Case Study 3: Business Intelligence and Analytics

Case Study #3: Business Intelligence and Analytics: Functionality and Scope Suitable for Business Intelligence Competency Center (BICC)

Trish Gomez Ahern

BUS 4200 Enterprise Information Management Systems

Professor: Rodney Heisterberg

February 1, 2017

Problem Statement

With the wide usage of various devices, data has become more unstructured compared to the traditional structured format.  This has added complexities in managing big data, deriving greater business value from them – making analysis and insights very challenging (Heisterberg & Verma, 2014, 237).  This paper will examine software application packages for business intelligence, business analytics, and predictive analytics that can be integrated with Enterprise Information Management Systems (EIMS) solutions – each capable of being deployed via Cloud Computing and facilitate the intended uses in terms of their functionality and scope suitable for Business Intelligence Competency Center (BICC) implementation.

Challenges and Opportunities

Today’s competitive business environment requires that organizations collect, analyze, and interpret vast quantities of data to enable more informed and effective decision making (Foster, Smith, Ariyachandra & Frolick 2015).  With an abundance of data available, without proper data quality and governance guidelines the data might prove useless.  Data quality is essential to extract meaningful information so that a company is not left making skewed decisions based on flawed data.  Data needs to be accurate, available, understandable, and relevant to the problem being solved to achieve high quality performance.

Business Intelligence Competency Center (BICC)

According to Gartner, “A business intelligence competency center (BICC) develops the overall strategic plan and priorities for BI. It also defines requirements, such as data quality and governance and fulfills the role of promoting the use of BI” (BICC, 2017).  The broad categories of BI applications and technologies gather, store, analyze, and provide access to data that helps organizations and users make better decisions (Viaene, 2008).  The challenge lies in extracting real intelligence from massive data feeds without being overwhelmed in the process.  BI provides organizations with a systematic and structured approach and incorporates a value chain that transforms data into information.  Companies ultimately realize the business benefits at the end of this chain.

Figure 1: Business Intelligence Value Chain (Viaene, 2008).

BICCs are critical to the success of an organization’s analytics (Sankar, 2013).  Regardless of the size of an organization, BICCs align business-driven objectives with information, applications, processes, training, policies, and technology.  Capabilities span Human Capital Knowledge Processes, Culture, and Infrastructure.  The three skills needed by effective BICCs fall into three broad areas – business, IT, and Analytics. According to Sankar. The capabilities needed within each of these areas are:

  • Business Skills: Linking to business strategy, defining priorities, leading organization and process change, and controlling funding. These often sit in the business, but IT needs to develop as well.
  • IT Skills: Defining vision, maintaining programs, establishing standards, creating the technology roadmap, providing methodology leadership, maintaining an adaptable infrastructure, and improving data quality. These are traditionally technical skills, but business users need awareness of these skills especially standards and roadmap and play a key role in data quality.
  • Analytic Skills: Developing user skills, defining business rules, identifying and extracting data, creating business views of data, discovering and exploring data, and enabling advanced analytical skills like statistical and text mining. Analytic skills are needed across the organization both in Business and IT areas.

 

Figure 2: BICC Skills Requirements (Sankar, 2013).

 

 

 

Business Solution

According to webopedia, “Business Intelligence software, also called BI software, is software that is designed to analyze business data to better understand an organization’s strengths and weaknesses.  Business intelligence software allows an organization’s management to better see the relationship between different data for better decision-making and optimal deployment of resources. Business Intelligence software plays a key role in the strategic planning process of the corporation” (“Business Intelligence software”, 2017).  Tesearch shows that BI systems help organizations create value through their impact on business processes, people, and firm operations (Lee & Widener, 2016).

BI software helps organizations organize and analyze data to make better decisions (Harris 2017).  This could include internal data from company departments as well as from external sources, such as marketing data services, social media channels or even macroeconomic information.  According to Daniel Harris, Market Research Associate at Software Advice (2017):

The BI market is growing rapidly because of the proliferation of data to analyze. Over the past few decades, companies that have deployed Enterprise Resource Planning (ERP), Customer Relationship Management (CRM) and other applications are now sitting on a mountain of data that can be analyzed. In addition, the growth of the Web has increased the demand for tools that can analyze large data sets.

