MIS420

Team 1

Lisa Bella, Brittany Harrison, Marie Ross, Kameron Torres, Christian Whiles

Managerial Summary: Data Governance

In order for companies to succeed in the business world today it is important to understand their data and how to manage it. Many companies use software to analyze data received from vendors, clients, and departments; however, many do not have the framework of a good data governance program. According to SAS, Data governance is “how you collect, manage and archive data in your enterprise”. (Web, 4/29/16) A good data governance program will enable users to receive accurate data efficiently for business analytics, and for data mining. Although, it is possible to establish the framework for a good data governance program, the main impediments with data governance are that many corporations do not understand the importance of a good data governance program, do not realize that they even have a problem, or do not have the proper structures in place to effectively manage the data.

Managerial Summary: Mobile BI 

           Mobile BI tools offer users the flexibility to make data-driven decisions anywhere anytime. With the right information users can make decisions faster giving companies that know how to leverage mobile BI an advantage over their competition. Unfortunately implementing successful mobile BI tools is very difficult. Users expect their mobile applications to function similarly to their desktop applications. Many people believe that mobile BI tools are simply shrunk down desktop applications, but designing mobile BI tools in this manner leads to messy slow applications. Mobile devices and therefore mobile BI tools are inherently limited by small screen sizes, touch screen capabilities, and inferior processing capabilities. These limitations make identifying a specific business need and user type crucial to developing a successful design for each mobile BI application. Mobile BI applications should be designed around simplicity and should break down data into bite sized pieces so that it can be easily consumed on a smaller screen. Each mobile BI application should have a highly focused front end and a highly connected backend that connects to the overall data warehouse and other applications. There are many design elements that can help make mobile BI applications successful some of the most important are tabbed pages, limited KPIs and visuals, large buttons, use of blank space, smaller form scale, and elements that adjust to different screen sizes. At this point mobile BI is still limited to consumption of information. Creating on such small screens with the limited functionality of a touch screen makes creating information frustrating. Tablets and syncing capabilities offer limited workarounds to the impediments to creation, but do not solve the problem. Mobile devices are also more easily stolen, lost, and hacked than traditional desktops. These security risks must be met with security measures that are baked into the mobile applications as they are being developed. Key security measures include passcodes, authentication, timeouts, data wipes, and mobile device policies.

Managerial Summary: Big Data

With the advancement of business intelligence and technology, the term big data emerged. The concept of big data is relatively new. With the technology age, consumers are always using their devices. With emails, SnapChat, Instagram, Facebook, and other apps we spend a lot of our time on our devices. According to the article Big Data Possibilities, we generate more data in one minute than we did in the years between the beginning of time and the year 20003.2. There are endless possibilities with increase in data. With big data, companies will be able to learn about their consumers and make informed business decisions. The impediments of big data include the mindset of C-level employees, skills and knowledge barrier, cost, regulatory concerns, and high volume. If we utilize big data to its full potential, we will be able to understand our businesses and consumers better.

Managerial Summary: Visualization

The size and complexity of data in an organization requires visualization to help users understand the data.  Visualization allows all users to understand the patterns and trends through visual representation.  The most common tool used in visualization is a dashboard.  The dashboard includes graphs, charts, and images to help users understand the data.  Understanding the data is vital to the success of a BI system within an organization.  
        Proper visualization takes real time data and forms relevant visual displays to keep users up-to-date.  Being able to process real time data into useful information can benefit any organization.  Seamless transformation of data into visuals is the key to a successful visualization strategy.  Finding the balance between visual aesthetics and relevant information is a struggle for many.  Some visualization strategies include very detailed aesthetics without the real time data.  Other systems can’t seem to create the correct visuals to provide the right information in the right way.  The perfect visualization uses creativity and BI to its fullest potential, delivering speed and reliability.  Investing in visualization requires an understanding of what data needs to be displayed.  It also requires being aware of what visualization techniques will best benefit the organization.

