In my previous post, I’ve introduced the idea of architecture t-shirt sizes to depict the idea that you BI architecture should growth with your requirements. In this blog post I position the four example t-shirt sizes on Damhof’s Data Management Quadrants.
T-Shirt Sizes in the Context of Data Management Quadrants
In [Dam], Ronald Damhof describes a simple model for the positioning of data management projects in an organization. Here, he identifies two dimensions and four quadrants (see also Figure 1). On the x-axis, Damhof uses the terms push-pull strategy, known from business economics. This expresses how strongly the production process is controlled and individualized by demand. On the right or pull side, topic-specific data marts and from them information products such as reports and dashboards, for example, are developed in response to purely technical requirements. Agility and specialist knowledge are the key to this. The first two T-shirt sizes, S and M, can be categorized as belonging on this side. On the left or push side, the BI department connects various source systems and prepares the data in a data warehouse. The focus here is on economies of scale and deploying a stable basic infrastructure for BI in the company. Here we can see the other two T-shirt sizes, L and XL.
On the y-axis, Damhof shows how an information system or product is produced. In the lower half, development is opportunistic. Developers and users are often identical here. For example, a current problem with data in Excel or with other tools is evaluated directly by the business user. This corresponds to the S-size T-shirt. As can be seen in my own case, the flexibility gained for research, innovation, and prototyping, for example, is at the expense of the uniformity and maintainability of results. If a specialist user leaves the company, knowledge about the analysis and the business rules applied is often lost.
In contrast, development in the upper half is systematic: developers and end users are typically different people. The data acquisition processes are largely automated and so do not depend on the presence of a specific person in daily operations. The data is highly reliable due to systematic quality assurance, and key figures are uniformly defined. The L- and XL-size T-shirts can be placed here in most cases.
The remaining T-shirt, the M-size, is somewhere “on the way” between quadrants IV and II. This means it is certainly also possible for a business user without IT support to implement a data mart. If the solution’s development and operation is also systematized, this approach can also be found in the second quadrant. This also shows that the architecture sizes are not only growing in terms of the number of levels used.
The positioning of the various T-shirt sizes in the quadrant model (see Figure 2) indicates two further movements.
The movement from bottom to top: We increase systematization by making the solution independent of the original professional user. In my own dashboard, for example, this was expressed by the fact that at some point data was no longer access using my personal SAP user name but using a technical account. Another aspect of systematization is the use of data modeling.
While my initial dashboard simply imported a wide table, in the tabular model the data was already dimensionally modelled.
The movement from right to left: While the first two T-shirt sizes are clearly dominated by technical requirements and the corresponding domain knowledge, further left increasing technical skills are required, for example to manage different data formats and types and to automate processes.
Summary and Outlook
Let’s get this straight: BI solutions have to grow with their requirements. The architectural solutions shown in T-shirt sizes illustrate how this growth path can look in concrete terms. The DWH solution is built, so to speak, from top to bottom – we start with the pure information product and then build step by step up to the complete data warehouse architecture. The various architectural approaches can also be positioned in Ronald Damhof’s quadrant model: A new BI solution is often created in the fourth quadrant, where business users work exploratively with data and create the first versions of information products. If these prove successful, it is of particular importance to systematize and standardize their approach. At first, a data mart serves as a guarantor for a language used by various information products. Data decoupling from the source systems also allows further scaling of the development work. Finally, a data warehouse can be added to the previous levels to permanently merge data from different sources and, if required, make them permanently and historically available.
Organizations should aim to institutionalize the growth process of a BI solution. Business users can’t wait for every new data source to be integrated across multiple layers before it’s made available for reporting. On the other hand, individual solutions must be continuously systematized, gradually placed on a stable data foundation, and operated properly. The architecture approaches shown in T-shirt sizes provide some hints as to what this institutionalization could look like.
This article was first published in TDWI’s BI-SPEKTRUM 3/2019
The boss needs a new evaluation based on a new data source. There isn’t time to load the data into an existing data warehouse, let alone into a new one. In this article I introduce the idea of architectural T-shirt sizes. Of course, different requirements lead to different architectural approaches, but at the same time, it should be possible that architecture can grow as a BI system is expanded.
