishing.

Galitz, W.O., 2007. The essential guide to user interface design: an introduction to GUI design principles and techniques. John Wiley & Sons.

Reeves, L.M., Lai, J., Larson, J.A., Oviatt, S., Balaji, T.S., Buisine, S., Collings, P., Cohen, P., Kraal, B., Martin, J.C. and McTear, M., 2004. Guidelines for multimodal user interface design. Communications of the ACM, 47(1), pp.57-59.

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