Managerial Summary: Security

                It’s 2016 and most, if not all companies are using some form of data warehousing to store valuable company information while transmitting this valuable information through VPN’s, the internet, cloudlets,  and other forms of data communication. Modern innovations such as tablet PC’s, mobile phones, and cloud computing have enabled users to access this data from just about any location, at any given time. This means that businesses must take the appropriate steps in order to properly secure this valuable information, and prevent it from falling into the wrong hands.

                In order to effectively secure data businesses must ask questions like “do we need to protect data at rest, during transmission, or when accessed?”, “do some privileged users still need the ability to view the original sensitive data or does sensitive data need to be obscured?”, and “what level of access or granularity of controls do we need?”. In any business case a variety of methods such as encryption, static data masking, dynamic data masking, tokenization, retention management, purging, etc., will be used to meet specified security requirements. These methods will vary greatly from business to business depending on a variety of internal and external variables.

                Knowing that no system will ever be entirely secure, businesses are expected to take the appropriate steps to develop an effective security model, composed of a variety of security methods used to meet a business's specified security requirements.

Section One: Data Governance

Data Governance Strategy:

According to the article Establishing Data Governance Policies: Four Issues to Get Them Right, “Electronic data and records are indispensable assets in any organization”. (Web, 4/28/16) Collection and management is important in a data governance program; however, many companies fail to see the urgency in implementing a data governance program, and fail to define any strategic targets for its data or a data governance program. In order to implement a good data governance program, an enterprise needs to evaluate their current processes, establish a corporate culture, create a data governance program, appoint a steward, obtain executive sponsorship, implement the program, and continually monitor benchmark targets. According to the article Enterprise Information Management: Best Practices in Data Governance states, “Data governance is not meant to solve all business or IT problems in an organization”, but to “define, approve, and communicate data strategies, policies, standards, architecture, procedures, and metrics”. (Sun, p. 5) Companies must develop short term and long term strategies before they begin to create a data governance program to ensure that the company’s strategic initiatives are inline with the data governance program.

Create a data governance program:

According to Jeff Bertolucci author of, Data Governance Plans: Many Companies Don’t Have One, “Forty-four percent of companies don’t have a formal data governance policy, and 22%...have no plans to implement one”. (Web, 4/28/16) Many corporations have databases abundant with customer information, and data that can be used for business analytics, such as “behavior tracking, audience measurement, ecommerce, and other aspects” (Web, 4/28/16); however, corporations do not see the value in the data they contain, specifically as an asset of the company. The amount of information contained within a company’s infrastructure is essential to many businesses to continue to gain market share and increase revenue by servicing or selling to existing or new clients. Corporations do not understand that they could be losing out on gaining market share by not having the correct processes in place to effectively utilize the company's data. As such, many corporations do not see the value in putting forth any effort to evaluate their current processes regarding the data they contain, and do not realize that their data governance in relation to the data they contain is insufficient.

Many corporations current processes use antiquated systems or language making it difficult for ETL to be successful for all end users. According to 5 Data Governance Mistakes, “data governance is a business-driven process”. (Web, 4/28/16) Without evaluating current processes, and developing a long term strategy for growth of big data, many companies will not be successful in implementing a data governance program. Because companies have expanding collection of data, it is important to control the data with a data governance program to “ensure ongoing compliance with corporate standards and requirements”. (Web, 4/28/16) According to Jeff Bertolucci, he claims that “Organizations should develop a formal data governance policy of reevaluate its current plan...can only increase the value your organization derives from its data”. (Web, 4/28/16)

Establish a corporate culture:

In order to use data to its fullest potential, it must be collected and transformed into standardized formats for all departments to be able to make use of the data, also known as ETL: extract, transform, and load. One of the impediments to data governance when collecting the data is there is no “data dictionary” that specifies the formula for which to collect the data, so that all departments are collecting the data in the same language. There are two causes to ETL inconsistencies: expanding collection of big data, and a shortage of knowledgeable work force. Data continues to grow at an extravagant rate, however, it is growing faster than the amount of work force trained. Therefore, many companies continue to have discrepancies between departments because the data is overwhelming and the level of skilled workers is decreasing. Furthermore, in order to develop a good data governance program both IT and business need to work together to encompass the corporate culture.

