Research Paper

 For your research paper, conduct research on how Descriptive Analytics, Predictive Analytics and Prescriptive Analytics can be utilized in any small or medium sized business and what must be done within the business to effectively implement and/or institutionalize Analytics (Descriptive, Predictive and Prescriptive Analytics must be discussed in your paper) 

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ITS 531 – Research Paper – Creating an Analytics-Centered Business

Imagine you work for an Analytics consultant who is developing a questionnaire that can be used to appraise

any organization’s ability to implement an analytics solution and/or determine how well an organization has

institutionalized analytics. Beyond the questionnaire, a professional research paper must be created.

Come up with 25-50 questions for the questionnaire using the bullets below (each bullet below should lead to

multiple questions). Go beyond these bullets and identify other areas that can lead to additional questions.

• size of organization (staff, transactions, sales, services, etc.) and how its size will affect the
implementation of Descriptive analytics and/or Predictive analytics and/or Prescriptive analytics.

• type of organization (local/regional/etc. & industry) and how its strategic and operational mission will
affect the implementation of Descriptive and/or Predictive and/or Prescriptive analytics.

• key performance indicators (KPI) or critical products/services that must take priority in the
implementation of Descriptive analytics and/or Predictive analytics and/or Prescriptive analytics.

• use of technology (software, hardware, databases, systems, etc.) and their ability to assist in
implementing Descriptive analytics and/or Predictive analytics and/or Prescriptive analytics.

• organization processes that produce or consume data and their impact on the implementation of
Descriptive analytics and/or Predictive analytics and/or Prescriptive analytics. (i.e.

Management/Administration, Operations/Manufacturing, Finance, Customer Relations and Sales,

Marketing/Communications, Public Affairs/Relations, Research/Innovation, Human

Resources/Purchasing, Institutional Advancement, etc.)

• software systems used in processing and producing information needed in the implementation of
Descriptive analytics and/or Predictive analytics and/or Prescriptive analytics.

• sources of data (systems/web/other and structured, unstructured); the data “V’s”; collection and
cleaning; storage/database/warehouses/management; analysis/outputs/outcomes, etc. that would be

needed in the implementation of Descriptive and/or Predictive and/or Prescriptive analytics.

• analytics tools and analysis/analytics applications that are used or could be used in the implementation
of Descriptive analytics and/or Predictive analytics and/or Prescriptive analytics.

• technical expertise (i.e. data scientist, IT specialist, super-user, etc.) and professional training required in
the implementation of Descriptive analytics and/or Predictive analytics and/or Prescriptive analytics.

• operational systems that already include embedded analytic tools

• organization’s ability to institutionalize the use of new processes & technology needed in the
implementation of Descriptive analytics and/or Predictive analytics and/or Prescriptive analytics.

• organization’ desire and ability to pursue cloud services to include AaaS.

Select one member of your group to submit the questionnaire by the Friday due date/time.

For your research paper, conduct research on how Descriptive Analytics, Predictive Analytics and Prescriptive

Analytics can be utilized in any small or medium sized business and what must be done within the business to

effectively implement and/or institutionalize Analytics (Descriptive, Predictive and Prescriptive Analytics

must be discussed in your paper). You can research how large businesses use analytics and then discuss how

these methods/tools can be used or are not viable for small and medium business. Explore many of the items

that were identified within your questionnaire. You may also select a small or medium business in your area as

an example for your analysis but remember the paper is about the use of Descriptive, Predictive and Prescriptive

Analytics in any small or medium sized businesses and NOT large businesses or specific industries.

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Analyze your research to identify one or more Research Questions. Example: you analyze the literature and

determine that Predictive analytics requires a strong knowledge of statistics and advanced database systems.

Other literature indicates that Prescriptive analytics requires a strong knowledge of data modeling, data

warehouses and more advanced data systems. One Research Question might be “can all analytics methods be

implemented in any size organization?”. Another Research Questions might be “do all businesses possess the

knowledge needed to implement and maintain all analytics methods?”.

