Business Intelligence Competency Center (BICC) is a cross-functional organizational team that has defined tasks, roles, responsibilities and processes for supporting and promoting the effective use of Business Intelligence(BI) across an organization.

As early as 2001, Gartner, an information technology research and advisory company, started advocating that companies need a BICC to develop and focus resources to be successful using business intelligence. ^[1] Since then, the BICC concept has been further refined through practical implementations in organizations that have implemented BI and analytical software.

In practice, the term “BICC” is not well integrated into the nomenclature of business or public sector organizations and there are a large degree of variances in the organizational design for BICCs. Nevertheless, the popularity of the BICC concept has caused the creation of units that focus on ensuring the use of the information for decision-making from BI software and increasing the return on investment (ROI) of BI. ^[2]

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.

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.”^[3]

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.

For strategizing on the BICC, here are the processes and current states which will be analyzed

BI Strategy and Roadmap

• Development and Deployment Strategies
• BI Projects in line with organization’s overall BI framework and linked with Integrated Enterprise Performance Management (IEPM)
• Project Deployment Priorities as per overall BI program
• Project Management / Organization Consulting
• Project and System reviews

Project Governance

• Project Scoping and Effort Estimation
• Business and System requirement specification
• Infrastructure (HW,SW, Network) specification
• Use case for test case preparation
• Review and Release planning

Solution Research

• Report layer-Dashboard, CPM, Opera
• /tional Reporting, Internal Compliance, Regulatory Compliance
• Analytical Compliance- OLAP, Analytical Modelling, Data Mining
• Data layer – Data, Prepared data, Meta Data, Data Quality

BI Technical Support (Technology CoE)

• Installation and Upgrade Services
• Tools evaluation and Recommendations
• Methods and tool consulting
• Sharing of best practices and Enforce Technology Standards
• Resolve Technical Query

Infrastructure Management

• Physical: Capacity Planning, Sizing and Performance Tuning
• Physical: Sizing and Performance Tuning
• Logical: Enterprise-wide LDM
• Logical: Analytical Models
• Logical: Analytical Templates

Competency Management

• Usage of BI tools training material
• Training of the project teams
• Training of the end-users
• Workshop of Special topics
• Faculty Development Plan

Knowledge Management/Internal Marketing

• Contact person for Administration
• Asset capture, Create and Share
• User helpdesk for general inquires and information

Contract Management

• Internal Contact processing
• BI tools License Administration
• Central Settlement of Maintenance fees
• Contract with service providing vendors

Coordination with joint development project with between customer and product vendor

• Coordination with Execution groups
• Service group
• Solution Development group
• Support Group
• QA and Testing Group
• Other Corporate IT Group

Pros

Web mining essentially has many advantages which makes this technology attractive to corporations including the government agencies. This technology has enabled ecommerce to do personalized marketing, which eventually results in higher trade volumes. The government agencies are using this technology to classify threats and fight against terrorism. The predicting capability of the mining application can benefits the society by identifying criminal activities. The companies can establish better customer relationship by giving them exactly what they need. Companies can understand the needs of the customer better and they can react to customer needs faster. The companies can find, attract and retain customers; they can save on production costs by utilizing the acquired insight of customer requirements. They can increase profitability by target pricing based on the profiles created. They can even find the customer who might default to a competitor the company will try to retain the customer by providing promotional offers to the specific customer, thus reducing the risk of losing a customer.

