💼Project Management: Project Execution (Part I: Redefining Projects' Success I)

A project is typically considered as successful if has met the beforehand defined objectives within the allocated budget, timeframe and expected quality levels. Any negative deviation from any of these equates with a project failure. In other words, the success or failure of a project is judged as black or white with no grays in between, which is utopic, especially for mid to big software projects, typically associated with lot of uncertainty. According to this definition a project which had a delay of a few months, or the budget was overrun by 10%, or the users got only 90% from the planned functionality, or any combination of these negative deviations, can be considered as failed.

If a small project needed 6 instead of 3 months to complete, which is normal for projects with reduced priority, as long the project costs haven’t changed, then the increase in duration can be ignored. In exchange, 3 months of a delay for a 2 years project is normal, especially when the project is complex. Even if additional costs incurred within this timeframe, as long they are a small percentage in comparison with the overall project costs, then the impact can be acceptable for the business. On the other side, when the delays have an exponential growth with further implications, then the problem changes dramatically.

Big projects have typically a strategic importance. It’s the case of ERP implementations, which besides the technology changes have in theory have the potential to transform an organization pushing it to reach further performance levels. Such projects are estimated to take on average one to two years for a medium organization, however the delays can easily reach 50% to 100% from the initial estimation. Independently of what caused the delay, as long the organization achieved the intended goals and can cover project’s costs, one can say that the project made a (positive) difference.

Independently of project’s size, if 90% of the important functionality is available, then more likely the 10% can be covered in a first step with manual work, following in time to further invest into the system as part of a continuous improvement process. It’s maybe not ideal for the users, however the approach incorporates also the learning curve of working with the system and understanding ist possibilities and limitations. Of course, when the percentage of the available functionality decreases below a given limit, system’s acceptance is endangered, which users eventually start looking for alternatives.

There are also projects which opened the door to new possibilities and which require more investments to leverage the full capabilities. Some ERP implementations have this potential, despite overruns. Some of such investments are entitled while others are not. Related to this last category, there are projects which are on time, on budget, and the deliverables satisfy the quality criteria and objectives, however they make no difference for the organization despite the important investments made. Sure, some of the projects from this category are a must (e.g. updates, upgrade, technology changes), however there are also projects which can be considered as self-occupational hazard. In extremis such projects run in the background and cost organizations lot of energy and resources, while their effects are questionable.

At least from these examples the definition of a project's success needs to be changed or maybe standardized to consider not only intrinsic but also extrinsic aspects. In theory, that is the role of a Project Management Office (PMO), however it’s challenging to find an evaluation methodology that fits all needs. Further on, from same considerations, benchmarking projects across organizations and industries can prove to be a foolhardy attempt.

🪄SSRS: Graphical Representations II (Sixth Magic Class)

Introduction 

Using a single chart to display multiple series in SQL Server Reporting Services (SSRS) or any other reporting tool works well when the number of series is somehow manageable - usually being enough to display 2-10 series within the same chart. The more series one adds, the more complicated will be for users to read the chart. One has the choice to find either
-  a level of detail (e.g. Category) which, when grouping the data, leads to a number of manageable series,

-  compare the data within a certain grouping (e.g. Category),

-  displaying the individual trends (e.g. for each Product). 

Let's consider the last choice. The report from this post will display the Sales Volume per Product and Year/Month of the Sales Orders available in the AdventureWorks2014 database. The logic uses the Sales.SalesOrderDetail and Sales.SalesOrderHeader tables, respectively the Production.vProducts view created in a previous post. 

Note:

A Sales Volume report is more appropriate to be built using a data warehouse's data, which are already aggregated and prepared for such reports. There's actually an AdventureWorksDW2014 data warehouse model made available which can be used to display the same information, however the intent is to demonstrate the techniques of working with data in an OLTP environment. 

