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The client for this particular project is Vista Concept Dimensions who are looking to implement a data analytics tool in their system. They are currently facing problems due to their manual data analytics process and hence, they require a new automated analytics system. The main objective of this particular project is to identify only one data analytics tool that is the best option for all the business organizations. Hence, in order to conduct the project, questionnaire survey method was used. Collection of secondary data from the organizational records of the analytics tools was rejected due to one particular reason: biasing. Developers of each of these tools always claim their product to be the best in the market. Hence, the target in this research was to capture actual user experience data to identify the best analytics tool available. The decided method to undertake the survey was by using questionnaires where 15 questions were asked. The target audiences of the survey were different organizational leaders for small scale organizations and organizational management staff for the large scale ones. The interview was conducted in form of direct interviews (if possible), video calls and online distribution of questionnaire copy.
This report is based on the project conducted on data analytics in business with special emphasis on IBM Watson Analytics tool. The survey data has been analyzed in order to identify the specific feedbacks on the IBM Watson Analytics tool as it is the main point of focus in this particular research. In addition, other analytics tools were also analyzed in order to determine whether IBM Watson is better than them in terms of services and features or not.
The main problems that need to be addressed in course of the project are as follows.
P1: The existing data analytics system has very low efficiency as it is mostly handled manually.
P2: Currently, Microsoft Excel is used to store business data that has large number of limitations.
P3: In the client’s existing manual analytics system, due to lack of sufficient data analytics results, the designs cannot be produced of the best quality.
The stakeholders of the project have been identified as follows.
Project Manager – To manage the different aspects of the project
Project Supervisor – To supervise and monitor the progress of the project
Financial Support – To provide sufficient financial support for the project
Sponsor – To provide the overall budget for the project
Software Developer – To develop the chosen analytics tool in the system
Software Tester – To test the developed software for bugs and glitches
The objectives of the project are as follows.
Data Analytics is a process by which different pieces of data are analyzed in order to extract a particular piece of information from it. Before the availability of advanced technology, data analytics were done manually. However, after the advancement of software technology, data analytics is now software driven. There are a number of software tools available for data analytics including IBM Watson Analytics, Pentaho, Clic Data, Tap Clicks and others.
According to Tsoi et al. (2017), data analytics is an important aspect of any business that wants significant progress in today’s market. Initially, the analytics operations were done manually and as a result, there were numerous errors in the calculations. However, after the arrival of the softwares, this process has now become much more easier. The data analytics softwares can now efficiently carry out any analytics operations within a matter of microseconds.
Hoyt et al. (2016) said that the software data analytics tools must be implemented by the business organizations in order to progress in modern market. Most business organizations utilize these analytics tools for business operations that are related to the use of numerous pieces of data and information.
Guidi et al. (2016) promoted the use of Pentaho by publishing data sets that it can utilize Big Data completely and its values are accelerated by NoSQL, Hadoop and other big data platforms. This tool integrates all the data collected on a specific subject and then classifies it using its customization analytics tool. It then analyzes the data and provide reliable business insights and suggestions based on the information contained in the data.
Lak et al. (2016) discussed about other tools like Clic Data and Tap Clicks. According to them, Clic Data has different and unique features than the other existing ones. Clic Data also acts as a data visualization and business intelligence tool that also collects, analyzes and cleans the data according to the requirements set by the user. It can also set up visual indicators in the data as well as KPIs and other key metrics so that the user can easily share the result data with the clients and colleagues. They also said that Tap Clicks is designed to provide marketing analysis services rather than only data analytics i.e. it is specific to marketing data for the analytic functions. Moreover, it is also used to create a workflow plan and order management plan that are some of the most essential parts of some business organizations.
Aggarwal and Madhukar (2016) recommended the use of IBM Watson by saying that it is by far the best tool available in today’s market. According to them, IBM Watson can efficiently determine a pattern or sequence within a specific set of data without any difficulty. IBM also has IBMSPSS predictive analytics and data science experience tools that further enhance the features of the data analytics tool.
In order to conduct this research, the use of some relevant theories and frameworks were necessary. The main framework that was utilized in this research was Technology Acceptance Model (TAM). The use of this particular framework helped to analyze how a data analytics tool like IBM Watson will benefit the given company under the field of the research.
