Recency, frequency and intensity (RFI), is an analytical technique typically used in customer relationship management (CRM) (but not exclusive to it). It aims to understand the behaviour or responsiveness [8] of customers within a rolling time period [9]. Depending on the granularity of the time dimension, recency is the temporal measurement of how recently an interaction has occurred [9, 4, 2]. Frequency measures how often interactions occur within the same time period. Intensity measures how productive the interactions have been [9, 4, 2]. Within CRM, intensity is commonly measured monetarily leading to the variation RFM [9, 4, 2].

A Segmentation Approach

RFI takes a segmentation approach to categorising customer behaviour. Hughes suggests segmenting customers by first splitting recency into quintiles [8]. The top twenty percent of customers, ordered by most to least recent, are given a recency score of five. The following twenty percent are awarded a four and so on. This is repeated for frequency and intensity. Each customer is then assigned a combined RFI score. Miglautsch highlights the flexibility of this method by adding weights to each metric depending on its perceived importance [11]. However, this segmentation approach fails to “take advantage of all available customer data and not simply RFM counter variables” [6].

Data Mining and Clustering

An alternative to quintile segmentation is to use data mining to unearth “valid, useful, and understandable” patterns [2]. It aims to answer questions posed by the business, for example how can customers be segmented or which are the most valuable customers [1]. It can find associations between data for example the likelihood of purchase of a product following the purchase of another [1]. Data mining applies descriptive and predictive analytical algorithms such as the K-Mean algorithm [3], Sequential Pattern Mining (SPM) [4] or the CHAID algorithm [9] to data. It segments customers by finding natural meaningful behavioural clusters from trends using the RFI metrics [9]. The discovered clusters are subsequently tagged.

Tagging

Tagging is the process of hierarchically classifying the meaning of identified clusters once the centroids (mean) [3] have been established. A tag will contain as alias together with a description. A customer is assigned a tag based on the cluster they appear in for the period analysed. Tagging allows non-technical users to understand how RFI analysis has classified the customer.
Dimensional Storage

Muntean suggests storing the RFI metrics and tag in the customer dimensions table [12]. If the business is only interested in the current state of customers, this data can be stored as a type 1 slowly changing dimension (SCD). However, it is likely the business will want to analyse customer behaviour over time, and identify how customers migrate from one cluster to another in response to marketing campaigns or seasonal behaviours. Kimball views tags as “facts summarising behaviour” [9]. He recommends storing tags for a set number of time periods in columns in the dimension table using a type 3 SCD. In comparison to using a fact table or a type 2 SCD, less data is generated and comparing tags across time periods is easier [9]. Eventually the number of columns available will be exhausted requiring more columns to be added, or creation of a new row [9]. There is a potential issue if there is a change in granularity, as this will change the periodic meaning of the tag columns. It could be argued based on the granularity of time whether these dimensions are rapidly changing [13]. Excessively wide rows can lead to poor performance so it is prudent to think about the alternative of using a mini-dimension table (type 4 SCD) to store the historical tag values. The current tag dimension will therefore be of type 1 SCD. This solution is a good compromise between the amount of data stored and with the fact table holding a foreign key to the mini dimension table, good performance for querying across periodic tag values.

Advantages and Disadvantages

RFI analysis is not geared towards making predictions unlike statistical models which aim to make predictions on, for example, those customers that will respond to direct marketing. What it can do is provide a business with the means to gain competitive advantage through an understanding of customer behaviour. For example, it can define patterns of similar customer behaviour within clusters and migration from one cluster to another [1]. The tagging of these clusters allows terabytes of data can be boiled down into simple tags [9] for storage.

Businesses already collect and store the data required for RFI analysis. The model is simple, intuitive and cost-effective [11]. It can be used as a base to build on by adding additional metrics and weighting. Wei et al argue that RFI is effective “as the purchase behaviour can be summarised by using a very small number of variables” [15].

Wei et al criticises RFI segmentation which by its very nature focuses on “the best customers” [15]. It can potentially miss the untapped potential of recent customers, who have made a single small purchase. The segmentation approach is also likely to result in a large bucket of 1-1-1 customers, but unlikely statistical analysis, it cannot predict the probability of response to marketing for these customers [5].

The alternative use of Data Mining provides the power to use as many metrics as collected in analysis. Although RFI metrics can be extended to include demographic and other captured attributes, Ye acknowledges that it fails to understand critical purchasing triggers “such as taste/brand preferences or price sensitivity” [16]. It also fails to distinguish between the number of purchases made and the “weight of importance” [16] of the items, for example between multiple apples and one television.

Although the model can make predictions, these will be based on historical data. It is unable to highlight prospective new customers or potential target markets [5].

Applications of RFI

The RFI analytical technique is applicable to a range of areas outside of CRM.
For example, RFI can find the most unresponsive or responsive constituents to direct mailing, whether this is for product advertising, fundraising or monetary donors. This information can then be used for tailored and targeted marketing.
Within the military, studies have used the variation Recency, Frequency, Duration (RFD) to measure the link between combat exposure and alcohol abuse. Within this study, the metrics were extended to include age, marital status and enlistment type. The outcome however found RFI to have limitations within its results, but deemed worthwhile due to it highlighting requirements for a more detailed study [14].