Preparing the Data

Creating a view to build the Sales Orders details is usually recommended, though for the current report we just need the Product Category, Subcategory, Number and Name, respectively Sales Date, Quantity and Value, which is only a small part from the attributes available. Another choice to consider is whether to use the raw data, though then the number of records sent to the client can be considerably high, or aggregate the data and the lowermost level of detail needed for the report, in this case the Category, Subcategory, Product, Month and Year:

-- Sales volume per Product   
SELECT ITM.Category
, ITM.Subcategory
, ITM.ProductNumber
, ITM.Name
, Month(SOH.OrderDate) [Month]
, Year(SOH.OrderDate) [Year]
, Sum(SOD.OrderQty) OrderQty
, Sum(SOD.LineTotal) OrderValue
FROM Sales.SalesOrderDetail SOD
     JOIN Sales.SalesOrderHeader SOH
       ON SOD.SalesOrderID = SOH.SalesOrderID
     JOIN Production.vProducts ITM
       ON SOD.ProductId = ITM.Productid 
WHERE ITM.ProductNumber IN ('BB-7421', 'BB-9108')
GROUP BY ITM.Category
, ITM.Subcategory
, ITM.ProductNumber
, ITM.Name
, Month(SOH.OrderDate)
, Year(SOH.OrderDate)
ORDER BY ITM.Category
, ITM.Subcategory
, ITM.ProductNumber
, [Year]
, [Month]

The query contains all the needed data, however one could have more flexibility if the data would contain cumulative or total values as well: 

-- Sales volume per Product (extended)  
SELECT SOD.Category
, SOD.Subcategory
, SOD.ProductNumber
, SOD.Name
, SOD.[Month]
, SOD.[Year]
, SOD.OrderQty
, SOD.OrderValue
, SUM(SOD.OrderQty) OVER (PARTITION BY SOD.ProductNumber) TotalQty
, SUM(SOD.OrderValue) OVER (PARTITION BY SOD.ProductNumber) TotalValue
, SUM(SOD.OrderQty) OVER (PARTITION BY SOD.ProductNumber ORDER BY [Year], [Month]) CumulatedQty
, SUM(SOD.OrderValue) OVER (PARTITION BY SOD.ProductNumber ORDER BY [Year], [Month]) CumulatedValue
FROM (-- cumulated values
 SELECT ITM.Category
 , ITM.Subcategory
 , ITM.ProductNumber
 , ITM.Name
 , Month(SOH.OrderDate) [Month]
 , Year(SOH.OrderDate) [Year]
 , Sum(SOD.OrderQty) OrderQty
 , Sum(SOD.LineTotal) OrderValue
 FROM Sales.SalesOrderDetail SOD
   JOIN Sales.SalesOrderHeader SOH
    ON SOD.SalesOrderID = SOH.SalesOrderID
   JOIN [Production].[vProducts] ITM
     ON SOD.ProductId = ITM.Productid 
 WHERE ITM.ProductNumber IN ('BB-7421', 'BB-9108')
 GROUP BY ITM.Category
    , ITM.Subcategory
 , ITM.ProductNumber
 , ITM.Name
 , Month(SOH.OrderDate)
 , Year(SOH.OrderDate)
  ) SOD
ORDER BY SOD.Category
, SOD.Subcategory
, SOD.ProductNumber
, SOD.[Year]
, SOD.[Month]

In the end one can use any of the above queries.
Note:When prototyping a report is usually recommended to consider only a small number of records (e.g. only two Products). In addition, do not forget to validate the number or records considered by the logic:

-- checking the view for internal data consistency
SELECT count(*)
FROM Sales.SalesOrderDetail SOD
     JOIN Sales.SalesOrderHeader SOH
   ON SOD.SalesOrderID = SOH.SalesOrderID
  JOIN Production.vProducts ITM
    ON SOD.ProductId = ITM.Productid 

Creating the Report
Using the Report Wizard create a new matrix report called "Sales Volume per Product" based on either of the above queries (I considered the second). Within "Design the Matrix" select the attributes as follows:

Design the Matrix

This will create the backbone for our report:

First draft in Design mode

Which is pretty basic, if we consider the output:

First draft in Preview mode

Now, returning in Design mode, right click on the "Sum of OrderQty" cell and from the floating menu select Insert/Chart, while from the list of available charts select Line. Do the same for "Sum of OrderValue". And here's the result:

Second draft in Design mode

As only one series will be displayed, select the Chart Title and delete the respective label. Delete the Series label as well. When running the report you'll observe that the horizontal axis values are not really appealing. To dix this right click on the respective area and from the floating menu select Horizontal Axis Properties. Within Axis Options section change the Axis type as 'Scalar', enter '1' as Minimum, '12' as Maximum, '1' as Interval and 'Number' as Interval type:

Horizontal Axis Properties

In the same window, within the Labels section select 'Enable auto-fit' and uncheck the "Labels can be offset", respectively the "Labels can be rotated" checkboxes. 

To include the Category, Subcategory and eventually the Product Name, select the Product Number cell, right click on it, and from the floating menu select Insert Column/Inside Group - Left, then select from the Category as attribute:

Inserting a column within the group

Repeat the process to add the Subcategory. Eventually you can add also the Product Name, though for it you'll have to select "Inside Group - Right". 

To improve the design, you can add a Page Header and move the report's title into it add a timestamp, respectively a page count textbox, resize the boxes to fit the columns. You can also align the column header values to the center, change the font to 10pt, etc.

Third draft in Design mode

Here's the report in preview mode:

Third draft in Preview mode

One can use the report the way it is, or add the Category and Subcategory as parameters. Similarly, one can use the cumulative values as input for the charts. 

Revamping the Report with Sparklines

Even if the charts allow displaying the scales, the problem with them is that they are too big, which makes it difficult to compare the data across records. One can correct this by using the other types of graphics available in reports, e.g. sparklines. For this make a copy of the report already built, and within the Detail cells select a Sparkine Column instead of a chart:

Sparkline types

In comparison with Lines, Column-based representations allow to see how many data points are represented. Because spartklines are more compact as graphic forms, you can resize the cells as follows:

Fourth draft in Design mode

And here's the report in preview mode (the constraints from the source query were removed):

Fourth draft in Preview mode

As can be seen one can easily identify the trends however the volume scale was lost, being impossible to compare which of the Products sold better. One can bring the Total Quantity and Value as display information and sort the dataset from the highest to lowest value. One can even select a top x, to reduce the focus only to the most sold Products.

If the prices remained relatively constant over time, there's actually almost no difference between the graphic displays for Order Quantity, respectively for Order Value. Therefore one can remove one of them (e.g. Order Quantity). Being more compact, sparkline-based representations allow to better use the space, therefore you can add more fields into the report. 

Happy coding!

🪄SSRS: Graphical Representations I (SQL Server CPU Utilization)

As described in a previous post, the Scheduler Monitor buffer exposed via the sys.dm_os_ring_buffers  data management view (DMV) provides a history of 4 hours uptime with minute by minute data points (in total 256 entries) with the CPU utilization for the SQL Server, other processes, respectively the system idle time as percentages. SSRS is ideal for showing the respective information within a chart.

For this create a new report (e.g. CPU Utilization) by using the Report Wizard based on the following query: 

-- cpu utilization for SQL Server and other applications
DECLARE @ts_now bigint = (SELECT cpu_ticks/(cpu_ticks/ms_ticks)
        FROM sys.dm_os_sys_info); 

