Running Head: BUSINESS INTELLIGENCE AND ANALYTICS 1

Case Study 3: Business Intelligence and Analytics

BUS 4200 Enterprise Information Management Systems

Daria Dulan

Notre Dame de Namur University

Dr. Rodney Heisterberg

February 1, 2017

BUSINESS INTELLIGENCE AND ANALYTICS

Problem Statement

Challenges resulting from the Big Data explosion are leading many organizations

to either develop a BI Competency Center (BICC) or enhance the capabilities of their

existing center (Rouse, 2014).

Challenges and Opportunities

TechTarget defines business intelligence (BI) as “a technology-driven process for

analyzing data and presenting actionable information to help corporate executives,

business managers and other end users make more informed business decisions”

(Rouse, 2014). CTO of Dell EMC Services, Bill Schmarzo, describes business

intelligence as the world of descriptive analytics: “retrospective analysis that provides a

rearview mirror view on the business—reporting on what happened and what is

currently happening” (Schmarzo, 2014). Predictive analytics, on the other hand, is

forward-looking analysis: “providing future-looking insights on the business—predicting

what is likely to happen (usually associated with a probability) and why it’s likely to

happen” (Schmarzo, 2014). BI includes a variety of tools, applications and

methodologies that allow organizations to collect data from internal systems and

external sources, prepare it for analysis, develop and run queries against the data, and

create reports, dashboards and data visualizations to assist in decision-making (Rouse,

2014). Figure 1 shows a sample of the info graphics that can be formed to display data

dashboards using BI.

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Figure 1: Data Collected in 60 Seconds

(Retrieved from: http://www.exelanz.com/blogs/use-of-big-data-analytics-and-predictive-analytics-in-marketing-and-the-role-played-by-cloud-computing/)

Business intelligence looks for trends at the macro or aggregated levels of the business,

and then drills up, down, or across the data to identify areas of under- and over-

performance. Areas may include: geography, time, products, customers, stores,

partners, campaigns, or other business dimensions (Schmarzo, 2014). Predictive

analytics, on the other hand, builds analytic models at the lowest levels of the business

—at the individual customer, product, campaign, store, and device levels—and looks for

predictable behaviors, propensities, and business rules (as can be expressed by an

analytic or mathematical formula) that can be used to predict the likelihood of certain

behaviors and actions (Schmarzo, 2014). Figure 2 shows a brief summary of the

differences between BI and predictive analytics.

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Figure 2: Business Intelligence versus Predictive Analytics

(Retrieved from: https://infocus.emc.com/william_schmarzo/business-analytics-moving-from-descriptive-to-predictive-analytics/)

CTI explains that, “the primary difference between predictive and business is that

business analytics is reactive in nature – analysts and management observe and act on

historic measurable information, such as revenue, profit, loss, employee turnover,

customer churn. Predictive analytics, on the other hand, is proactive: it can assist

companies in actionable intelligence and anticipative action where a human intervention

might not be required at all.” The software will trigger action, be it sending a marketing

email with a coupon or automatically creating a position vacancy notice on a job site, for

example. Forecasting, projecting, and predicting: this is what distinguishes predictive

analytics from traditional business analytics (CTI, 2013).

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The challenges driving this interest in BICCs are: user demand for analytics, data

management and access, and data quality and data confidence issues (Saporito, 2013).

With regards to demand, in response to the sheer volume and variety of new data

types, users are demanding more analytics, the long wait times for IT to deliver

analytics is causing user frustration and user frustration is growing as the ability to do it

themselves is hindered by the lack of robust, yet easy-to-use, visualization and

exploration tools (Saporito, 2013). On another note, IT is pushing for user self-service

so that it can focus on higher value and more skilled activities such as data

management. “The issue of data management and access sparks BICC interest

because mobile devices, social media, and machine-generated data are key

contributors to the data explosion phenomena specifically since finding, evaluating, and

selecting the right tools to manage, analyze, and enable action on non-structured data

types such as videos, web logs, and social media comments has IT scrambling”

(Saporito, 2013). Issues around data quality and data confidence also bring interest to

BICCs as these issues are forcing organizations to enhance their data governance

programs to improve usability of data. Lack of data confidence has led users to create

their own data sets, creating yet more data islands and requiring already strained

resources (both in the business and IT) to spend time on data validation or

reconciliation instead of analysis (Saporito, 2013). All of these challenges can be

addressed with BICCs.

