Since the birth of science, decision-making has been based on evidence or data. However, to decide, the data must first be processed and analyzed to unearth the patterns in it (Rysavy, Bromley & Daggett 2014). But again, before data is analyzed, it must first be collected. One of the most useful inventions of the information age has been the increase in the methods of collecting data.
According to Rysavy, Bromley and Daggett (2014), the efficient and effective collection of data has only been made possible by the development of the relevant technologies. Perhaps the most important invention of the information age has been the possibility of automating the process of data collection, such as through the sensors, for instance. There are more technologies for data collection than for data analysis (Patil & Guruprasad 2015). For example, the phrase ‘big data’ describes a situation in which information comes in from many sources at a fast pace and in extensive variety. The complex structures in which data is collected have led to challenges in analyzing these data. The science of data analytics developed to offer a solution to this challenge. It enables the decision-makers to get insights without necessarily having to understand to develop models for the same. Visual analytics makes it possible for the managers to make decisions in real-time, as long as the developers have set up ‘intelligent’ algorithms.
Visual analytics has been variously defined. The term was coined by Jim Thomas, who was describing the context of research that the US Homeland Security has adopted (Choo & Park 2013). After Thomas’ usage, the term was later defined as the use of interactive visual interfaces to support analytical thinking processes (Horn & Ellsworth 2012). In modern times, the concept of visual analytics has been expanded to include the combination of interactive visualizations and the methods of automatic analysis to handle massive sets of data to support the process of decision-making. As such, visual analytics has become a multi-disciplinary issue in the contexts of data analysis and management, data processing and statistics as ways through which humans interact with computers.
To understand the utility of visual analytics, it is essential to understand the nature of analytical problems. According to Heer and Agrawal (2007), analytical problems require specific logic to solve them. In many cases, it has been difficult or time-consuming to come up with the methods of rational assessment of these problems. Visual analytics developed as a way of solving these analytical problems, and one of the bases of visual analytics is automatic analysis.
Figure Error! No text of specified style in document.‑1 Visual Analytics Representation (Keim, Mansmann & Thomas, 2010)
Although automatic analysis can party solve analytical problems (such as those with comparison values and concrete measures), it has its shortcomings. Fekete (2013) described that automatic analysis is based on algorithms; these algorithms require that the person designing them to have excellent knowledge of the problem area to define the problem area and possible solutions. If that does not happen, algorithms would fail because they would be limited to a local optimum that does not match the problem at the global level. The other reason why automatic analysis does not always work is that parameters are required to define a problem area, which may be expensive to set up valid and extensive parameters.
Due to the weaknesses of automatic analysis, there has to be another basis for the solution of analytical problems in the age of big data. The good news is that human beings can use their intuition and creativity to recognize patterns in data complex data (Choo & Park 2013). It is on this basis that visualization has become a good way of solving analytical problems. Although visualization still has its limitations, they are fewer compared to those of automatic analysis. Visualization is a useful approach since, unlike automated analysis, human beings have background knowledge about specific analytical problems. This aspect differs significantly compared to algorithmic analysis that may be unable to exhaustively cover (because of developer’s limited knowledge of the nature of the problem or the field), or because it would be too expensive to set up parameters for artificial intelligence. Since automatic analysis and visualization have their shortcomings, it only makes sense to combine both methods to reap from each of their advantages and offset the disadvantages in each. Visual analytics combines both of these approaches to solve analytical problems.
There are many reasons why visual analytics has high utility in the modern world of decision-making. According to Rysavy, Bromley and Daggett (2014), over and above combining the strengths of automatic analysis and visualization, visual analytics ensures that the decision-maker increases their confidence in the decision that they make (the confidence in decisions made of each of the individual methods is lower than when combined). The confidence in visual analytics is so high that it does not think that the resultant decisions can be wrong if the exact scientific process of decision-making was followed (Horn & Ellsworth 2012).
As already implied, there are three possible ways of solving analytical problems. The first two are automatic analysis and visualization. The third one is visual analytics, the intersection between these two. The utility of visual analytics is that it provides not only a path to the solution of the decision problem but also an IT solution. According to Heer and Agrawal (2007), visual analytics makes daily work processes more efficient because automatic analysis collates data from databases: there is no step of data extraction. In his book “Exploratory Data Analysis,” John W. Tukey stated the need for scientists to move from the time-wasting statistical data analysis to “discovering knowledge in databases” (Keim 2010). In most cases, descriptive statistics are an adequate source of information for decision-making, but visualization technologies such as interactive devices and graphical user interfaces enhance this. Visual analytics also makes processes of daily work more effective because it offers summaries of enormous amounts of data, which shortens the time between data processing and decision-making.
