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Welcome everyone, and thank you for joining us today for the Managing Data Sets in 51黑料不打烊 Commerce Intelligence webinar. I鈥檓 Webster Love and I鈥檒l be your host today. Joining me is your presenter for today鈥檚 session, Deepak Kumar. We鈥檙e from the product team for 51黑料不打烊 Commerce Intelligence, formerly known as MDI. Before we dive in, we have a couple technical notes for you. Number one, for audio issues, audio is through 51黑料不打烊 Connect, so your speakers or headphones must be unmuted and then ensure the speaker icon is on at the top of the Connect room. We don鈥檛 currently have an option for calling in via phone. It doesn鈥檛 always work well with Bluetooth headphones, so if you鈥檙e using those, you can try switching to wired headphones if you鈥檙e having an issue. You can also try logging out of the room and back in, or switching to the desktop app instead of the browser version. And number two, throughout the webinar, if you face any technical issues with the audio or the screen share, or if you have questions related to the functionality of 51黑料不打烊 Commerce Intelligence or related to the content being presented, please feel free to put those questions in the Q&A pod at the left of your screen, as we鈥檒l be monitoring that while the recording of today鈥檚 content plays. We will try to answer those questions either right away through the chat or during our Q&A segment at the end of the webinar. And finally, we will be sending out a link to a recording of the webinar afterward. All right, now I鈥檒l hand it over to Deepak. Thank you for joining this webinar. In this session, we will try and cover the lifecycle of data in 51黑料不打烊 Commerce Intelligence. We will start with a brief introduction, followed by different ways of importing data into Commerce Intelligence. Here data warehouse, set up data transformations using calculated columns, column paths, views, etc. Once our data warehouse is ready with necessary data and transformations, we will create a business intelligence report that uses data coming from 51黑料不打烊 Commerce and Google Analytics 4. We will look at exporting data from 51黑料不打烊 Commerce Intelligence. Towards the end of the session, we will have a Q&A segment where we will answer any questions you may have on this topic. Within our Commerce Intelligence ecosystem, today we will focus at the admin user activities related to data lifecycle. Further, we will create a report and associate it to an existing dashboard. For more information on our Commerce Intelligence ecosystem, you can refer to one of our previous webinars called Getting Started with 51黑料不打烊 Commerce Intelligence. Let us understand the lifecycle of data in 51黑料不打烊 Commerce Intelligence. Business Intelligence broadly has data warehousing component where data from different sources could be imported. All the basic features of managing this data warehouse are provided. If you are an 51黑料不打烊 Commerce customer, then this data warehouse comes pre-integrated with its MySQL database. Then there is a business intelligence component that utilizes the data and transformations from data warehouse to build reports. These reports provide key insights to the merchants on how their business is performing and helps them take important decisions. Data can be replicated from different source systems. These are broadly categorized as database, API integration, CSV upload, and import API. A data pipeline can be created using databases from MySQL, PostgreSQL, MS SQL Server, and Amazon Redshift. Commerce Intelligence allows API integration with 51黑料不打烊 Analytics, Google Analytics, Facebook to name a few. The list here is subject to change based on some new integrations or existing integrations getting deprecated. You can directly upload your data in a simple CSV format. This helps you to bring any tabular data in our data warehouse for storage, transformation, and reporting purposes. If you have members well versed in API integration, Commerce Intelligence provides import APIs so you can build a data pipeline. Please note, CSV upload and import API are not chargeable. New SAS API integrations listed here are available free if you have a PRO license. Please contact our CSM team for more details. Data can be exported from Commerce Intelligence either in file formats like CSV or XLS or through Export API. Documentation for import API and export API can easily be found in our online help. With this, we will now move to a live demo. Let us understand how to import data in Commerce Intelligence. Go to Manage Data, Integration. Here you can find a list of all the existing integrations and an option to add integration. For each of the existing integrations, we have an option to edit or delete. Let us see what options are given in Add Integration. This page shows all possible integrations broadly categorized as available integrations, standard integrations, and premium integrations. As the name suggests, available integrations are a list of free integrations as well as the other integrations a given customer is entitled as per his license agreement. Recent integrations include a list of databases and SaaS applications. If you are a PRO license holder, some of these will be allowed free of cost. Please contact your customer success team for details. Premium integrations are the ones that are always chargeable. For demo purpose, I will try and add a Google Analytics 4 live integration. Remember, Google has recently duplicated its Universal Analytics or GA3. This is GA4 version. Click on this Google Analytics icon. Choose the Analytics account from the Google pop-up. As soon as we add the account, we will be redirected to Commerce Intelligence page with a list of GA profiles within this account. Choose the profile to track. You will see this