Healthcare spending is a prime example of how RFI analysis can be used effectively to highlight patients with the highest regular expenditure who could be targeted for intervention to minimise long term costs. Recency measures how recently the patient has required treatment. Frequency is the number of treatments within the time period measured. Intensity measures the expenditure over this period. Data mining can use this data to find the clusters of patients who are current, regular and expensive where intervention, such as private treatment, would be recommended. This study can be modified to use RFD to analyse patients with particular ailments that may require regular stays in hospital. An alternative to hospitalisation, such as home care schemes, can be crucial in minimising costs [7]. Data mining can be used to highlight those patients where this intervention can be recommended.

Conclusion

RFI has the ability to highlight behaviours and trends to institutions that would not otherwise be found.

It is not as powerful as more complex statistical modelling tools which are geared towards prediction [5]. In terms of CRM, RFI analysis has the ability to provide competitive advantage to a business through simple customer analysis, providing an understanding of current and past customer behaviour.
RFI has been shown to be a useful tool. It can even be used to compliment predictive models [3].

References

[1] Buttle, F. Customer Relationship Management, 2nd Ed, Elsevier, 2009. Pg 114-117

[2] Chao, P. Fu, H. Lee, H and Chang Y. Identifying The Customer Profiles for 3C-Product Retailers: A Data Mining Approach, International Journal of Electronic Business Management, 2008, Vol. 6, No. 4, pp. 198-202.

[3] Cheng, C. and Chen, Y. Classifying the segmentation of customer value via RFM model and RS Theory, Expert Systems with Applications Journal, No. 36, 2009.

[4] Chen, Y. Kuo, M. Wu, S. and Tang, K. Discovering Recency, Frequency, and Monetary (RFM) Sequential Patterns From Customers’ Purchasing Data, Electronic Commence Research and Applications Journal, No. 8, 2009.

[5] D’Auria, T. The Fall of RFM Analysis, Business Intelligence Unlocked, June 2009.

[6] Drozdenko, R. G. And Drake, P. D. Optimal Database Marketing: Strategy, Development, and Data Mining, SAGE, 2002

[7] Escarrabill, J. Discharge Planning and Home Care for End-Stage COPD Patients, European Respiratory Journal, Vol. 34, Issue 2, pp. 507-512, August 2009

[8] Hughes, A. M. Strategic Database Marketing, 2nd Edition, McGraw-Hill, 2000

[9] Kimball, R. Wrangling Behaviour Tags, Intelligent Enterprise, 2002.

[10] McCarty, J. A and Hastak, M. Segmentation Approaches in Data-Mining: A Comparison of RFM, CHAID and Logistic Regression, Journal of Business Research, Vol. 60, 2007 pp. 656-662.

[11] Miglautsch, J. Thoughts on RFM Scoring, The Journal of Data Marketing, Vol. 8, No. 1, August, 2000.

[12] Muntean, O. Data Warehouse Solutions for CRM, International Conference on Computer Systems and Technologies (CompSysTech), 2004

[13] Ponniah, P. Data Warehousing Fundamentals for IT Professionals, John Wiley and Sons, 2010, pp. 249- 261

[14] Spera, C. Relationship of Military Deployment Recency, Frequency, Duration, and Combat Exposure to Alcohol Use in the Air Force, Journal of Studies on Alcohol and Drugs, Vol 72, Issue 1, pp 5(10), January 2011

[15] Wei, J. Lin, S. and Wu, H. A Review of the Application of RFM Model, African Journal of Business Management, Vol. 4 (19) December 2010

[16] Ye, N. The Handbook of Data Mining, Lawrence Erlbaum Associates, 2003, Pg 259-261

Related posts:

1. Logically Modelling Requirements for Analytical Systems A brief discussion of relational and dimensional logical modelling techniques, to model requirements for analytical systems. The discussion extends into a comparison of some prominent dimensional modelling techniques such as the Bus Matrix, the Starnet Model, the Sun Model and the Star Schema. Click the title above to read more....

Kimball believes this approach is hazardous for analytical modelling [4]. The type of requirements differ with an emphasis on defining aggregated measures like profit, with context supplied by the intersections of base data, or ‘dimensions’ [5]. Running analytical queries against a relational database is likely to result in performance issues [6, 7]. A logical analytical model needs to be easy to navigate with a data structure easily understood by the business user for the database to be used effectively [2]. The model should be created with dialog between the business user and data modeller. This approach can provide clarification for existing requirements, highlight limitations, additional detail, and provide an insight into the business through visualisation. This technique describes dimensional modelling. Kimball states, “Dimensional modelling models the proposed solution from “a user perspective, rather than a data perspective”.” [3]. Dimensional modelling takes data from the transactional system and restructures it into a multidimensional cube creating an optimised, logical model of the data [6].