T-shirt sizes for BI solutions
The boss needs a new evaluation based on a new data source. There isn’t time to load the data into an existing data warehouse, let alone into a new one. It seems obvious to reach for “agile” BI tools such as Excel or Power BI. After several months and more “urgent” evaluations, a maintenance nightmare threatens. But there is another way: In this article I introduce the idea of architectural T-shirt sizes, illustrated using our own dashboard. Of course, different requirements lead to different architectural approaches, but at the same time, it should be possible that architecture can grow as a BI system is expanded.
The summer holidays were just over, and I was about to take on a new management function within our company. It was clear to me: I wanted to make my decisions in a data-driven way and to offer my employees this opportunity. From an earlier trial, I already knew how to extract the required data from our SAP system. As a BI expert, I would love to have a data warehouse (DWH) with automated loading processes and all the rest. The problem was that, as a small business, we ourselves had no real BI infrastructure. And alongside attending to ongoing customer projects, my own time was pretty tight. So did the BI project die? Of course not. I just helped myself with the tool I had to hand, in this case Microsoft Power BI. Within a few hours the first dashboard was ready, and it was published in our cloud service even faster.
The Drawbacks of Quick and Dirty
My problem was solved in the short term. I could make daily-updated data available to my employees. Further requirements followed, so I copied the power BI file and began fitting it in here and there. Over the next few weeks, I added new key figures to it and made more copies. However, I was only partly able to keep the various dashboard copies “in sync”. In addition, operational problems came up. Of course, I had connected the SAP system under my personal username, whose password has to be changed regularly. This in turn led to interruptions to the data updating and required manual effort in reconfiguring the new password in Power BI.
T-Shirt Sizes for Step-by-Step Development of BI Architecture
I guess a lot of professional users have been in my shoes. You have to find a solution on the quick – and before you know it, you’re in your own personal maintenance nightmare. At the same time, a fully developed BI solution is a distant prospect, usually for organizational or financial reasons.
As an expert in the adaptation of agile methods for BI projects, I have been dealing with this problem for a long time: How can we both address the short-term needs of the professional user and create the sustainability of a “clean” solution? As a father of two daughters, I remembered how my children grew up – including the fact that you regularly have to buy bigger clothes. The growth process is continuous, but from time to time we have to get a new dress size. It is exactly this principle that can also be applied to BI solutions and their architecture. Figure 1 shows four example T-shirt sizes, which are explained in more detail below.
Think Big, Start Small: The S-Size T-Shirt
This approach corresponds to my first set of dashboards. A BI front-end tool connects directly to a source. All metadata, such as access data for the source systems, business rules, and key figure definitions, are developed and stored directly in the context of the information products created.
This T-shirt size is suitable for a BI project that is still in its infancy. It is often only then that a broader aim is formulated involving everything that you would like to analyze and evaluate – this is when “thinking big” begins. Practically, however, not much more known than the data source. But you would also like to be more explorative and produce first results promptly, so it makes sense to begin with the small and technically undemanding.
However, this approach reaches its limits very quickly. Here is a list, by no means complete, of criteria for deciding when the time has come to think about the next architectural dress size:
There exist several similar information products or data queries that use the same key figures and attributes over and over again.
Different users regularly access the information products, but access to the data source is through the personal access account of the original developer.
The source system suffers from multiple, and mostly very similar, data queries of the various information products.
Developing a Common Language: The M-Size T-Shirt
In this size, we try to save commonly useful metadata such as key figure definitions and the access information for the source systems at its own level rather than the information product itself. This level is often also called the data mart or the semantic layer. In the case of my own dashboard, we developed a tabular model for it in Azure Analysis Services (AAS). The various specifications or copies of the dashboards as such in large part remained – only the substructure changed. However, all variants now rested on the same central foundation. The advantages of this T-shirt size in comparison to the previous are clear: Your maintenance effort is considerably reduced, because the basic data is stored centrally once and does not need to be maintained for every single information product. At the same time, you bring consistency to the definition and designation of the key figures. In a multilingual environment, the added value becomes even more apparent, because translations are centralized only once and maintained uniformly in the semantic layer. All your dashboards thus speak a common language.
