Business intelligence: Understanding BI, and Case studies

Business analysis is the process of collecting data from varied data sources (it can be a challenge in most cases to get clean data), and then process that data into datasets of information, and then finally render those datasets into meaningful ways so that it can easily be understood by the end-user (and it also needs to be useful to the end-user).

The core of Business Intelligence is reporting.

Doesn’t matter what sort of BI solution a company uses, at its core is reporting; it needs to show meaningful information. And that is why companies employ experts who can generate meaningful information from the huge amount of data that most companies possess.

Business factors that drive business intelligence include:

• Provide more value to the customer based on their past interactions with the company, improved customer service
• Offer better pricing
• More convenience
• Demand forecasting
• Improve safety and overall quality

Business Intelligence – A Definition

“BI – To present data to business people so they can use it to gain knowledge. BI enables access and delivery of information to business users. It is the visible portion of the corporate data systems”….. “BI is what business people see via tools and dashboards. The data comes from relational data sources or enterprise applications such as enterprise resource planning (ERP), customer resource management (CRM)”.

Data & Information

Businesses need information to understand a variety of things: -operations, customers, competitors, suppliers, partners, employees and stockholders.

Using Information helps businesses now what is happening in the business, analyse their operations, react to internal and external pressures, and make decisions that will help them.

Data should now be viewed as a business asset.

Business environment

• The environment in which organisations operate today is becoming more and more complex, creating new challenges and opportunities (e.g. globalization, Brexit)
• Business environment factors: markets, consumer demands, new technologies and societal expectations

Competitive intelligence

Competitive intelligence is one aspect of business intelligence and is limited to information about competitors and the ways in which the knowledge can inform strategy, tactics and operations.

It embraces the entire environment and stakeholders: customers, competitors, distributors, technologies, and macroeconomic data.

A Framework for Business Intelligence

• BI is an evolution of decision support concepts over time
• Then: Executive Information System
• Now: Everybody’s Information System (BI)

Benefits of Business Intelligence

• BI’s major objective is to enable easy access to data (and models) to provide business managers with the ability to conduct analysis
• BI helps transform data, to information (and knowledge), to decisions, and finally to action

Brief history of BI

• 1970s – MIS reporting – static/periodic reports
• 1980s – Executive Information Systems (EIS)
• 1990s – OLAP, dynamic, multidimensional, ad-hoc reporting > coining of the term ‘BI’
• 2010s – Data/Text/Web Mining; Web-based Portals, Dashboards, Big Data, Social Media, and Visual Analytics
• 2020s – yet to be seen

BI System Components

A BI system has four major components:

• Data warehouse, with its source data
• Business analytics, a collection of tools for manipulating, mining, and analysing the data in the data warehouse.
• Business performance management (BPM) for monitoring and analysing performance
• A user interface (e.g., dashboard)

Successful BI implementation

• Involves all levels of the management hierarchy
• Provides what is needed to whom he/she needs it
• Is of benefit to the enterprise as a whole
• Is aligned with the company’s business strategy
• Transforms decision making to a more data/fact/information driven activity
• Operates on a real time / on demand basis
• Improves business processes

Developing or acquiring BI systems

• BI shell?
• In-house versus outside consultants
• Justification and cost-benefit analysis
• Security and protection of privacy
• Integration of systems and applications

BI Maturity Scale

Where is your organization on the BI maturity scale?

• 1: Multiple BI and reporting tools, Chaotic environment, No alignment or consistency, No organization
• 2. Pockets of team working together, Still multiple groups, but starting some collaborative approaches, Virtual team may be in place
• 3. A set of standards chosen, IT and business working together, BI team in place, Gaining consistency in approaches and processes
• 4. Connecting strategy and execution; Formal organizational approach in place; IT, finance and business working together; Technology, people and process standards for BI and PM in place

Analytics

• The analysis of large volume, high velocity and highly variable data types has become a major problem for most businesses.
• In addition to this, increasing numbers of staff within a business are using this new Information e.g.
• Shipping Data is used by Internal Dispatch, Carriers and Customers
• Website Analytics are used by Marketing to measure the success of social media campaigns
• TV Streaming Services compile Data on customer viewing habits and this is used to recommend other movies and products to the customer.