- Analytics Using Visual Process
Analytics using visual processes incorporates visual and automatic methods of analysis, although its application requires several phases to be accomplished first. Figure 3-3 shows in a visual representation of all the steps. Arrows represent the transitions between the sections or processes within the sections. The first one is to merge the data from different sources. This is done by first pre-processing the data (grouping, cleaning and normalization) followed by transformation (Keim 2010). Thereafter, one can choose between visual or automatic analysis.
Figure Error! No text of specified style in document.‑2 Visual Analytics Process (Keim, 2010)
For the automatic data analysis process, the following steps are followed: data mining, model estimation and parameter refinement. In order to create a model, these steps of data mining are followed: cluster analysis, association, classification and outlier detection (Larry 2017). The model can be improved by improving the parameters, aided by interactive visualizations that show the results of adopting different algorithms or parameters. The interactive visualizations are, therefore meaningful because they lead to the early detection of any abnormalities or interesting initial insights from the big data being handled. Just as in automatic analysis, data visualization does not only help in gaining new ideas, but also it is useful in improving the overall models. One of the ways of gaining insights into the data is to use different visualizations on the same data points and see which one ‘tells a better story.’ The other method of visual analysis is through zooming in on different data points. Data that is improved through visual analysis can, in turn, be used for automatic analysis; therefore, the two methods are interdependent if used together.
Visual analytics leads to gaining knowledge on the subject issue. In order to make the most out of visual analytics, there has to be an active interaction between the data model, the visualization and the analysis. The more these parties interact, the more knowledge is likely to be gained from the model. At the end of this interaction, a feedback loop has a new data requirement. The inclusion of any new data into the model implies a repetition of the entire process hence better decisions because of the new information. Unlike in the traditional data analysis where getting insights takes time due to lengthy data collation processes, visual analytics leads to real-time decision making because the information is automatically fed into the model and the new results visualized.
In a vehicle, a dashboard provides information on how to operate its safety. In information technology and business management, dashboards offer information that is required to make day-to-day or strategic decisions (Few 2004). The information is provided in a single window or ‘at a glance’ to enable quick decision-making. In order to improve their functionality, dashboards use visual indicators, charts, alert mechanisms, or summary reports. From a cooperate perspective, dashboards are detailed as much as possible to define the decision frameworks of the companies. A dashboard can show the extent to which a company has achieved or deviated from its goals as it uses the performance metrics and indicators.
There are many categorizations of dashboards, depending on the point of view. By considering the uses of a dashboard, they can be grouped into three categories: analytical dashboards, operational dashboards, and strategic management dashboards (Jaiswal 2019). At this point, it is essential to note that the different types of dashboards are tailor-made for specific user groups. However, some user groups may require various kinds of dashboards at the same time.
The strategic management dashboards aim to offer a general overview of the company’s performance over time. It gives the management an idea of how the company is doing for a specific period. This type of dashboard also includes several key performance indicators for the particular timelines (Jaiswal 2019). Importantly, strategic management dashboards usually contain historical data. Therefore, it is possible to know how the business is doing at any given point in time for the various indicators. In addition, strategic management dashboards may contain information on competition in the industry or for a particular product of interest.
Unlike the strategic management dashboards, operational dashboards provide day-to-day data on essential operations. According to Taras (2018), the information provided in operational dashboards is time-sensitive, hence the related tasks urgent. Examples of functional dashboard data types are deviations from expectations and critical levels of resources. For instance, a dashboard for the customer care department would contain the number of calls made, what the main issues were and how many of those issues were not conclusively addressed.
As indicated above, both operational and strategic management dashboards are explorative and descriptive in nature. In the context of analytical dashboards, the aspect of exploration and description is not the main focus. In this case, the goal is to provide details on each of the KPIs and metrics or a clearer understanding of the company data upon which decisions are to be based. Therefore, the analytical dashboards can provide both strategic and operational data, but they are not time-sensitive as the latter are (Taras 2018).
In order to come up with an impressive dashboard, some principles guide how layout, design and charts should be generated and arranged. This section presents the charts layout, design and types of charts to be included in the dashboard.
