Decision Analytics - Decision Making and Crowdsourcing

Question: Discuss about theDecision Analytics, Decision Making and Crowdsourcing. Answer: Data Analytics and Decision Making Data Analytics and Decision Making are two different terms that we will discuss here in this report. Data Analytics is a science of examining raw data and its purpose is to draw conclusions about that information. In various industries data analytics is used to allow companies for making better business decisions and to exist models or theories. The main focus of data analytics is inference and process of deriving a conclusion based on information. On other side, decision making is also an essential activity for business organizations and there are various critical decisions have to make at management level. In this case, data analytics can provide help to make decisions on the behalf of analytical data. As per the given information, I am in-charge of a medium-large organization XYZ Pvt. Ltd. This company provides software solutions to its customers and serving this service for long times. In this company different departments handle different operations such as finance, accounts, en gineering and logistics. In these departments, data analytics are used to perform required functions and as a manager I have analyzed that sales department is getting benefits from data analytics or in particular big data and it has become easy to make decisions on the behalf of data analytics report. Now in this report, I will emphasize on sourcing, refinement and exploitation of data, management and structuring of data and some specific barriers into success of organization (Smart Insights, 2014). How Data is Sourced, Refined and Exploited? Data analytics are helpful to source, refine and exploit data. In case of XYZ organization, data analytics has improved sales of this company by using sourced and refined data. By sales team members, key sources of information are used that work as strategic approach for sales department. In this case, data analytics helps to monitor customer activities and to tie them to specific sales efforts. In case of so many brands, the use of analytics helps to power business organizations to sales and marketing. By using data analytics, campaign sources can be used for tracking sales. Campaign sources consists of ad campaigns, use of adwords, email, social media and other special events such as webinars and conference presentations. But while implementing these campaigns time and budget are also important to manage. This management can be successfully done by refining best choices from given sources. It means company must decide that which option will be better among above given campaign opti ons for tracking sales of company. Other way to track the performance of various marketing channels is Google Analytics which is a most sophisticated way of self-service business intelligence platform. In this why, after sourcing and refinement of data, exploitation of data can be done regarding sales of business. Structure and Management of Project As we have discussed above that how data analytics is helpful to source and refine data properly, so implementation of data analytics for sales of company should be done effectively for effective decision making. Now here we will emphasize on appropriate structure and management of this project of data analytics (Salesforce Blog, 2016). The structure of this project will consist of following points: Find Analytical Approach that works Best Generate Insights by Integrating Capabilities Make an Analytical Approach The main purpose of above structure of data analytics to handle sales of company and these main structural points work as below: Find Analytical Approach that works Best According to this first structural point, it is necessary for every company to work out pros and cons of all appropriate analytical tools and it is necessary to gain the most effective marketing mix. The main choices that comes to non-direct marketing consists of reach, cost and quality (RCQ). Here RCQ reduces each touch point into its component parts and here structured judgment and data is required. These parts include quality of engagement, target customers and cost per unique touch point. Other essential method that is used to find analytical approach is attribution modelling. This technique or method has become so important for marketing execution and online media purchasing. Attribution modelling is that where a set of algorithms or rules manage that how credit for conversion of traffic to sales can meet to various touch points. These basic touch points are online ads, social media feeds and email campaigns. Generate Insights by Integrating Capabilities Some business organizations use single analysis technique but to get better results there is requirement of using MROI. This approach includes data and insights from direct responses. This method allows business leaders to be flexible in any movement in budgets that are made to perform various essential activities. Make an Analytical Approach Analytical approach is important for every department in a business organization. As we have discussed that how sales department of XYZ Pvt. Ltd has got benefits from analytical approach, so this approach should be center point. This will be helpful for organizations to perform various business activities effectively. In this way by following above discussed structure and by managing data analytics approach, company can handle sales activities in an easy way and effective outcomes can be found. Now in next segment of this report, we will emphasize on specific barriers to success in organization. Barriers to Success of Organization As we know that data analytics is beneficial for business organizations to handle its activities of different departments. But here are some barriers in usage of data analytics that are also known as Big Data barriers and those analytics are listed as below: Reskilling Delivering Cultural and Communication Barriers Handling Integration of new Types of Data Reskilling: This process of reskilling the workforce take advantage of Big Data Analytics which is a barrier in implementation of data analytics (Enterpriseappstoday.com, 2016). Delivering: Delivery of clear business case to optimize big data strategy is also considered as barrier for data analytics. Cultural and Communication Barriers: Cultural and Communication barrier within and between IT and other business is also a big problem in implementation of data analytics. This problem must be resolved to achieve a synchronized strategy. Handling Integration of new Types of Data: It is necessary to flood in as Big Data is implemented. Crowdsourcing Beyond the Firm Here in this segment of report we will discuss about an article of crowdsourcing and emphasize on some essential