An analytical matrix like Kimball’s Bus Matrix [6] is a tabular matrix where measures are listed against common dimensions. Marking the intersection signifies a relationship. It depicts which measures are achievable from which set of common dimensions, however does not detail the granular levels of each dimension. It is unclear which slicing of dimensions can be achieved and the potential range of analytical measures the system can provide. It is not instinctive to navigate although there is some clue to the data structure [2]. The author feels that the model is conceptual, placing an emphasis on the business rules and requirements without the critical detail of the logical data structure and relationships between entities.

The Starnet Model [5] shows hierarchical attributes in granular order along a radial line. Each line represents a separate dimension with the most atomic side meeting other dimensions at a centre point. It depicts the granularities available for OLAP operations, like roll-up and roll-down navigation [5]. However there is no representation of the analytical measures. Without these measures the data modeller cannot grasp the underlying aims of the system to produce an optimised model for the requirements. The Sun Model bridges this gap [8].

The Sun Model displays dimensions in granular order along a radial line however the most atomic sides meet at a centre circle containing a list of measures. Each listed measure can be analysed by all combinations of the surrounding dimensions [8]. The model is easy to navigate and provides a bridge between the business user and the physical modeller. This is where the Sun Model excels beyond the Star Schema, the preferred model for “user understandability and predictable high performance, for both atomic and aggregated data.” [8].

Each dimension in a Star Schema is a table listing all granular attributes, and linked to the central Fact table which contains the measures. [11] The granularity of dimension is not depicted. In [8], the Sun model is converted into a physical Star Schema. Kimball states, “The physical model should mirror the logical model as much as possible” [9] indicating that the category of model depends on the level of detail. The reason the Sun Model excels is from the perspective of the business user in understanding, navigating and slicing hierarchies.

The writer has provided a case for the requirements of a good logical dimensional model and highlighted the advantages of the Sun Model over other more prominent alternatives. The success of dimensional modelling is based on the business user expressing their analytical needs correctly. Once the system is created, structural changes may prove difficult due to conformed dimensions producing duplicate data [1]. Both the ER and dimensional approaches to analytical modelling have deficiencies [1, 6]. Inmon [1] and Kimball [6] maintain the two styles are fundamentally different, but others [2] have shown this may not be the case. As systems continue to get faster and memory and storage continues to get larger and cheaper, performance issues may become less prominent. With this in mind, combined modelling techniques such as the Snowflake Schema [12] which attempts to combine the best aspects of both relational and dimensional techniques, may then become more widely used.

References

[1] Inmon, W. H. Building the Data Warehouse, Fourth Edition, Wiley, 2005, pp. 357-376.

[2] Moody, D. L. and Kortink, A. R. From ER Models to Dimensional Models Part I: Bridging the Gap between OLTP and OLAP Design, Journal of Business Intelligence, 8, Summer 2003.

[3] Simpson, G. C. and Witt G. C. Data Modelling Essentials, Third Edition, Elsevier, 2005, pp. 16, figure 1.3.

[4] Kimball, R. Is ER Modelling Hazardous to DSS, DBMS Online, October 1995.

[5] Simpson G. C. and Witt G. C. Data Modelling Essentials, Third Edition, Elsevier, 2005, pp. 485.

[6] Kimball, R. The Data Warehouse Toolkit, Second Edition, Wiley, 2002

[7] Han, J. and Kamber, H. Data Mining: Concepts and Techniques, Second Edition, Elsevier, 2006, pp. 110-126.

[8] Whitehorn, M. Business Intelligence for Small Businesses with SQL Server, iTExpertMag.com, June 2009.

[9] Kimball, R. The Data Warehouse Lifecycle Toolkit, Second Edition, Wiley, 1998

[10] Hoberman, S. Physical or Logical, Information Management Magazine, October 2006.

[11] Malinowski, E. and Zimányi, E. Hierarchies in a Multdimensional Model: From Conceptual Modelling to Logical Representation, Data & Knowledge Engineering 59, 2006, pp. 348-377

[12] Moody, D. L. and Kortink, A. R. From ER Models to Dimensional Models Part II: Advanced Design Issues, Journal of Business Intelligence, Autumn 2003.

Related posts:

1. Recency, Frequency and Intensity (RFI) Recency, frequency and intensity (RFI), is an analytical technique typically used in customer relationship management (CRM) (but not exclusive to it). It aims to understand the behaviour or responsiveness [8] of customers within a rolling time period [9]. Depending on the granularity of the time dimension, recency is the temporal measurement of how recently an interaction has occurred [9, 4, 2]. To read more, click the title above....
2. Software Development Models – Quick And Dirty Guide A quick personal guide to some sequential and agile software development methodologies that i've come across. To read more, please click on the title above....

The above paragraph is simply confusing. The terms data, information and knowledge, are used interchangeably, but it is correct to do so, and is one person’s data another person’s information?