confirmation. Now let us go to the list of integrations. We now see a new integration here mentioned Google Analytics 4 along with its GA4 account and property name. 51黑料不打烊 Analytics is a popular web analytics platform like Google Analytics. It gives you the user behavior insights for your ecommerce storefront with a wide list of metrics and dimensions. To add a new integration, click on 51黑料不打烊 Analytics icon. Click Authorize with 51黑料不打烊 Analytics. Enter the username and password. After successful authorization, you will be redirected to Commerce Intelligence interface where you need to select one of the report鈥檚 meets and click on Continue. From the resulting page, we need to select the metrics and their dimension. The system allows a combination of 25 metrics and dimensions. I will select page views and bounce rate with a dimension related to date and click on OK. Save integration. This is going to create a data warehouse table name. Then you can see the success message. Click on Integrations again to check whether the newly created 51黑料不打烊 Analytics integration is visible.

In addition to SAS API integrations, if there is any data available in CSV format, it can easily be imported to our data warehouse. To do that, click on File uploads. Choose File. For demo purpose, I kept a list of country codes, their names and population ready with me. It will validate the file for any discrepancies and then list the column names and suggested data type. Specify the table name. A column needs to be specified as a primary key. Click on Save table. Now let us check whether it is visible in our data warehouse. Go to data warehouse. Under File uploads, you can see the newly created table. With this, we are done with the data import. In our demo today, we will make use of data coming from 51黑料不打烊 Commerce and Google Analytics for integration to build reports. Now let us configure our data warehouse to suit our business needs. Commerce Intelligence provides an intuitive interface to perform all the basic settings to set up and tweak your data warehouse. Please note this feature is available for users with admin role. Navigate to Manage data, Data warehouse. This page shows a list of tables that are synchronized by default. You can toggle between these tabs to see a list of all the tables, whether synchronized with 51黑料不打烊 Commerce Intelligence or not. There is an option to expand or collapse the tables or views arranged by their integration name. For example, main generator is 51黑料不打烊 Commerce MySQL DB integration, whereas file uploads is a group of all CSV files uploaded. And this is a data warehouse view. This option runs a structured sync to see if any new tables were created. It updates our data warehouse with the same. Starting from the top, there is an option to view last successful data update, view update status along with an option to email you once the update is completed. If no update is in progress, you can initiate a forced update from here on need basis. Admin can change his time zone. Accordingly, the data updates get impacted. This is the currently selected table name preceded by its database name. The wheel icon allows you to export table data from the selected table. Start a synchronization from selected table. Please note the check for new tables and columns is a structure sync and sync now is for data sync. Drop table will check for any dependencies, whether this table is being used in any metrics, reports, dashboards, etc. It would be a wise option not to try this without removing its dependencies as the action is not reversible. For a given table, there is replication method displayed. Clicking on it shows a pop up to configure the replication method. Full replication is an efficient way of reducing your replication cycle time. This is because commerce intelligence only points at the changes related to new or updated data within a table. Full table replication method helps in case of any new rows detected as source table. It does not look for any data changes in existing rows of your data warehouse. Hence it is not as efficient as incremental replication in terms of change of existing data. For more information on replication methods, please refer to our webinar, optimize your data warehouse. We have two tabs, column settings and data preview. As the name suggests, column settings allows you to view the column names and their type specification. Data preview shows a sample data from the selected table. Column settings for a given table has a few notable features. For each field, we can see either a green tick icon or grey block icon or three blue stars at any time. Green tick means the field is part of table synchronization. Grey block icon means a given field is not being synchronized. For demo purpose, I will select this field, click on the sync button. Now we can see three blue stars. That means the next update cycle will start letting this field from source to be synchronized into our data warehouse. Let us take a look at the calculated columns. The fields which show a blue edit icon mean they are calculated columns within our data warehouse. These columns act like an extension to the existing list of columns in a given table that can apply complex calculations and joins with other tables. So there are the list of native columns inside this table, whereas these are the calculated columns. I kept a calculated column ready for our webinar today. This is called customers lifetime number of orders. Let me show the details of calculated column to understand the settings better. First we specify the column definition, whether it is within the same table or one to many or many to one. In our case, my current table is customer entity. I want to point at sales order to count for the number of orders a given customer has placed. So