The next and the last segment that we are going to discuss about in data management is Data.
Data Archiving.
So what does data archiving data archive when it comes into picture then the exceed other data storage
like other systems.
Sandoval's also has guarded its limits for storage and then we exceed that data storage.
Then deals 100 percent of the data storage that is given to our arg.
Then we talk about data OK but then what do we do.
Because it is so much of data right and we have almost used 100 percent of our space.
So what do we do with that our additional data.
Because data is incoming every day and new records are getting created in the system.
So how do we make space so then we discuss this concept of data archiving so data archival means basically
it is defined as the process of moving data which is no longer actively used to separate storage space
beget because we can no longer accommodate that in our sensible system.
So we are moving that additional data to a separate storage space.
And this process is called this data update.
So in this session and also in the upcoming sessions we discuss about the various steps involved in
archiving data what all the precautionary measures that we need to take and also what on the various
options that got available to us for archiving of our data.
Let's go all of them one by one before we go to the actual process of our data archiving.
First let's understand about the importance of doing data archiving.
So what is the significance of archiving data in the system.
The main significance of data archival is about the credit performance and about the user experience.
So when there is large volume of data in it all of it obviously slows down your credit performance.
So whenever user goes to a particular object tries to retrieve some records in the background but what
happens in the background clearly is being performed to fetch the data that the user is requesting for
and then such goodies are being requested.
Then you have anonymous data.
Obviously that performance will be slow meaning slower credit performance.
SULLIVAN The performance of the queries become smaller.
It will directly impact the user experience.
So large of data.
WILLIAMS The results in slower credit performance which in turn negatively impacts the user experience.
So Data Archiving comes for rescue data.
Archival has to be in place in args this enormous data.
So what do we do.
We consolidate the data so we do data consolidation and that in turn improve the user's experience and
also glean data archiving gives you a good control of the information that you have.
Information is spread across every bit and once you do data useful which is on that data that is actively
being used in the system and which is all the data that they are not actively being used and they will
also be data that is not being used at all.
So once a data archival is in place you'll know exactly that the data that you have is actively being
used that to win it for them help you improve your business.
So data archiving is very essential for you and not only that this process of archiving it also reduces
the storage cost because many times you are storing unwanted data and the data that is not being used
the data that does not have any use to your business.
So deleting data archive will give a much more clarity about you about your business about the data
that you store and thereby introduce a storage cost because if you buy additional storage is going to
cost a lot of money.
So having proper archiving techniques in place reduces the storage cost and also it keeps your data
safe.
Right.
Because we know exactly what we want and we know that the data is there in a safe place and we can retrieve
it whenever you want.
So the data is not lost right.
So Data Archiving has to be in place for your business and for your system to be more effective.
So these are some of the key benefits of data aka Buffett.
So now that we know what is the significance of archiving data in the system let's get into action.
So how do we start the process of data.
OK.
But the first step the very first step in the whole process is to know your storage limits you should
know what does the maximum storage limit that is allowed for your org.
And how do we know that go to a developer dietitian go to setup and here look for data storage.
Sorry.
Look for just storage and you under data management you have storage usage.
Go for it.
Here it will tell you what does the maximum data storage and what does the maximum file storage limit
for your honor.
All right so that gives you the maximum limit.
And this also tells you like how much you have used.
And basically the pleasant days that was used.
So this is the old what our data storage limit.
And they also split it by the racquet type.
You can see that a video that I got by Zach got listed.
And the storage that is occupied by each after the record.
Type on top of it.
You also get for the file storage and also the users basically taking up more storage space.
So we get this whole information from here.
In fact like we have discussed all these in detail in our previous sections.
So here is where we go and understand about the storage limits and how much we have consumed it now.
Perfect.
Now if you are thinking that about the limits is the same across all args.
Absolutely not.
The storage limit varies from OK to.
OK so what he had is some documents from a fanciful said which helps to give more information about
that.
And here you can see it was based on the addition and it defaults based on the number of uses that you
have.
So here you can see data storage allocation for use that license.
All right.
And you can see for contact manager for group professional enterprise performance unlimited.
All right.
So it depends on the addition that you have and it gives out.
What does the storage that is allocated for use user license.
And according to that the limits will be set for your order.
All right.
So based on this the limit would be set for you.
OK.
So if you all you go to setup and storage then you can see what is the maximum limit that is allowed
for your order perfect.
So now we have got a good understanding about the hold the limits are set for each arm.
And what is the limit that is specific to what.
OK.
All right.