In order to conduct the research, questionnaire survey method was used. Collection of secondary data from the organizational records of the analytics tools was rejected due to one particular reason: biasing. Developers of each of these tools always claim their product to be the best in the market. Hence, the target in this research was to capture actual user experience data to identify the best analytics tool available. The decided method to undertake the survey was by using questionnaires where 15 questions were asked. The target audiences of the survey were different organizational leaders for small scale organizations and organizational management staff for the large scale ones. The interview was conducted in form of direct interviews (if possible), video calls and online distribution of questionnaire copy. Before starting the interview, the organizational members and leaders were contacted and prior appointments were made. Most of the organizational leaders actively helped by answering the questions accurately as per the requirements of the survey.
This survey was conducted among 150 members from different organizations and it has been found in the result that 70% votes were in favor of IBM Watson Analytics i.e. 70% of the people said they are currently using IBM Watson Analytics as they find it the most helpful for their data analytics operations. Moreover, from this survey, it has been found that the following reputed organizations are actively using IBM Watson Analytics in their data analytics department.
It has also been found that the business operations of these companies have been significantly enhanced by the use of IBM Watson Analytics tool. However, this is not due to only the use of this tool in the data analytics department. These organizations innovated their own virtual systems and connected it to the analytics tool that has revolutionalized their businesses. For instance, Staples provides a red button in their order processing system. By pressing this button, one simply has to utter “order me some red pens” or “order me some blue pens”. The IBM Analytics tool will instantly process this voice message and will order for the pens immediately. Similarly, the IBM tool enhanced the business processes of all the organizations by linking up the business processes and data analytics operations.
As discussed in the methodology section, the main data collection has been done from the survey interviews of different organizational members and leaders. However, before proceeding with the survey, an in-depth analysis of literature has been conducted. During this phase, the works of researchers based on the data analytics and the influence of software in data analytics have been studied. When sufficient data had been collected, the survey was conducted to capture data regarding user experiences on using software driven data analytics tools. Conducting both the data collection methods helped to reduce biasing of opinions as well as verification of the hypotheses that were proposed before the start of the research.
The data collected included the processes of manual data analytics, software driven data analytics, different analytic tools available in the market and the types of services and advantages they provide. In order to collect sufficient data for literature survey, online databases like google scholar and other online libraries. After the data collection for literature survey was complete, the survey was started. The interview was conducted in form of direct interviews (if possible), video calls and online distribution of questionnaire copy. Before starting the interview, the organizational members and leaders were contacted and prior appointments were made. Most of the organizational leaders actively helped by answering the questions accurately as per the requirements of the survey.
When all the data was collected from literature review and the survey, analysis was conducted using all the data. Firstly, a comparison was made between the literature data and survey data for verification of the hypotheses proposed before the starting of the project. After that, the survey data was analyzed in order to identify the specific feedbacks on the IBM Watson Analytics tool as it is the main point of focus in this particular research. In addition, other analytics tools were also analyzed in order to determine whether IBM Watson is better than them in terms of services and features or not. The data analysis results are as follows.
From the data analysis process, it has been seen there are a number of data analytics tool currently available in the market. Some of the most popular ones are discussed as follows.
IBM Watson Analytics – This has been found to be the most popular of all the data analytics tools available in the market. This tool was invented by IBM and has the features of data analysis and visualization within a cloud based virtual interface. IBM Watson analytics can refine, explore, predict and assemble the data. IBM Watson can efficiently determine a pattern or sequence within a specific set of data without any difficulty. It also provides complex cloud based services along with guidance and data prediction. IBM also has IBMSPSS predictive analytics and data science experience tools that further enhance the features of the data analytics tool. It also has cognos analytics in which it provides services as hosted solution via IBM cloud. In addition to achieving vista concept dimensions’ business client through the free choice, Watson Analytics additionally guarantees availability through representation and self-benefit usefulness.
Pentaho – Pentaho is another business data analytics tool that has been designed by Hitachi. Pentaho is slightly different from IBM Watson in terms of services and features. Pentaho mainly utilizes Big Data and its values are accelerated by NoSQL, Hadoop and other big data. This tool integrates all the data collected on a specific subject and then classifies it using its customization analytics tool. It then analyzes the data and provide reliable business insights and suggestions based on the information contained in the data.
