Deliverable 6 – Data Grain
Support proper formulation of decision-making criteria using Business Intelligence assets.
You work for a tourism board at a top destination within the United States that among other tasks, acts as a third-party reseller of attraction tickets in one of the world’s top vacation destinations. The organization has an e-commerce presence where tickets are sold online, as well as a physical store location where people can go to purchase physical tickets in person. In all instances, the ticket purchases are recorded and referenced back to various marketing databases that allow the organization to see how well different promotional campaigns have done, which products sell better than others, and what time of year the sales are highest and lowest, to name but a few.Jeff, a junior financial analyst, conducts reviews of the ticket sales data with painstaking detail, often producing reports that show detail lines for each individual sale from the online web storefront and the physical store. Strategic decisions regarding sales are often made based on a few small examples of individual rows from the report and often do not always reflect the sales trends accurately.The board director does not see any issue with the reports that Jeff presents, in fact hailing his work as the best analysis he’s ever seen. You have been asked to discuss the current reporting practice with the board, including Jeff, in hopes you can offer some suggestions on how the data can be better presented, as well as explain why there is too much detail in the reports to support effective decision making.
The questions they are presenting you with include:

What is the major issue with how Jeff is using the data?
Why is too much data grain not necessarily a benefit to the decision-making process?
What might be a better measure of the success of ticket sales than line by line review of every ticket sold?

The task:
Record a presentation using the screen sharing Webware/software of your choice (an Internet search will reveal many free options). Your presentation can be recorded with your own voiceover and visuals, just as you would if you were giving the presentation live.Your presentation should explain the concepts contained within the DIKW Triangle. Explain how it applies in the decision-making process for ticket sales, as well as making suggestions on different report granularity, and how ticket sales data may be better presented through aggregation over months, quarters, and years.Create a summary report to accompany your presentation as a takeaway for your executive management team.

DIKW Pyramid

The DIKW pyramid is a visual representation to show the relationship between data, information, knowledge, and wisdom – terms that share similar characteristics and quality at a high level, but are separate and distinct in their own meaning. While working in the business intelligence space, it is important to understand the differences between these terms, since they are often used incorrectly and somewhat interchangeably. Let us begin by first visualizing the actual model itself. Imagine a pyramid divided into four layers, with the bottom layer being the widest, and each layer above the previous approaching the top of the pyramid.
· The first layer, the widest part of the pyramid base, is the “data” layer. The term data in this example refers to discrete units, such as words, facts, and numerical information. In this simple model, the data is raw and has no meaning, context, or organization. It, therefore, has little value in its current state. This concept is a fundamental departure from the traditional understanding of what people call data since the term is used so generically within general and professional contexts.
· The next layer above the data layer is “information.” This layer is narrower than the data layer, which begins to demonstrate how raw data can be focused and concentrated on creating meaning. Connections are made between pieces of data, and organizational processes are applied, such as sorting, indexing, and grouping. At this point, the data begins to be applied in a specific context or toward a given purpose, and the result is information that begins to take on value.
· The third layer in the pyramid is “knowledge” and it narrows yet again, suggesting that the information derived from data has now been focused on bringing greater value. The knowledge layer differs from the information layer, as it is less about the organization and structure of the small and discrete pieces but more about forming a framework for relating and integrating additional information to generate expertise, skills, and experience. At this point, information becomes useful and is applied in meaningful ways to bring value.
· Finally, the last layer at the top of the pyramid is “wisdom.” This layer effectively integrates all the layers of the model below and allows for the application of concepts, ideas, theories, and understanding. As this is the highest level within the model, it also depicts the highest level of thinking, such as determining between right and wrong, or good and bad.
Based on this description we begin to see how data in its rawest form does not begin to take on value until it is organized and refined and then subjected to application and, finally, interpretation. To further demonstrate this principle, consider the following example as an application of the DIKW pyramid:
➢ Data: The English language and written word.
➢ Information: The novel “Frankenstein,” by Mary Shelley.
➢ Knowledge: Frankenstein was not the monster, it was the name of the doctor who created the monster.
➢ Wisdom: Frankenstein was the monster, because his creation caused destruction and horror.
Many things can be said about the DIKW pyramid and how it captures the relationships described above. In the context of business intelligence, this model should serve as a guiding principle for people working to take data and ultimately derive knowledge, as it does require passing through all levels in the model to reach that end state. Further, the first three layers (data, information, and knowledge) can all be thought of as being either in the past or present. Only the top layer of the model (wisdom) deals with the future, and the application of every layer beneath.
Effective Reporting and Decision-Making
Many businesses, as well as the individual teams and departments within, rely heavily on the data presented within the reports and dashboards they produce. This data is necessary for an organization to make decisions that are operational, tactical, and strategic in nature, but the effectiveness of these reports is also directly related to the detail within. Note that detail and accuracy are not the same – data accuracy speaks to the quality or state of being correct and free of error as well as the level of precision. Detail in this context speaks to the discrete-level granularity, or grain, of the information within the report, which allows for it to be segmented into different groups.
The grain of data may vary depending on how it will be used. In some instances, a level of fine grain is required because specifics are required, or the focus may be narrow. In other situations, less granular detail may be sufficient as the focus may be broad. Consider the sales data for a company collected throughout its years of operation. As individual transactions are collected, the data associated may be initially stored at a customer, transaction, or product level. That is, it is possible to get to down to the detail of the purchases made by a single customer, the customers that purchased a particular product, or what transactions contained purchases of a given item. These types of reports may be needed in order to provide basic customer service, or report on sales volume for a given day, week, or even month.
As the timeframe for a report grows, so does the amount of data within it. But as that amount of data increases, the need for finer grain decreases. For instance, a report that shows all the sales for a given day may show a breakdown by major customers, regions, products, etc. But reports that encompass multiple geographic territories or regions would not benefit from such granularity, as these reports are intended are tailored in a picture centered more around a larger audience than specific detail. Similarly, for reports that show figures broken up by month or by quarter (three-month intervals), the need for fine grain is diminished.
These types of reports will use special processes to consolidate the more granular data into distinct subsets of transactions that are better suited for this style of “roll-up” reports (reports that show data at a much higher level of timeframe, region, or division). A quarterly report, for instance, would show sales figures for the first quarter (January through March), second quarter (April through June), and so on. If this report is run after a quarter is complete, there should be no need to re-total all the sales for quarter 1 or quarter 2 since the data will not change. This technique of presenting the same data at different levels of grain is referred to as aggregation. Aggregation can be done based on the timeframe (month, quarter, year), location (city, state, region, country), or product category (product, manufacturing location, warehouse), to name only a few.
As the volume of data increases, it becomes necessary to aggregate the data in a number of different combinations to support growing reporting needs. A quarterly breakdown of sales may not be sufficient detail, yet the individual sales transactions may be too detailed to be meaningful. In these situations, data may have to be aggregated by the different combinations of criteria, such as the month, product, region, sales agent, and so on – the compilation of each of these dimensions is called a data cube, and can be found in larger reporting implementations of a business intelligence team.
Effective reporting of data not only requires a good understanding of the data itself but an equally good understanding of how the data will be presented. Though the initial capture of data may require a fine grain to support transactional operations, sustaining that fine grain becomes prohibitive as the reporting timeframe and dimensions in question increase. Understanding what level of granularity is necessary and how to effectively aggregate the data for reporting purposes is a key component to a successful business intelligence strategy.

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