SELECT DAT.record_id
, DAT.EventTime
, DAT.SQLProcessUtilization 
, DAT.SystemIdle 
, 100 - (DAT.SystemIdle + DAT.SQLProcessUtilization) OtherUtilization
FROM ( 
 SELECT record.value('(./Record/@id)[1]', 'int') record_id
 , record.value('(./Record/SchedulerMonitorEvent/SystemHealth/SystemIdle)[1]', 'int') SystemIdle 
 , record.value('(./Record/SchedulerMonitorEvent/SystemHealth/ProcessUtilization)[1]', 'int') SQLProcessUtilization
 , EventTime 
 FROM ( 
  SELECT DATEADD(ms, -1 * (@ts_now - [timestamp]), GETDATE()) EventTime
  , [timestamp]
  , CONVERT(xml, record) AS [record] 
  FROM sys.dm_os_ring_buffers 
  WHERE ring_buffer_type = N'RING_BUFFER_SCHEDULER_MONITOR' 
    AND record LIKE N'%<SystemHealth>%') AS x 
 ) AS DAT
ORDER BY DAT.record_id DESC;

After creating the report delete the available table and add a chart by right-clicking inside the report and from the floating menu choose Inser/Chart. Within the Select Chart Type select Line/Shape as chart type:

Resize the chart to provide an acceptable level of detail, then click within the chart area and add the SQLProcesUtilization, SystemIdle and OtherUtilization as values Values, respectively the EventTime as Category Group. 

It is needed to edit horizontal's axis properties - select the respective region and from the floating menu chose Horizontal Axis Properties. Within Axis Options chose Scalar as axis type, ideal for numeric and date values:

Axis Options

Within the Number section select the Time as category and provide the type as below (e.g. 13:30):

Number Section

As last change, add a header, move report's title within the header and add a text box next to it with the following formula to show the time when the report was run:

= Now().ToString("dd.MM.yyyy HH:mm:ss")

With these changes the report is set to be run:

Design mode

Here's the preview

Preview mode

Unfortunately the default choice of colors is not really ideal as red is used for warnings, and green for positive trends, which is not necessarily the case for a CPU's utilization. 

One can play with the various chart types, for example by selecting the chart area and changing the chart type as Area/Range one can obtain the following chart (it is needed to change the Axis Type as Scalar again):

Happy coding!

🪄SSRS (& Paginated Reports): Ranking Rows in Reports

Introduction

In almost all the reports I built, unless it was explicitly requested no to, I prefer adding a running number (aka ranking) for each record contained into the report, while providing different background colors for consecutive rows. The ranking allows easily identify a record when discussing about it within the report or extracts, while the different background colors allow differentiating between two records while following the values which scrolling horizontally. The logic for the background color can be based on two (or more) colors using the ranking as basis.

Tabular Reports

In a tabular report the RowNumber() function is the straightforward way for providing a ranking. One just needs to add a column into the report before the other columns, giving a meaningful name (e.g. RankingNo) and provide the following formula within its Expression:
= RowNumber(Nothing)

When 'Nothing' is provided as parameter, the ranking is performed across all the report. If is needed to restrict the Ranking only to a grouping (e.g. Category), then group's name needs to be provided as parameter:
= RowNumber("Category")

Matrix Reports

Unfortunately, in a matrix report based on aggregation of raw data the RowNumber() function stops working, the values shown being incorrect. The solution I use to solve this is based on the custom GetRank() VB function:

Dim Rank as Integer = 0
Dim LastValue as String = ""

Function GetRank(group as string) as integer
if group <> LastValue then
       Rank = Rank + 1
       LastValue = group
end if

return Rank
end function

The function compares the values provided in the call against a global scope LastValue text value. If the values are different, then a global scope Rank value is incremented by1, while the LastValue is initialized to the new value, otherwise the values remaining the same. The logic is basic also for a non-programmer.

The above code needs to be added into the Code section of Report's Properties for the function to be available:

Adding the code in Report Properties

Once the function added, a new column should be added similarly as for a tabular report,  providing the following code within its Expression in exchange:
=Code.GetRank(Fields!ProductNumber.Value)

Note:
As it seems, on the version of Reporting Services Extension I use, the function has only a page scope, the value being reset after each page. However when exporting the data with Excel the ranking is applied to the whole dataset.