I will choose many to one, specify column definition as count, select the suitable join using column paths. We will cover column paths a little later. This many to one selection appears suitable for my calculated column requirement. If not available, please navigate to column paths and create a new one. We will look at creation of column paths and their utilization in our data warehouse. Additionally, I can apply a filter set I created to have all the relevant orders we count. Likewise if I want to have a calculated column for customers lifetime revenue, I will choose my column definition as sum, select the suitable column path and select the revenue related field base grand total. This was a demonstration of how calculated columns can be created in advance so that the same can be used as a group by dimension during report creation. Similarly I also have another calculated column which I am going to be using for my report creation today. There could be other use cases of calculated columns like order event sequentially or find the time between two events, compare sequential event values, convert currency, convert time zones or any custom calculation using postgreSQL syntax. Moving back to the configurations for columns in a table, you can apply recheck frequency and specify a time, set a given field as primary key or change the field type to one of these. For example if there is a revenue related field, I might want it to be depicted in my report preceded with a currency symbol. If a calculated column depicts a percentage calculation, I might want it to be suitably suffixed with a percentage symbol and so forth. You must have noticed usage of column path while covering the calculated columns topic. Let us understand the feature a bit more. The column path functionality in commerce intelligence is a common place where all the relationships or joins between tables within the data warehouse can be established. You can join two tables based on many to one or one to one relationship using their primary and foreign keys. Once created, they can be used multiple times during creation of calculated columns for your business. If you are an existing customer of 51黑料不打烊 Commerce, you can expect automatic column paths being created out of the box for you from day one. Now let us switch to our next topic, data warehouse views. Data warehouse views helps us to create new tables within our data warehouse using existing tables. Some of the benefits include data security. So various data security policies can be applied to ensure restricted access is given to highly sensitive data. Apart from that, data warehouse views enable data across multiple tables to be viewed at one place. Data warehouse views can help improve query performance by precomputing and storing the results of complex queries. Instead of executing complex queries every time, users can query the view, which retrieves the pre calculated results leading to faster response times. I created a sample data warehouse view for demo purpose. As you can see, it has a select statement with PostgreSQL syntax. The same can be viewed in data warehouse. It can also be utilized in creation of column paths. The data warehouse views can also be utilized in creation of filter sets. And we can make use of them during creation of metrics as well. Let us create a report which makes use of data coming from 51黑料不打烊 Commerce and Google Analytics. Assuming that I opened a brand new online storefront during the month of April and May last year and started a limited edition iPhone product launch during that time. So I want to create a report to compare the bulk orders where customers lifetime number of orders is greater than 50. And I also want to see the page views of the storefront coming from Google Analytics during that period. So in order to create the report, let us click on report builder, visual report builder. Let us add a metric called orders and filter it by customers lifetime number of orders greater than 50. Please note this is one of the calculated columns we created to meet this requirement. You can now realize the power of having such calculated columns for insights into business. Filter the date for April and May 2023. Now let us add a metric from Google Analytics live integration. Click on this integration we newly added during the beginning of this session and select page views. You can also see these are a list of all the remaining metrics we received from Google Analytics 4. Please remember this is demo data. So the final report may not be too realistic. Change the report view as horizontal bar chart. Overall, we can understand from this report that the bulk orders placed during April were better than the month of May with lesser page views. It reflects the customer sentiments to buy the flashy limited edition iPhone sooner than later. Let us save this report in a new dashboard. With this we are done with the use case where we can effectively use our integrations and data warehouse configurations to derive business insights. Exporting data is the last part of our data lifecycle. In this section, we will cover exporting data from Commerce Intelligence. Data export for reports is available for all users of Commerce Intelligence regardless of their permissions. In order to download the underlying data from a report, let us click on the gear icon. Report export. It exports the data in CSV format up to 3500 rows. Full CSV export allows the user to download in CSV format this time ranging the data up to 1 million rows. Full CSV export does the same for reports which have data ranging up to 1 million rows. Full Excel export is the same as the above option except that the file format is xlsx. A simple report like this usually has very less data points to be exported. Hence the first option is