One of the biggest trends in the BI market is the shift in software architecture and design to more user-friendly applications. These applications are now being used by business users—not just IT staff—to analyze particular sets of departmental data, including marketing, procurement, retail and Web data.

BI software can be divided into three broad application categories: data management tools, data discovery applications and reporting tools (including dashboards and visualization software). In the next section, we’ll explain how these applications can help your organization’s decision-making process become more data-driven.

What BI tools you need depends on how your data is currently managed and how you would like to analyze it. For example, if it is currently scattered across disparate transactional databases, you might need to build a data warehouse to centralize it and invest in data management tools that offer Extract, Transform and Load (ETL) functionality to move and re-structure it.

Once data is given a common structure and format, you can invest in data discovery solutions such as Online Analytical Processing (OLAP), data mining and semantic or text mining applications, with the capability to create custom, ad hoc reports. And because information is stored within the warehouse, users can quickly pull reports without impacting the performance of the organization’s software applications, such as CRM, ERP and supply chain management solutions.

in general, the features you should seek in a new BI solution should include:

  • Data quality management
  • Extract, transform and load (ETL)
  • Data mining
  • Online analytical processing (OLAP)
  • Predictive analytics
  • Semantic and text analytics
  • Visualizations
  • Dashboards
  • Report writers
  • Scorecarding

 

 

 

 

 

 

 

 

 

Figure 3. Concept of Business Intelligence (Harris, 2017).

SharePoint

SharePoint is a web-based application that integrates with Microsoft Management.  SharePoint is primarily sold as a document management and storage system, but the product is highly configurable and usage varies substantially between organizations.  According to Daniel Harris, Market Research Associate at Software Advice (2017):

Offered as both an on-premise or a web-based solution, Microsoft SharePoint 2013 helps organization collect and analyze key business data in order to gain an accurate, actionable view of operations and the company as a whole. It can be combined with existing business management software, such as Dynamics CRM or Dynamics ERP, as well as Microsoft Office, providing a powerful, intuitive end-user experience.

Users across the organization can quickly and easily monitor and analyze critical business data from a PC, smartphone or the Internet, and this anytime, anywhere access can be tailored to a company’s changing needs. You can choose from an online subscription, software installed on a local server or a hybrid of both, optimizing IT resources while also controlling costs.

Using Microsoft SharePoint users from across the company can collaborate easily and effectively by setting up websites to share information. Using the comprehensive set of tools provided within the software, companies can build sites that range from internal team sites, a partner extranet and a customer portal, all built on a single infrastructure that streamlines site management.

In addition to creating websites, SharePoint allows users to manage documents through their entire lifecycle as well as publish dynamic, collaborative reports. With its interactive dashboards and scorecards, users within the organization can quickly see, and then address, needs specific to their department and the company as a whole.

Microsoft SharePoint 2013 is a must-have for companies looking for a business intelligence solution that increases collaboration while gaining accurate, actionable business insights.

Figure 4. SharePoint Dashboard (Harris, 2017).

Domo

Domo is a fully cloud-based business management suite that integrates with multiple data sources, including spreadsheets, databases, social media and any existing cloud-based or on-premise software solutions.  According to Daniel Harris, Market Research Associate at Software Advice (2017):

It is suitable for company sizes ranging from small businesses to large enterprises, and is compatible on Windows or Mac platforms, iPad tablets, and on mobile devices.

Domo provides both micro- and macro-level visibility and analysis; from cash balances and top-selling product lines to forecasted revenue by region, marketing return on investment (ROI) by channel, and more. It offers real-time, up-to-date dashboards with data pertaining to multiple business areas and performance metrics (e.g., key performance indicators, ROI, etc.). Domo’s key selling points include interactive visualization tools and instant access to company-wide data via customized dashboards.

Pricing is offered on an annual subscription basis, and depends upon the number of users that need access.

Figure 5. Domo Dashboard (Harris, 2017).

Qlik

Qlik Sense Cloud Create allows users to securely access and share content from anywhere at any time and collaborate with authorized users with no setup or infrastructure.  According to Daniel Harris, Market Research Associate at Software Advice (2017):

Qlik Sense is a Business Intelligence (BI) visual analytics platform that supports a range of use cases, including centrally deployed guided analytics apps and dashboards, custom and embedded analytics, and self-service visualization, all within a scalable, governed framework.