In this M-size t-shirt, we still do not store any data permanently outside the source. Even if the source data is transferred to the tabular model in AAS, it must be re-imported for larger adjustments to the model. Other manufacturers’ products come completely without data storage, for example, the Universes in SAP BusinessObjects. This means that a high load is sometimes applied to the source systems, especially during the development phase. Here is a list of possible reasons to give your “BI child” the next largest dress size:
The load on the source systems is still too large despite the semantic layer and should be further reduced.
The BI solution is also to be used as an archive for the source data, for example in the case of web-based data sources where the history is only available for a limited period of time.
If the source system itself does not historize changes to data records, this can be interpreted as another reason for using the BI solution as a data archive.
Decoupling from the Source: The L-Size T-Shirt
The next larger T-shirt for our BI architecture, the L-size, replaces the direct data access of the data mart to the source. To do this, the data is extracted from the source and permanently stored in a separate database. This level corresponds to the concepts of a persistent staging area (PSA) (see also [Kim04] pp. 31ff. and [Vos]) or an actively managed data lake (see also [Gor19], for example p. 9). They all have one thing in common: that the data is taken from the source with as little change as possible and stored permanently. This procedure means that the existing data mart can be reassigned to this new source relatively easily. In my own dashboard example, we’re not at this stage yet. But in the next step, we have planned to extract the SAP data using the Azure Data Factory and store it permanently in an Azure SQL database. For people who, like us, use the ERP as a cloud solution, this layer reduces the lock-in effect of the ERP cloud provider. Other advantages of this persistent data storage beyond the source systems include the archiving and historization function it brings with it: Both new and changed data from the source are continuously stored. Deleted records can be marked accordingly. Technically, our data model becomes very close to the data model of the source we use, and under certain circumstances, we are already harmonizing some data types. While this layer can be realized practically and fast, there are also indicators here when to jump to the next T-shirt size:
The desired information products require integrated and harmonized data from several data sources.
The data quality of the source data is not sufficient and can’t simply be improved in the source system.
Key calculations should be saved permanently, for example for audit purposes.
Integrated, Harmonized and Historicized Data: The XL-Size T-Shirt
The next, and for the time being last, T-shirt, the XL-size extends the existing architecture to a classical data warehouse structure between PSA and a data mart. It foregrounds the integration and harmonization of master data from the various source systems. This is done using a central data model, which exists independently of the source used (for instance, a data vault model or a dimensional model). This enables systematic data quality controls to be carried out when initially loading and processing data. Historization concepts can also be integrated into the data here as required. The persistent storage of data in this DWH layer means it is also permanently available for audit purposes.
The various T-shirt sizes don’t only differ in the number of levels they use. To characterize the four architectural approaches more comprehensively, it’s worth taking a brief look at Damhof’s model of Data Management Quadrants [Dam]. I’ll do this in the next blog post.
Practically every BI project is about requirements, because requirements communicate “what the client wants”. There are essentially two problems with this communication: the first is that clients often do not end up with what they really need. This is illustrated in the famous drawing in Figure 1: What the customer really needs.
The second problem is that requirements can change over time. Thus, it can be that, especially in the case of long implementation cycles, the client and the contractor share a close consensus about what is wanted at the time of the requirement analysis. By the time the solution goes into operation, however, essential requirements may have changed.