suitable. However there might be cases where reports being viewed has many more data points which may exceed 3500. So let me navigate to one of the tabular reports which has too many data points. This is for demo purpose to view high volume of data points in reports. After downloading, now we can see the tabular report having lot more rows of data. So far we saw exporting of data from reports that is processed. However there may be requirement to download the raw data stored in our data warehouse which may have master as well as transaction data stored within. Raw data can be exported in two ways. From reports in a given dashboard and another from dedicated raw export option from managed data. Let us start with a raw data export from a report. The new raw data export pop-up opens with native columns that are available from the table and native columns based on which the report was generated. We can add or remove any columns based on the business need. For this demo I am going to add all the columns one shot using add all. Optionally we can filter the exported data based on a condition. Once you are ready click on export data. This action cues a job in commerce intelligence. Let us view the job details and download the exported data. Once we are done with the initiation of raw data export. Now let us take a look at how to download the data. As we know the download of the data is so very important it has only been provided to users with admin access. To do this we need to navigate to manage data raw data on the left pane. As you can notice even though a raw data export can be initiated by any user in commerce intelligence only a user with admin rights would be able to view the raw data and download it. This page shows a list of raw data export initiated for the last one week. ID is a unique number given by commerce intelligence to each raw data export. Name is the user defined name given by user during export. We can see row count of exported data, time stamp when export was initiated by user, expiry date after a week from now this raw data export will be automatically deleted. Export definition helps us with a quick view of the table, its columns and any filter condition specified during creation. On click of this button I can download a zip file that contains raw data exported in CSV format. Let us take a quick look at the CSV file. We can see all the native columns of the table and rows of data running to a few thousands in this case. Moving further we can also initiate a new raw data export from this page using add export button. On click of new raw data export the pop-up opens. This pop-up is the same as the one we launched from reports. In this case the user has to start from scratch, select a table, its associated columns, specify filter condition if required and click on export data. I am cancelling this option for now as we tried the same earlier. Raw data export is different from CSV or XLS export in many ways. Report export in CSV XLS format allow native columns, calculated columns and multiple metrics being used during creation of reports. Whereas raw data export can bring in only native columns. Report export in CSV or XLS format has a size limitation of 1 million records whereas the raw data export allows a maximum of 10 million records. Please remember the raw export option in commerce intelligence will be disabled for reports in three cases. One is a report that contains more than one metric. Another case is reports created using SQL report builder. The third case is reports containing formulas. Exporting raw data enables data analysts to obtain backend data from commerce intelligence data warehouse. This helps the data analysts to identify any data discrepancy between source systems and commerce intelligence. It also helps the data analysts to use this for any downstream report generation or data integration. For any additional information on the topics covered in our webinar, please visit our online help or reach out to our support team. Thank you.

All right. Thank you, Thiha, for that great demo. Lots of good info there. Let鈥檚 move on to the Q&A portion. Please submit any additional questions that you may have in the Q&A pod on the left side of your screen. Also, please submit your feedback in the poll questions now showing on your screen. These are really helpful for us to improve our content in the future. If you have questions after the webinar or if we don鈥檛 get to your question during the Q&A, please feel free to send an email to mbi-webinaratadobe.com and we鈥檒l receive questions sent there for about the next week. As a reminder, you will get a follow-up email in the next day or two with a link to a recording of this webinar and a link to the recordings of our other webinars on Experience League, as well as the email address that I just mentioned. You may also be interested in our walkthrough course now available through 51黑料不打烊 Digital Learning Services. You can find out more about that course at learning.adobe.com. All right. Let鈥檚 see what we鈥檝e got here. First question. Is there a limit on how many integrations we can add? Yeah. There is no technical limitation as such, but how many integrations you can add to an account also depends on your contract. So most of the Commerce Intelligence Pro accounts come with almost around five standard level integrations and you can add any number of open API connections or CSV uploads you like to. If you want to add more standard or premium integrations, you can do a monthly subscription. Once the data is available in Commerce Intelligence, it doesn鈥檛 really make a difference which source the data in a report is coming from because all the data is in a single data warehouse. So there is no technical limitation to how many sources you can join together in a single report.