Wikipedia describes a Business Intelligence Competency Center (BICC) as “a

cross-functional organizational team that has defined tasks, roles, responsibilities and

processes for supporting and promoting the effective use of Business Intelligence (BI)

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across an organization” (“Business Intelligence Competency Center,” n.d.). A BICC

coordinates the activities and resources to ensure that a fact-based approach to

decision making is systematically implemented throughout an organization. It has

responsibility for the governance structure for BI and analytical programs, projects,

practices, software, and architecture. It is responsible for building the plans, priorities,

infrastructure, and competencies that the organization needs to take forward-looking

strategic decisions by using the BI and analytical software capabilities (“Business

Intelligence Competency Center,” n.d.).

Figure 2: BICC Functional Areas

(Retrieved from http://www.rci.com.br/en/services/bicc)

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The two adaptive strategic planning processes for virtual enterprise integration in a

business ecosystem, as expressed by Heisterberg, Verma, and Wallace (2014) are:

Building virtual enterprise infrastructures using the Ecosystem Hub architecture

Making decisions to optimize business performance of the virtual enterprise

value chain (p. 17)

To stay ahead in today's rapidly changing business environment, organizations need

agile business processes that allow them to adapt quickly to evolving markets, customer

needs, policies, regulations, and business models. “The convergence of these emerging

technologies and business practices— cloud, social, mobile, video, and big data– driven

analytics, along with business process management (BPM)— is opening up interesting

avenues for businesses” (Heisterberg et al., 2014, p. 36).

The business value of IT is realized by creating business agility using a strategy

of innovation for CIO-CMO alignment around big data analytics based on collaborative

marketing best practices. This is made actionable as a decision framework by

incorporating a holistic customer-centric performance management system using an IT

balanced scorecard as the basis for a Business Agility Readiness (BAR) Road Map

(Heisterberg et al., 2014, p. 329). “Collaborative commerce strategic concepts provide

the foundation for digital business practices that enable business ecosystem trading

partners to create, manage, and use data in a shared environment to design, build, and

support their whole product throughout its life cycle, working separately to leverage their

core competencies together in a value chain that forms a virtual enterprise (Heisterberg

2003)” (Heisterberg et al., 2014).

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BUSINESS INTELLIGENCE AND ANALYTICS

A challenge in the new twenty-first-century economy is to adapt quickly to new

business opportunities or threats by realigning enterprise strategy to efficiently and

effectively create value. An adaptive strategic planning solution for enterprise business

management decision making can provide a holistic approach to strategy management,

business planning, target setting, rolling and event-driven forecasting, and business

performance management based on financial and leading nonfinancial key performance

indicators (KPIs) (Heisterberg et al., 2014, 25).

A BICC’s influence transcends that of a typical business unit, playing a crucial

central role in the organizational change and strategic process. Accordingly, the BICC’s

purpose is to empower the entire organization to coordinate BI from all units. Through

centralization, it “…ensures that information and best practices are communicated and

shared through the entire organization so that everyone can benefit from successes and

lessons learned” (Miller, Bräutigam, & Gerlach, 2006). The BICC also plays an

important organizational role facilitating interaction among the various cultures and units

within the organization. Knowledge transfer, enhancement of analytic skills, coaching

and training are central to the mandate of the BICC. A BICC “should be pivotal in

ensuring a high degree of information consumption and a ROI for BI” (“Business

Intelligence Competency Center,” n.d.).

They are chartered to align business-driven objectives with information,

applications, processes, training, policies, and technology regardless of the size of an

organization. Their capabilities span human capital, knowledge processes, culture, and

infrastructure. Yet many organizations struggle with what skills and roles are needed in

a BICC (Saporito, 2015).