The system offers visualization and discovery for individuals, collaboration and mobility for groups and teams, and data integration, management and governance capabilities for large organizations. Qlik Sense helps users find all the possible associations that exist in their data, across all their data sources, with the Qlik Associative Data Indexing Engine. Users can see the whole story instead of only the partial views offered by hierarchical tools. Qlik Sense also offers an HTML5 client, which helps businesses gain useful insights into their information to help each employee make better business decisions.

Figure 6. Qlik Sense Dashboard (Harris, 2017).

            Comparison of SharePoint, DOMO, and Qlik

SharePoint is primarily sold as a document management and storage system, but the product is highly configurable and usage varies substantially between organizations of all sizes (Harris, 2017). It is an affordable software with a high rating that comes highly recommended. Domo is a fully cloud-based business management suite that integrates with multiple data sources, including spreadsheets, databases, social media and any existing cloud-based or on-premise software solutions. Users highly rate domo, which more expensive than the other systems and has the lowest recommendation rating.  Qlik Sense Cloud Create allows users to securely access and share content from anywhere at any time and collaborate with authorized users.  Qlik has the highest user rating of the three systems, is reasonably priced, and comes with the highest recommendation.

Figure 7. Comparison of SharePoint, DOMO, and Qlik (Harris, 2017).

Business Analytics (BA)

Business Analytics are “the extensive use of data, statistical, and quantitative analysis, exploratory and predictive models, and fact-based management to drive decisions and actions” (Purba., Ray, & Kumar, 2013).  Business Analytics also refers to skills, technologies, applications, and practices to investigate past and current business performance to gain insight and drive business processes to more efficient and effective business planning in the future. In a very broad perspective Business Analytics today refers to different approaches for modeling different business situations and arriving at assessing and predicting risk, predicting market preferences, project feasibility, customer segmentation, inherent and underlying dimensions in consumer preferences, factors leading to probability of purchase, preferred segments in financial and credit card industry, probability of attrition in large organizations etc.

Business Analytics translates data into information that is necessary for businesses to make informed decisions and investments (Havlena 2013).  BA is a way of organizing and converting data into information to answer questions about the business. By looking for patterns and trends in the data and by being able to forecast impact on decisions before they are taken, BA leads to better decision making.  Examples of BA uses include exploring data to find new relationships and patterns (data mining), explaining why a certain result occurred (statistical analysis, quantitative analysis), experimenting to test previous decision (A/B testing, multivariate testing, and forecasting future results (predictive modeling, predictive analytics).

Figure 8. High-level Business Analytics Architecture (Havlena, 2013).

 

 

            Business Intelligence (BI) versus Business Analytics (BA)

Traditional Business Intelligence (BI) has been associated with providing executive dashboards and reporting to monitor the assumptions and key performance metrics (Havlena, 2013).  Business Analytics (BA) has a broader character and offers deeper insight that can answer questions like why this is happening, what if the trends continue, what will happen (prediction), and what is the best that can happened (optimization).

Figure 9. BI versus BA (Rouse, 2013).

Sisense

Sisense is an end-to-end business intelligence (BI) solution that was developed to be accessible for any type of user, even those with little or no prior experience with BI software. According to Software Advice Market Research Associate, Daniel Harris (2017):

Their full suite of applications provide users with the tools they need to manage and support business data with analytics, visuals and reporting. This out-of-the-box system doesn’t require lengthy implementation or training, so businesses can have it up and running quickly.

Sisense’s standalone applications offer data and text mining with interactive analytics tools. ElastiCube is their analytics database that utilizes In-Chip technology, enabling a single server and minimal hardware with the ability to handle big data. Sisense lowered limits on data usage to maximize shared information between users.

Integrated within the suite, Sisense includes functionality for dashboards and scorecards, data warehousing, extract, transform and load (ETL) and a query and report writer. Everything is managed through one interface designed with the end-user in mind. Sisense can be deployed on-premise or over the cloud.