Appoint a steward:

In order for a data governance program to be fully realized, ownership of the program is essential to ensure the long term goals are met by appointing a data governance steward. The stewards responsibility is to oversee the entire process from creating the program to monitoring the program to the company's long term strategies. The stewards main objective is to “ensure effective control and use of data assets” (Sun, p. 16). The steward must also ensure that they are “showcasing good examples, instilling sustained enthusiasm, and promoting desired cultural changes within an organization”. (Sun, p. 17)

Obtain executive sponsorship:

Executive sponsorship involves C-level executives to enact a data governance program because it takes a great deal of strategy and money that must be integrated with the company’s vision. Without the sponsorship of the C-level executives the data governance program will not be fully realized. According to the article Houston, We Have a Problem” - Why Data Governance Programs Fail, “the key to success is the sustainment of sponsorship”, (Web, 4/28/16) for it is the sponsorship from executives that can ensure the program has the budgeting to continue, and the power to remove any roadblocks to the program. The article Enterprise Information Management: Best Practices in Data Governance, “Without strong backing from the executive level, none of the above will happen.” (Sun, p. 16)

Implement the program, and monitor benchmark progress:

Lastly, implementation impediments restrict the company from incorporating a data governance program, such as policies and procedures. Many governance programs fail because companies do not see the value to incorporate the programs into policies and procedures that must be followed enterprise wide. According to Data Governance and Stewardship, “Data stewards are responsible for monitoring data-related activities for compliance”, and the “data governance committee should track program implementation progress with key metrics, and periodically report...to stakeholder”. (Web, 4/28/16. Any program that is implemented whether it be data governance or any other program needs to be evaluated to ensure that the initiative is achieving benchmark targets, and continues to be in line with corporate strategies, drivers, and standards.

Section Two: Mobile BI

User Expectations:

                For firms that have the vision and knowledge to leverage mobile BI, mobile BI can add a lot of value. For those that only use it because everyone else is using it, or because it’s the next “big thing” it can be a waste of time and resources. If not done right it can become a hindrance to productivity and add to employees’ workloads. One natural gas company has found a way to combine locational intelligence and mobile BI to help users make decisions in the field on whether to make rights-purchase offers to owners on the spot. When the company receives a call from an owner looking to sell gas rights the company can look up which agent is closest and send them out to check out the property. On site the agent can use the app to look up the nearest pipelines and refineries to decide if it’s worth making an offer. The app allows users to make offers on the spot before the competition has time to react. Most other companies still have to return to the office to perform their analysis. Agents using the app can make offers on the spot which often results in purchasing the rights for lower prices (2.19). Instead of a mobile app to help employees make decisions Starbucks has implemented an extremely successful app that allows customers to purchase, pay, and receive promotions. In 2015 21% of all US transactions happened through the app. In the future Starbucks plans to offer recommendations and promotions based on customer history, preferences, and location. One interesting facet of Starbuck’s future strategy is to monitor customer’s individual privacy preferences and only send as many promotions as each customer is comfortable with (2.13).

                The previous two examples illustrate the value that mobile BI apps can add, where companies run into problems is when they implement mobile BI apps without a clear business need or lose focus of the business need and start making the apps too complicated. Mobile BI tools like other forms of business intelligence need to have a clear business need and be designed for specific types of users. An executive and a field agent are going to have very different needs. One mobile BI application will not be able to meet everyone’s needs, especially considering the size and use complications of mobile devices. Companies also tend to want mobile BI tools to be free or close to free along with their main business intelligence tools. This does not give vendors much incentive to invest in these applications (2.1). More and more companies and employees expect to be able to have access to information anytime anywhere on any device. Mobile BI users want to be able to have access to the latest data, be able to perform analytical procedures, and be able to move easily between devices. To meet these expectations mobile BI tools must work similarly to existing desktop programs and have comparable processing capabilities. The solutions to meeting user expectations includes rethinking how people should and could work in today’s environment, having a clear vision of how the application will meet a specific business need and user type, and training employees on what to expect and how to use new mobile tools. The next section Design is an impediment to mobile BI when done wrong, but is also the most crucial step to meeting user expectations.  