Clic Data – This is another data analytics tool that some different and unique features than the other existing ones. Clic Data also acts as a data visualization and business intelligence tool that also collects, analyzes and cleans the data according to the requirements set by the user. It can also set up visual indicators in the data as well as KPIs and other key metrics so that the user can easily share the result data with the clients and colleagues.)
Tap Clicks – The final data analytics tool featured in this research is tap clicks that is specially designed to provide marketing analysis services rather than only data analytics i.e. it is specific to marketing data for the analytic functions. Moreover, it is also used to create a workflow plan and order management plan that are some of the most essential parts of some business organizations.
The main points are compared as follows.
| IBM Watson | Pentaho | Clic Data | Tap Clics |
| It utilizes a cloud based interface | It uses the functions of big data tools like MySQL, Hadoop, etc. | It uses normal online interface features | It uses normal online interface features |
| It identifies the patterns within the data and processes it accordingly | It classifies the data into different categories and then processes it | It analyzes the data only based on user requirements | It analyzes only marketing data |
| It can classify any kind of business data and hosts it in IBM Cloud | It utilizes various tools to analyze and store the data | It requires user input for classifying, analyzing and hosting the data | It hosts the marketing data on its own cloud interface |
It is the next generation, using software powered user experience; the automated data pattern detection system can be created. This will also accept the natural language query as it can handle both IT related data or business user content data. The main design pattern of the automated data analytics systems are such that the users can understand the working and features of the application easily. It provides strong analytic techniques that massively enhance the quality of the business operations of any organization. IBM also has IBMSPSS predictive analytics and data science experience tools that further enhance the features of the data analytics tool.
Of all these data analytics tools, it has been found that IBM Watson is the most popular tool and is used extensively by business organizations worldwide. There are several reasons as to why it is the most popular whereas the other tools discussed in the research also have their own unique features as well as intelligence. The several aspects for the reason of popularity of IBM Watson are listed as follows.
Analytics Intelligence – IBM Watson is one of the most intelligent tools in the market today. It helps the users to identify different patterns in a particular data set by automatic classification and reading of the data. Moreover, IBM Watson has support for multiple languages and hence, based on the entered user data, this tool can read through any language and perform data analytics processes. However, some researchers have suggested including more personalized data analytics feature so that the user himself can personalize the analytics criteria to be performed on a specific piece of data.
Recommender System (RS) – This is a unique feature of IBM Watson that provides personalized recommendations to the user after analysis of the input data. The bases of this recommender system are prior usages and analytics history set by the user in the system. Recently, a more advanced recommender system has been developed namely, Context Aware Recommender System where the tool will analyze the context of the input at a particular time and provide suitable personalized recommendations to the user.
Database System – Every analytics tool requires a suitable database to store the data it deals with everyday. IBM Watson has dedicated logging database powered by ElasticSearch. This ElasticSearch provides a browser based user interface namely, Kibana for viewing logs and exploring real time events. This feature is very useful for the business users who can easily extract data from the events that have been inputted into the database of the analytics tool.
The findings of the research are summarized as follows.
There are several data analytics tools available in the current market that include IBM Watson Analytics, Pentaho, Clic Data, Tap Clicks and others. Each one of them has its own unique and advantageous features that are utilized by different business organizations worldwide. IBM Watson can efficiently determine a pattern or sequence within a specific set of data without any difficulty. IBM also has IBMSPSS predictive analytics and data science experience tools that further enhance the features of the data analytics tool. It also has cognos analytics in which it provides services as hosted solution via IBM cloud. It also provides complex cloud based services along with guidance and data prediction. IBM Watson analytics can refine, explore, predict and assemble the data. Pentaho is another business data analytics tool that has been designed by Hitachi. Pentaho is slightly different from IBM Watson in terms of services and features. Pentaho mainly utilizes Big Data and its values are accelerated by NoSQL, Hadoop and other big data.