Develop a Hypothesis using your Research Question(s) and your analysis of the research. Example, using the

example Research Questions above, “Because aaa requires bbb, only ccc business have the ability to implement

ddd analytics.”. A Hypothesis is a precise Statement that your research will either prove or disprove. Do not

make it too generic – it should be specific.

In the introduction section of your research paper, provide a general explanation of Descriptive, Predictive and

Prescriptive analytics and define any terms used within the questionnaire and paper (i.e. how do you define size

<# of employees or total-sales or total-investment>, organizational type, software & systems, etc.) as it relates

to your paper. At the end of the introduction section, include your problem statement and your hypothesis(es).

The content of your paper (from Introduction to Findings) must be at least 7 pages in length, double-spaced,

12-font. It must include at least 7 references, with at least 2 being peer-reviewed. The cover page, abstract

and references are NOT included in the 7-page length requirement. Your paper must be formatted using APA

guidelines.

The quality and thoroughness of the paper, as defined in the rubric, will determine the grade assigned.

Papers containing the minimum number of references and/or minimum number of pages will most likely not

earn a high grade.

Use the Research Paper template and Research Presentation template, provided in the Residency folder, for

your Research paper and presentation. Make sure it contains all items shown in the Notes.

Make sure the Hypothesis and References are included in the presentation and that the peer-reviewed

references are identified in the last page of the presentation PPT.

• Select one member of your group to submit the paper and presentation in a single submission by the
Sunday due date/time.

I recommend the following goal deadlines:

o Complete the majority of the research before leaving on Friday

o Complete a majority of the Research paper by Saturday lunch

o Complete the Research presentation by Saturday afternoon

o Reconcile differences between the Research paper and presentation before leaving on Saturday.

▪ After receiving comments on your presentation on Sunday, you can make any final changes to

your Research paper and submit the Research paper for grading (changes to the Research

presentation are not needed). Check your SafeAssign score and make changes as needed if your

score is too high (typically above 20%)

Things I look for when grading:

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• I will look for high SafeAssign scores where large blocks of text have been obtained from other sources

without citation OR where blocks of text have been obtained from other students. Students will be

assigned a ZERO when plagiarism is indicated.

• I will use the WORD Review-Split option on the Research paper, with the top pain looking at the

paper’s content and the bottom pain looking at the Reference page.

o I will search the content using the author’s name, for each Reference, to make sure each

Reference was Cited.

o I will search for every Citation to make sure a Reference exists on the Reference page for each

citation.

• I will use the WORD Review-Check Document to look for Spelling and Grammar errors.

o The 1st sentence of each paragraph must be indented.

o Add page-breaks to ensure the abstract page, start of the content and reference page start on a

new page.

o Ensure blank line spacing is consistent throughout the paper.

o Make sure the spacing is set to double spaced and contains zero before and after spacing.

• I will look for instances where main topic areas are not linked together – i.e. if each group member

researches and writes a portion of the paper, someone needs to go thru the final content to make sure

everything ties-together.

• I will want to see a Hypothesis that is detailed enough (see examples above) and is based on the research

(i.e. the Hypothesis is specifically addressed within the text).

• I will look for extraneous content that does not link back to the Hypothesis and topic.

Marr (2016) provided some examples of how analytics can be used in business’ systems.

Financial Analytics

Predictive Sales Analytics: determines success of sales forecast and assists in improving sales predictions

using data mining which looks for trends and relationships in the sales data. For smaller companies, third-

party or cloud computing can provide these services.

Customer Profitability Analytics: identifies customers who are profitable. By segmenting or clustering

customers, the marketing message and level of service can be tailored to each group.

Product Profitability Analytics: discovers individual product profitability. It uncovers hidden profit and

losses of a product range and helps business uncover insights across the range.

Cash Flow Analytics: shows how money is moving in and out of the business and identifies ease of

converting assets to cash. Using regression analysis, it allows a business to plan for peaks and valleys.