Design:

                More than any other form of business intelligence the usability and success of mobile BI tools depends on the quality of its design. Mobile BI tools are inherently limited by small screen sizes, touch screen capabilities, and inferior processing capabilities of the mobile devices they run on. The biggest contributor to poor mobile BI design is the expectation that mobile BI tools will be able to do everything that desktop applications do. This leads developers to design mobile BI tools that are “shrunk” down versions of traditional applications. On a mobile devices this results in clutter, slow processing speeds, and applications that are difficult to maneuver. Another contributor to poor mobile BI design is unfocused or changing expectations of what each mobile application should accomplish. Clients and developers often fall into the trap of over providing or designing for many types of users at once. Again this results in clutter, slow processing speeds, groupings of unrelated information, and applications that do not meet the needs of users.    

                To design a successful mobile BI tool there must first be a specific business need and a specific type of user identified. Executives, middle managers, and employees will all need very different types of data. Clients and developers often fall into the trap of designing catch-all apps that attempt to meet every need and type of user. Mobile BI tools are most successful when they are simple and include only the most important one or two elements. This can be accomplished by designing multiple small highly focused applications that are connected in the back. As users need more information they should be able to tap on specific elements to drill down for more detail (2.2). With this type of design each app can focus on a specific need and user and as user needs change they can easily switch between apps. An example of this is Tinder on the front end users only have to swipe to find what they are looking for all of the details are kept in the back. This results in an application that is uncluttered, easy to use, and effective (2.1). Keeping applications small also improves their processing speeds (2.20). To optimize processing efficiency mobile BI design should have similar back end architectures to other business intelligence systems with elements such as database denormalization, indexing, and summarization. As with all business intelligence initiatives it is important to invest in data quality, aggregation, and governance initiatives (2.9).  

                While mobile BI tools should not be mini versions of desktop applications they should feel similar to users. Actions such as doubleclick, pinch, and swipe should be consistent with other desktop applications and commercial mobile applications (2.11). Successful mobile BI design revolves around breaking information into bite-sized pieces. Having multiple tabbed pages with previous and next navigation buttons is a good way to breakdown information. To make reports more interactive and to add drill down capabilities design elements such as taping column headers to sort data, and pressing +/- to expand or collapse elements are all important (2.2). Peer collaboration is an important part of any business and something that mobile devices are naturally good at. Adding elements that allow users to leave comments connected to visuals and reports can add a lot of value to mobile applications. Similarly being able to send screen shots of reports along with notes can be extremely useful to users. Other basic design concepts for developing mobile BI applications are logical data groupings, positioning the most important elements in the top left corner and working right and down, filters, contrast, charts and visuals, large buttons, and creating on a small form scale (2.10, 2.11, & 2.18). It is also important to utilize blank space between elements this make reports easy to read and buttons and elements easier to navigate. Another important design concept is to deliver the same HTML code regardless of size and use CSS to rearrange the elements based on the width this allows mobile BI applications to adjust to various screen sizes (2.18). Similarly charts should show more or less detail depending on the resolution and size of the screen with the ability to zoom in and out for more detail (2.19). Systems like AngularJS also provide two-way binding which allows the application to “listen” for screen resizing. The next time the application loads it will use this information to automatically load the element to the right size (2.19). Another important consideration is what platforms employees will be using and how to design for them. Currently there are two main approaches for mobile app development browser based and native based. Browser-based is compatible with all mobile browsers, but has less functionality. Native based has greater functionality, greater storage capacity, and has offline capability, but is not universally compatible with all platforms (2.15). Firms must decide which approach best fits their needs. If they would like a high functioning application they should develop using the native based approach and buy each employee the devices they will be expected to use for work. This way they can use and design for a consistent platform throughout the organization. There are many design elements to consider when designing mobile BI tools. It can be tricky to decide which KPIs and visuals will add the most value to users. The most important thing to remember is the business need and the specific user who will be using the application. Remembering this will guide developers to the data they should include. After that the most important thing is to keep the application simple and easy to use by breaking down information by pages or into different applications.  