This tool integrates all the data collected on a specific subject and then classifies it using its customization analytics tool. Clic Data acts as a data visualization and business intelligence tool that also collects, analyzes and cleans the data according to the requirements set by the user. It can also set up visual indicators in the data as well as KPIs and other key metrics so that the user can easily share the result data with the clients and colleagues. Tap clicks is specially designed to provide marketing analysis services rather than only data analytics i.e. it is specific to marketing data for the analytic functions. Moreover, it is also used to create a workflow plan and order management plan that are some of the most essential parts of some business organizations. Of all these data analytics tools, it has been found that IBM Watson is the most popular tool and is used extensively by business organizations worldwide.
IBM Watson is one of the most intelligent tools in the market today. It helps the users to identify different patterns in a particular data set by automatic classification and reading of the data. Moreover, IBM Watson has support for multiple languages and hence, based on the entered user data, this tool can read through any language and perform data analytics processes. IBM Watson has dedicated logging database powered by ElasticSearch. This ElasticSearch provides a browser based user interface namely, Kibana for viewing logs and exploring real time events. This feature is very useful for the business users who can easily extract data from the events that have been inputted into the database of the analytics tool.
Based on the findings, the main problem statements can be addressed as follows.
S1: The main answer to the first problem statement is a software driven data analytics system. Among the many data analytics softwares available in today’s market, IBM Watson Analytics is the recommended for the purpose.
S2: The use of IBM Watson will solve this problem as well as it has its own database hosted in cloud server and hence, there will be no need for further use of excel sheets for data storage.
S3: Again, IBM Watson is the best possible solution. IBM Watson can efficiently determine a pattern or sequence within a specific set of data without any difficulty. IBM also has IBMSPSS predictive analytics and data science experience tools that further enhance the features of the data analytics tool. It also has cognos analytics in which it provides services as hosted solution via IBM cloud. It also provides complex cloud based services along with guidance and data prediction. IBM Watson analytics can refine, explore, predict and assemble the data. In addition to achieving vista concept dimensions’ business client through the free choice, Watson Analytics additionally guarantees availability through representation and self-benefit usefulness.
The analysis results based on the use of TAM were as follows.
Perceived Usefulness – It has been determined that the IBM Watson will be extremely useful in enhancing the business operations of the company as it will replace the manual analytics with faster, efficient and automatic solution.
Perceived Ease of Use – It has also been found that IBM Watson is very easy to understand and operate. Any person with sufficient training can expertly handle the different features of the analytics tool.
There are several limitations of this research that are discussed as follows.
Biasing – In spite of taking of several steps to ensure there is no opinion biasing for the review of IBM Watson Analytics, there are several deliberate opinion biases that are still existing within the research. This is because most of the managers who were interviewed for the personal usage experience of analytics tool, provided biases reviews on IBM Watson. From the research, it has been found that IBM Watson does have some unique features that help the users in business operations. However, they did not mention that these features are also available in other analytics tools as well. The main reasons for the preference of IBM Watson are brand value, low cost, guaranteed service in case of software failure and others. However, these are not highlighted by the interviewees and they all emphasized on the fact that they chose it because it is the most suitable and efficient analytics tool that they estimated to benefit their existing business processes.
Low Focus on Secondary Data – As discussed earlier, lesser focus has been given on secondary data that are collected from the different organizational data on their own analytic tools developments. Instead, the research was conducted based on actual user experiences. As a result, although sufficient data has been captured from the survey, real specifications of the softwares from their own developers have not been analyzed during the course of this research.
Lack of Focus on Other Analytics Tools – The final limitation of this research is the lack of sufficient focus on other analytics tools. It has been revealed in the research itself that there are many other tools that have their own unique features and advantages on business systems. However, since the main focus of this research is on IBM Watson, other tools have been mostly ignored except summarized discussions of some of them. Hence, there is a lack of sufficient comparison between IBM Watson and the other analytics tools like Tap Clicks, Pentaho, etc.
Finally, based on the overall analysis and research, the following recommendations can be provided for the future research activities.
More Focus on Competitive Comparison – Future researchers should focus more on competitive comparison of any analytic tool instead of focusing particularly one in order to find more about the competitive advantage of the chosen analytics tool. This will also further explain why one tool becomes more popular than the others at a certain period of time.
Preparation of Broader Hypothesis for Research – Again, another thing the future researchers should focus is the preparation of a broader hypothesis for research. This particular research is only narrowed down to some specific aspects as it is based on a case study and the problem statements were limited for the research.
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