The DIKW model(2) depicts a linear continuity upwards from data to information to knowledge (and wisdom) implying information is derived from data, and knowledge from information. Ackoff views understanding as a separate level above knowledge(3), however Bollinger et al. insist , “it is understanding, that support the transition from each stage to the next”(4). Surely it makes more sense to view understanding as knowing the context to apply. As Callahan stated, “context is a key ingredient acting as an underlay to all three concepts of data, information and knowledge”(5).

Ackoff believes data is, “raw. It simply exists…It does not have meaning of itself” (6). However, it can be argued that data cannot be collected without first understanding what is being collected which implies data has context, although the data may be unstructured, factual and have “nothing to teach us”(7). Snowden states, “Better to think that KNOWLEDGE is one way we collect INFORMATION from DATA”(8). The writer would go further and insist knowledge has gone into defining the data, a view shared by Fricke (9).

One person’s data IS another person’s information if the latter has understanding of the context surrounding the data and is informed. Shedroff states, “If it does not inform, it can’t be information”(10). By arranging and presenting data in ways that can provide answers, we have information to those that understand the questions relating to the “context and intent of the person interpreting it”(11).

Context is applied from real world knowledge already possess. Shedroff states, “Knowledge is more personal and relies on the individuals interpretation of the information absorbed and how it should be applied, hence build from personal experience“(12). Information thus becomes knowledge when it is understood how it can be applied. Einstein agrees stating, “Knowledge is experience. Everything else is information”(13). Some believe knowledge is an iterative process, as Whitehorn wrote, “we take it in and incorporate it into our existing understanding of the world and end up with a deeper and/or broader knowledge”(14).

T.S.Elliot wrote “Where is the knowledge we have lost in information?”(15). It is imperative that the terms and their transitions are properly understood, as Ingebrigtsen states, “you can better understand how to make better decisions based on the accurate facts”(16). Without capturing the correct data, information cannot enlighten, but it can also be difficult to “clarify what is mis-information, dis-information, or propaganda”(17), leading to no or inaccurate applications of knowledge. Although “data is always correct…information can be wrong”, because “the data changes over time”(18), hence the “information may not be an accurate reflection of the data”(19). As Wurman states, “organisation of data itself changes the meaning of it, or at least its interpretation”(20).

So after all this, are we any closer to understanding what is data, what is information and what is knowledge. Looking at the vast numbers of differing points of view, it is simply down to interpretation. One view held in common is that there is a difference between the three terms and that they are not really interchangeable. (The potential danger of mixing the terms has also been discussed). However the main variant in view is of when and how data becomes information, and information becomes knowledge. This can be pictured on a sliding scale for when one term changes into another. Here, the link between context and understanding can be crutial with it’s application being key.
______________________________________________________________________________________________________________________________________________
1 Richard Saul Wurman, Information Anxiety, 1991, pg. 38
2 Nikhil Sharma, The origins of the Data Information Knowledge Wisdom Hierachy, 2004:
http://go.webassistant.com/wa/upload/users/u1000057/webpage_10248.html
3 Gene Bellinger, David Castro and Anthony Mills, Data, Information, Knowledge and Wisdom cites Russell Ackoff: http://www.systems-thinking.org/dikw/dikw.htm
4 Gene Bellinger, David Castro and Anthony Mills, Data, Information, Knowledge and Wisdom: http://www.systems-thinking.org/dikw/dikw.htm
5 Shaun Callahan, Data, Information and Knowledge: a sensemaking perspective, 2006: http://www.anecdote.com.au/archives/2006/03/data_informatio.html
6 Gene Bellinger, David Castro and Anthony Mills, Data, Information, Knowledge and Wisdom cites Russell Ackoff: http://www.systems-thinking.org/dikw/dikw.htm
7 Richard Wurman, Information Anxiety 2, 2000, cites Nathan Shedroff, pg. 28
8 Patrick Lambe citing Dave Snowden at the KM4Dev Listserve
http://www.greenchameleon.com/gc/blog_detail/from_data_with_love/
9 Patrick Lambe, Blowing up the Pyramid, cites Martin Fricke: http://www.greenchameleon.com/gc/blog_detail/blowing_up_the_pyramid/
10 Richard Wurman, Information Anxiety 2, 2000, cites Nathan Shedroff, pg. 28
11 Richard Wurman, Information Anxiety 2, 2000, cites Nathan Shedroff, pg. 28
12 Richard Wurman, Information Anxiety 2, 2000, cites Nathan Shedroff, pg. 28
13 Milan Zeleny. Human Systems Management:Integrating Knowledge, Management and Systems, 2005, pg 5
14 Mark Whitehorn, Data, Information and Knowledge – it’s all about context. Published in the Server Management 2007 paper
15 T.S Eliot, The Rock (poem), 1934
16 N Ingebrigtsen, The Difference Between Data, Information and Knowledge: http://www.infogineering.net/data-information-knowledge.htm
17 Richard Saul Wurman, Information Anxiety, 1991, pg. 42
18 N Ingebrigtsen, The Difference Between Data, Information and Knowledge: http://www.infogineering.net/data-information-knowledge.htm
19 N Ingebrigtsen, The Difference Between Data, Information and Knowledge: http://www.infogineering.net/data-information-knowledge.htm
20 Richard Wurman, Information Anxiety 2, 2000, cites Nathan Shedroff, pg. 28

Related posts:

1. Logically Modelling Requirements for Analytical Systems A brief discussion of relational and dimensional logical modelling techniques, to model requirements for analytical systems. The discussion extends into a comparison of some prominent dimensional modelling techniques such as the Bus Matrix, the Starnet Model, the Sun Model and the Star Schema. Click the title above to read more....