Providing Alternate Colors

Independently of the report type, one can provide an alternate color for table's rows by selecting the row with the data and adding the following expression into the BackaroundColor property:
=Iif(ReportItems!RankingNo.Value Mod 2, "White", "LightSteelBlue")

Notes:
1) For a tabular report the cost of calling the RowNumber function instead of referring to the RankingNo cell is relatively small. One can write it also like this:
=llf(RowNumber(Nothing) Mod 2 = 0, "White", "LightSteelBlue")

Power BI Paginated Reports

The pieces of code considered above can be used also in Power BI Paginated Reports. Even if there's no functionality for adding custom code in the standard UI, one can make changes to the rdl file in Visual Studio or even in Notepad. For example, one can add the code within the "Code" tag at the end of the file before the closing tag for the report:

<Code>Dim Rank as Integer = 0
Dim LastValue as String = ""
Dim Concatenation = ""

Function GetRank(group as string) as integer
if group <> LastValue then
       Rank = Rank + 1
       LastValue = group
end if

Concatenation = Concatenation & vbCrLf & Rank & "/" & group &amp; "/" & LastValue
return Rank
end function</Code>
</Report>

Note:

One can consider using a pipeline "|" instead of a forward slash.

🪄SSRS (& Paginated Reports): Matrix Report Display (Fifth Magic Class)

Introduction

SQL Server Reporting Services (SSRS) allows grouping data into a matrix format based on one or more groups. By using the Report Wizard one can simplify considerably the volume of work.

The considered example is based on the AdventureWorks2014 database and considers Product's Inventory as base for building the report. 

Preparing the Data

Usually it's useful to incorporate the logic for a report in one or more views, allowing thus to reuse the views in multiple reports. For the current report is needed to create two views, one for the Products, respectively Production.vProductInventory for the inventory. 

-- dropping the vProducts view (cleaning after)
--DROP VIEW IF EXISTS [Production].[vProducts]

-- creating the vProducts view
CREATE VIEW [Production].[vProducts]
AS 
SELECT p.[ProductID] 
, p.ProductNumber
, p.[Name] 
, IsNull(p.Size, '') + IsNull(' ' + p.SizeUnitMeasureCode, '') Size
, p.Color
, P.Style
, p.ProductModelID
, pm.[Name] AS [ProductModel] 
, p.StandardCost 
, P.ListPrice
, P.SafetyStockLevel
, P.ReorderPoint
, p.SellStartDate 
, p.SellEndDate
, p.ProductSubcategoryID
, PPS.Name Subcategory
, PPS.ProductCategoryID
, PPC.Name Category
, P.MakeFlag
, P.FinishedGoodsFlag
FROM [Production].[Product] p 
     LEFT JOIN [Production].[ProductModel] pm 
       ON p.[ProductModelID] = pm.[ProductModelID] 
	 LEFT JOIN Production.ProductSubcategory PPS
	   ON P.ProductSubcategoryID = PPS.ProductSubcategoryID 
	      LEFT JOIN Production.ProductCategory PPC
		    ON PPS.ProductCategoryID = PPC.ProductCategoryID 
GO

-- reviewing the data 
SELECT *
FROM [Production].[vProducts]

-- checking the view for internal data consistency
SELECT count(*)
FROM [Production].[Product] p 
     LEFT JOIN [Production].[ProductModel] pm 
       ON p.[ProductModelID] = pm.[ProductModelID] 
	 LEFT JOIN Production.ProductSubcategory PPS
	   ON P.ProductSubcategoryID = PPS.ProductSubcategoryID 
	      LEFT JOIN Production.ProductCategory PPC
		    ON PPS.ProductCategoryID = PPC.ProductCategoryID 

-- dropping the vProductInventory view (cleaning after)
--DROP VIEW IF EXISTS Production.vProductInventory