Of course, there is no simple remedy for these challenges in practice. Various influencing factors need to be optimized. In particular, the demand for speed calls for an agile approach, especially in BI projects. I have already written various articles, including Steps towards more agility in BI projects In that article, among other things, I describe the importance of standardization. This also applies to requirement analysis. Unfortunately, the classic literature on requirement management is not very helpful; it is either too general or too strongly focused on software development. At IT-Logix, we have developed a framework over the last ten years that helps us and our customers in BI projects to standardize requirements and generate BI-specific results. Every child needs a name, and our framework is called IBIREF (the IT-Logix Business Intelligence Requirements Engineering Framework)
Overview of IBIREF
IBIREF is divided into three areas:
The area of requirement topics addresses the question of what subjects should be considered at all as requirements in a BI project. I’ll go into a little more detail about this later in this article.
In the requirements analysis process, the framework defines possible procedures for collecting requirements. Our preferred form is an iterative-incremental (i.e. agile) process; I have dealt here with the subject of an agile development process through some user stories. It is, of course, equally possible to raise the requirements upfront in a classic waterfall process.
We have also created a range of tools to simplify and speed up the requirement collection process, depending on the process variant. This includes various checklists, forms and slides.
Overview of requirement topics
Now I would like to take a first look at the structuring of possible requirement topics.
Here are a few points about each topic:
The broad requirements that arise from the project environment need to be considered to integrate a BI project properly. Which business processes should be supported by the BI solution to be developed? What are the basic professional, organizational or technical conditions? What are the project aims and the project scope?
If the BI solution to be created includes a data warehouse (DWH), the requirements for this system component must be collected. We split the data requirements into two groups: The target perspective provides information about the key figures, dimensions and related requirements, such as historiography or the need for hierarchies. This is all well and good, but the source perspective should not be forgotten either. Many requirements for the DWH arise from the nature of the source data. In addition, requirements for metadata and security in the DWH have to be clarified.
The BI application area includes all front-end requirements. This starts with the definition of the information products required (reports, dashboards, etc.), their target publication, purpose and data contents. One can then consider how the users navigate to and within the information products and what logic the selection options follow. One central consideration is the visualization of the data, whether in the form of tables or of diagrams. In this area, advanced standards such as the IBCS provide substantial support for the requirement analysis process (read an overview of my blog contributions to IBCS and Information Design here). The functionalities sub-item concerns requirements such as exporting and commenting. When it comes to distribution, it is interesting to know the channels through which the information products are made available to the users. And it is important to ask what security is required in the area of BI application too.
The issue of requirement metadata is often neglected; however, it is useful to clarify this as early as possible in the project. This concerns the type of additional information to be collected about a requirement: Does one know who is responsible for a requirement? When was it raised, and when was it changed again? Are acceptance criteria also being collected as part of the requirement analysis?
Lastly, requirements need to be collected for the documentation and training required for the use and administration of the BI system.
In this article, I have indicated that requirement analysis presents a challenge, both in general and especially in BI projects. Our IBIREF framework enables us to apply a standardized approach with the help of BI-specific tools. This allows both our customers and us to capture requirements more precisely, more completely and more quickly, thus enhancing the quality of the BI solution to be created.
Upcoming event: Please visit my team and me at our workshop at the TDWI Europe Conference in Munich in late June 2017. The theme is “Practice Makes Perfect: Practical Analysis of Requirements for a Dashboard” (though the workshop will be held in German). We will use the IBIREF framework, focusing on the BI application part, in roleplays and learn how to apply them. Register now—the number of seats for this workshop is limited!
Another aspect of this project is that I’m working with the Microsoft BI stack only. SQL Server 2016 with SQL Server Reporting Services (SSRS) as well as PowerBI is a great experience especially in combination with the IBCS dataviz standard.
Regarding blogs and publications, I was a bit more active contributing German articles:
My personal highlight of today, I’ll be speaking during Agile Testing Days 2017: I’ll do a 2.5 hours workshop regarding the introduction of Agile BI in a sustainable way.
It would be a pleasure to meet you during one of these events – in case you’ll join, send me a little heads-up!
Last but not least, let me mention the Scrum Breakfast Club which I’m visting on a regular basis. We gather once a month using the OpenSpace format to discuss practical issue all around the application of agile methods in all kind of projects incl. Business Intelligence and Datawarehousing. The Club has chapters in Zurich, Bern as well as in Milan and Lisbon.