concepts of crowdsourcing such as how in normative decision model crowdsourcing is used, overcoming of conventional decision making problems or distortions through crowdsourcing and objections and resistance to use crowdsourcing. Before discussion about crowdsourcing, it is necessary to know that what crowdsourcing is. It is a process of using a large group of people and general public to accomplish a required task. To possible crowdsourcing at grand scale, internet has made it possible at grand scale. How Normative Decision Model is used by Crowdsourcing Normative Model is basically used for decision making and it predicts the effectiveness of decision making procedures. This model is made by Victor Vroom who is professor at Yale University. As we know crowdsourcing is a process of accomplishment of required task with the help of a group of people. In this case, effective decision making is required. In this case, normative decision model will be helpful for crowdsourcing. In case of cloud sourcing, normative decision model will tell about how people should make effective decisions. This model is helpful to evaluate change or performance, searches for alternative answers to question and it also provides standards that how things and tasks should be done. These all factors are required in case of crowdsourcing to make completion of tasks. So crowdsourcing uses this model to make all critical decisions that are required to fulfill task and in this decision making, cost, time, components and other elements of project can be considered ( Gaudino, L. rarr; 2014). Conventional Decision Making Problems or Distortions In any field, decision making is not an easy task to perform. Critical decision making requires strategic approach and appropriate information about project. Today various new techniques and tools are available that can be used by people to make decisions and in case of crowd sourcing those techniques can also be used. But in case of conventional decision making in crowdsourcing, some problems and distortions are faced that are listed as below: Lack of confidentiality is found in management information that is required for decision making. Due to this, some information is not available on time and destroyed by hackers. This is big problem with conventional decision making. In earlier days, not proper or accurate data is available that is collected through data analytics. That is why not so much accurate result is found. Other barrier is lack of experts those have experience to collect, refine and exploit information. In conventional decision making, no proper availability of experts may lead wrong decision making and leadership. In conventional decision making, big data or data analytics were not used properly due to its lack of availability. These are some barriers that commonly observed in conventional decision making in case of crowdsourcing. Due to these barriers, various problems have faced in accomplishment of task in crowdsourcing (Harvard Business Review, 2014). Objections and Resistance to use Crowdsourcing for making Decisions According to analysis about crowdsourcing, we got to know about some limitations or objections in crowdsourcing or failure. Crowdsourcing is considered to be nightmare when it is not properly handled. Following are some objections or resistance regarding crowdsourcing for making decisions: The first objection is regarding unfairness. Sometimes it is found that crowd feels Crowdsourcing Company is not treating fairly or provided information by them is not sufficient. In this case they can raise objections and crowdsourcing will not be considered for decision making. In process of decision making, information plays an important role and without accurate information, it is difficult to make effective decisions. Besides above problems, other source of crowd resistance consists of manipulation. Manipulation is considered particularly dangerous for crowdsourcing companies and this is happened when members are totally disagree with companys rules or actions. These rules, actions and policies are important and these must be made with agreement of all members of business organizations. Next objection or resistance that is apart from crowd resistance is that company could be affected by bad quality of the results that are delivered by virtual employees. The reason for this object or resistance is failure of company to provide clear instructions to the crowd to filter applicants. These are some particular resistance or objections to crowdsourcing and due to this most of the members are agreed to work with crowdsourcing companies and with their decision making. After this whole discussion we can say that data analytics, decision making and crowdsourcing are some essential concepts that are commonly used in business organizations. The implementation of these important concepts is important for business organizations to perform various business activities. But before start its implementation above discussed points must be considered properly. These activities will be helpful for collection of accurate and appropriate data, refinement of required data and implementation of data according to choices. Further this information will be helpful for effective decision making to perform various business organizations. The barriers of crowdsourcing and other risk factors that are required to consider are also discussed here in this report. At last we can say that today v arious advanced technologies are used by business organizations for effective decision making and to analyze business information thoroughly. But all these techniques will be helpful, if will be implemented properly and by fulfilling its all requirements. References Enterpriseappstoday.com.(2016). 4 Barriers to Big Data Success -- and Ways to Overcome Them - Enterprise Apps Today. Retrieved 22 October 2016, from https://www.enterpriseappstoday.com/business-intelligence/4-barriers-to-big-data-success-and-ways-to-overcome-them.html Smart Insights. (2014). Marketing Analytics techniques to increase sales - Smart Insights Digital Marketing Advice. Retrieved 22 October 2016, from https://www.smartinsights.com/digital-marketing-strategy/analytics-techniques-increase-sales/ Salesforce Blog. (2016). How Businesses Use Data Analytics to Improve Sales. Retrieved 22 October 2016, from https://www.salesforce.com/blog/2016/06/businesses-use-data-analytics-improve-sales.html Gaudino, L. rarr;, V. (2014). Crowdsourcing benefits, limitations and how to avoid failure. Crowdsourcedtesting.com. Retrieved 22 October 2016, from https://crowdsourcedtesting.com/resources/crowdsourcing-benefits/ Harvard Business Review. (2014). Improve Decision-Making With Help From the Crowd. Retrieved 22 October 2016, from https://hbr.org/2014/04/improve-decision-making-with-help-from-the-crowd