-- creating the view 
CREATE VIEW Production.vProductInventory
AS
SELECT PPI.ProductId 
, PPD.ProductNumber
, PPD.Name ProductName 
, PPD.ProductModel
, PPD.Size
, PPD.Category
, PPD.Subcategory
, PPD.Style
, PPD.StandardCost
, PPD.ListPrice 
, PPD.StandardCost * PPI.Quantity InventoryValue 
, PPD.ListPrice * PPI.Quantity SalesValue
, PPD.MakeFlag
, PPI.Locationid 
, PPL.Name Location 
, PPI.Shelf 
, PPI.Bin 
, PPI.Quantity 
FROM [Production].[ProductInventory] PPI
     JOIN [Production].[vProducts] PPD
	   ON PPI.ProductID = PPD.ProductID
	 JOIN [Production].[Location] PPL
	   ON PPI.LocationID = PPL.LocationID

-- reviewing the data
SELECT *
FROM Production.vProductInventory

-- checking the view for internal data consistency
SELECT count(*)
FROM [Production].[ProductInventory] PPI
     JOIN [Production].[vProducts] PPD
	   ON PPI.ProductID = PPD.ProductID
	 JOIN [Production].[Location] PPL
	   ON PPI.LocationID = PPL.LocationID

Note:
It's important to check the internal consistency of the views or queries used, on whether the logic removes or duplicates data. For this one can run the query for the uppermost table, and add repeatedly one more join for each run to see whether the number of records remains the same. One can shortcut the validation by checking only the number of records from the base table and for the whole query, and only if there are differences use the previously mentioned approach. (This is how I observed that the Production.vProductDetails view is not usable, because it considers only the Products having a valid Model.)

Creating the Report 

We can now use  the Production.vProductInventory view to create the Product Inventory by Location report based on the following query:

-- Product Inventory by Location
SELECT PPI.Category
, PPI.Subcategory
, PPI.ProductNumber
, PPI.ProductName 
, PPI.ProductModel
, PPI.Size
, PPI.Style
, PPI.StandardCost
, PPI.ListPrice
, PPI.Location 
, PPI.Quantity 
, PPI.InventoryValue 
, PPI.SalesValue 
FROM Production.vProductInventory PPI
ORDER BY PPI.Category
, PPI.Subcategory
, PPI.ProductNumber

Note:
The attributes can be provided in the order in which they should be displayed in the report, reducing thus the overhead in the further steps. 

Using the Reporting Wizard via the Add New Report select in the first step the data source, while in the next step provide the above query:

Design the Query

In the next step select the "Matrix" Report Type:

Select the Report Type

Within the "Design the Matrix" section assign the fields as follows (all the fields except the ones considered as Columns and Details will be considered as Rows):

Design the Matrix

Into the last step give the report a meaningful name (e.g. Product Inventory by Location):

Completing the Wizard

In theory the report is ready to run, however before doing that change the formatting by aligning the headers to the center and eventually change their size from 11 to 10 pixels, respectively rename the dataset (e.g. Inventory). To obtain the same information about the grouping as below change into the "Advanced Mode".

Design View

And here's the output (I had to scroll through the pages to find meaningful values, therefore part of the Details header is not shown):

Preview

Restructuring the Grouping

As can be seen into the Design Mode, the wizard created a grouping for each attribute provider into the Details (see matrix1_Category, matrix1_Subcategory, etc.). Therefore, the values will not be repeated for each row, which is typically desirable for visualizations but not when exporting the data to Excel for further processing. I prefer the latter version, therefore to obtain this behavior one can go on and delete all the grouping via "Delete group only" except the matrix1_Category:

Deleting the groups

This action deleted unfortunately all the Detailed headers except Category. To bring them back into the grouping double click on the and add each field into the Group expressions as below:

Group Properties

As final change before running the report one can add header names for the Detail attributes. After these changes reports' design looks as follows:

Report Design with one grouping

And here's the final report with the values repeating for each row:

Preview Report without formatting

Note:
To avoid removing the groupings, I prefer to add only one Detail field into the query, typically the field which will make the row unique into the output (e.g. Product Number) and add the further fields (actually replace the below query with the one above) after the Wizard created the report. One still needs to add the columns manually into the report. In the end the effort is similar. 