“We now do Agile BI too” – such statements we hear often during conferences and while discussing with customers and prospects. But can you really do agility in Business Intelligence (BI) and data warehouse (DWH) project directly? Is it sufficent to introdouce bi-weekly iterations and let your employees read the Agile BI Memorandum [BiM]? At least in my own experience this doesn’t work in a sustainable way. In this post I’ll try to show basic root cause relations which finally lead to the desired agility.
If at the end of the day we want more agility, the first step towards it is “professionalism”. Neither an agile project management model nor an agile BI toolset is a replacement for “the good people” in project and operation teams. “Good” in this context means, that the people who work in the development and operation of a BI solution are masters in what they do, review their own work critically and don’t do any beginner’s mistakes.
Yet, professionalism alone isn’t enough to reach agility in the end. The reason for this is that different experts often apply different standards. Hence the next step is the standardization of the design and and development procedures. Hereby the goal is to use common standads for the design and development of BI solutions. Not only within one team, but ideally all over team and project boundaries within the same organization. An important aid for this are design patterns, e.g. for data modeling, the design and development of ETL processes as well as of information products (like reports, dashboards etc.).
Standardization again is a prerequisite for the next and I’d say the most important step towards more agility: The automation of as many process steps as possible in the development and operation of a BI solution. Automation is a key element – “Agile Analytics” author Ken Collier dedicateds even multiple chapters to this topic [Col12]. Because only if we reach an high degree of automation we can work with short iterations in a sustainable way. Sustainable means, that short iterations don’t lead to an increase in technical depts (cf. [War92] and [Fow03]). Without automation, e.g. in the areas of testing, this isn’t achievable in reality.
Now we are close to the actual goal, more agility. If one can release new and changed features to UAT e.g. every two weeks, these can be released to production in the same manner if needed. And this – the fast and frequent enhancement of features in your BI solutions is what sponsors and end users perceive as “agility”.
This blog post is inspired by a recent customer request to challenge their decision to use Design Studio for some “dashboard requirements”. Showing how you can create a dashboard in Webi doesn’t mean I told the customer not to use Design Studio. Much more it is to show that finally a dashboard as well as every other type of BI front end solution is made up of requirements and not primarily by the tool you build the solution. Please refer to my Generic Tool Selection Process for more details as well as my post regarding BI specific requirements engineering.
Having said this, let’s have a look at how we can use latest Webi 4.1 features to quickly build an interactive dashboard without the need of (much) scripting. First of all here is what the final result looks like:
You can select values from the left side bar (Product Lines), you can select States by directly clicking into the table and you can switch from the bar chart to a line chart. Here you see it in action:
The first step to achieve this, is to create the basic table and the two charts. Until the dynamic switch is implemented, I placed them side by side. Next add a simple input control in the left side bar:
Next thing is to define the table as an additional input control – right click the table and choose “Linking” and “Add Element Link”, choose the two chart objects as dependencies:
Next we need to create the “switch” to toggle the two charts. As I would like to position this switch at the top right corner of the chart, I again use a table input control. To generate the two necessary table values (namely “Bar Chart” and “Line Chart”) I prepared a simple Excel spreadsheet:
In 4.1 you can now finally upload this sheet directly into the BO repository:
If you need to update the Excel sheet later on, this is now feasible as well:
Finally, in Webi add the Excel sheet as a second query:
In the report we need now two tables: A visible one to represent the chart switch and a (hidden – see the “Hide always” option) dummy table to act as a dependency for the first:
The most tricky part is to create a variable to retrieve the selected value:
Here the formula for copy / paste:
=If( Pos(ReportFilterSummary(“Dashboard”);”Chart Type Equal “) > 0)
Then Substr(ReportFilterSummary(“Dashboard”);Pos(ReportFilterSummary(“Dashboard”);”Chart Type Equal “) + Length(“Chart Type Equal “);999)
Else “Bar Chart”
(The idea for this formula I grabed from David Lai’s Blog here)
Finally you need to configure the hide formula for both charts:
Positive: I’m not too technical anymore (I do more paperwork than I wish sometimes…). Therefore I don’t consider me a “developer” and I like solutions for the so called “business (power) user” more and more. Therefore I like Webi. It took me about 60 minutes to figure out how to create this kind of interactive dashboard. I didn’t need to install anything – I could do everything web based. Except for one single formula (which I didn’t need to write myself) I could click together the above sample. And I dare to say it looks like some kind of a dashboard 🙂 In addition I have all the basic features of Webi like a broad range of data source support, plenty of export possibilities, Office integration and so on. Even integrating an Excel spreadsheet as a data source is now finally a no-brainer.