Creation:

                Business intelligence is the process of developing actionable decisions through data analysis. Mobile BI accomplishes this well when the decision only requires consuming information from previous analysis. But if users need to create new information mobile BI tools can be infuriating to use their small screens and touch operated controls make creating reports or visuals nearly impossible. So far the traditional mouse and keyboard far surpass touchscreen in ease of use and speed. Other factors that make creation difficult on mobile devices are the inherent limitations of storage space and processing capabilities. Right now most users use mobile devices to check information while they are in the field or on the move, but have to return to their desktops or laptops to write reports or perform analysis (2.4). The lack of flexibility to create is a huge impediment to mobile BI, as it only accomplishes a small portion of what business intelligence should be. Tablets can be used and are a good middle ground with larger screens and attachable keyboards and mice, but why not just bring a laptop. For mobile BI to become more than just a way to view a few KPIs and visuals creation of at least simple reports will have to become easier. For this there are no simple solutions. To work around the limitations of mobile BI tools it is extremely important for mobile BI tools to stay in sync with desktop applications so that users can work seamlessly between discovery and creation on multiple devices.

Security:

                With mobile BI come additional security risks. Mobile devices are easier to steal, lose, hack, and can end up anywhere. An example of the types of risks that mobile BI apps bring is the vulnerability found in Apple’s iOS sandbox applications used by enterprises to automatically share applications, configuration settings, and access rules with company devices. The vulnerability was created in the process of installing the application and setting up the MDM (mobile device management) account for each new device. During this process their configuration settings including URLs, usernames, and passwords are stored in a “world readable” location. This means that the same configuration that allows the company to monitor and control company devices also allows other applications installed on the device to read this sensitive information. After the vulnerability was discovered Apple hardened the sandbox profiles of apps with managed preferences in the iOS 8.4.1 released August 13, 2015 (2.16). This example illustrates how difficult it can be to secure mobile devices. To further complicate matters many employees want to be able to use their own devices for work. Companies have to balance policies that allow employees freedom to use their devices as they wish, while still keeping company data secure.  

                There are many ways to improve the security of mobile BI tools. One of the best things companies can do to protect themselves is to buy employees the devices they will be expected to use. This gives companies more leverage and enforcement power over employee devices. Buying employees’ devices allows companies to choose the carrier, put different application on different tiers of service quality, and monitor and control the applications on each device. Specific security measures include application passwords, authentication requirements for specific documents, blocking saving options, encryption, blocking screen shots with blank screen masks, low resolution thumbnails, and smudging capabilities. Other options include tethered mode which only lets users that are connected to a server access documents or applications and deletes information when the connection is lost. Application time outs let employers to set a time limit on inactivity after which applications are frozen and users must re-enter passcodes to regain access. Blacklisting allows the company to block certain people and devices from connecting to the server and applications. Along these lines are data wipes which wipe data from the device when it tries to connect to the server. These security measures are not completely foolproof, but do give companies a certain level of security over their devices and applications. It is important to note that to be most effective security measures should be “baked into” the applications themselves as they are being developed. Security measures from third party providers as an afterthought are rarely as effective as measures developed to work within a specific application (2.1).

Section Three: Big Data

Section Four: Visualization

Visuals can be poor:

                Even with the best intentions, visuals can still lack the necessary aesthetics to help make sense of data.  Poor visuals create a distraction for the users and eventually waste money and time for everyone involved.  Focusing on the key elements of design and researching what visuals make the most sense will help.  Investing in better displays can enhance the system's capabilities, but should only be used if it makes financial sense (4.12).  If the visuals can’t be designed to what the overall strategy requires, visualization should not be used at all.  Visuals can be amazing in design, but sometimes they don’t work with the data.  If patterns and judgments can’t be made from the visuals, the visuals aren’t doing their job.  Making sense of the data is the whole point of visualization.  Either restructuring the data or redesigning the visuals to make more sense with the data will create the benefit.  This can be done through programming and data science.