So now that we have that information what does that the total storage limit of org and the use to storage
limit of all.
So what do we do next.
That is step two.
Step would be to analyze the use to storage.
All right.
So now that we know what does the maximum limit and what does the user percentage of it all.
Next we should analyze how those bases being used.
Holiday into storage is being used that is called less analyzing of a storage space if the order of
is showing that 96 percent of the storage space is being concealed.
The V should know which data is consuming that 96 percent.
We should know which data is consuming most.
We should know which data has glass nearly any records.
So that to step to analyzing.
They used to storage.
If you go to set up.
We just a few minutes before we understood how to go to set up storage limits and there we can see the
percentage of the percentage of space that is occupied by record types.
Right.
So that gives you the numbers but for a more pictorial representation and better understanding of your
data there are free apps available.
So take advantage of those free apps you have the field trip app you have the data quality analysis
dashboards app.
So you have these apps and they are for free.
Here is the lead field trip app.
So this gives you a good understanding about the space the storage space that you have and what are
the objects that are being occupied in the storage space what is the percentage of space that is occupied
by each objects how many feels and object cast.
And what is the data that is populated on those fields that all the that guards have data populated
on those fields only some of the records of none of that guards have values populated on those fields.
So on those analysis you can do with the help of these apps.
In fact the field trip app discussed in detail another other basic admin goals.
So if you want to recap please go check out that and the data analysis dashboard does data analysis
dashboards again.
We discussed about this app in detail in the previous section.
So please go take a look at it to see how we can make use of this data quality analysis dashboards.
This is again a pretty cool app that gives us a good pictorial representation a good understanding about
the total data usage.
So these apps will help you understand about your data storage space and then exactly you need to pay
attention for effect.
So make use of these apps and understand and analyze the use to storage.
So once you take care of these apps you will get a good picture about the use distorted space in your
arm and how it is being used already.
So that could give a good idea about that effect on seeing those ad reports on dashboards from those
apps you know you might think oh my god make 10 percent of the spaces being occupied by accounts.
Really do I have so many icons in the arm.
Right.
Well it's a one way after those analysis one of you after those that report do you get that understanding.
So on analyzing those reports and dashboards and also there are the other details you would know that
exactly the objects that you are going to data are right.
So once you design those objects that you need to archive then the for the strategy goes on.
That is step three.
So step three is about the data that is to be archived in those objects.
So first you will list down the objects that you want to be archived.
All right.
So once you list down the objects.
Say for example the icons or context or tasks activities.
So once you finalize on those objects the next step is to finalize the records on that object so which
is all those that I cards that I want to archive.
That is our next question.
So what you feel comes the data.
Say for example icons or maybe activities.
So if you consider icons that don't they icons a data that comprises of the needed data that you want
in the system.
Basically the active data that you need for your business plus additional data.
Right.
Because it is not that 100 percent of the data you are using for your business.
That would be definitely some additional data which is not needed.
Right.
So that does that additional data.
So any object would have that needed data or the active data plus additional data so when you say additional
data what does this additional data this additional data may comprise of some unwanted data that you
absolutely don't need at all.
It is of no use to your business and you may also have data that you have not sure about.
You are not sure about whether you will need that data in the future or not.
All right.
So this additional data game comprises of the unwanted data that does absolutely of no need for your
business and the data that may be needed in the future.
All right so we are splitting that additional data so this unwanted data what do we do with unwanted
data.
Obviously go ahead and put it to them.
It is of no use to you or to your business.
It is just occupying space.
So what do we do with that unwanted data.
We just put it to them and what do we do with the data that may be needed in the future.
We need to archive that information.
So this is how we understand other data.
And this is just the common strategy that applies to any object out there.
So this is how you split the data basically which is all that active data and which is all that unwanted
data and which is all that data that you are going to archive so now that we have got that understanding
the next step would be to plan that data retention strategy and this data retention strategy deflect
from object to object.
So how do we end up with that strategy.
Basically the final output of this step would be to frame the retention policy.
So once we are done with this step we'll have that retention policy in place for the objects for which
all objects for the virtual objects that we are planning to archive the records.
So how do we do that first list all the objects that you want to archive the data and how would you
decide on patrol the objects.
Basically we have garnered good understanding from other data analysis reports and dashboards.
Right.
So from that reports on dashboards you know your technical team you'll have a meeting.
You'll also have a meeting with the business and the new final you to decide.
OK.
So these are all the objects that are occupying space.
So we need to pay more attention to these objects and aka the data.
All right.