Negative: Clearly, Webi is not a “design tool”. For example I wasn’t able to show icons for my chart switch instead of the text lables. Putting a background image to the table doesn’t work well if the table is used as input control. When I discussed this prototype with the customer they also mentioned that there are still too many options end users might get confused with (e.g. that there is a “filter” section showing whether the Bar Chart or the Line Chart value is chosen). In Webi you can’t change that. Toolbars, tabs etc. are just there where they are. Live with it or choose a different tool.
Bottom line: Have a look at my Generic Tool Selection Process and the mentioned hands-on test. The above example is exactly what I mean with this: Create a functional prototype in one or two tools and then do a fact based decision depending on your requirements and end user expectations.
Important remark: This post focused on the technical aspect of the dashboard. The visual representation doesn’t yet fit to best practices mentioned in my earlier articels (e.g. about SUCCESS) In a next blog post I will outline how to optimize the existing dashboard in this regard.
Join my teammate Kristof Gramm during sapInsider’s BI2015 conference at Nice (June 16-18): He will go into much more details about how you can use Web Intelligence as a dashboard tool for business users. Use this link to see more infos and save 300€ on your conference registration!
Illustrate available options using a BI Picture Book
A BI Picture Book is a structured collection of “pictures” aka screenshots of features illustrating one or multiple products. It describes and illustrates the available options in a compact and easy to handle manual. It should help the user to identify what options they have in a given BI front end application. Referring to scenario A and B above, in an ideal world one would create a BI Picture Book during the initial tool selection process (scenario B). In this context, the BI Picture Book helps to illustrate the available features of the different tools under consideration. Some (or all) of these tools will become “strategic” and therefore the preferred tools to be used during subsequent BI projects. In the same way, the corresponding parts of the original BI Picture Book will also be included in the “daily business” BI Picture Book, which only contains the available options regarding the strategic tool set.
One main characteristic of a BI Picture Book is that we compare feature (or requirement) categories one after another and not a tool (with all its different features) after another tool. This helps to clarify specific differences between the tools for each category.
Based on the previously described structure, the BI Picture Book should contain notes which highlight unique features of one tool compared to the rest of available (or evaluated) tools, e.g. a specific chart type which is only available in one tool. On the other hand, one should highlight limitations regarding specific features that are initially “not obvious”, e.g. in cases where the color palette of charts cannot be customized. Another example is to specifically highlight a tool which does not contain an Excel export (because end users might assume that there is an Excel export for every imaginable BI tool, so that they think they do not have to specify this).
How to build a BI Picture Book
Building a BI Picture Book is primarily about taking screenshots and arranging them in a structured manner, e.g. following the seven feature categories introduced above. As with every other project, certain points need to be planned and clarified before you start:
What is the primary purpose of the BI Picture Book? This refers to either scenario A) requirements engineering or scenario B), creating a front end tool strategy.
Which BI tool vendors are to be taken into consideration? Which concrete tools of these vendors are to be integrated into the BI Picture Book? For scenario A) this is defined by the available strategically defined BI toolset. For scenario B) it depends on the procedure for evaluating and selecting tools for your front end tool strategy.
Once you know which tools you want to take screenshots of you need to define which software version to use. Depending on the release cycle of the BI vendor, the software version can make quite a difference regarding available features. Therefore a BI Picture Book is mostly specific to a certain version.