The Importance of Analytics

“Businesses cannot underestimate the importance of their analytics initiatives. While enterprises still need leaders and decision makers with intuition, they depend on data to validate their intuitions. In this sense, data becomes a strategic guide that helps executives see patterns that the otherwise might not notice.” Sherman, R., Business Intelligence Guidebook: From Data Integration to Analytics, (p7), 2015, Elsevier Inc.

• Studies show that businesses with better analytics capability show the following attributes:-
• Improved financial performance
• Have faster decision making processes
• Are more likely to execute decisions as intended
• Will use data more frequently in decision making

Analytics Challenges

• Predictive Analytics: An advanced form of analytics that uses business information to find patterns and predict future outcomes and trends. Determining credit scores by looking at a customer’s credit history and other data is a typical use.
• Data Visualisation: Presenting data in a visual way, such as with graphs and charts, helps business people glean insights they might not otherwise discern from tabular data. Dashboards and Self-Service BI are examples.
• Businesses are concerned that they do not have enough staff with the right digital, high level skills needed.

“The use of analytics is not just growing in volume; it is also growing more complex. Advanced analytics is expanding to include predictive analytics, data visualisation, and data discovery” Sherman, R., Business Intelligence Guidebook: From Data Integration to Analytics, (p7), 2015, Elsevier Inc.

Business Intelligence Roadmap / Lifecycle

“A BI assessment provides a road map to achieve your business and technical objectives in as cost-and-resource-effective manner as possible.” Sherman, R., Business Intelligence Guidebook: From Data Integration to Analytics, (p4), 2015, Elsevier Inc.

The BI Assessment includes:-

• Business and IT Requirements
• Architectures (Data, Information, Technology and Products)
• Organisation and Skills
• Program, Project, Processes and Policies

Requirements Definition

The requirements need to provide a sufficient level of detail in the following areas:

• Business Requirements – High level business requirements, the business processes supported, business rules and metrics
• BI Functional Requirements – Use cases, process workflow and user interaction, analytical styles and functionalities
• Data Requirements – Data sources, data conformance, consistency and currency, data integration, data quality
• Regulatory and compliance requirements – Country, industry, privacy and security

Architectural Framework

“An architectural framework, i.e., a set of of architectural blueprints, is needed as each new BI project is undertaken to enable these projects to complement each other and create a cohesive, cost effective BI solution. The framework needs to be designed to accommodate expansion and renovation based on evolving requirements, capabilities and skills .” Sherman, R., Business Intelligence Guidebook: From Data Integration to Analytics., (p66), 2015, Elsevier Inc.

The BI Frameworks consists of four architectural layers:

• Information Architecture
• Data Architecture
• Technical Architecture
• Product Architecture

Information Architecture

The Information Architecture defines the what, who, where and why for BI analytical applications:

WHAT business processes or functions are going to be supported, WHAT types of analytics are going to be needed, WHAT types of decisions are affected.
WHO will have access – employees, customers, prospects, suppliers, other stakeholders
WHERE the data is now, WHERE will it be integrated, WHERE will it be consumed in analytical applications
WHY the BI solution(s) will be built.

“The Information Architecture defines the business context necessary for successful BI solutions to be built on a sustaining basis. To often IT skips Information Architecture and goes straight to designing the technology and product architectures.” Sherman, R., Business Intelligence Guidebook: From Data Integration to Analytics., (p67), 2015, Elsevier Inc.

Data Architecture

The has been a longstanding acknowledgement that an Enterprise Data Warehouse (EDW) is needed. The classic EDW data workflow is as follows:

Data is created, updated and modified in Systems of Record (SOR), e.g. CRM, Data Services, Enterprise Applications, Unstructured files, Cloud Applications, Business Processes, Big Data etc.

Data from SOR’s is then integrated, transformed and cleansed
Data then loaded into the Enterprise Data Warehouse (EDW)
Data is then accessed by the BI tools for reporting and analysis.

“The Data Architecture defines the data along with the schemas, integration, transformations, storage and workflow required to enable the analytical requirements of the information architecture.” Sherman, R., Business Intelligence Guidebook: From Data Integration to Analytics., (p70), 2015, Elsevier Inc.