So the first thing is to list all the objects to archive.
And what does the next thing that we need to see then you are working on the objects that type will
be archived.
You should pay attention to them related objects because you know if you want to add deleting a record
from the master object and if it has a master detailed relationship the child objects then automatically
the child records also will be deleted.
All right.
And data deletion has got a big impact.
So when you are working on the data please pay more attention to every single step that you do.
All right.
So whenever you finalize on the list of objects to archive chuckled all the related objects and all
as a whole they are related with the pattern the object of it that it is a master digital relationship
or lookup relationship.
All right.
So pay more attention to the relationship with the other objects and then and then you'll decide on
the retention criteria.
Say for example accounts you know so for account the retention criteria.
Maybe you like your business but basically like this a call is made by the business.
The business would tell you like a I wouldn't need records that does inactive for more than three years.
I wouldn't need records that is inactive for more than five years.
Right.
So they give you the analysis and that basically forms zero retention criteria and then you say that
inactive.
What do you mean by inactive.
Because you know not every object has guard that inactive flag.
So holiday you say a record is inactive How do you say the data is inactive well each varies from object
to object and basically does that get the business.
Who makes all this glide like a say for example.
Consider icons.
If that is no new opportunity created for the duck island in the past three years then the business
may consider that as an inactive account.
All right or maybe four.
Consider contacts if there are more activities created for that contact in the last five years.
Then that contact may be considered as inactive.
So every business has guard data one criteria for defining this inactive record.
All right.
So that goes away entirely depends on your business how they define this concept of being inactive so
those inactive records for certain period of time to year three is five years so that forms a retention
criteria.
Those are the records that are to be archived and once you finalize the retention criteria you also
plan on the frequency of data archiving in what frequency we should archive our records every week every
other week every month every other month in what frequency we should archive over rockets.
So that is again we sit with the business and we understand that.
So the final output of this step will be to frame that retention policy so that the retention policy
will contain details of the object and what does it retention criteria for that object and also the
frequency of archiving the data for that object.
So these are the three main details that helps you frame the retention policy.
The object the retention criteria and the frequency of doing that and once you frame does it attention
policy for one object you redo all the steps and then do the same retention policy for the all the other
objects as well.
So in a similar fashion we do all day retention policy for all the objects for which we need to archive
the data.
Perfect.
So good amount of work has already been done for data archiving.
Right.
We have started up this analysis we analyzed and then we came up with the list of objects and then we
have also framed the retention policy.
Great.
So now that we know the objects and the percentage of the active data and the percentage of the inactive
data what do we do next.
In that percentage of inactive data the gains bleed them into the data.
That is to be purged because that is absolutely of no use to the system and the data that may be needed
in the future.
That is the data to be archived right.
So we have that split up and what do we do with the data that is to be purged.
We obviously go ahead and schedule some belly jobs and watch the data.
All right.
So that percentage of data which is inactive or which the business totally doesn't need that data.
You go ahead and talk to them.
So how do you put then you have a few options here.
You can use comes to jobs.
Apex could do a jobs that will automatically close direct records based on the criteria that you set.
You can using any of the data handling tools for deleting those right cards.
But for a scheduled automatic run it picks bad jobs would be the best bet.
Once you set up the criteria and ones just do the job if will do the luck for you.
All right.
So that would be definitely a good option for skidoo.
Automatic a daily jobs.
All right so that you can use for deleting data on a regular basis order you can use any of the data
handling tools for deletion so the data has to be deleted.
Yes we can delete that but while you delete your data you have to pay more attention because I gain
data is the key and why deleting data all you have to pay more attention because you are not deleting
any needed data.
All right.
So pay more attention to that and always check the legal team for any legal implications and always
check for data integrity implications because as I mentioned before if you're deleting a master record
and if it does go to master digital relationship with the child record then automatically the child
records also when you get deleted.
So do all those analysis and see about the deletion of those records.
And yes take a backup before deletion.
You know so when you take a backup even in case like you know that is a semi error that happens.
You have that data backup too.
All right.
So be very cautious while doing the data deletion and also take necessary backups.
Perfect.
So now what we have done we have punched the unwanted data which forms the percentage of that additional
data we have pushed that unwanted data.
So where does the next one.
That is the major part of the task is to archive that data.
That may be needed in the future.
Even that does against good old archival right.
So you are basically archiving the data to a separate storage space so that you may return which in
the future if needed.
So how do we archive.
So this process of archiving worked on the various options that are available to us.
We'll discuss another upcoming session.