- Layout and Design
Dashboards present information on a single screen. This implies that one should use the space sparingly, for instance, taking away things that do not need to be on the screen. According to Few (2013), if there is no longer anything else to be taken away, that is when perfection is achieved. But at the same time, data should not be compressed so much that it loses meaning.
Depending on their intended use, dashboard designs can vary widely. Operational, Analytical and Strategic dashboards have a different design approach. Therefore, it is essential before starting with the dashboard design to choose first the dashboard type (JustInMind 2020). This will help the user to identify easily which data are relevant and which types of charts can be used.
There are general guidelines that are followed in arranging charts in a dashboard area. For the sake of enhanced visual perception, it is suggested that the primary data is put at the center while the following important information is placed at the top left corner, followed by the bottom left side, then top right followed by top left. This is because, as per Few (2006), in western countries, people begin reading from left to right, top to down. This proposed basic layout or arrangement of the dashboard is aimed at making visualization easy, which is one of the tools of visual analytics. According to Liebowitz (2010), relevant data must be grouped together. When users have to scroll down or check different pages, they cannot group the data effectively and the data analysis becomes significantly difficult.
The presentation of dashboard charts should be such that the ratio of data pixels to non-data pixels to be as high as possible. According to Few (2013), the pixels that change when the data itself changes are the data pixels. Examples of non-data pixels include, for instance, gridlines, background patterns and the 3D aspects of the charts to be used in the dashboard. These do not add much value to the understanding of the data; they are not data themselves; hence should not be prioritized in relation to data pixels.
In order to arrange the layout of the dashboards in the best way possible, Few (2013) suggests two methods. First, the non-data pixels should not be emphasized. Second, the data pixels should be increased and highlighted. With regard to the first one, the non-data pixels that are not needed should be removed while those that are needed should be limited. With regard to the second one, the data pixels that are not very useful should be removed and those that are the most important to be highlighted.
The highlight of data pixels is a guided process. Guidelines are offered on this so that they can aid visualization. It is suggested that standard colors are used for data whose priority is normal (Few 2013). These colors have a softer effect because they are not highly saturated. If some data has more than normal priority, accentuating colors should be used, but with caution, lest their intended effect is lost. According to JustInMind (2020), it is important to use fixed colour palettes and language in the dashboard. It helps to deliver clear visibility and simple navigation to the user. In addition, the font type and number should be consistent according to specific titles, charts and text in the dashboard.
It is critical for the dashboard to provide consistent labelling and formatting for all the elements of the dashboard. Using different labels can cause confusion and increase the possibility of making mistakes. In addition, this doesn’t help the end-user to proceed faster with the data analysis (Dursevic 2019).
According to Dursevic (2019), another element that makes the design of the dashboard easier and more effective for the user is the functionality that supports interactivity between different individuals. This further enables them to make use of the charts and other elements as temporary filter values. Consequently, by clicking a specified area of interest, the dashboard is easily amended by the filter function.
Selecting a relevant diagram is essential for creating a great user experience. A better diagram helps the audiences to interpret data the right way hence achieve the intended purpose, such as making the right decisions within a department or organization. It also helps the analyst to get insights much more easily since it shows different types of distributions, relationships, compositions, and trends. There are, however, rules that are followed despite the type of diagram that one chooses to represent the dashboard insights. Citing Miller 1956,  advises that the chunks of information in a diagram should be seven plus or minus two; meaning between five and nine. If there is only one value to present, there is no need of using a diagram, as it will even make it harder to understand it.
Some types of diagrams are presented below.
A time series chart is a vital statistical tool that is that is employed in an array of applications to indicate consecutive points of data in specific time intervals. The charts has several points where each point represents quantity and time of data being measured, whereby the vertical axis represents the values being measured while horizontal axis is usually a plot of change in duration. Once points are plotted on the graph, they are then connected using a straight line in their respective order.
Time series charts are very instrumental in illustrating certain trends. This is, however, not easy to perceive when jotting down the variables. But once the graph is drawn, the trends now start appearing and thus they can be used to make future projections.
Bar charts can show many dimensions such as the Y-X-Z axes, but the Y-axis and the X-axis are the most commonly used. It is one of the most commonly used diagram types especially with continuous variables. For a business, a bar chart can be useful, for instance, for comparing product sales per region, quarter, year and the like. They can also be used to analyze the levels of competition in the industry.