Cons

Web mining the technology itself doesn’t create issues, but this technology when used on data of personal nature might cause concerns. The most criticized ethical issue involving web mining is the invasion of privacy. Privacy is considered lost when information concerning an individual is obtained, used, or disseminated, especially if this occurs without their knowledge or consent. The obtained data will be analyzed, and clustered to form profiles; the data will be made anonymous before clustering so that no individual can be linked directly to a profile. But usually the group profiles are used as if they are personal profiles. Thus these applications de-individualize the users by judging them by their mouse clicks. De-individualization, can be defined as a tendency of judging and treating people on the basis of group characteristics instead of on their own individual characteristics and merits.
Another important concern is that the companies collecting the data for a specific purpose might use the data for a totally different purpose, and this essentially violates the user’s interests. The growing trend of selling personal data as a commodity encourages website owners to trade personal data obtained from their site. This trend has increased the amount of data being captured and traded increasing the likeliness of one’s privacy being invaded. The companies which buy the data are obliged make it anonymous and these companies are considered authors of any specific release of mining patterns. They are legally responsible for the contents of the release; any inaccuracies in the release will result in serious lawsuits, but there is no law preventing them from trading the data.
Some mining algorithms might use controversial attributes like sex, race, religion, or sexual orientation to categorize individuals. These practices might be against the anti-discrimination legislation. The applications make it hard to identify the use of such controversial attributes, and there is no strong rule against the usage of such algorithms with such attributes. This process could result in denial of service or a privilege to an individual based on his race, religion or sexual orientation, right now this situation can be avoided by the high ethical standards maintained by the data mining company. The collected data is being made anonymous so that, the obtained data and the obtained patterns cannot be traced back to an individual. It might look as if this poses no threat to one’s privacy, actually many extra information can be inferred by the application by combining two separate unscrupulous data from the user

Text Mining

Text mining, sometimes alternately referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the divining of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. ‘High quality’ in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities).

Personally, I’ve encountered web mining during my internship program at IBM SINGAPORE. IBM has a web portal which they keep track the unique IP addresses and etc, that passes through the network. Base on that, I had to create a report of the findings which I’ve noticed. It certainly helped in various ways such as leads creation, etc.

In regression, the objective is to predict the value of a (usually) continuous variable: tomorrow’s stock price, the fuel consumption of a car, next year’s profits. In this case, the output required is a single numeric variable.

The performance of a regression network can be examined in a number of ways.

1. The output of the network for each case (or any new case you choose to test) can be submitted to the network. If part of the data set, the residual errors can also be generated.
2. Summary statistics can be generated. These include the mean and standard deviation of both the training data values and the prediction error. We would generally expect to see a prediction error mean extremely close to zero (it is, after all, possible to get a zero prediction error mean simply by estimating the mean training data value, without any recourse to the input variables or a neural network at all). The most significant value is the prediction error standard deviation. If this is no better than the training data standard deviation, then the network has performed no better than a simple mean estimator. A ratio of the prediction error SD to the training data SD significantly below 1.0 indicates good regression performance, with a level below 0.1 often said (heuristically) to indicate good regression. This regression ratio (or, more accurately, one minus this ratio) is sometimes referred to as the explained variance of the model.The regression statistics also include the Pearson-R correlation coefficient between the network’s prediction and the observed values. In linear modeling, the Pearson-R correlation between the predictor variable and the predicted is often used to express correlation – if a linear model is fitted, this is identical to the correlation between the model’s prediction and the observed values (or, to the negative of it). Thus, this gives you a convenient way to compare the neural network’s accuracy with that of your linear models.
3. A view of the response surface can be generated. The network’s actual response surface is, of course, constructed in N+1 dimensions, where N is the number of input units, and the last dimension plots the height. It is clearly impossible to directly visualize this surface whereN is anything greater than two (which it invariably is).

Neural Networks

Neural networks can actually perform a number of regression and/or classification tasks at once, although commonly each network performs only one. In the vast majority of cases, therefore, the network will have a single output variable, although in the case of many-state classification problems, this may correspond to a number of output units (the post-processing stage takes care of the mapping from output units to output variables). If you do define a single network with multiple output variables, it may suffer from cross-talk (the hidden neurons experience difficulty learning, as they are attempting to model at least two functions at once). The best solution is usually to train separate networks for each output, then to combine them into an ensemble so that they can be run as a unit.

This topic is quite difficult to understand without in-dept thinking.

1. DO start with just a few business metrics, like profit, revenue, cash flow, new leads, website visitors – whatever you’re measuring and tracking now. DON’T wait until you have worked out all the measures that matter for your business: it will take forever, you’ll get bored with it and you’ll be missing profit pumping opportunities from the measures you already use.