                Other times the visuals have the capabilities that are required, but they don’t fulfill the quality standards.  Fortunately, the poor visuals can be improved with investment in higher quality and better designers.  The hardest aspect of the visuals is making sure they make sense, quality visuals is part of that.  No matter how well the data is structured, it still needs to be presented in a pleasing way.  Focusing on aesthetics will help improve any visualization strategy.

The visuals aren’t useful:

                With the vast size of data and its complexity, every aspect of a visual needs to have a useful purpose.  Graphs, charts, and other visuals need to present the information in such a way that makes sense to all users.  The visuals can be beautiful in design, but make no sense to the user.  When developing a visualization strategy, the goals of the data need to be aligned with how the visuals will present them.  If the data is clean and organized, what will be the best way to show patterns?  Keeping questions like this in mind will help during the development process.  Historically, visuals have been used to display information for hundreds of years.  Studying past patterns and uses of graphs and charts can help formulate a strategy for any visualization (4.1).

                Aligning the organization's strategy with visualization can focus on a few aspects specifically.  The desired patterns of the organization can be displayed via graphs and charts.  The progress of the company statistically can be displayed in number format as well as graphs and charts.  Focusing on the best ways to visually understand something will make the whole integration easier.  If customer satisfaction is important, having a visual that shows the average that day would be useful.  Showing a pie chart to represent customer satisfaction data wouldn’t make sense in this situation.  Also, being aware of what the current trends in visual technology are can help avoid useless visuals.

The visuals aren’t fast enough:

                The speed and processing required to formulate data into a visual pattern can prove overwhelming for many systems.  Many visuals aren’t capable of keeping up with the speed and complexity of the data.  Organization sometimes jump into making visuals without giving them the proper allocation of resources.  After the visuals are completed, they aren’t able to process the real time data and eventually fail in their goal (4.3).  Instead of jamming a bunch of data into underperforming visuals and dashboards, organizations need to start smaller.  By focusing on smaller portions of data, the visuals can produce more accurate information and process the data much quicker.  When ready, an organization can slowly implement larger amounts of data while still maintaining the speed and quality.  The resulting focus of integration can benefit the entire system and help decision makers be better informed.  

Section Five: Security

Data Security (General)

                “The base of modern information systems has been altered. The structure of primitive economic areas has totally changed and new types of economic growth have appeared. Basic Areas such as Education, Banking, National Defense, and Health Care have been fully transformed. Their main structure has been totally digitalized in order to be adapted to the needs of the new century. Traditional Operational Areas, where the human factor was necessary, have been totally reconstructed minimizing or eliminating the participation of humans.” So just how secure are the information systems that bind together the structure of modern America’s economic structure?

A data breach is an intentional or unintentional release of secure information to an untrusted environment. In 2014 the estimated average organizational cost of a data breach was 5.9 million dollars, meaning each individual compromised record costed the company $201.00. These costs that companies accrue during a data breach do not only include the value of the compromised records but also the abnormal turnover of customers, the increased customer acquisition activities, and the reputation losses and diminished goodwill. Most commonly company’s sensitive records are compromised by hackers, cyberattacks, their own employees, and vendors/other third parties. In order to reduce the chances of becoming victims of a data breach, common solutions inquired by companies include increased encryption protection through the use of pendrive password protection software, and strong cryptography methods such as PGP, AES, and other strong algorithmic encryption types.

Hackers

                A hacker is someone who has a high level of skill in computer technology or programming and uses their skills to circumvent security and gain unauthorized access to networks, computers, files, etc. Hackers usually operate with malicious intent and are capable of stealing highly sensitive information anonymously; however some hackers claim to ethically hack in order to exploit vulnerability and weaknesses within a business, forcing them to increase their security measures. Hackers use a variety of resources such as hacking tools, malware samples, source code, hacker forums, etc., in order to access and exploit secured data without authorization. The most dangerous hackers to businesses are those operating with the intentions to steal. Cyber theft comprises crimes in which a computer is used to steal money or other things of value. Cyber theft includes embezzlement, fraud, theft of intellectual property, and theft of personal or financial data.