One of the important aspects of a business is the issue of time. In order to show trends, line graphs are the best option. It has been good practice to include gridlines in order to make visualization better; the use of data labels is also common if the gridlines are not use. According to Few (2013), a sparkline is a smaller version of the line chart that provides an at-a-glance view of a trend. For the line charts, the comparison of trends can be done across categories. It is usually a good practice to break the lines as it becomes easier to read and interpret. Line graphs are indeed popular as they are easily interpreted: the slope indicates the rate of change. Playing around with color and thickness of the line usually make visualizations easy.
Also known as tree-maps, they consist of connected rectangles whose sizes are proportional to the figure they are linked. They are best for presenting categorically or hierarchically ordered data. The visualizations make sense by varying not only the size of the rectangle but also the color. The larger rectangle would represent a category or hierarchy while the smaller one would represent a sub-category (Few, 2006).
A pie chart is a statistical tool that is used to represent data in form of a circle. The circle has divisions with varying sizes where each sector corresponds to a given data. The pie charts are majorly used to visualize data and not necessarily determine certain trends or future projections. Pie charts are majorly used in the media and business to illustrate data owing to its high visualization.
The requirement analysis brings the vision of a system or application into a detailed and understandable form and documents it. This can range from text documents and use cases to process and data modeling. The result can be used to set business goals for the new system, determine the scope of the project, assess the feasibility of the project, and create an initial work plan.
This section expounds on the specific requirements in the designing of dashboards units. We engaged the project team members through the Skype social platform to gather useful insights and clarify emerging questions that will be useful in the creation of dashboards.
Generally, non-functional and functional requirements are the most common categorization of dashboard requirements. A statement and a description for each of these requirements are provided below. The analysis of the specific requirements will be useful in designing the dashboard as well as the subsequent data sets.
Functional requirements determine how the system supports the user and define all the functionality that should be included in the prototype (Sommerville, 2010). Functional requirements specify a concrete behavior or information that the system such as software attributes, specification of functionalities, the requirement for the interface, scope, functions, purpose as well as the database requirements (Balaji & Sundararajan, 2012).
All the functional requirements are formulated and listed below:
FR1: Display Average Quality Scores
The dashboard must provide different types of average quality scores at a glance. More specifically, the following categories of average quality scores should be included in the dashboard:
- Average Category Score
- Average Dimension Category Score
- Average Rule Score
FR2: Graph Types
The new SAC dashboard must support the following chart types for all the different quality scores: horizontal bar charts, numeric points, and time series charts. Numeric points are needed in order to provide to the end-user a direct presentation of the quality average scores. In addition, the horizontal bar charts are used in order to compare scores based on specific dimensions. Time series charts are used to show trends over time.
FR3: Display Variance
The horizontal bar charts of category scores and dimension category scores must provide the difference in value between the actual scores and the target scores. All the negative variances must be presented in red and positive values in green.
FR4: Descending Order
By default, all the types of scores (category scores, dimension category scores, and rule scores) are displayed in the horizontal bar charts, sorted by score descending, meaning the worst performed will be on top.
FR5: Automatically Update of Charts
The time series and horizontal bar charts should be updated automatically when a specific category or dimension category is selected.
FR6: Backend Navigation
The horizontal bar chart which includes all the quality rule scores by rule name must make possible the navigation to the Data Quality Rule Application in the backend S/4 HANA System. The chart must make available the link to a page when the end-user clicks a specific rule; this page contains an analytical description for the selected rule.
FR7: Customization of the charts
Further customization of the charts must be possible for the end-users, including filters, dimensions, legend and grouping of bars. The dashboard will be delivered to the customers by default. It will contain specific charts, and dimensions. However, the end-users will have the chance to make changes according to their needs.
FR8: Full Screen View
It must be possible to have a full screen view of the Dashboard as a whole, and of the numeric points, horizontal bar charts, time series charts separately as well. All the components of each chart must remain the same in a full-screen view.
FR9: Live Update of Master Data
The new SAC dashboard should provide a live update of the master data reflecting any changes in SAP’s analytical cloud in case alterations are made in the source data.
FR10: Analytics ABAP CDS Views
To support Data Quality Analysis in SAP Analytics Cloud, analytical ABAP CDS Views needs to be provided. ABAP CDS Views improves performance for calculations that are close to the data, by reducing the data volume of communication between the application server and the database. In addition, ABAP CDS Views will help to link data from different databases. More specifically a query view and a cube view must be provided.