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Pat Saporito (2015) stresses that BICCs face a number of challenges, but two

key related ones are visibility and funding. “Having an executive business intelligence

champion at the senior management level is a must. But even with an executive

champion, the BICC’s value may not be fully recognized” (Saporito, 2015). Saportio

urges to increase awareness of BICC value by defining measureable key performance

indicators and communicating them through a BICC dashboard or scorecard (Saporito,

2015). The most frequently used metrics are in delivered return areas, “such as

pipeline, cost, usage, and quality, and operational performance areas such as uptime,

load time, outage, and education” (Saporito, 2015). Next steps are suggested to be to

“post the dashboard on a BICC wiki or community site; post the enterprise’s charter;

share BI success stories; post upcoming educational courses/webcasts; and add links

to analytic sandboxes” and this will ensure that communication is constant, which is key

as BICCs that keep awareness of their capabilities and value front and center struggle

far less with funding than those that aren’t visible (Saporito, 2015).

The goal is to be able to capture data that can predict the clients’ needs and

desires and to provide an infrastructure that can be monitored in real-time to enable

decision-making. Figure 3 highlights that the functions of process, governance and

people all intersect to create a BICC.

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Figure 3: BICC Setup

(Retrieved from http://i4bi.be/framework-3/)

Creating Business Agility (2014), describe the five most important issues that IT leaders

need to consider when evaluating cloud BI: (a) deliver trusted data; (b) consider a

hybrid model (cloud base and in-house hardware); (c) keep your data secure; (d) build

the right infrastructure; and (e) facilitate change management. (Heisterberg et al., 2014).

Selecting the proper software that meet the five criteria listed previously is the priority.

Enterprises need to be able to provide extensive training in the BI tools, roll out an

efficient change management plan, as well educate users on the benefits of the BI

system’s capabilities (Heisterberg et al., 2014). If experts are unable to facilitate the

BICC; there are really no benefits of having it.

Opportunities

TechTarget argues that the potential benefits of business intelligence programs

include accelerating and improving decision making; optimizing internal business

processes; increasing operational efficiency; driving new revenues; and gaining

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competitive advantages over business rivals. BI systems can also help companies

identify market trends and spot business problems that need to be addressed (Rouse,

2014). Furthermore, the function of the BICC creates many improvements in business

intelligence (BI) and analytics, specifically. Pat Saporito (SAP) argues that the top three

benefits from BICCs to BI and analytics are: “(1) better collaboration between business

and IT, ensuring a business driven BI/analytics strategy; (2) increased use/adoption of

BI and analytic investments; and (3) improved data quality and data management,

enabling more time for analysis vs. preparation and validation” (Saporito, 2015). These

three areas should ultimately result in increased revenue, decreased expenses, and

improved operational efficiency for enterprises. Thought leaders such as McKinsey,

Gartner, Forrester Research, Harvard Business Review, and the MIT Center for Digital

Business agree that companies that adopt digital information asset-based policies and

processes generate significantly higher growth, market share, and profits (Heisterberg

et al., 2014, 14).

Business Solution

If an enterprise does not already have a BICC, then it needs the basics of

building an organization: find an executive business champion; define your

charter/mission; decide on your form, define roles, and fill with the right candidates;

create a roadmap for the BICC, with initial vs. near future state; launch the BICC,

focusing on defining your strategy and documenting/amassing current BI related

standards and processes (Saporito, 2015).

The goal is to identify the best cloud-based software collaboration tool(s) that can

be used within a digital workplace platform to perform business intelligence and

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analytics with customers or peers following the BICC model. The BICC needs to be able

to manage a large amount of data, be accessible on any device, adapt to internal

change, and provide real-time data. It will operate in a shared data environment in order

to gain insights into company performance and customer value, and aid in prompt

decision-making for the business thereby allowing the enterprise to compete in the

business ecosystem.