Anyhow, I have just read ‘The Art of Agile Development’ by Shire and Warden, which concentrates on the principles, practices and potential benefits of introducing the Extreme Programming methodology into your development teams. If Extreme Programming is a road you feel you may want to go down, or if you require some help with putting the ideas it brings in practice, I can highly recommend this book. For me, XP is currently not an option with the size of the development team I work in, which has been reduced to me, myself and I, together with an, I guess, Product Manager/Customer/Domain Expert, whilst we both double as testers. However, you never know what the future will bring (but you can hope!), so I wanted to give myself some background knowledge on the current popular methodologies.

So, I am going to start by looking at some of the more conventional models for a Software Development Process or Lifecycle, before moving onto the Agile models. The aim of adopting any such model is to achieve improved ‘productivity’ and ‘quality’ (big buzz words) by using a repeatable and predictable structured process. By incorporating such a model within your project management strategy is to ensure that your projects are delivered ‘successfully’, i.e. on budget, on time and with all the features in the agreed specification. All sounds fantastic in an ideal world where requirements do not change over the length of the project, and design specifications have no mistakes. Up until the revolution that is Agile, this is what we were taught. I also want to mention here that Agile type modelling has probably been used way before the term Agile was coined and applied to defined models. There are probably a large number of software teams who work to their own ideals or models using Agile type processes to enhance the holy grail of productivity and quality, and I should now add to that, delivering what the customer needs to improve their business. All models from what I have read use pretty much the same phases described below, just in a different way. I’m going to discuss these phases, how they work and the ideas behind them in another blog.

The Waterfall Model, the daddy. This uses sequential movement from Analysis (requirements), to Design, to Construction, to Integration, to Testing, to Deployment to Maintenance. This is also known as a top down strategy. This is a strict ideal where once each phase is completed, only then can the next be started. Moving onto the next phase may be dependent upon a review, which I guess can allow some flexibility if changes are required, however once a phase has been formally completed, the model discourages revisiting or changes to the previous phase.

The Big Design Upfront (BDUF) model comes from the idea of the Waterfall model, and advocates investing a large amount of time on design (without the need for prototyping or any initial implementation at this stage) as to reduce the chances of bugs being created and found within later phases.

The Spiral model is similar in phases to the Waterfall model, but like the BDUF model in that the design phase is seen as the most important. It comes with the aim of combining both Design and Prototyping, resulting in a top down/bottom up combo strategy. The idea is iterative, however, not in the way Agile defines iterations. After exhaustively defining the requirements through a number of strategies, emphasis is placed on creating a preliminary design in which all possible alternatives are investigated and strategies are used to determine the best way forward. The aim of this phase is also to find and resolve risk in each element of the design. If risks show there is uncertainty within the requirements, prototyping can be used to find a solution and an update to the requirements can be made. A preliminary scaled down version of the system is then build from the resulting design. This model is then evolved via firstly evaluation (strengths, weaknesses and risks), and then following the model in another iteration of refining requirements, then design, construction and so on. This can then be repeated.

So what is iterative development in the way Agile sees it? Well, it is basically splitting a project up into small independent chunks, and then applying to Software Development Lifecycle to each chunk until the project is created. Agile development uses this is its basis, but it places higher value on communication and feedback through Test Driven Development practices (TDD) and release of features at regular intervals, rather than meticulous planning.

Coming back to the traditional goals of increased productivity, increased quality and development success (on time, on budget, and with all features) I should start by saying that XP is no magic bullet. It is described as a “Way of working differently, not faster, although it has been noted that Agile teams usually have above average productivity”.

So maybe it can hit these goals? But then that depends on whether you define success as stated, or whether this definition of success is limited as it does not take into consideration whether the project hits its business targets of generating cash or improving process or whatever it may be.

This is where an Agile model differs vastly from its predecessors. The aim of an Agile approach is to not only bring personal (increased productivity) and technical (quality of work) success, but also “organisational success”, i.e. to deliver value for decreasing costs by changing and adapting to changing business needs and requirements as and when decisions are made and new data is uncovered. This is done through TDD, regular reviews, continual code improvements and therefore enhanced maintainability.

Let’s look at the most complete Agile model, Extreme Programming or XP. XP gears itself towards technical excellence and code longevity using pairs programming where two people review every piece of code. It focuses on operational success by only releasing new features (a feature contains a number of tasks or stories) when they make business sense. This requires code integration from developers so features are ready for release when required. By only working on a single feature at a time and requiring completion before the next is started, this reduces unexpected delays and allows teams the freedom to change direction when business needs dictate them to. A team will commit to a number of tasks/stories for the length of a single iteration, usually a week or two. In a nutshell, XP is about responding to change and technical excellence. The phases of Analysis (requirements gathering), Design, Construction, Integration and Testing are performed simultaneously each day.