Apart from the aforementioned functional requirements, various non-functional requirements for the prototype were identified. In contrast to functional requirements, it is not explicitly stated here what the application should do, but prospective factors (constraints, criteria, limitations, and requirements) that might limit the software on replicating the prescribed behavior must be described (Balaji & Sundararajan 2012).
All the non-functional requirements, which are needed for the implementation, have to fulfill in order to maximize the usability of the dashboards. Below, all the non-functional are described in detail.
NF1: User-Friendly Visualization
The dashboard should be easy to read and understand. It should be clear right away what the dashboard does and how users are supposed to use it. The results will demonstrate the role of the Chief Data Officers and Master Data Specialists and their subsequent interactions with the various dashboards. Additionally, the findings will highlight the relationship between the qualities of different data sets without the risk of overburdening different functionalities especially where their role is irrelevant.
NF2: Adaptation of the user interface
The current FIORI scorecard dashboard is designed to be used with computers. The new dashboard must be adaptable also to other devices such as mobiles and tablets. In concrete terms, this means that all the interface elements must be resized in order to fit to different screens.
NF3: FIORI Analytics Look and Feel
Since the current scorecard dashboard is implemented in FIORI, the new SAC dashboard should be designed according to the new SAP standards for FIORI Analytics. Specific layout guidelines, font formats, color palettes and charts must be applied.
The dashboard will be delivered to the customers by default. However, in order to make the dashboard flexible, users should be able to apply new filters, change dimensions or types of charts according to their needs.
System administrators are the only ones permitted to access, retrieve and alter permission in information systems. The authorization concept must prevent unauthorized persons from accessing the data. The maintainability must be simple. For the connection of the sources, attention must be paid to known sources that can be connected to the new reporting system via live connection.
The precision of calculations is needed in order to provide to the end-users accurate results.
NF7: Supported platforms
In the near future the new cloud Data Quality Management solution will be delivered to the customer. This step wll create the need of unified platform to analyze data and process quality for core master data (SCP) and all different application master data (e.g. S4 OP, S4 CE, Ariba, C4 etc.). The dashboard should be explicitly designed in such a way that different systems can be supported with as little effort as possible.
All the content of the dashboard must be written in English language.
In the following section, all the steps of the dashboard design procedure in SAP Analytics Cloud according to the requirement analysis from section 4.3 are presented and explained. First, all the different ways of providing data for the SAC are explained, followed by the connectivity selection made in the context of this work. Data modeling is also explained, which can be used for analysis by means of interactive data visualizations. At the end of this chapter, a detailed presentation of the SAC dashboard is made.
1.1.1. Data Connection
Importation of data from local files, on-premise databases and cloud database in SAP Analytics Cloud could take place in several approaches. As Figure 5.3 shows, there is a distinction between Import Data Connection and Live Data Connection.
With import data connection, the data is replicated in the SAC databases (SAP SE). This enables the user to make use of the data modelling functions of SAC. By using this connectivity option, changes in the SAC stories are made only when the data models are updated. This can be done either manually by uploading a local file, or by importing once or periodically from databases or file servers. More specifically, manual localized files do not necessarily require a connection when uploading since they are easily integrated through XLSX, TXT and CSV files. In such scenario, source systems such as SAP BW, SQL and ERP act as centres for data importation. Other programs that are used in such instances include Google Drive, SalesForce, SAP SuccessFactors which are all cloud based. The disadvantage of the import data connectivity option is that data collection is limited to about 100 columns, 800,000 rows and 150,000 relationships of a parent-child hierarchy  which can cause problem when we have huge amount of data.
The non-occurrence of replication of data in the SAC database (SAP SE) depends significantly on live data connection. Based on this connections are established to the system consequently limiting the exportation of data to the SAP Analytics. The source system is the only location used for data at this phase. According to Kraus and Kerner (2018), the process of visualization of queries in the SAP Analytics Cloud depends on limited datasets such as metadata. The SAP HANA systems and SAP S/4HANA systems are also dependent on the SAP Analytics Cloud to provide on-premise solutions as well as cloud solutions.
The definition of metadata and content definition is the first action that occurs in the system after carries out analytics in the SAP Analytic Cloud. However, the user has to make a request through the SAC. The intervention of the web browser is vital in completing requests through the AJAX which is conducted directly to enable data query. The web browser also concurrently executes the function of defining content and initiation of analytics process. These processes are important since they enhance the security features of the data in the firewalls since end-users can directly query datasets especially in the web browser without necessarily using the SAC.