System of Record (SOR) – data is captured and updated in various systems.

System of Integration (SOI) – this gathers, integrates and transforms data from SOR’s into consistent, conformed, comprehensive, clean and current information. If a person or process requires integrated data, the SOI should be used.

Systems of Analytics (SOA) – provides business information that has been integrated and transformed to BI applications for business analysis. If integrated and transformed data is required, the SOA is the source.

“One of the best practices for a BI data architecture is to have the EDW serve two different roles: systems of integration (SOI) and systems of analytics (SOA).” Sherman, R., Business Intelligence Guidebook: From Data Integration to Analytics., (p71), 2015, Elsevier Inc.

Technical Architecture

The BI technical architecture is composed of four major layers:

• 1: Business Intelligence – Dashboards, OLAP, MS Office Integration, Data Discovery, Data Visualisation, Big Data analytics, Mobile BI, Ad-hoc Analysis, Reporting and Alerts.
• 2: Data Warehouse and BI Data – BI Repositories, Refined Big Data, OLAP Cubes, Unstructured Databases etc.
• 3: Data Integration
• 4: Data Sources – Application Services, Cloud Databases, Business Processes, Unstructured Documents, Spread sheets etc.

“The technical architecture defines the technologies that are used to implement and support a BI solution that fulfils the information and data architecture requirements”. Sherman, R., Business Intelligence Guidebook: From Data Integration to Analytics., (p72), 2015, Elsevier Inc.

Product Architecture

Before considering which products should be used, First of all you should take time to define the Technical metadata and the Business metadata – metadata is ‘data about data.

Technical Metadata is the description of data as it is processed by software tools, databases for example need to define columns, tables and indexes. This data is used to enable software (not people) to understand and process the data.

Business Metadata is the description of information from the business perspective e.g. weekly sales, budget variance reports. Most of the data the business person cares about is not used by the software tools.

Once the Metadata is defined we can then consider the products.

“The product architecture defines the products, their configurations, and how they are interconnected to implement the technology requirements of the BI framework”. Sherman, R., Business Intelligence Guidebook: From Data Integration to Analytics., (p78), 2015, Elsevier Inc.

Data Collection

Data collection is an important part of Business Intelligence; however, this step can pose a challenge because data in large organizations can be stored in multiple sources and in different standards and formats, but it is an importance step in BI which can help in effective operations. Check-in, ticketing and seat allocation, demand forecasting, pricing, marketing and customer service, all rely on good data to operate effectively.

While automation is important for reducing the time that is usually spent on gathering data, there needs to be an enterprise wide strategy on how to collect, maintain and govern data, considering the importance of data to companies, and the reliance of BI on good data.

Also, once a BI solution is implemented, the various processes should ensure only clean data resides in the data warehouse, which will then feed on to the various business processes and systems of BA.

Once you have a proper BI system in place and with adequate safeguard to keep out bad data, analysts would be in a better position to quickly produce more accurate analyses which can then better support strategic and operational decisions for the business.

Thorough analysis of the data and going through the history of a customer’s interaction can help the company offer better customer service, learn about the peak or lag seasons, offer better pricing, and in general help streamline the business operations.

These are the areas where organizations will need to put in more efforts to derive more benefits from Business Intelligence.

Data Governance Strategy

In the modern business environment, data is an asset for any business. So, companies will need to give more importance to its data and capture it effectively in order to build the company’s competitiveness.

Organizations will need to put in an enterprise-wide data governance strategy where they need to setup a team that will be responsible for managing the data. In order to manage data effectively, besides the person/s governing the data, this team should also include key people from departments such as marketing, sales, customer service, finance and bookings.

Hire Experts

Organizations will need to hire more experts, especially in data science and business analytics, who have the expertise to come with useful models that will make sense of the underlying data.

Business Intelligence helps companies by turning their data into meaningful insights which can enable them to take action, but you need people with the right skillsets who can do this for you. You need experts who can work with data and render them in meaningful ways for use by top management (Steve Seeber, 2018).

Change in Mindset

Most industries have a test-and-learn attitude where they are willing to try new things, learn from their mistakes and in general employ a much faster pace of development.