2. DO use basic tools you already have – like Microsoft Excel – until you get into the groove of dashboarding and using your business metrics to manage your business. DON’T invest in flashy software until you know how you want your dashboard to serve you.

3. DO use simple line charts to display your measures so you can see trends over time – the trends are more important than the points. DON’T compare this month to last month, or do any other two-point comparisons like this – you never see the real trends or get any real insights this way.

4. DO include Pareto charts to dig into your data, such as to show you the 20% of marketing campaigns that bring 80% of your customers, or the 20% of sales that bring 80% of the revenue.DON’T ever use pie charts – they absolutely suck when it comes to giving you any kind of useful information from your data.

5. DO let form follow function, and only include the measures that matter to your business, and the graphs that best reveal those measures’ trends and insights. DON’T go all Picasso on it – limit your creativity to choice of colours, not to find out how many forms of bling you can build into it.

6. DO use your dashboard weekly, to look for clues about the best ways to ramp up your business results. DON’T lose the discipline of regularly tracking, testing and tuning your business – sure, the dashboard will only be one input to managing your business but it’s an essential input.

7. DO get help from someone who’s really ofay with Excel and graphs, or someone who is experienced with measuring and tracking. DON’T rely on dashboard software people who can’t demonstrate skills in choosing meaningful measures and displaying them appropriately (they usually get carried away with pretty but useless guages and dials). DON’T freak out trying to do it yourself either – the difference that a business dashboard will make to your success and sanity is too valuable.

Extracted from 7 small business dashboard do and don’ts.

The following link shows examples of good dashboards.

I hope the business dashboard that I will be using in the future would be well organized like the mentioned tips.

What is an OLAP?

It stands for Online Analytical Processing.

OLAP allows business users to slice and dice data at will. Normally data in an organization is distributed in multiple data sources and are incompatible with each other. A retail example: Point-of-sales data and sales made via call-center or the Web are stored in different location and formats. It would a time consuming process for an executive to obtain OLAP reports such as – What are the most popular products purchased by customers between the ages 15 to 30?

Part of the OLAP implementation process involves extracting data from the various data repositories and making them compatible. Making data compatible involves ensuring that the meaning of the data in one repository matches all other repositories. An example of incompatible data: Customer ages can be stored as birth date for purchases made over the web and stored as age categories (i.e. between 15 and 30) for in store sales.

It is not always necessary to create a data warehouse for OLAP analysis. Data stored by operational systems, such as point-of-sales, are in types of databases called OLTPs. OLTP, Online Transaction Process, databases do not have any difference from a structural perspective from any other databases. The main difference, and only, difference is the way in which data is stored.

On the other hand, data warehouse stores data from different data sources in a relational database for end use analysis.

Data from different data sources is stored in a relational database for end use analysis Data is organized in summarized, aggregated, subject oriented, non volatile patterns.

Data is a data warehouse is consolidated, flexible collection of data Supports analysis of data but does not support online analysis of data.

Data warehouse’ and ‘OLAP’ are terms which are often used interchangeably. Actually they refer to two different components of a decision support system. While data in a data warehouse is composed of the historical data of the organization stored for end user analysis, OLAP is a technology that enables a data warehouse to be used effectively for online analysis using complex analytical queries.

I hope my fellow course mates can understand the differences and apply them effectively.

Once you have your base information that you want your users to see, you display them in an appropriate format on the dashboard. Some information is better suited to be in tabular and some can only make sense in a Line chart or Stacked bar chart.
(extracted from BI article)

What is a balanced scorecard?

A balanced scorecard is a business performance management tool for measuring whether the smaller-scale operational activities of a company are aligned with its larger-scale objectives in terms of vision and strategy.

It follows the organization’s strategic theme. In addition, it is a breakdown of objectives, measures, target and initiatives into different business perspectives.

There are 4 perspectives in a balanced scorecard.