In the live data connection, the advantage is that data security depends on the database system or provider selected. Furthermore, data modelling directly on the database level can lead to more efficient calculations and thus faster results. The dashboards and their data visualizations are linked to the actuality of the connected database, which makes them “live” (also called “real-time”).
In the context of this work, the live data connection option is used in order to offer a real-time visualization to the end-users. For the selection of the live data connection three different are necessary. First, it is required to select the SAP HANA and SAP BW system type. Then, the connection with QM7 SAP backend system is selected in client 405. At the end, the data source from the query view is needed. In this case, we use as a data source the sqlviewname MDQLTYANSCORESQR from the query view.
Data models serve as the basis for output of data in SAP Analytics Cloud. Depending on the data connection, a number of data modelling and pre-processing functions are offered.
For import data connections, you can decide whether to create an empty model first, a model based on a local file, a connected data source or application, or by uploading a local file.  For a live data connection, these are very limited, as modeling directly on the database system is intended. The SAC model is created based on that of the live data connection in use.
Models generally consist of rows and columns of data. Each column is defined as a measure or dimension. Measures are numerical numbers to which mathematical functions can be applied. An example is the quantity of items sold. Dimensions are qualitative data such as the item name, geographical dimensions such as the sales location, and time dimensions such as the quarter. [1, p. 33]
Although it is also possible to skip the model creation, it is recommended to use it, as there are many advantages associated with it, as described below. For data preparation, the spelling of entries can be standardized and corrected. For example, “Los Angeles” and “LA” can be unified using a find and replace function. Or it can be created from a single dimension called “City”, with entries following the scheme “Los Angeles, California”, two separate dimensions called “City” and “State”, with the entries “Los Angeles” and “California”. The common term for this is data wrangling. You can also define units and currencies, create hierarchies between several dimensions such as “continent → country → state → state → city” for drilldown functionality, and create formulas such as “price excluding VAT”. 
An important point in the value proposition is the ability to link data sources within SAC. This linkage can be compared with the compound operator “JOIN” of the database language SQL. Similar to SQL, it is possible here to link different data models based on at least one dimension with common values. The advantage is that SAC can be used to connect data sources of different origins, for which data warehouse solutions must usually be used [20, p. 81]. On the one hand, the connection between different data sources is possible at the time of model creation. On the other hand, it is possible to link two already created models within a story. At the time of writing, both variants only supported the linking of import data connections, which is limited to about one million lines. 50] Since the first quarter of 2019, it should be possible for the first time to link different live data connections, such as the data warehouse application SAP Business Warehouse. 
Another advantage of model creation is that they can be used in different stories and by different users. Thus, a data model can be maintained centrally and used collaboratively. For example, general financial data of a company is often relevant for the analyses of different departments, which can therefore be made available to several stakeholders with less effort. 
In developing the project, the programmer created the dashboard based on a model that extensively relied on files retrieved from the PC. In this particular case, the model was provided with a unique name which each coinciding view receiving specific data model. The user will be required to approve the data modelling by ascending to the “OK” button to develop the new model. In this phase, the user is able to define and describe the different data dimensions and key figures as well as varied types of aggregation (limit ranges and decimal places). End users can then create different diagrams by using the data models generated from the preceding phases.
Data visualization of stories in SAP Analytics Cloud is the next step after data collection and modelling. Typically, in the dashboard SAC represents a story. According to Kraus and Kerner (2018, p.26) visualization and analysis of stories can occur after data models are prepared. Several pages can constitute an SAC.
During the creation of a new page, a distinction is made between four types: Smart Discovery, Responsive pages, Canvas and Grid. Smart Discovery pages give automatically suggestions to the user according to the data. This type of page creates a story for the user, which can be easily changed. Specific types of charts or texts can be display in a Smart Discovery page. Canvas and Grid are empty pages where the user adds specific content and they are not adjustable in other devices. Grid pages contain tables with numbers and gives to the users same functionalities like Excel. On the other hand, in Canvas pages various types of charts can be added. Responsive pages help in order to create a flexible story which automatically adjusts to different devices. All the responsive pages consist up to six lanes instead of a blank canvas or grid. The user can insert tiles in each lane, which are automatically resized to fit the allotted space.
Elements such as text boxes, tables, charts, geo maps, check boxes and images can all be incorporated into the pages. Additionally, the functionality allows for the importation of data models into the subsequent stories. Users can also insert elements while linking them with data models at the same time.
