You may think that an Excel Workbook already provides a table of data to you, and in a sense it does, an area consisting of Rows and Columns, with column and row headings and values.
It is, however, possible to create formal Tables from ranges of cells on an Excel worksheet.
Why would we want to do this?
Well, here are a few reasons:
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Having spent over 25 years working with customers on their data warehouses, there are few mistakes that I haven’t seen made during their construction. The reality in fact, is that many organisations underestimate the complexities of building a data warehouse – or at least, building the RIGHT data warehouse for their business needs.
Here are six crucial aspects to building a data warehouse that will help you avoid the many pitfalls along the way of its construction.
In many instances you could choose either, but 8 key questions to ask before making your decision are:
Are you planning on deploying to Azure Analysis Services or On-Premise Analysis Services?
Do you wish to be able to extend your analytics model with custom dimension fields/expressions?
Do you wish to embed hyperlinks into your analytics model?
Do you wish to carry out ETL operations within your analytics model or using a service such as SSIS prior to building your analytics model?
Do you wish to have automatic support for Ragged Depth Hierarchies and Custom Rollups?
Do you require writeback capability to your analytics model?
Do you have any Multidimensional or Tabular design experience with your business?
Are you planning on using Power BI to deliver enterprise wide reports and dashboards?
This article was inspired after one of our training delegates (Declan!) asked the question “How would you calculate how many egg cartons you need for a given number of eggs?”.
This is a common challenge that we come across. We have an order quantity of a product, we know how many products we can fit in a box, pack or carton, but we want to calculate how many packs we need to be able to ship or deliver the ordered products.
Do you remember the days when business data was just a series of digits presented in a variety of formats, none of which seemed to give you an overall understanding of what you were actually looking at?
The emergence of platforms that compile key business stats into easily understood dashboards and reports, mean that various forms of data visualisation are now being adopted by most companies. In fact, you could go as far to say that it is now a pre-requisite for efficient business management.
Exporting simple tabular SSRS reports to Excel usually doesn’t create too many problems. But what about those reports with multiple regions (maybe mixed matrix, table or reports) and textboxes external to regions? These can be a different case altogether and are particularly prone to producing reports with merged cells.
Of course, merged cells are not necessarily a problem if all you want to do is view or print your Excel workbook. However, they can make it impossible for you to use filters or work with the report data in any meaningful way.
Having spent over 25 years working with customers on their data warehouses, there are few mistakes that I haven’t seen made during their construction. The reality in fact, is that many organisations underestimate the complexities of building a data warehouse – or at least, building the RIGHT data warehouse for their business needs.
Here are six crucial aspects to building a data warehouse that will help you avoid the many pitfalls along the way of its construction.
I’m sure we have all come across a scenario where a parameter report has allowed a user to specify a combination of conditions that results in no matching records for a dataset behind a region in an SSRS report.
If the report has headers, footers or textboxes then there may be some output produced when the report is run to make it obvious that there is no valid data for the report, as in the following example:
Many data analysts will be familiar with exploring data using Pivot Tables in Excel workbooks. The following example shows the detail sales data for bike related products, and an Excel Pivot Table created from that data:
The Totaldue values are grouped by Quarter and product.