• Financial
• Customer
• Internal process
• Learning and growth

The financial perspective examines if the company’s implementation and execution of its strategy are contributing to the bottom-line improvement of the company. It represents the long-term strategic objectives of the organization and thus it incorporates the tangible outcomes of the strategy in traditional financial terms. The three possible stages which are rapid growth, sustain, and harvest. Financial objectives and measures for the growth stage will stem from the development and growth of the organization which will lead to increased sales volumes, acquisition of new customers, growth in revenues etc. The sustain stage on the other hand will be characterized by measures that evaluate the effectiveness of the organization to manage its operations and costs, by calculating the return on investment, the return on capital employed, etc. Finally, the harvest stage will be based on cash flow analysis with measures such as payback periods and revenue volume. Some of the most common financial measures that are incorporated in the financial perspective are EVA, revenue growth, costs, profit margins, cash flow, net operating income etc.

The customer perspective defines the value proposition that the organization will apply to satisfy customers and thus generate more sales to the most desired (i.e. the most profitable) customer groups. The measures that are selected for the customer perspective should measure both the value that is delivered to the customer (value proposition) which may involve time, quality, performance and service, and cost, and the outcomes that come as a result of this value proposition (e.g., customer satisfaction, market share). The value proposition can be centered on one of the three: operational excellence, customer intimacy or product leadership, while maintaining threshold levels at the other two.

The internal process perspective is concerned with the processes that create and deliver the customer value proposition. It focuses on all the activities and key processes required in order for the company to excel at providing the value expected by the customers both productively and efficiently. These can include both short-term and long-term objectives as well as incorporating innovative process development in order to stimulate improvement.The clusters for the internal process perspective are operations management, customer management, innovation and regulatory & social .

The learning and growth perspective is the foundation of any strategy and focuses on the intangible assets of an organization, mainly on the internal skills and capabilities that are required to support the value-creating internal processes. The learning and growth perspective is concerned with the jobs, the systems, and the climate of the enterprise.

(Credits to wikipedia)

Next, we have to link appropriate objectives within each perspective together to form a logical design.

Last but not least, we have to add measures to the different objectives, target to the respective measures and initiatives that we had to do in order to achieve our target.

Before a balanced scorecard…

We have to develop strategic foundation, assessment of internal and external environments and formulation of strategic theme for the organization.

Types of Assessments

• PESTEL
• Porter’s 5 forces
• SWOT analysis

I’ve learn these assessment techniques in 1 of the module, Systems Analysis aka SAS.

Links

Balanced scorecard examples and success stories!

All about balanced scorecard!

I’ve done balanced scorecard in my tutorials and group project hence, I am quite familar with it.

Additional information: A balanced score should not be messy and too populated. The objectives should relate to the strategic theme that you’ve chosen.

Business Intelligence look at the historic data to find out underlying problems or solutions in decision making. It can also find out trends and figures to prepare marketing strategies. Business Intelligence is carried out to gain competitive advantage and is a valuable core competence in some instances.

Tools in Business Intelligence?

Business Intelligence is used in tools such as SAP or Performa. It extracts data from the database/inputs and converts them into visual diagrams or tables which we can easily read it from.

Visualization Types

• OLAP Cube
• Report and Query
• Dashboard

OLAP cube is a multidimensional diagram where data are inputted into it. The cube consists of numeric facts called measures which are categorized by dimensions. The cube’s structure may be created from a star schema or snowflake schema of tables in a relational database such as MySQL. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables.

A static report shows the data on a periodic basis which means that it cannot be generated/updated anytime, anywhere. On the other hand, ad hoc queries can be generated when a user sends a query to the database, the data can appear almost immediately infront of the user.

A dashboard shows the highlight and problem areas which requires immediate attention to the selected users. It is usually shown in visuals and data charts to capture the attention of the user. The key performance indicator would be shown in the dashboard to remind the user what the company is aiming for.

Links

Business Intelligence

Business Intelligence Tools

I feel that these are the key details in topic 1. It is an overview/preview of Business Intelligence and what we will learn in the next few topics.