Please watch the video in 1080p because it is recorded in High Resolution.
Please note the script of the presentation is below so you can read along with the demonstration if you like.
Hello and welcome.
My name is Mihai Neacsu and I am the business development manager here at BIDA.
Firstly I’d like to say thank you very much for coming along and watching our latest video today.
I really appreciate your attention so thank you very much for that.
Todays video is going to be the very detailed demonstration of our prior summary video about Meta5 and Excel reporting.
The prior video we just released talks at a very high level.
It is just a 10 minute video that shows you that it is possible to create what is called a Meta5 capsule.
Capsules can run in your ETL stream after all your data is prepared and capsules can create many reports.
Capsules can send out individual, customized Excel dashboards containing only the data that is necessary for the individual people who are the users of those dashboards.
These dashboards are sent out via such tools as One Drive, and then your One Drive distributes the reports according to the rules that have been defined.
Your users receive their excel dashboard via such mechanisms as the one drive or sharepoint portal or whatever mechanism is being used to receive reports.
Even email is supported.
The placement of excel reports in folders is controlled through the capsules.
The prior demonstration was to show you at a high level that this is possible, this is viable, and it gives you the ability to do excel reporting out to the masses across your company, faster, cheaper and better.
So if you have a large number of users who want excel reports across your organization and it is costing you time and effort and trouble to do that?
Or if you have to be linked back to an SSAS server for them to do that and you have to have high speed connections?
What this demonstration shows you is that it’s possible to produce the excel spreadsheet in a centralized location, say, near your data warehouse or near your ER.
Then put it into the One Drive, and have the one drive send it out to the users, ready waiting for them at 9 o’clock in the morning when they come in to work.
That is what the previous demonstration was all about.
Todays demonstration is the details of showing you how that is possible.
How we do it.
This is a demonstration for more technical people who are asking:
“What makes Meta5 different to SSAS? Why would we use Meta5?”
That’s what we are going to show you today.
So sit back, relax and enjoy the show!
So here we are on my meta5 workstation desktop.
This is just the starter edition.
You can see I have opened up a folder called Adventure Works.
And inside that folder I have another folder called report examples.
And inside that folder I have a capsule with Adventure Works Example Report 01.
This is the capsule I am going to show you today.
Now the first thing we have is this thing called a sales territory query.
This is a query icon.
We will open it up.
We click show controls.
You see this is dim sales territory out of the adventure works database.
It is getting us all the territories except the NA one.
We are calling the result at A A which is called a capsule variable.
We will turn on capture SQL so you can see what is going on.
When we hit show data and you can see the SQL that has been issued.
It has done a select distinct DimSalesTerritory.SalesTerritoryGroup from DimSalesTerritory where not DimSalesTerritory is equal to ‘NA’ order by DimSalesTerritory.
So, a simple sql statement to get the SalesTerritoryGroup from the adventure works database.
Now I will show you the query catalog.
These are all the tables available in the adventure works sample database.
You have the Product Sub Category.
Fact Internet Sales.
Fact Reseller Sales.
Now you should recognize these from the adventure works database.
If you are watching this video you should be familiar with SQL Server and the Adventure Works Sample database.
So that’s our query catalog.
We can query any of those tables.
Now what this capsule does is it sends those three records, one at a time, in to the second capsule.
It sends them through the arrow in to the region name controls of the second capsule.
These are the control variables of the receiving capsule.
It is sending the region name to at A A which is a variable.
So when I open up the capsule and I click show controls and I go to at AA you can see Sales Territory Group.
You can see it is set to Pacific which was the last value from the previous run of the capsule.
I want you to understand that it runs the capsule three times.
We call it iterations of the capsule and it runs that capsule and sets that variable.
Now in front of you, you have a fairly strange thing that you have probably never seen before.
Capsules are 40 years old.
They were invented in the early 80s.
They were the first real graphical programmable environment to build applications in and many companies have copied the capsule idea.
For example if you look at datastage or informatica you will see they have copied the meta5 capsule.
So let us say you want to build a dashboard.
Let us go and look at a finished dashboard.
We come over here to Europe and we are going to open this example dashboard.
And we are going to look at what’s inside it.
So your user comes to you and says:
Hi Mihai, I want you to build a dashboard with the reseller order amounts and the internet sales amounts from the adventure works database.
