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Most people don't realize just how much work there is in populating the data for a dashboard or scorecard.  Let’s make a list of some of the things that contribute to the problem.

The Query.  Getting the data

To get useful data, SQL is too difficult and expertise is scarce and expensive.  Moreover, SQL can’t do all the things you need. Often, the solution requires extra tables which are usually undesirable.  MDX & Cubes are an alternative, but they are expensive to design, create and maintain especially if the needs are likely to change.

Once the query has been submitted, too much data returns.  The ODBC layer or DataSet Object uses too much memory and causes failures at the presentation layer.

Putting The Data Into The Chart

Now that you have the data, there's too many labels, and large ones affect how the legends and chart labels appear. It is way too easy to get a chart that looks horrible.  Visualizations fail on large number of rows and columns. Overwriting risers, lines, symbols are all quite common.

Most chart package support some  local data manipulation and calculation operations, but they are not integrated with the query, causing a design & implementation burden for the presentation layer developer.

Crosstabs & Datagrid Objects

Just like with charts, too many and large labels take up too much screen real-estate. There’s no room for numbers and you can’t visualize anything about your data. Too much scrolling.

As with chart packages, local math and sort operations are not integrated with the query, and it’s difficult to save the user’s actions to the same state the next time they open the view.

UI Controls  (Combos, listboxes, etc )

Plainly said, these fail given a normal amount of production data.  UI developers resort to hard-coding but slowly changing data (in the business) makes the dashboard a maintenance nightmare.  Metadata mitigates this, but at a great overhead expense .

Three Part Solution

Reduce the volume, provide subsets as a result of analytical workflows, not just arbitrary hierarchy like OLAP provides.  From the user interface, let the users choose an analyzed subset to look at with their scorecard or dashboard.  Alternatively, use analytics to aggregate lower level detail into upper level views but avoid the overhead of a cube or temporary database tables.

Use analytic workflows to improve the content. Pre-calculate patterns, trends, outliers, comparisons, and whatever else suits your business. 

Provide your users with valuable "already analyzed" numbers.  If you do this, you don’t need to chart as much data, and the data being shown is more useful than raw “database data”.  

Avoid the trap of encouraging them to export to Excel, spreadsheets are an entity that requires auditability per Sarbanes-Oxley. Don't get yourself into a legal mess.

Improve your charts and crosstabs by using programmatic techniques to perform text and numeric substitutions.  This makes your visualizations more readable.

Three easy steps. That's all it takes to make scorecards & dashboards more useful.   

Nextanalytics makes these steps easy.  It offers additional processing on query results and transparently provides the analyzed data to presentation layers.  A simple, elegant solution that is fast and easy to implement.