According to the book I just read, XP is about:

• Satisfing customers first with continuous delivery of functionality
• Always welcoming changing requirements
• Preference to shorter timescales for delivery, 2 weeks to 2 months
• Working with the business in a joint effort
• Using working software is the best measure of progress
• Attention to technical excellence for best agility and longevity
• Reflecting continuously to improve efficiency

The Analysis phase will consist of the Product Manager, someone who talks with the business and decides what needs to be done will create the requirements for a feature. Estimates by the coders will then allow more details to be added to the requirements before they can be coded.

The Design phase is integrated with coding and testing. As mentioned previously, TDD is used. The requirements are used to create customer tests, preferably with a customer, or someone who knows how the feature needs to work providing the insight and business logic. The tests are passed on to the coders who in pears create work on tasks/stories of the current feature, constantly refactoring and improving the code base. Once coding is complete, unit, integration and end to end testing are done. The code is then passed over for exploratory and performance/stability testing, with root cause analysis applied to any issues found. Regression testing is applied on integration to ensure nothing else has broken.

Deployment first occurs to a demo system. This is timely at the end of the current iteration. The real deployment is dependent on business needs.

After completion of the current iteration a period called retrospectives occurs. Here, the team talk about what went well, what didn’t, how the process can be improved, and anything which is hindering progress. Finally, the next iteration is discussed and planned.

This is a quick and dirty summary of the XP model, which is far more complex then what I have written here. For more information, I would suggest reading ‘The Art of Agile Development’ by Shore and Warden.

Lastly, I want discuss Scrum, another popular Agile model. Scrum is arguably a scaled down version of XP, for smaller development teams. The aim is to produce a feature, or set of features, which are tested and shippable, within a given time-frame (time-boxed), which rather than being pre-defined, is set by the team.
This iteration is called a Sprint. The features going into the Sprint come from what is known as the Product Backlog, a maintained (by the Scrum Master) set of prioritised (by the Product Manager) high level requirements. Any items not complete are removed from the Sprint. Which items go into the next Sprint is determined at a Sprint planning meeting.

As with XP, the aim of Scrum is to be able to adapt to changing business needs and by working in small Sprints, the focus for development can change and when required.

There are a number of further Agile models such as Feature Driven Development (FDD) built around a core set of industry best practice, Agile Unified Process (AUP) and Dynamic System Development Method (DSDM). I hope to look at these in due course.

Related posts:

1. Logically Modelling Requirements for Analytical Systems A brief discussion of relational and dimensional logical modelling techniques, to model requirements for analytical systems. The discussion extends into a comparison of some prominent dimensional modelling techniques such as the Bus Matrix, the Starnet Model, the Sun Model and the Star Schema. Click the title above to read more....

Contribution: Planning and Requirements gathering, creative design, code structural architecture, project management, design and development, syntax and code block testing, end user training and ongoing maintenance and management.

URL mdemo.ipointweb.net

[View with PicLens]

Technologies VB.Net, ASP.Net 3.5, 51 Degrees Redirection using WURFL, CSS (floating DIV construction), XHTML-MP, SQL Server 2008 and linked SQL Server (Accord Payroll System), linked Sybase ASA (IQX Recruitment Solution) Database and Adobe Fireworks.

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1. Pulse Healthcare’s iPoint Version 3.0 A ground-up re-creation of the Pulse iPoint web application, which is currently used in a number of NHS Trusts for managing Shift, Vacancy and Staff ordering, Online Timesheeting, Invoicing and Reporting. Click on the title above to read more......
2. Pulse Jobs Facebook Application The application allows prospective healthcare job seakerson Facebook, to search our jobs database, based on Sector, Profession, Location, Job Type and Keywords. Click on the title above to read more......
3. Metrodome Consumer Website A dynamically constructed and fully content managed consumer website for Metrodome Distribution. This website forms one of a set of three target driven websites which all use the same data source. Click on the title above to read more......

The all new marbleblue.co.uk is created using Wordpress and a customised version of the Studiopress Lifestyle theme.

This is the second version of the site for which the original was a ASP.Net 3.5 and C# based site, with a seperate Wordpress blog.

Firstly, id like to say a big thank you to James Reeve, a respected network guru, work colleague, and webmaster of singleandflirty.com, for drawing my attension to Brian Gardners Studiopress Wordpress themes. Some of these themes have been SEO optimised by Joost de Valk, a well known Wordpress ninja! After looking into these, I decided to create the all new marbleblue website directly from Wordpress – saved me a bunch of coding and optimising too! This is the first time I have really played with Wordpress, and I have to admit, it has been great fun! It has also inspired to me look deeper into the massive subject that is SEO.

If you are starting off with Wordpress, I recommend having a look at the video by Matt Cutt’s at this years Wordcamp as a starter for ten.