Sisense was chosen as one of the Top 10 BI Vendors of 2013 by CIO Magazine and “Best in Show” at the O’Reilly Big Data Strata conference.

Figure 10. Sisense Dashboard (Harris, 2017).

BOARD

Created to combine business intelligence, corporate performance management and business analytics, BOARD is a full-featured business intelligence system that suits midsize and enterprise-level companies in a variety of different industry segments.  According to Software Advice Market Research Associate, Daniel Harris (2017):

Within the reporting functionality, BOARD allows users to pull from almost any data source, as well as generate full self-service reporting. These reports can be exported into several different formats, if necessary, such as CSV, HTML and more. The system also features extensive multi-lingual capabilities, making suitable for companies that need to deliver reports in another language.

The dashboard application allows BOARD users to create a fully customizable experience, featuring drill-down and drill-through functionality, as well as several different types of data visualization options. By implementing BOARD’s data collecting and analysis functionalities, companies can view data in a relevant way that helps drive intelligent business decisions.

Figure 11. BOARD Dashboard (Harris, 2017).

 

Adaptive Discovery

Adaptive Discovery from Adaptive Insights is a web-based visualization platform with drag-and-drop design that allows end users to create new visualizations without the need for additional IT resources. According to Software Advice Market Research Associate, Daniel Harris (2017):

This solution can incorporate data from a company’s CRM, financial, payroll or operations systems, and information can be broken down by a specific range of time.

Multiple dashboards and perspectives provide users with the tools to customize their views and settings with different key indicators for various departments. Drill down options segment data by specifics, such as customer, product, vendor, or geographic region.

Once Adaptive Discovery is connected to the data sources of choice, charts and graphs can be updated in real time. Adaptive Discovery is compatible with any browser on Macs and PCs.

There is an app for phones or tablets, which can be accessed in real time.

Figure 12. Adaptive Discovery Dashboard (Harris, 2017).

Comparison of Sisense, BOARD, and Adaptive Discovery

Sisense’s full suite of applications tools needed to manage and support business data with analytics, visuals, and reporting.  It is affordable and comes highly recommended.  BOARD is a full-featured business intelligence system that suits midsize and enterprise-level companies in a variety of different industry segments. It is an affordable system that has the highest recommendation.  Adaptive Discovery from Adaptive Insights is a web-based visualization platform that can incorporate data from a company’s CRM, financial, payroll or operations systems, and information can be broken down by a specific range of time.  It is much higher in price compared to the other systems, and is the only one without a recommendation.  Users very highly rate all three systems (Harris, 2017).

 

Figure 13. Comparison of Sisense, BOARD, and Adaptive Discovery (Harris, 2017).

Predictive Analytics (PA)

According to webopedia, “Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends.  Predictive analytics does not tell you what will happen in the future. It forecasts what might happen in the future with an acceptable level of reliability, and includes what-if scenarios and risk assessment” (“Predictive Analytics”, 2017).  Predictive Analytics (PA) is a component of business intelligence that received increasing attention in the equipment leasing and finance industry (Halladay, 2013).  Development of powerful predictive models and the processing of large volumes of data has been enabled by the advances in technology and computer processing.  While traditional BI software usually examines past and present trends within a company, predictive analytics solutions look to the future to help decision makers plan ahead (Harris, 2016).  Daniel Harris, Market Research Associate at Software Advice (2016), notes that:

These systems extract and code a company’s historical information to determine patterns. Armed with these patterns, predictive models are then created and used to forecast possible trends and outcomes.

This is not an exact science, and forecasts do contain a margin of error. But the key advantage of predictive analytics software is that it can highlight upcoming opportunities and potentials for risk to improve the quality of decision-making around those events.

There are four types of analytics users should know about, which can aid a business in different ways:

  • Descriptive: Descriptive analytics uses incoming data to identify trends occurring in real-time.  This can help answer the question, “What is going on right now?”
  • Diagnostic: Diagnostic analytics uses historical data to determine the cause of an event in the past.  This can help answer, “Why did this happen?”
  • Predictive: Predictive analytics uses historical data to find trends and uses them to predict future events.  This can help answer, “What will happen next month?”
  • Prescriptive: Prescriptive analytics uses both descriptive and predictive data to determine a specific action to take.  This can help answer, “How can I solve this problem?”