And I want it by region and I want to show the categories and the currencies and the countries and those sort of things. Mihai, can you do that for me please?
And the user gives you a sketch of this sort of report and says “this is what I want it to look like”.
Ok? Let’s say that happens.
So you sit down and say Ok, the user wants a new dashboard and he has given me a description of what needs to be on it, and maybe he has created a sample excel spreadsheet, and it’s my job to build it.
And the user wants it distributed out to the individual region managers.
How do I do all that?
Think about how you do that today.
We are going to show you how that is done with Meta5 tomorrow.
On the left hand side all the normal things with dashboards are supported.
I can click on multi-select for country and then click on France and then it is also going to give me data for France as well as the United Kingdom.
I can click on Germany and it will show me data for Germany.
I have got multiple currencies selected which is adding up different currencies which does not make sense of course.
So if I click on Euros it’s going to show me euros.
If I click on british pounds it’s going to show me pounds.
If I click on US dollars it’s going to show me US dollars.
I can click on bikes. It’s just going to show me bikes.
So let us have a look inside this spreadsheet.
Just to see what is in here.
We are going to go into power pivot, and we are going to look in to the data model.
We are going to click on manager data model and look inside the data model to see what is there.
We are going to look at the diagram view.
So what do we have in here?
We have the years dimension.
A months dimension with month number and month name.
A countries dimension with country.
A product categories dimension with product category key and product category.
And currency dimension with currency key and currency.
And we have a sales fact table.
The grey fields means they are hidden from tools.
We have the sales group, the country, the region, the product sub category, the reseller order amount and the internet order amount.
That is what we have in our little mini-fact table.
Of course, with power pivot for excel I can have lots of dimension tables and lots of mini-fact tables.
I can have them all linked together.
I can have the slicers linked to these dimension tables.
Ok, to get my final excel workbook working I need years, months, countries, product categories, currencies and sales facts.
Now if I am working in a mature data warehousing environment maybe I already have an SSAS cube that has this information in it.
Maybe I can just link my excel spreadsheet to that cube.
Or maybe I have a well developed data warehousing enviornment and a well developed way of getting to this data and I can dump the data into Excel and build this dashboard.
Today what we are going to show you is how to do this with meta5.
Obviously when we go over to data view and we click on years dim there are the years.
We click on months dim there are the months.
We click on countries, there are the countries.
Remember, for countries, this is only after the selection for regions has been done.
Notice this data is just for the European region.
We click on product categories, there are the categories.
We click on currencies, there are the currencies.
And lastly sales facts.
And in sales facts we scroll down and we see 1,629 records.
And you can see the greyed out columns are the columns that can’t be presented to a tool.
And indeed, we could drop the greyed out columns from the data to have less data in the table.
We have kept them in the workbook just so you can see they are there.
We come over to the right to product sub category and you can see the reseller order amounts. When we scroll down a bit we can see the internet order amounts.
We can take out the greyed out fields if we want to, this is just an example to show you what is possible.
So this dashboard is what your user wants, and he wants it distributed to the correct users across a network.
And let us say they just want the data in the workbook for the person using the workbook.
So let us say that is what the users want.
How would you do it with Meta5?
This is called a Meta5 capsule and what we are going to do today is we are going to go through this in detail to show you exactly how you would produce the finished Excel dashboard.
We are not going to show you the construction of the dashboard itself.
We are going to show you how the data gets in to the dashboard to then be used inside the dashboard supported by the ability to be distributed across a one drive network.
You could use other ways of distributing the dashboards.
We are using one drive as an example.
You saw inside the Excel dashboard we had Years, Months, Countries, Product Categories and Currencies.
And you can see these capsules labeled with these names.
So we open up years.
And you see this little visual programming environment.
We open up the years query icon and click show controls and you can see the query for years.
We see the order calendar year.
We have restricted it to just 2012 and 2013 because that is all the data there is in the adventure works database.
We will select capture sql just so you can watch this.
We click on show data.
You can see the sql was generated and run.
You can hit pause read the sql generated for your self.
So the meta5 query tool returned me the years and then it has sent the years in to the spreadsheet next to the query tool.
You can see the data in the spreadsheet.
Then it runs an icon called “remove header”.
It says “rows to delete” and it deletes 1 row being the header row.
We have another spreadsheet which is called “headings”.
And it says “Year”.
Why do we have this?