Unfortunately, you don’t see this in some industries where you will see the various departments work in silos, which ideally should give way to transparency and visibility so that the data from these departments can be used effectively.

This requires a cultural change, and also perhaps a change in organization structure.

Organizations that make the necessary changes should see benefits like revenue improvement, and improved customer satisfaction within a short span of time.

References

Recommended Books

Sherman, R., Business Intelligence Guidebook: From Data Integration to Analytics, 2015, Elsevier Inc.

Steve Seeber (2018) How Business Intelligence Helps Companies [Online] Available at https://www.xtivia.com/how-business-intelligence-helps-companies/

Business intelligence: Assignments, Questions and Answers

Question: Produce a report named “A Critical Analysis and Evaluation of the Impact of Business Intelligence within the Case Study Organisation”.

The report should research the use and impact of Business Intelligence within the Case Study Organisation. The findings should be analysed and evaluated and then recommendations made for organisational development, service/organisational improvement and opportunities for innovation and growth within the Case Study Organisation in relation to Business Intelligence.

Report Structure: 1: Introduction 2: Analysis and evaluation of Business Intelligence within the Case Study Organisation. 3: Recommendations for organisational development, service/organisational improvements and opportunities for innovation and growth in the Case Study Organisation in relation to Business Intelligence. 4: Conclusion

The Report should be professionally written. Your report should be guided by relevant theories and concepts and it is important to include academic research to underpin your analysis and critical findings where applicable. Refer to appropriate books/journals, supporting text books, academic papers and case studies should be included and referenced correctly using Harvard referencing.

Question

Case Study: Zynga Wins with Business Intelligence

1. It is said that Zynga is “an analytics company masquerading as a games company.” Discuss the implications of this statement.

Ans: Zynga’s success has been example for video gaming industries. Zynga used the information available free for them i.e. the data of player frequency, preferences. It didn’t interested much on short term income charging for customers but applied analytics to better improve users experience so that customers are retained more.
It maintained an eco-system of player, games and company data analysis system. Although Zynga’s developers disliked the company’s strategy of data analysis rather than game development it continued the way of retaining customers on simple games instead of plugging users on hard games.

2. What role does business intelligence play in Zynga’s business model?

Ans: Zynga have dedicated Vertica cluster for developing real-time graph of data on daily basis. Zynga’s feeding and notification system is highly influenced by analysis of previous night’s graph. It aims to better match level and type of gift for active players and lowering spamming inactive players.
When Zynga proposes and add certain feature to any game it can quickly get picture of its pro and cons, effectiveness by that day end graph so that they can improve or remove such feature.

3. Give examples of three kinds of decisions supported by business intelligence at Zynga.

Ans: Zynga have system to get day to day analytics graph that interprets terabytes of data. It determines user preference so that user preferences are measured more accurately. Depending upon user interest and involvement it can feed virtual goods likely to be purchased.
Zynga on the other hand have access to players profile, background and interest from their Facebook profile. Most users sharing things on Facebook so Zynga can make this profile a source for greater accurate information.
Zynga attempts to suffer inactive members less through spams but prioritize active ones offering limited edition offers which the gamers prefer much. Similar behavioral users group are detected and targeted for game-related promotions and activities.

4. How much of a competitive advantage does business intelligence provide for Zynga? Explain.

Ans: Zynga accounted $91 million dollar profit in 2010 with revenue of $600 on same year jumping from the figure of $121 in 2009. Zynga followed different track than traditional business and have been role model for some running and some startup game industries. The revenue figure concludes its success of strategy. Zynga doesn’t target revenue from original game but from virtual goods which are offered very precisely with detail analysis of available data.

5. What problems can business intelligence solve for Zynga? What problems cannot it solve?

Ans: Business Intelligent system can be used to reduce spam and promote active players interaction. Moreover it can use data analytics to get information of good and bad products so that they can trash rubbish piling up in their system. It can measure users’ likelihood of accepting changes and upgrades on games by daily updates.
If Zynga underestimated requirement of creativity and only concern data analysis then they fail on success. They parallel should focus on both. Moreover its higher dependency on Facebook make it parasite to it so if Facebook raised some change in its strategies or policies then the direct effect is on Zynga.