So how vulnerable are businesses when it comes to data breaches? Security experts like to say that there are now only two types of companies left in the United States, those that have been hacked and those that don’t know they’ve been hacked. Recently Verizon released a report stating that the company counted 621 confirmed data breaches last year, and more than 47,000 reported security incidents. The majority of these hacks were due to distributed denial of service attacks, in which hackers flood a site with traffic until it falls offline, but do not necessarily break into a company’s network. This is just one of many common methods hackers use to exploit sensitive information.

In this day and age since nearly all information is transmitted or stored via the internet, it can be said that all businesses and all data are highly vulnerable to being hacked by cyber-criminals. A few simple ways that businesses can reduce their chance of being hacked are by being aware of suspicious emails, checking link locations, by using two-factor authentication, by using advanced passwords,  by not sharing sensitive data while connected to public Wi-Fi networks, and by being wary about what’s shared or stored on cloud servers. With that being said, hacking is a phenomenon which is deeply rooted in the world of computing and will probably never die, so businesses must continuously seek out new security measures in order to keep sensitive data out of the hands of the wrong people.

   Cloud Computing Vulnerability

A cloud is a network of remote servers hosted on the Internet and used to store, manage, and process data in place of local servers or personal computers. One of the major issues in cloud computing currently is finding a way to achieve mutual authentication so that both of the parties authenticate themselves to the other before the communication begins. “Proper authentication is an essential technology for cloud-computing environments in which connections to external environments are common and risks are high,” One solution to this issue is to implement a protocol that achieves mutual authentication using steganography and secret sharing. This protocol would use out-of-band authentication, secret sharing, steganography, and mutual authentication with password change options in order to build a resistance to DoS attacks, masquerade attacks, replay attacks, and insider attacks.         

Mobile cloud computing is another type of cloud computing that is commonly used by employees who are constantly on the go or businesses that cannot afford a private cloud. Mobile cloud computing uses a combination of mobile devices, cloud servers (cloudlets), and wireless networks to store and transmit data. For example, an employee accessing company emails through Gmail on a mobile device. The goal of mobile cloud computing is to enable the execution of rich mobile applications on a variety of mobile devices, with a rich user experience. The reasoning behind the use of mobile cloud computing is that it provides business opportunities for mobile network operators as well as cloud providers. Coinciding with the ease of access that mobile cloud computing provide to its users is the increase in security concerns. The security concerns include insecure data storage, weak server side controls, insufficient transport layer protection, client side injection, poor authentication and authorization, improper session handling, security decisions via untrusted inputs, side channel data leakage, broken cryptography, and sensitive information disclosure. One proposed solution to the lack of inherent security measures in mobile cloud computing is proposed by authors Niroshinie Fernando, Seng W. Loke, and Wenny Rahayu. Their proposal aims to provide a security services architecture by acting as an intermediary for identity, key, and secure data access policy management. This is achieved by protecting information belonging to mobile users through the use of security isolations, and by assessing risks through the monitoring of the mobile clouds centralized data collection and processing. By doing so the risk management service can identify malicious nodes and take preventive measures according to estimated risks.

Cloud computing provides businesses, and employees remote access to data, reduces costs, improves business agility, provides self-service, provides on demand IT, and much more. However, although mobile cloud computing provides many beneficial services to businesses and end-users, it also has many security vulnerabilities that need to be considered in order to properly secure sensitive data.

Unauthorized Access by Human in the Environment

Commonly businesses focus the majority of their security efforts on the security of their servers, and networks and often forget about the threat of unauthorized data access by humans within the environment such as employees, IT persons, etc. Protection of in house information systems that store critical company information is vital in order to keep sensitive data out of the hands of untrustworthy individuals who have physical access to this data. Often, critical company data is shared with company stakeholders, or stored on information systems that are physically accessible. This leads to the issue of data leakages that are often undetectable due to limited traces of unauthorized physical access to databases. One proposed solution to this issue is to decrease data accessibility by implementing a lattice of rights model. A lattice of rights model breaks data into four categories; public, confidential, secret, and top secret. The model then proposes the use of a variety of security mechanisms that will be used in order to allow and restrict the access of data by subjects based upon a need to know basis.

Although there is no foolproof mechanism in place that can entirely secure all data from being leaked or used without authorization, the lattice of rights model provides a basic structure in which a company can modify in order to appropriately secure their data.