If we come along later and we change the description of a column in the meta5 dictionary that name change will flow through the capsule in to the workbook.
This will then cause a problem in the workbook.
So we must create fixed heading names that will never change and can not be accidentally changed in the meta5 dictionary.
So in the final spreadsheet in this capsule we see two small regions, one for the heading “Year” and one for the data for the two years we have.
And then the data goes along the arrow to this out icon called Years.
The Out icon sends the data out of this little capsule to a capturing spreadsheet.
If we click on the arrow you can see that it is copying data from the region name Years. That is the little out icon inside the Years capsule.
In the target spreadsheet we have the year heading and then 2012 and 2013 as the two years.
That is the dimension table for years that is going to wind up in the excel spreadsheet.
Then we have months. And we can click show data. And we see the months 1 through 12 and January through December.
And we see the headings month num and month.
And we see the final spreadsheet with month num and month.
Being sent to the months out arrow.
And we see the arrow is reading the data from region name months in the source capsule and sending it to the target spreadsheet.
So in the months spreadsheet we have month num and month, 1 through 12 and January through December.
Ok? Do you see how that works?
Same with countries.
Now countries is a little different because when we look at the query for the countries we see at A A set as the constraint for sales terriroty group.
If we click on capture SQL and we click on show data we will see “Australia” as the returned country and we will see in the SQL the constraint on Sales Territory Group of “Pacific”.
The at A A variable has inherited the value “Pacific” and sent that to the database to get the countries just for Territory Group “Pacific”.
So whatever constraints we want to place on a query to the database we feed those values in to the capsule through the controls and they come from the controlling query.
Then we would produce a spreadsheet just for Pacific.
Again we have headings for Country and we are going to have the final spreadsheet contain the heading Country and the value Australia.
So countries are sent to this spreadsheet.
Next we have product categories.
And you can see product category keys and values.
And this is going to be the data in the dimension table in the power query for product categories.
We open up the capturing spreadsheet and we can see product category key and product category values.
And then we have currencies.
In the database there is a long list of currencies but there are only sales records for a small number of currencies.
The rest of the currencies will be ignored by the slicers because there are no sales records for them.
In the currency spreadsheet you see currency key and currency value.
So these are the dimension tables that are going to be in the power query which are going to be attached to your slicers.
Your business user says I want these slicers on my dashboard and I want these charts sliced by these slicers.
These capsules are the way you get the data to build the slicers.
Of course in a mature environment these capsules will be created for you and you will copy them in to your capsule. You will not develop them your self.
The next thing you need to get are your sales facts.
Now remember there were internet orders and reseller orders and these rows are in two different fact tables.
We open up the sales facts capsule and the first one we will look at is reseller facts.
We click on show controls.
You can see the query that has been created on top of the adventure works database.
You have the product category, product sub category, product, order date, territory and then the reseller facts.
You can see the constraint on the sales territory group set to at A A so it is going to inherit the value of that variable and send it to the database as a constraint.
We have set the order date to just 2012 and 2013.
Now we do capture sql and click show data.
The sql is a little hard to read because it does not insert new lines but you can hit pause and read the sql for your self.
You can see it is normal generated sql from the query tool.
It has found 194 rows for the pacific sales territory group.
It sends the rows in to a spreadsheet.
We open the spreadsheet and here is that data.
The other data we need is we need internet sales facts.
So when we click show controls here you have fact internet sales as the fact table.
This is a different fact table, but of course, the dimensions are the same and the constraints are the same. But it is a different fact table.
If you are familiar with building dashboards where the facts come from different fact tables you are familiar with the problems of doing that.
What we do with Meta5 is we go and query individual fact tables and we go and get the details. And then we join them together to send in to whatever the reporting tool is. In this case, Excel.
When we hit show data we get 245 items because, obviously, the number of rows should not be exactly the same.
Then in the capsule there seems to be 2 streams of data and then there seems to be a join.
I want to just to mention that you do not need all these spreadsheets in this capsule.
We are putting the data in to spreadsheets so you can see the data at each step.
So we get the reseller sales.
We get the internet sales.
We then run the reseller sales through a sort.
And we sort by columns a through m.
We then run the internet sales through a sort.
And we sort by columns a through m.
So we are doing 2 sorts and putting the results in to 2 spreadsheets.
And then we are going to do a join.
And this is the most difficult piece of building dashboards with lots of measures on the dashboards.