Lessons Learned / Business Case

The business case for digital businesses is implied in IT investments to facilitate

collaborative business practices with shared demand/ supply data and virtual enterprise

visibility information in real time that will reduce uncertainty to improve decision

effectiveness (Heisterberg et al., 2014). In order to secure government funding,

education institutions are under more pressure than ever to demonstrate performance

measurements in an easy-to-understand fashion. We are going to compare BI tools

within some of the highest ratings on the TrustRadius website for software reviews by

real users to determine which tool would be best for helping our education instituation,

Daria’s Public School, figure out ways to improve and report on performance around

fundraising. Below are details describing each of the BI tools:

Dundas BI is business intelligence (BI) and data visualization software that includes customizable dashboards, reporting and visual data analytics. According to the vendor, their solution is the most flexible BI platform, with seamless integration into the user’s business applications, allowing them to deliver real-time visual access and analytics for their data, information and results for better decisions and faster insights.

Birst Networked BI is a BI product that promises to enable a single view of data and eliminate data silos. Additionally, decentralized teams and individual users can augment this shared analytic fabric with their own local data, without compromising data governance. This consistency comes from a unified semantic layer, which maintains common definitions and key metrics, no matter where users sit.

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Oracle Business Intelligence Standard Edition One is a business intelligence suite geared toward small and midsized companies. Its drag and drop features simplify report and dashboard creation, and users can generate a variety of documents, including shipping labels and tax forms. It is easy to set up and scale, allowing users to add features as needed; compatible with the Oracle Business Intelligence Suite Enterprise Edition, which is helpful for organizations as their usage requirements grow. Applications within this system include the Oracle Business Intelligence Publisher, which allows users to create and manage their reports, invoices, sales orders, checks, etc., and download them as PDF, HTML and Microsoft Excel files.

For the purpose of a large high-school whose goal is to get increased funding for the

next year, Dundas would be a great solution! Dundas has great visualizations and tons

of features to choose from. It provides a wizard-like feel by giving users options to load

data, prepare data views, and create performance and analytical dashboards. Instead of

requiring IT developers to create initial dashboard interfaces, an end user who

understands where data resides and how tables interrelate can develop their own

dashboards from scratch. The tool is simple for non-technical users to engage with and

allows for integration.

A dashboard can help track alumni donations, fundraising procurements, and

annual budgets very easily. Daria’s Public School could use the features of Dundas to

consolidate and analyze our real-time donor statistics, habits and demographics on a

dashboard and create more effective and targeted advertising and fundraising

campaigns.

IT Central Station compares Tableau and Alteryx, two great predictive analytics

tools. Tableau is an enterprise analytics platform that enables organizations to explore

trusted data in a secure and scalable environment. It gives people access to intuitive

visual analytics, interactive dashboards, and limitless ad-hoc analyses that reveal

hidden opportunities and eureka moments alike. With Tableu, an enterprise can get the

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security, governance, and management required to confidently integrate it into the

business—on-premises or in the cloud—and deliver the power of true self-service

analytics at scale.

Pros:

Multi-dimension graphs

Formulas/calculations are familiar structure/format (IF/Then, Else)

Great geo/mapping capabilities

Built-in table calculations

Story creation!

Easy cube connection

Drag and drop hierarchy feature

Cons:

Heavy data prep is needed

No way to easily “clear” or remove applied filters

Image thumbnails not supported in table format

On the other hand, Alteryx which seems to be a less popular tool is also great! Alteryx is

a visual workflow tool that combines Extract, Transform and Load (ETL) capabilities with

spatial processing. It allows you to rapidly access and transform multiple databases,

including spatial databases, to provide geographic business intelligence in support of

sales, marketing and operations challenges.

Pros:

Blending capabilities are wonderful

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Reports are insightful and easy to obtain

Email capability makes for efficiency

Integrates easily with existing apps

Accurate geo/mapping technology

Ability to import store level POS data in just few minutes

Cons:

Visualization could be improved

Popular in a small number of industries

Stability could be improved

Visier is a tool that EA uses for workforce planning. Visier is a cloud-based analytics

polatform that enables HR professionals to answer critical workforce strategy questions.