Works Cited

Lisa Bella, Section One: Data Governance

Brittany Harrison, Section Two: Mobile BI

Marie Ross, Section Three: Big Data

Kameron Torres, Section Four: Visualization

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to put the scale and flexibility of cloud data warehousing in the hands of data         professionals.  Retrieved from http://www.marketwired.com/press-release/                snowflake-computing-tableau-software-partner-put-scale-flexibility-cloud-data-        warehousing-nyse-data-2108079.html

4.4        Hall, S. (2016, February 29).  How do you know IT costs too much?  Retrieved from         http://ww2.cfo.com/it-value/2016/02/know-costs-much/

4.5        Woodie, A. (2016, January 26).  Data quality trending down? C’est la vie.  

Retrieved from

http://www.datanami.com/2016/01/26/data-quality-trending-down-cest-la-        vie/

4.6        Ring, D. (2015, June 30).  Cultural readiness vital for HR data analytics.  Retrieved

from http://searchfinancialapplications.techtarget.com/news/4500249076/Cultural-

readiness-vital-for-HR-data-analytics

4.7        Gatte, T. (2016, February 26).  Are you struggling with your data?  Retrieved from  

http://        www.cio.com/article/3037942/leadership-management/are-you-still-struggling-        

With-your-data.html

4.8        Pohlmann, T. (2016, February 16).  What design thinking means for data and

analytics.  Retrieved from http://www.datanami.com/2016/02/16/what-design-thinking-                

means-for-data-analytics/

4.9        Kernochan, W. (2013, May 13).  4 barriers to big data success — and ways to                 overcome them.  Retrieved from http://www.enterpriseappstoday.com/business-                intelligence/4-barriers-to-big-data-success-and-ways-to-overcome-them.html

4.10        Wood, C. (2014, December 11).  Open data visualization tool challenges traditional

GIS.  Retrieved from 

http://www.govtech.com/data/Open-Data-Visualization-Tool-Challenges-Traditional-GIS.html

4.11        Castle, S. (2015, November 12).  Why overhyping data visualization will cause your

BI project to fail.  Retrieved from http://venturebeat.com/2015/11/12/why-                        overhyping-data-visualization-will-cause-your-bi-project-to-fail/

4.12        McKenna, B. (2016, March 17).  Data matters.  Retrieved from http://                        www.computerweekly.com/blogs/Data-Matters/2016/03/beauty-is-not-truth.html

4.13        Burns, E. (2015, September 1).  Effective big data visualization starts with simplicity.          Retrieved from http://searchbusinessanalytics.techtarget.com/feature/Effective-                big-data-visualization-starts-with-simplicity

4.14        Krause, W. (2016, April 4).  Advance analytics in healthcare:  The challenges and                 benefits.  Retrieved from https://icrunchdatanews.com/advanced-analytics-                

healthcare-challenges-benefits/

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4.16        Loshin, D. (2014, March 1).  BI dashboards need measured approach on data                 visualizations.  Retrieved from http://searchbusinessanalytics.techtarget.com/tip/                BI-dashboards-need-measured-approach-on-data-visualizations

4.17        Cotgreave, A. (2016, February 16).  To move forward with data visualization, look         backwards.  Retrieved from http://www.computerworld.com/article/3032653/data-        analytics/to-move-forward-with-data-visualization-look-backwards.html

4.18        Myers, J. (2016, February 19).  Be careful when implementing data warehouse                 automation.  Retrieved from http://www.infoworld.com/article/3035476/data-                warehousing/be-careful-when-implementing-data-warehouse-automation.html

4.19        Miseta, E. (2015, July 16).  How one bio company tackled its data visualization                 challenge.  Retrieved from         http://www.clinicalleader.com/doc/how-one-bio-                company-tackled-its-data-visualization-challenge-0001        

4.20        Cotgreave, A. (2015, November 19).  Living with data.  Retrieved from http://                www.computerworld.com/article/3005740/data-analytics/why-do-we-visualize-                data.html

Christian Whiles, Section Five: Security