Figure 14. Predictive Analytics Visualization (Harris, 2016).

Halo

Halo provides analytics tools that are tailored to specific industries, such general ledger (GL) analysis for finance and inventory forecasting for supply chain management.  According to Daniel Harris, Market Research Associate at Software Advice (2016):

The system uses data from all sources – big, small, and in-between – to form a cumulative view of all business information.

By combining the BI data analytics with a social platform, companies can use the data to spark dialogue between employees, helping to generate ideas and guide business decisions. Deployment options for Halo include on-premise, SaaS (Software as a Service) or PaaS (Platform as a Service, which allows users to develop, access and run custom apps in the cloud).

This system is suited for mid-size companies in several target verticals, including Manufacturing and Distribution.

Figure 15. Halo Dashboard (Harris, 2016).

 

Splunk

The Splunk Enterprise platform allows users to process and index most forms of data in their native format. According to Daniel Harris, Market Research Associate at Software Advice (2016):

It includes data indexing tools, which enable users to locate specific data across large datasets.

This software was created with the non-technical user in mind, making for a more intuitive user experience. A key selling point is the platform’s scalability, which allows it to grow with the amount of data it is needed to process; up to at least 100 terabytes per day. To ensure users always have access to their data, even in the event of a system disruption, this platform features built-in failover and disaster recovery capabilities.

Deployment options include both on-premise and SaaS (Software as a Service). Subscription pricing is based on the amount of data indexed per day, and pricing decreases as the amount of data indexed increases. There are perpetual and term license pricing options as well.

Figure 16. Splunk Dashboard (Harris, 2016).

Phocas

Phocas Software’s Phocas Business Intelligence Solution offers a cloud-based, fully integrated suite of Business Intelligence tools for small-to-medium-sized businesses in the manufacturing industry.  According to Daniel Harris, Market Research Associate at Software Advice (2016):

The system works on any modern browser and can be used on any connected mobile device or tablet.

Phocas Business Intelligence includes Dashboards and Scorecards; Data Mining and Predictive Analytics; Extraction, Transformation, and Loading (ETL); Online Analytical Processing (OLAP); and Data Warehousing. The system provides users the ability to view data from many different perspectives- users can analyze sales information, including trends and buying patterns, to determine cross-selling opportunities and sales opportunities. Users can identify customers who fall into a pattern of not purchasing or decline in purchasing frequency. The system also helps businesses form intelligent purchasing decisions by providing access to vendor performance, average prices, and purchase-to-sales ratios, in addition to flow rates, output, production cycle, and materials costs.

Figure 17. Splunk Dashboard (Harris, 2016).

 

Comparison of Halo, Splunk, and Phocas

Halo provides analytics tools that are tailored to specific industries for supply chain management.  The Splunk Enterprise platform allows users to process and index most forms of data in their native format.  Phocas offers a cloud-based, fully integrated suite of Business Intelligence tools for small-to-medium-sized businesses in the manufacturing industry.  Users rate all three systems very highly, all three a very affordable, and are very highly recommended (Harris, 2016).

Figure 18. Comparison of Rapid Insight, Splunk, and Phocas (Harris, 2016).

 

 

Lessons Learned/Business Case

Although Business Intelligence (BI) may be one of the most used buzzwords in today’s modern business landscape, the use of BI systems has proven to be beneficial for organizations (Goewey, 2015).  Goewey notes the 10 most obvious benefits of implementing Business Intelligence software for a business are that it removes guesswork, gives you quicker responses to your business-related queries, the ability to obtain important business metrics reports whenever you need them, valuable insight gained into your customer’s behavior, pinpointed up-selling as well as cross-selling opportunities, streamlining operations, developing efficiency, knowing what your real manufacturing costs are, conducting better inventory, and gaining a better understanding of your business’ past, present,  and future.

Organizations are inundated with data about their customers, prospects, internal business processes, suppliers, partners, and competitors, but often cannot leverage this flood of data to convert it to actionable information for growing revenue, increasing profitability, and efficiently operating the business (Sherman, 2015).  Implementation of Business Intelligence and Analytics systems offers the ability to make better business decisions.  Business Intelligence (BI), Business Analytics (BA), and Predictive Analytics (PA) systems enable knowledge workers to transform data into information that will help their business.  The market for these systems is vibrant and constantly innovating and evolving to meet the ever-expanding needs of businesses of all sizes and industries.