You may also find it worthwhile looking at John Chow’s website, and downloading and flicking through his free ebook. John Chow is a very successful and well known blogger, in terms of generating traffic and money if that is what you are interested in.

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Contribution: Everything, from start to finish!

URL: www.marbleblue.co.uk

Technologies: Wordpress 2.8+, Wordpress plugins, php, mysql, customised (by me) Studiopress Lifestyle Theme.

By the way, for hosting, I am using and I can highly recommend, Rick Stevens’ Soft Sys Hosting.

Related posts:

1. Getting started with Wordpress and SEO, Video by Matt Cutts and Wordcamp 2009 Video and summary of the great presentation by Matt Cutts at Wordcamp 2009 about getting started with Wordpress and SEO. Click on the title to read more and see the Video......

- Install the Video Quicktags plugin by the legend that is Viper (viper007) – dont forget to make a donation

Without using this plugin, and by adding the embeding code directly, I had nothing but a ton of script errors in IE, but as always Firefox was perfect!

- Create a Featured Video category.

- Set the area on your homepage where you want your videos to be displayed, to hit posts from this category.

- Add a post.

- Within the post, create a Custom Field. Call it for example, Video_Link, and for the value, add a quicktag to the video you want to display, e.g.


[ viddler id=bc656bb0&w=280&h=166 ]


where w is the width and h is the height.

- Open home.php in the wordpress editor, and locate the area which you have set to display your featured videos.

- Comment out the following:

<a href="<?php the_permalink() ?>" rel="bookmark"><?php the_title(); ?></a>
<? php the_content_limit(80, ""); ?>

- Add replace with:

// If post contains a vid_link custom field
<?php $vid_link = get_post_meta($post->ID, 'Video_Link', $single = true);
if($vid_link !== '') {
// first display the title
?>
<a href="<?php the_permalink() ?>" rel="bookmark"><?php the_title(); ?></a>
<?php
       // Get the contents of the value of the custom field and apply Quicktag filter
$vid_link = apply_filters('the_content', $vid_link);
      // display video
echo $vid_link;
}
?>

You might notice that if you have other plugins installed, such as TweetMe or Share/Save buttons, unless you have unchecked the ‘display on home page’ checkbox on the plugin settings, this will also be displayed with the video (from the apply_filters command), so I would recommend checking this option to hide them in the home page.

Related posts:

1. Getting started with Wordpress and SEO, Video by Matt Cutts and Wordcamp 2009 Video and summary of the great presentation by Matt Cutts at Wordcamp 2009 about getting started with Wordpress and SEO. Click on the title to read more and see the Video......
2. Wordpress on IIS Cannot Handle 404 File Not Found A solution to the problem where Wordpress sites hosted on IIS cannot handle 404 File Not Found errors using the standard 404.php page, even though it exists. Click on the title above to read more......

- How to do well in Google. ‘Wordpress solves a ton of SEO issues, 80% to 90% of the mechanics of Search Engine Optimisation’, i.e. the crawability of a site.

- Useful Plugins to improve your site, for example, Cookies for Comments (spam prevention). On a side note, I have been looking at Plugins for a couple of weeks now, look out for my Post on this, coming shortly!

- Pagerank, the higher your Pagerank, the faster your pages will be found, the deeper your site gets crawled, and the more often your site gets crawled. It is not just about how many people link to you, but the ‘QUALITY’ of those links.

- Keywords and how they can be used to structure titles and URL’s. The Google Keyword Tool can be used to find top ranking keywords for your subject, hence aid you in picking up traffic via Word Targetting.

- Wordpress Permalinks and how to structure your URL’s to give ‘meaning’ for better ranking.

- Finding your niche and gaining reputation!

Finally, I would just like to say, thank you Matt! – and I hope you like my site!

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1. Adding a Featured Video Section to a Wordpress Homepage Instructions on how I created the Featured Video section on my homepage. Click the title above to read more......
2. marbleblue. website Welcome to my website! I hope Matt Cutts likes it! This site is created using Wordpress with a Studiopress theme. Click on the title above to read more......
3. Wordpress on IIS Cannot Handle 404 File Not Found A solution to the problem where Wordpress sites hosted on IIS cannot handle 404 File Not Found errors using the standard 404.php page, even though it exists. Click on the title above to read more......

Click HERE to download.

Summary:

• search for AD users using LDAP
• view details of a selected AD user using LDAP
• allow AD user’s to edit only their own AD contact details using LDAP

Before using any of these components, you will need to know your LDAP connection string, username and password. Once the components have been added to your Sharepoint site as web parts, you will need to ‘Modify the web part’, to add the connection details.

Once downloaded, copy each dll into the global assembly cache on your Sharepoint server(C:\Windows\Assembley). Right click on the assembly in the GAC to retrieve the ‘Public Key Token’ from the properties.