Taking data from multiple different fact tables, maybe from multiple source systems and presenting them on one consistent dashboard.
This is a two way join.
We have a two way join and a five way join.
We have table 1 and table 2.
And then output data.
We click on show controls.
You can see the columns that are being used to join are a through m for each table.
You see there is 1 heading row.
You see the join type is outer join.
You see that when a row is not present in both input spreadsheets that the default value of 0 will be used.
So where there are reseller sales but no internet sales the amount for internet sales will be set to 0.
So where there are internet sales but no reseller sales the amount for reseller sales will be set to 0.
This ability for end users or report developers to join data streams from multiple fact tables or sources like this is one of the big features of meta5 and what makes meta5 so useful.
Also, in our first demonstration dashboard we had 6 mini fact tables in the power pivot model. So you can build multiple fact tables in to the power pivot model and have the dimensions around them and have the slicers slicing in to multiple fact tables in the power pivot model.
It is actually pretty cool.
On the right hand end of the out table you can see how the two extended amounts are present from the two input fact tables. The join has been successful.
Then it is going to put the joined data in to a spreadsheet.
You can see in the spreadsheet how we have the extended amount for both internet sales and reseller sales which are coming from two different fact tables.
Then we run it through a clean icon. In this case this will do nothing.
Then we run it through a compress icon to delete the heading row.
We have a headings spreadsheet.
In this spreadsheet we have the column headings we want to pass in to Excel and they can never change once set and sent to Excel.
These headings will not change even if the meta5 dictionary names are changed.
We send the headings and data to a final spreadsheet.
You can see the heading row is coming from the headings spreadsheet and not the query icons.
In the end there are 340 rows in the final spreadsheet.
If we provided all the data for all the regions and included a slicer for region we would have many more rows in this workbook.
We would send out the workbook and each person would have to click on the slicer for their region.
Instead of that we create 3 excel workbooks and send each workbook just to the people for that region.
The data is going to go out of the capsule through the out icon sales facts 01.
When we option the arrow you can see it is reading data from region name sales facts 01.
Now you are seeing that the top set of capsules are providing all the data we need for our slicers.
For this report we need just one sales fact table.
Now obviously what happens is that after you have built the dashboard for the user he will come back and ask for more data to be included in the dashboard.
We all know that.
So you might come back into this capsule and build another sales fact capsule to get more data to put on the dashboard.
All these spreadsheets are sending data to the collect data spreadsheet.
So what happens in collect data?
Meta5 spreadsheets have regions.
We will go down and select the years region.
You will see the years are selected.
Then we select months dim.
Then we select countries dim.
Then we select product categories dim.
Then we select currencies dim.
Then we scroll right and select sales facts 01.
So this spreadsheet is collecting the data from all the other spreadsheets and placing them in to regions named for the data.
The regions will expand and contract depending on how much data is sent to the region.
When we option the arrow for Years we see that it is copying data from the years spreadsheet and sending it to the years dim region in the collect data spreadsheet.
We can see the same for the months arrow.
We can see the same for the product categories arrow.
We can see the same for the sales facts 01 arrow.
So now all our data we need for our dashboard, all with the appropriate constraints set for each iteration of the capsule, is in the collect data spreadsheet.
You can do this for any data from anywhere using meta5.
So your users tell you what data they want on their dashboards.
Getting that data from the data warehouse, or other sources, and updated and in to the spreadsheet every morning before 9am when the users are coming in to work to see their dashboards.
That is the hard part, right?
That is your problem and it is quite a problem to solve.
This problem of repeatedly running spreadsheets so that they are up to date at 9 am with the data from close of business yesterday is quite a problem.
That is what we have just seen.
This portion of the capsule does the work of collect the data for this spreadsheet.
It solves that problem.
And it uses the parameter of territory to collect just the data needed for this version of the spreadsheet.
Now the magic happens in getting this data in to excel.
When we option the arrow you are going to see something very cool.
Notice that we are copying data from the collect data spreadsheet and that all the region names are named.
In the copy data to area you see the excel spreadsheet name.
You see region names for the excel spreadsheet match the region names in the collect data spreadsheet.
So when this arrow runs it copies all the data from the collect data spreadsheet to the excel workbook.
So let us look in the excel workbook.
Here is our years dim.
Here is our months dim.
Here is our currencies dim.