Value Driver Analytics: uses strategy mapping to identify value drivers within the business by building a

simple model or conducting a business experiment to test the hypothesis.

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Shareholder Value Analytics: helps investors determine the strength of a business and influences whether

stakeholders decide to buy, sell or hold stock.

Market Analytics

Unmet Need Analytics: identifies unmet product or service needs by determining the gap between what the

market wants and what your business provides so new products can close the gap and take advantage of the

opportunity. It can use qualitative surveys, focus groups and interviews that can then be analyzed using text

analytics

and sentiment analysis.

Market Size Analytics: determines the market size and potential growth for a business’ products and

services. It is used in strategic planning and may use customer surveys that can be analyzed using text

analytics and sentiment analysis.

Demand Forecasting: seeks to estimate demand for a product or service by using predictive analytics

against historical or current sales data. It uses forecasting techniques such as time series analysis, data

mining and neural network analysis.

Market Trend Analytics: establishes whether and how quickly a market is growing, stagnant or declining.

It can use business experiments and scenario analysis to gauge the market and trends that might impact the

business. In addition, it can use customer surveys and focus groups that can be analyzed using text analytics

and sentiment analysis.

Competitor Analytics: allows a business to understand competitors by understanding their strengths and

weaknesses. It can use text analytics and sentiment analysis to evaluate competitor product reviews.

Pricing Analytics: analyses price sensitivity in market segments to increase profitability. It can use

business experiments, data mining and forecast models to predict future outcomes.

Marketing Channel Analytics: assesses marketing channels to identify the most effective ones for the

business. It can use web analytics and data mining to obtain insights.

Brand Analytics: determines the strength of the business’ brand compared to the competitors. It can use

text analytics and sentiment analysis to identify positive or negative opinions.

Customer Analytics

Customer Satisfaction Analysis: assesses customer expectations and satisfaction when purchasing

products or services. It can use text analytics and sentiment analysis to identify positive/negative opinions.

Customer Lifetime Value Analytics: analyses customer value to the business over the customer’s lifetime.

It can use regression analysis and big data analytics to increase the length of the relationship and total value.

Customer Segmentation Analytics: finds sub-groups or segments within the market. It can use text

analytics and data mining to analyze data extracted from the web.

Sales Channel Analytics: analyzes the distribution of products to see which market channels are the most

effective.

Web Analytics: analyzes online behavior to optimize website use and increase engagement. It looks at off-

site analytics to measure potential audiences, competition and trends as well as on-site analytics to analyze

the business web presence.

Social Media Analytics: gathers and analyses data from social media. It can use data mining, text analytics

and sentiment analysis to analyze social media extracted from the web.

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Customer Engagement Analytics: maps the entire customer interaction and engagement over time. It can

use social media analytics and surveys to gauge engagement as well as text analytics and sentiment analysis

to analyze text content.

Customer Churn Analytics: assesses the loss of customers over time. It can use regression analysis to

identify the reasons for the churn.

Customer Acquisition Analytics: establishes how effective a business is in acquiring new customers. It

can use regression analysis to analyze the data.

Operational Analytics

Fraud Detection Analytics: uncovers fraudulent actions or behaviors to predict and reduce fraud. It can

use video and voice analytics as well as text analytics to identify patterns.

Core Competency Analytics: identifies the company’s core competencies to provide access to markets,

determine customer benefits of end products and ensure competitors find it difficult to imitate. It can use

factor analysis to identify patterns.

Supply Chain Analytics: assess each stage of the supply chain or process that create products and services.

It can use neural network analysis, linear programming and Monte Carlo simulation to optimize and detect

issues with the supply chain.

Capacity Utilization Analytics: focuses on equipment and plant operations. It can use video analytics to

determine utilization levels.

Project and Program Analytics: assesses the effectiveness of internal projects and programs. It can use

text analytics and sentiment analysis to analyze the opinions of the project team and process owners/users.

References

Marr, B. (2016). Key Business Analytics. Hoboken: Pearson.