-- Product Inventory by Location
SELECT PPI.ProductNumber
, PPI.Location 
, PPI.Quantity 
, PPI.InventoryValue 
, PPI.SalesValue 
FROM Production.vProductInventory PPI
ORDER BY PPI.Category
, PPI.Subcategory
, PPI.ProductNumber

Changing the Design

Report's design can be slightly improved by adding various formatting of the cells or values. One can use similar formatting as the ones consider in the previous post. The only thing difficult to implement will be a ranking function (see Ranking Rows in Reports). After the design changes here's the report:

Final Report

Note:
Of course, together with parameters one can also add totals after each Category or Subcategory to the report if needed, though the latter is more appropriate for design purposes and not for further data consumption. 

Happy coding!

💠🛠️🪄SQL Server: Undocumented (Part III: SQL Server CPU Utilization via the Ring Buffer)

Introduction

If no proper monitoring solution of the SQL Server and the hosting server is in place to review the CPU utilization, one can use the Scheduler Monitor buffer provided by the undocumented sys.dm_os_ring_buffers data management view (DMV). Introduced with SQL Server 2005, the DMV provides significant amount of diagnostic memory information in XML form via several buffers: Resource Monitor, Out-of-Memory, Memory Broker, Buffer Pool, respectively Scheduler Monitor buffer [2]. A ring buffer is a recorded response to a notification [1].

The view changed between the various versions of SQL Server, while with the introduction of Always On availability groups in SQL Server 2017 further buffer rings were made available (see [5]).

Warning:

According to Microsoft (see [4] the sys.dm_os_ring_buffers is provided only for information purposes, the future compatibility post SQL Server 2019 being not guaranteed!

Querying the Scheduler Monitor Buffer

Within the Scheduler Monitor buffer, the DMV stores a history of 4 hours uptime with minute by minute data points (in total 256 entries) with the CPU utilization for the SQL Server, other processes, respectively the system idle time as percentages. It allows thus to identify the peaks in CPU utilization and thus to determine the intervals of focus for further troubleshooting. As the data are stored within an XML structure, the values can be queried via the XQuery syntax as follows: 

-- cpu utilization for SQL Server and other applications
DECLARE @ts_now bigint = (SELECT cpu_ticks/(cpu_ticks/ms_ticks)
        FROM sys.dm_os_sys_info); 

SELECT DAT.record_id
, DAT.EventTime
, DAT.SQLProcessUtilization 
, DAT.SystemIdle 
, 100 - (DAT.SystemIdle + DAT.SQLProcessUtilization) OtherUtilization
FROM ( 
	SELECT record.value('(./Record/@id)[1]', 'int') record_id
	, record.value('(./Record/SchedulerMonitorEvent/SystemHealth/SystemIdle)[1]', 'int') SystemIdle 
	, record.value('(./Record/SchedulerMonitorEvent/SystemHealth/ProcessUtilization)[1]', 'int') SQLProcessUtilization
	, EventTime 
	FROM ( 
		SELECT DATEADD(ms, -1 * (@ts_now - [timestamp]), GETDATE()) EventTime
		, [timestamp]
		, CONVERT(xml, record) AS [record] 
		FROM sys.dm_os_ring_buffers 
		WHERE ring_buffer_type = N'RING_BUFFER_SCHEDULER_MONITOR' 
		  AND record LIKE N'%<SystemHealth>%') AS x 
	) AS DAT
ORDER BY DAT.record_id DESC;

If the SQL Server is not busy as all, the SQL Server utilization time may tend to 0%, while the system idle time to 90%. (It's the case of my SQL Server lab.)

CPU Utilization for my home SQL Server lab

Notes:

If the server was restarted within the last 4 hours, then the points will have a gap between two readings corresponding to the downtime interval.