Our countries dim.
Our product categories dim.
Our sales facts.
As an excel user you would be familiar with how we set up regions inside excel.
To send data collected by meta5 in to excel all you have to do is transfer the data via the arrow region name to region name.
It is that simple.
Then what does it do?
The excel spreadsheet has a name.
We option the arrow and we put the name of the excel worksheet in the out data region name for the PC Directory.
It will write the excel spreadsheet to the PC directory with the name specified here.
The PC Directory is set to a directory on the C Drive as named in the directory name.
And the directory name can be any drive including a mapped drive.
We are sending it to C drive A BIDA HOSTING.
Notice that the spreadsheet name has no at variable.
This spreadsheet is going to be over-written with each execution of the capsule.
It is a temporary data workbook.
So now you think.
Ok, that is the data prepared in a temporary data workbook.
But how does the data get in to the power pivot model in the dashboard?
You do that exactly like this.
This spreadsheet is called Example Report 01 Template.
So it is a template. It is not a finished report.
Let us open it up.
Ok, it looks like the finished dashboard.
That is interesting.
Let us go over to power pivot.
Go to manage model.
And what are we going to find in here?
We look in existing connections.
And here we are.
Power pivot data connections.
Each of those connections is linked to a region in the source data workbook.
Those regions are linked to the tables in the power pivot model.
So this dashboard only contains the data in the power pivot model.
It does not contain the data in worksheets or regions.
The power pivot model is also highly compressed to make it faster to send over networks.
Then inside the template we have pivot tables selecting data from the power pivot models.
We built a pivot table by category and a pivot table by country.
And we put some charts on them.
Of course the worksheets for the pivot tables can be hidden so only the dashboard is visible.
The data in this template is from Europe. France, Germany, United Kingdom.
So obviously you understand that the data is being queried and collected by Meta5 in to collect data.
It is being send in to the temporary data workbook.
That temporary data workbook is linked to the template power pivot model in the dashboard.
But how does Meta5 get the data from the temporary data workbook in to the power pivot model in the template workbook and produce a final workbook?
We have this tool called iExcel, the Excel integrator tool.
I have a spreadsheet called dummy and it is going to send dummy data to the data region name.
Because I am not sending any data in to iExcel.
I am getting my data from the temporary data workbook.
But that still leaves me with the question.
How do I tell Excel how to get the data from the temporary data excel workbook?
You do that using iExcel which is a magic, magic piece of software.
This is what makes meta5 a really cool tool for excel reporting.
It have lots and lots of options.
iExcel is a huge piece of software.
All I need to do is to put – rad in the advanced commands parameter.
This will cause iExcel to read the template workbook and refresh the data, which will cause excel to re-read the data in from the temporary data workbook.
It will also refresh all the charts in the workbook ready for the user to open the workbook.
We have designed the workbooks that they do not re-load the data on open, they reload the data in the power pivot model on first slicer click.
iExcel will then save the newly created workbook to the Final workbook.
If we open this up this is another spreadsheet.
And you notice this is the one for Australia because that is the last workbook that was produced by the prior execution of the capsule.
It is not the workbook for Europe which is the template.
And then, some more meta5 magic.
When we option the arrow we see that the name of the spreadsheet is EX01 Sample Report 01 Final and it has the variable at AA at the end.
This means it will have the region name in the actual excel file name.
So now we have the final Excel file name and we need to know where to send it to.
In the PC directory icon you name the full path of the one drive on this computer in the directory name parameter.
In this example you can see that it is the one drive for BIDA and it is the public portion of the one drive. You can see the whole name on the screen.
You can see at the end of the name is at AA which is the region.
So the final dashboard will be placed in to the region name folder on the one drive.
This could also include the names of individual people if you wished to.
You could send customized reports for individual people via the one drive.
Just to prove this is what it does we will go to file manager.
You can see the name of the folder is the same as was in the PC directory icon.
You can see the region name is included in the path and included in the name of the spreadsheet itself just for demonstration purposes.
Now just to prove to you that this works I am going to delete these spreadsheets and then run the capsule to put them back.
So no spreadsheets in any of the folders any more.
Now we are going to run the capsule to re-create the spreadsheets.
I will hit pause on the video so you don’t have to watch the capsule run.
Ok, so the capsule has completed.
So let us go back to the folders.
You can see that the Europe workbook is back in the folder with the current time.