For cars, there are tuning shops which provide extra features not offered by the car manufacturer. Similarly, in the BI world, there are many add-on providers who extend the available features of BI products. If such add-ons are already in place, it is important to include their features in the BI Picture Book. Nevertheless, one shouldn’t forget to label features from add-on products specifically as they might be charged additionally.
Do not show options which are not applicable in practice, e.g. system wide customizations on a multi-tenant BI platform. An example is customizing the look and feel of the BI portal by modifying the portal’s CSS style sheet. Although, in theory, this option might exist, depending on your organizational and technical setup, to changing the style sheet might not be allowed because many other stakeholders would be affected.
After having answered these questions, you can start: Take whatever screen capture program you like and start taking the screenshots. Use either a tool like Microsoft Powerpoint or Word to collect and layout the screenshot in a meaningful way. Keep an eye on the point that the BI Picture Books’ main characteristic is about comparing a specific feature over multiple tools. Therefore, put the screenshots of a given feature for multiple tools side by side on the same page or slide.
The subsequent paragraphs will illustrate how a concrete BI Picture Book might look. Screenshots are taken from various SAP Business Intelligence front end tools.
1. Content Options
Content options are difficult to illustrate using screenshots regarding scenario A). For scenario B) we can, for example, compare the different available data connectivity options:
2. Navigation & Selection Options
For navigation options outside of information, products typically screenshots of a BI portal are to be taken. This can be either based on a vendor specific portal or your company’s intranet site (or both if end users have a choice and need to decide which one to use).
On the other hand, a tool provides navigation and selection features inside information products. We usually take screenshots for at least the following elements:
Parameter & Prompts
Groups / Hierarchy View and Navigation
Drill Down features
Some of these elements are illustrated as follows:
The drill-down example, in particular, shows that it is not enough for an end user to simply specify “we need drill-down functionality” as a requirement. End users need to specify requirements in alignment with the different options of drill-down available.
3. Layout Options
We suggest taking screenshots for the following elements:
Make sure you list all important features and highlight the unique ones as well as limitations that are not obvious. This helps end users to compare the different options. In some cases, it is important to shed more light on the settings of features such as charts. By way of example, specify if it is possible to change the colors of a pie chart?
4. Functional Options
Next up are functional options, for example export. It is quite simple to find the available options and therefore it is easy for end users to choose from the existing options. It is useless, for example, if you let someone define that he wants a PowerPoint export from a front end tool, if it does not exist. Of course this would be nice, but it is simply not part of the catalog.
Another category of functions is printing. Usually it is not precise enough if an end user specifies he needs to print a document. Giving them a picture book, they can easily find out the available printing options. The BI Picture Book should clarify points such as if you can mix landscape and portrait page mode or choose «Fit to page». Below is our list of typical functions which could be integrated into the BI Picture Book:
MS Office Integration
5. Delivery Options
An up-to-date topic which falls into the category of delivery options is mobile-device compatibility. This is becoming increasingly important at a time when all information should be available independent of the end users geographical location. Depending on the BI vendor and the BI tool itself, mobile devices support can differ considerably. Some serve the information products 1:1 to mobile devices. Others transform existing information products into specific mobile versions, which might have quite a different look and feel compared to the original information product.
6. Security Options
As with content options, it is somehow difficult to visualize security options using screenshots in a meaningful way. Try to focus on the comparison aspect between different tools and highlight unique features and limitations that are not obvious. The following example illustrates the available access rights for two different tools. One tool can simply restrict the export functionality in general, whereas the other tool can control the different export formats.
7. Qualitative Options
It is hard to illustrate this category using screenshots. Yet, as indicated in a previous paragraph, you can try to find other illustrations to guide your end users in specifying qualitative requirements.
As with my other blog posts this article doesn’t aim to be a complete list of something. A BI Picture Book is neither the only way to define BI specific requirements nor is it enought to define a complete BI front end tool strategy. It shows you a particular idea and it is up to you to apply it in your organization in combination with other appropriate methods.
Please share your experience – I’m looking forward to reading your comment just below!