Added functionalities in the SAP Analytics Cloud allows for the distinguishing of the display and edit mode. Under the display mode, the end-user interface will be able to observe the pages or stories while being able to amend changes under the edit mode. Other functionality tools in the system allow for the incorporation of stories in addition to the common elements. Page Filters and Story Filters are the most common tools. Depending with the value of selected constructs, story filters allows the system to filter entire stories while in page filter users have the capability to alter and filter pages they own.
Associated analyses link two or more data models by one dimension so that when filtering or sorting a chart, other charts automatically adopt these settings. Input controls allow users to switch between the different metrics of a data model to better analyze and view the data individually.
The design of the dashboard is made according to the new standards of the SAP Fiori for Analytics. Specific guidelines are provided about the layout, font formatting and colour palletes. An analytical description of the SAP Fiori for Analytics standards can be found in the Appendix.
For the realization of this project, a responsive page is used in order to ensure the flexibility of the story in different types of devices. It is essential to create a story that will also be displayed in mobile and tablet devices. Relative data are grouped in different lanes of the SAC story. For the horizontal spacing between the lanes, a header widget is used between the lanes in gray colour.
The dashboard have a with design, as do other user interfaces in the currect scorecard dashboards in Fiori. All the colours are taken from SAP SE’s for Fiori Analytics recommended colour palettes, which are part of the brand identity strategy. Figure 5-7 shows the colour palette, which is used for the representation of the charts in the dashboard. For the representation of variances in charts SAP Analytics Cloud, apply on default specific colours. In addition, all the texts in the dashboard such as headers, charts titles and axis titles have specific font types, colours and sizes according to SAP SE’s standards.
Figure Error! No text of specified style in document.‑3 Colour Palette for Fiori Analytics
In the dashboard, specific page filters have been applied in order to display all the data that are needed. Page filters have been selected because they are invisible to the users, which helps them to interact easily with the dashboard and not becoming overburdened by different functionalities.
In this section, the SAP Analytics Cloud dashboard developed in the context of this work as well as the functions will be explained in more detail. The dashboard consists of one page and provides a real time quality abstract by using an intelligent and integrated approach. All the desired requirements from section 4.3 are visualized.
Figure 5-8 shows an overview of the dashboard which is the contains all the required information about the Data Quality for Products. The dashboard provides all the details the user needs to quickly identify the root cause of quality problems and continuously monitor the data health. More specifically, it contains data which are related with three different types of scores: Category Score, Dimension Category Score and Rule Score.
The SAC story consists of four different lanes which are part of the responsive story creation. The first lane contains the title of the SAC story. The other three lanes represent three different types of scores and contains different types of charts such as horizontal bar chart, time series and numeric point.
Numeric points are used in the lanes for the identification of the average quality scores. These indicators are needed in order to provide to the end-user a direct presentation of the quality average scores.
In the center of each lane, horizontal bar charts provide the quality scores according to a specific category, dimension category, or rule name. By default, each horizontal bar chart displays the five lowest values in order to show at a glance the categories, rules and dimension categories with the lowest data quality. However, as figure 5-12 shows, the end-users can also create their own options if it is needed.
The horizontal bar charts of category scores and dimension category scores also contains another bar chart, which shows the variance between the actual and the target quality scores. All the negative variances are displayed in red and positive values in green.
On the right side of each lane is a time series chart that shows the score trends over time. The colour of each line corresponds to the specific category of the bar chart. Specific data points are shown in the chart when the user moves the mouse pointer in order to help the user to identify the quality score on a specific date.
One function enables the return of all dimensions and key figures of the patient when moving the mouse pointer over the different data points in the respective diagrams.
To compare only certain data, the whole page can be filtered by certain dimensions, such as a patient ID. All diagrams on the page are then automatically updated and show only the filtered data. Thus, it is also possible to search for a specific patient in the clinical study. Figure 4.11 shows such a comparison option, which was realized with a page filter.
As Figure 5-14 shows, in the last lane of the dashboard, the end-user can click a specific rule name from the horizontal bar chart. After the selection, a link is available, which makes possible the navigation of the user to the Data Quality Rule Application in the backend S/4 HANA system.
Data Quality Rule Application in the backend S/4 HANA System provides the user with additional information related to the selected rule. In addition, the end-user can make changes to the rule and send them for implementation.
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