Add the following entries to the web.config file of your Sharepoint site (change the public key tokens if necessary):

SafeControl Assembly="ActiveDirectorySearch, Version=1.0.0.0,
Culture=neutral, PublicKeyToken=81cbcca0d610705b"
Namespace="Kushel.Com.WSSADSearch" TypeName="*" Safe="True"

SafeControl Assembly="ActiveDirectoryUserProfile, Version=1.0.0.0,
Culture=neutral, PublicKeyToken=b5bf2e0d7a1500e2"
Namespace="Kushel.Com.WSSADUserProfile" TypeName="*" Safe="True"

SafeControl Assembly="ActiveDirectoryEditUserProfile, Version=1.0.0.0,
Culture=neutral, PublicKeyToken=4a6f057aa1895b65"
Namespace="Kushel.Com.WSSADEditUserProfile" TypeName="*" Safe="True"

Please note, these components are very simple and not excessively configurable. If you need help in using them, or want the code to expand on yourself, please add a comment below (and if you have twitter, please hit the tweet button at the start of the article).

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1. Failure adding assembly to the cache: Unknown Error – GACUtil A solution for: Failure adding assembly to the cache: Unknown Error - when adding an Assembly to the Global Assembley Cache (GAC) Click on the title to read more......
2. Sharepoint WSS 3.0 Intranet Portal and Document Repository A Sharepoint WSS 3.0 based Intranet Portal and Document Repository for Pulse Healthcare. Click on the title above to read more......
3. Alphabet Selector Web Server Control A free control will display the alphabet, A to Z, where each letter is clickable. On clicking a letter, an event is raised detailing the letter which was clicked. This letter can then be captured and used as needed, for example to display a set of results beginning with the specific letter. Click the title above to read more......

If you want to limit the character pool the password generator selects characters from or change the maximum number of characters selected in your randomly generated passwords, you will need to implement your own Custom Membership Provider to override this behaviour.

1 – Create a new class in your project and name it say ‘MyMembershipProvider’.
2 – Inherit from the default ‘SqlMembershipProvider’.
3 – Override the Initialize method to reset the password length.
4 – Override the GeneratePassword method with your custom method for generating a password.

The VB.Net code listing can be found at the bottom of this blog entry. If you would like this converted to C#, let me know.

For your Web application to make use of your new Custom Membership Provider, you need to make some changes to your web.config file.

In your web.config file, you will see the following -

<membership defaultProvider="DefaultMembershipProvider">
    <providers>
    ...
    </providers>
</membership>

Update this to

<membership defaultProvider="MyMembershipProvider">
    <providers>
        <add name="MyMembershipProvider" type="MembershipProvider" minRequiredPasswordLength="6" .... />
    </providers>
</membership>

Code listing for MyMembershipProvider class:

Public Class MyMembershipProvider
    Inherits SqlMembershipProvider 

    ' private variable declarations
    Private _random As System.Random
    Private _chars() As String
    Private _passwordLength As Integer 

    ' Constructor
    Public Sub New()
        _random = New Random
        _chars = InitialiseCharArray()
        _passwordLength = 6
    End Sub 

    ' Override Initialize method to reset the password length
    Public Overrides Sub Initialize(ByVal name As String, ByVal config As  system.Collections.Specialized.NameValueCollection)
        Dim _l As String = _passwordLength
        Dim _lc As String = config(”resetPasswordLength”)

        If Not IsNothing(_l) Then
           ' Have to remove the config entry as the provider we are inheriting from
           ' doesn’t understand it and will throw an exception.
            config.Remove(”resetPasswordLength”)
            If Not Integer.TryParse(_lc, _l) Then
               _l = _passwordLength
            End If
        End If
        MyBase.Initialize(name, config)
    End Sub

    ' Procedure to override the password generation functionality and return a random password of 6 alphnumberic characters.
    Public Overrides Function GeneratePassword() As String
        Dim _password As String = String.Empty
        For i As Integer = 1 To _passwordLength
            _password &= GenerateRandomCharacter()
        Next
        Return _password
    End Function

    ' Create a character map of characters we want our passwords to be constructed from.
    Private Function InitialiseCharArray() As String()
        Dim _s(34) As String
        ' Add numbers, 1 to 9.
        For i As Integer = 0 To 8
            _s(i) = i + 1
        Next
        ' Add letters, a to z - lowercase only.
        For j As Integer = 97 To 122
            _s(j - 88) = Char.ConvertFromUtf32(j)
        Next
        Return _s
    End Function  

    ' Procedure to return a random character from our character list.
    Private Function GenerateRandomCharacter() As String
        Return _chars(_random.Next(_chars.GetUpperBound(0)))
    End Function
End Class

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1. Creating a Custom Web Server Control – Alphabet Selector Web Server Control Instructions on how to create a Web Server Control. These instructions will use the example of creating your own Alphabet Selector Control. These principlals can then be applied to creating any control of your choice. Click the title above to read more......
2. Component to Connect to Active Directory with Sharepoint WSS Download a component for your Sharepoint (WSS) Intranet to connect to the Active Directory(AD) via LDAP. This component will allow users to search for colleagues in the AD. It will also allow users to update their own contact details. This component requires Window Authenticated access to your Intranet. Click on the title above to read more......