The query is supposed to run also on Linux machines, though the SystemIdle time will be 0. One can thus consider the SQL and non-SQL CPU utilization.

Storing the History

The above query can be run on a regular basis (e.g. every 3-4 hours) via a SSIS package and push the data into a table for historical purposes. Because is needed to have a continuous history of the readings, it's better if the gap between runs is smaller than the 4 hours. No matter of the approach used is better to check for overlappings when storing the data:

-- dropping the table
-- DROP TABLE IF EXISTS dbo.T_RingBufferReadings 

-- reinitilizing the history
-- TRUNCATE TABLE dbo.T_RingBufferReadings

-- creating the table
CREATE TABLE dbo.T_RingBufferReadings (
  Id bigint IDENTITY (1,1) NOT NULL
, RecordId bigint 
, EventTime datetime2(3) NOT NULL
, SQLProcessUtilization int NOT NULL
, SystemIdle int NOT NULL
, OtherUtilization int NOT NULL
)


-- reviewing the data
SELECT *
FROM dbo.T_RingBufferReadings 
ORDER BY EventTime DESC

If there are many records, to improve the performance, one can create also an index, which can include the reading points as well:

-- creating a unique index with an include 
CREATE UNIQUE NONCLUSTERED INDEX [UI_T_RingBufferReadings_EventTime] ON dbo.T_RingBufferReadings
(
	EventTime ASC,
    RecordId ASC
) INCLUDE (SQLProcessUtilization, SystemIdle, OtherUtilization)
GO

The above query based on the DMV becomes:

-- cpu utilization by SQL Server and other applications
DECLARE @ts_now bigint = (SELECT cpu_ticks/(cpu_ticks/ms_ticks)
        FROM sys.dm_os_sys_info); 

INSERT INTO dbo.T_RingBufferReadings
SELECT record_id
, DAT.EventTime
, DAT.SQLProcessUtilization 
, DAT.SystemIdle 
, 100 - (DAT.SystemIdle + DAT.SQLProcessUtilization) OtherUtilization
FROM ( 
	SELECT record.value('(./Record/@id)[1]', 'int') record_id
	, record.value('(./Record/SchedulerMonitorEvent/SystemHealth/SystemIdle)[1]', 'int') SystemIdle 
	, record.value('(./Record/SchedulerMonitorEvent/SystemHealth/ProcessUtilization)[1]', 'int') SQLProcessUtilization
	, EventTime 
	FROM ( 
		SELECT DATEADD(ms, -1 * (@ts_now - [timestamp]), GETDATE()) EventTime
		, [timestamp]
		, CONVERT(xml, record) AS [record] 
		FROM sys.dm_os_ring_buffers 
		WHERE ring_buffer_type = N'RING_BUFFER_SCHEDULER_MONITOR' 
		  AND record LIKE N'%<SystemHealth>%') AS x 
	) AS DAT
	LEFT JOIN dbo.T_RingBufferReadings RBR
	  ON DAT.record_id = RBR.Recordid 
WHERE RBR.Recordid IS NULL
ORDER BY DAT.record_id DESC;

Note:
A ServerName column can be added to the table if is needed to store the values for different SQL Servers. Then the LEFT JOIN has to consider the new added column. 
Either of the two queries can be used to display the data points within a chart via SSRS, Power BI or any reporting tool available. 

Happy coding!

References:
[1] Grant Fritchey (2014) SQL Server Query Performance Tuning: Troubleshoot and Optimize Query Performance in SQL Server 2014, 4th Ed.
[2] Sunil Agarwal et al (2005), Troubleshooting Performance Problems in SQL Server 2005, Source: TShootPerfProbs.docx
[3] Sunil Agarwal et al (2008), Troubleshooting Performance Problems in SQL Server 2008, Source: TShootPerfProbs2008.docx
[4] Microsoft SQL Docs (2018) Related Dynamic Management Views, Source
[5] Microsoft SQL Docs (2017) Use ring buffers to obtain health information about Always On availability groups, Source