We can open the report.
You can see it has got the data for France, Germany, United Kingdom.
You can click on multiple currencies and click on Euros and it will update the dashboard.
Click on GBP and the dashboard updates.
You can see the date and time for North America.
You can see the date and time for Pacific.
To prove the dashboards have been sent to our portal I will open up our sharepoint portal.
You can see the report for europe was sent about a minute ago.
You can see the timing of the arrival of the workbooks for North America and Pacific.
So you can see the capsule ran and the reports were sent to the one drive and were then distributed to our sharepoint portal.
So let us go back to the inside of the capsule.
Right now at A A is set to Pacific.
This portion of the capsule to collect data and send it in to the collect data spreadsheet has the ability to literally get data from anywhere.
You can get data from web sites, logs, other databases, spreadsheets, any data from anywhere.
You do not have to have your data in a data warehouse.
You can get data from virtually any data source at all.
Meta5 can read just about everything and anything.
And if you ever come up against a data source that is not supported?
Meta5 can add support for a modest fee.
You can clean the data up.
There are many tools to perform many functions on the data including statistics if you wish.
And anything that can run on the host computer can also be called to process data inside Meta5.
Most importantly you can get data from anywhere and integrate it in to these applications and send it forward in to a spreadsheet in a parameterized repeatable batch processing mode.
So you don’t have to go back to your IT people and beg them to put the data you need in to an SSAS cube to get it into your report. Ok?
The big beauty of meta5 is that I can go and collect data from anywhere and send it into a spreadsheet.
For people who want reports and dashboards in spreadsheets this is a BIG feature.
This makes populating your power pivot models much easier than any other way you have of doing this today.
The way that these capsules are run is via the BIDA scheduler.
Today we have one customer who uses the BIDA ETL software and models to populate data from their ERP in to their data warehouse.
They do this via the scheduler that runs each night.
And at the end of the batch the scheduler runs a set of capsules to generate Excel reports and it puts those reports on to a shared drive mapped as a drive letter.
The BIDA software is doing end to end BI support producing Excel reports and dashboards with Meta5 as the software that takes data from the data warehouse and populates the Excel workbooks.
There is no need for SSAS in that environment.
This makes report development and support much faster, cheaper and easier.
Now what I am going to do is to run this capsule in front of you so you can see it run.
I will also open up the task manager so you can see Excel running.
Please remember this is a small development machine running Excel 32 bit so it runs a little slowly.
So here you can see Excel is running and Meta5 is sending data in to Excel.
Now it has finished putting data in to the temporary Excel data workbook and it is now putting the data in to the dashboard.
Obviously excel single threads so you have to be a little careful about how you set up job processing to produce the reports.
You can have capsules run on their own dedicated capsule servers if you want to parallelise processing.
You saw that it did not take Meta5 very long to go and collect the data.
And then it takes a little while to push the data in to the excel spreadsheets.
Ok, just to summarize.
What I wanted to show you today was a very detailed, drill down look in to how do you actually build excel dashboards which will collect data from many different places using the meta5 software.
I also wanted to show you how those dashboards can be parameterized for different regions so that only the data needed is in the dashboard for the user who will use it.
We wanted to show you how those dashboards could be delivered to different people in different places via the one drive feature.
That is what we have demonstrated to you today.
By using Meta5 what you will be able to do is build excel dashboards better, cheaper, faster than any other way.
Given that you are going to be building excel dashboards for a very long time out in to the future?
Building them better, cheaper, faster and easier to maintain to save time and money is a good investment.
It will reduce the cost of maintenance for all those excel spreadsheets you have out there for years and years to come.
Now as I said at the beginning this is a very detailed demonstration.
This demonstration is for those of you who have the job of building excel reports and dashboards.
This if for those of you where your business users come to you and say “Mihai, I have this new dashboard I would like to build, would you build this dashboard for me please?
And would you put it into the daily schedule to make sure it is delivered to all the people who need it by 9am please?”
That is who this video is for.
If that is your job, building excel reports and dashboards?
What we showed you today is a better, faster, cheaper way of building those excel reports and dashboards using the power of power query and meta5.
You can build your excel reports and dashboards regardless of where your data is.
You don’t need one of those big and expensive data warehouse projects to get started.
Though, of course, having a data warehouse helps.
If you do have a data warehouse that is a bonus.
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