 

 

Why I Care

Using Business Information and Analytics systems are imperative for effective decision making within an organization.  Businesses must implement Business Intelligence (BI) systems utilizing a Business Intelligence Competency Center (BICC) to stay agile in our modern, data-driven business environment.  There is an overflow of data available to organizations, so implementing effective systems are essential to get the most value from the available data.

For the group project, this information will be valuable being that SF Travel has so many complex and robust systems.  It is essential that these systems operate cohesively to maximize the value of the systems and the data within these systems. The systems in place will need to manage data both internally and externally for a wide range of operational usage.

Professionally, this information, along with the information in Case Study 2, has been extremely relevant to my current work projects.  I am heading the operations for a brand-new, small business and currently researching different systems for us to maximize our time, work, and efficiencies.  I now know first-hand how many moving parts there are to operating even the smallest of businesses, and the importance of your systems not only integrating, but integrating in a way that leverages all features for a collaborative, efficient business ecosystem.

References

Business Intelligence Competency Center (BICC). (2017). Retrieved from: http://www.gartner.com/it-glossary/bicc-business-intelligence-competency-center/

Business Intelligence Software. (2017). Retrieved from http://www.webopedia.com/TERM/B/Business_Intelligence_software.html

Business Intelligence Software. (2017, January 25). Retrieved from: https://en.wikipedia.org/wiki/Business_intelligence_software

Foster, K., Smith, G., Ariyachandra, T., & Frolick, M. N. (2015). Business Intelligence Competency Center: Improving Data and Decisions. Information Systems Management, 32(3), 229-233.

Goewey, B. (2015, October 31). The 10 Most Important Benefits of Business Intelligence. Retrieved from: http://www.datamensional.com/the-10-most-important-benefits-of-business-intelligence/

Halladay, S. D. (2013). Using Predictive Analytics to Improve Decisionmaking. The Journal of Equipment Lease Financing (Online), 31(2), B1-B6.

Harris, D. (2016, December 9). Compare Predictive Analytics Software. Retrieved from http://www.softwareadvice.com/bi/predictive-analytics-comparison/#buyers-guide

Harris, D. (2017, January 28). Compare Business Intelligence (BI) Software Tools. Retrieved from: http://www.softwareadvice.com/bi/#buyers-guide

Havlena, M. (2013, January 27). What is Business Analytics? Retrieved from http://www.havlena.net/en/business-analytics-intelligence/big-data-analytics-part-1-what-is-business-analytics/

Heisterberg, R., & Verma, A. (2014). Creating Business Agility:  How convergence of Cloud, Social, Mobile, Video, and Big Data Enables Competitive Advantage. John Wiley & Sons. ISBN 978-1-118-72456-9.

Lee, M. T., & Widener, S. K. (2016). The Performance Effects of Using Business Intelligence Systems for Exploitation and Exploration Learning. Journal of Information Systems, 30(3), 1-31.

Predictive Analytics. (2017). Retrieved from: http://www.webopedia.com/TERM/P/predictive_analytics.html

Purba, H. R., Ray, S., & Kumar, P. (2013). Business analytics: A perspective. International Journal of Business Analytics and Intelligence, 1(1), 1-12.

Rouse, M. (2013). Business Analytics (BA). Retrieved from http://searchbusinessanalytics.techtarget.com/definition/business-analytics-BA

Sankar, D. 2013, July 12. Business Intelligence Competency Center, the Glue that Hold Business and Technology Together. Retrieved from https://blogs.sap.com/2013/07/12/business-intelligence-competency-center-the-glue-that-hold-business-and-technology-together/.

Sherman, R. (2015, June). Understanding BI Analytics Tools and Their Benefits. Retrieved from: http://searchbusinessanalytics.techtarget.com/feature/Understanding-BI-analytics-tools-and-their-benefits

Viaene, S. (2008). Linking business intelligence into your business. IT Professional Magazine, 10(6), 28-34.