Visier delivers workforce analytics and planning solutions that enable organizations to

move from intuition- to fact-based decision-making, and to more accurately plan for the

future. Implemented in just four to eight weeks, Visier delivers answers to over 300

workforce questions. It is incredibly easy to use, the visualization is great and the

integration with other systems seems to be smooth (we use Workday). Though Visier

isn’t exactly a tool that produces predictive analytics, it can be helpful in doing so,

specifically for HR-related data.

Why I Care

Heisterberg et al. (2014) define business agility as innovation via collaboration to

be able to anticipate challenges and opportunities before they occur (p. 1), which has

the potential to create a competitive advantage. “Sustainable competitive advantage

has been realized by creating a culture for adoption of digital business strategies and

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customer-centric business models” (Heisterberg et al., 2014, p. 5). The only way for

enterprises to sustain their competitive advantage is for digital businesses to sense

changes in their business ecosystem, and then adapt their plans based on predictions

of how these insights will produce customer delight and advocacy, made possible by

managing information (Heisterberg et al., 2014, p. 10).

“Fragmented data, disjointed systems, and multichannel interactions make it

difficult to deliver a consistent customer experience” and it is these experiences that

lead to decreased customer satisfaction, and, in the long-term, loss of business

(Heisterberg et al., 2014, p. 38). There are multiple opportunities to up-sell and cross-

sell products that impact the bottom line. If companies are unable to identify the multiple

opportunities there are to up-sell or cross-sell products, bring a product to market

quickly, or offer the right product to the right customer at the right time, there will be

huge profitable misses (Heisterberg et al., 2014, p. 38). Working at EA, I’ve learned

about the value of micro-transactions and in-app purchases for the games industry.

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Figure 4: Comparison of Revenue from Subscription v. Micro-transactions

(Retrieved from https://inplayer.com/2015/subscriptions-vs-micro-transactions-comparing-the-major-content-monetization-models/)

There is a significant market around micro-transactions, but the timing, the price, the

customer interest all has to be in place before the in-app purchase is introduced,

otherwise, the deal is pretty much dead. Having the systems, processes and technology

in place to capture that data and make it useful for our game teams is critical, without a

BICC, there’s be an entire branch of our industry and business that EA wouldn’t be

profiting from.

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References

Business Intelligence Competency Center. (n.d.). In Wikipedia. Retrieved November 5,

2016, from

https://en.wikipedia.org/wiki/Business_Intelligence_Competency_Center

Heisterberg, R. J., Verma, A., & Wallace, C. (2014). Creating business agility: How

convergence of cloud, social, mobile, video, and big data enables competitive

advantage. Hoboken, New Jersey: Wiley.

Miller, G., Bräutigam, B, & Gerlach, S. (2006). Business intelligence competency

centers: a team approach to competitive advantage. Hoboken: Wiley

Rouse, M. (2014, October). Business intelligence (BI). Retrieved from

http://searchdatamanagement.techtarget.com/definition/business-intelligence

CTI. (2013, February 28). Predictive analytics vs. business analytics: what’s the

difference? Retrieved from http://www.cptech.com/blog/predictive-analytics-

difference/

Saporito, P. (2013, October 7). BI competency centers help “big data” deliver big value.

Retrieved from http://blog-sap.com/analytics/2013/10/07/bi-competency-centers-

help-big-data-deliver-big-value/

Saporito, P. (2015, February 9). Part 2: BICC basics – models, benefits and challenges.

Retrieved from http://blog-sap.com/analytics/2015/02/09/part-2-bicc-basics-

models-benefits-and-challenges/

Saporito, P. (2015, February). Part 3: BICC skills, roles, and responsibilities. Digitalist

Magazine, Retrieved from

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http://www.digitalistmag.com/technologies/analytics/2015/02/23/part-3-bicc-skills-

roles-and-responsibilities-02250522

Schmarzo, Bill. (2014, January 9). Business analytics: moving from descriptive to

predictive analytics. Retrieved from

https://infocus.emc.com/william_schmarzo/business-analytics-moving-from-

descriptive-to-predictive-analytics/

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