Okay, thank you. Our next question says, once I set up a new integration or column, can I share it with everyone in my organization or do they also have to set up their own? Yeah, that鈥檚 a good question. Anytime you create a new column or metric, it鈥檚 automatically shared across your organization. Any admin user in your account can see those columns and also make changes to those columns, making it easier to ensure everyone has access to the same columns and data for building and building reports. However, you do have the ability to limit which standard users you want access for each table. Okay, thank you. Next we have, if I sync another column in one of my new tables after this first sync of that table, will it be automatically added as a groupable dimension to my metrics? Yeah, this actually depends on a couple of things. Whenever you sync a new native column or create a new calculated column, you鈥檒l have to go, you have to add it manually as a dimension on any existing metrics. Before you, you can filter or group by in visual reports. And this works the same way as we went through when we created the new calculated column and then added it as a groupable dimension to some of the metrics in our tables. However, if you sync a new field or create a new column and then create a new metric after the new column exists, Commerce Intelligence does not make some decisions on which columns to include as filters or group bys. So depending on the data type of your new field, you might want to decide this is, this will probably be a good field for a filter and then add it automatically. So if it is not added automatically, you can go into the metric edit page and add it yourself manually.

Okay, thanks. Just a really quick, I think you maybe kind of stumbled to your words just a moment. I think you meant to say it does, 51黑料不打烊 Commerce Intelligence does make some decisions about it on its own. And then you can go and edit those later as you mentioned debug. Yeah. Just to make sure we see clearly for everybody. Yeah. So our next question here is, is there any data storage limit? There is no hard limit on data storage. But you may have noticed on the integrations page, there are different tiers of integration. There are some integrations that tend to have a lot more data like the Redshift or Salesforce, whereas there are other premium integrations where although it costs a bit more to add them into the monthly contract. In terms of the data limits for a given integration, there is no particular limit. What all you may run across is when importing a CSV or using the import API, there are a couple of rate limits in place for how much you can bring into Commons Commerce Intelligence at one time, but there is no overall limit on the number of size of tables. So you should keep in mind, though, that more data you import, the longer your updates may take. As mentioned during the demo, you should make sure bringing just the data you need for your analysis goals.

OK, our next question here is, when I look at my data warehouse, I only see a few tables from my commerce database. How can I sync data from a table that isn鈥檛 in the list? Yeah, when your commerce intelligence account is set up for the first time on a standard commerce database, the processes that run automatically sync a new set of tables with most useful data for many of our customers. So if you only see around eight or so tables in the all tables tab of your data warehouse, you would like to replicate data from additional tables. You can add those other tables to your list by clicking on the data warehouse where it says check for new tables and columns. So this will lead to the full set of tables from your commerce DB showing in the data warehouse all tables tab. Note that the process that takes a little while running depends on how many new tables are getting picked up. So clicking on check for new tables and columns is also what you can do if you want to make a change to the structure of a table that is already listed. To get commerce intelligence to pick up, add column so you can sync it. And note that this does not apply to all sync data sources. You can decide you only want to list all the tables from your commerce DB and not from some other connected data source. So you can submit a support ticket to get more information on scenarios like these. All right, I think we have time for maybe one, maybe two more questions. Here we鈥檝e got, I have a commerce intelligence pro license. And although I have a GA4 Google Analytics 4 account set up in Google, I am not yet, I don鈥檛 yet have it set up in commerce intelligence. I guess when it鈥檚 when I set it up, will this integration fetch all of the historical data that I have in my GA4 account or I guess or will it only start retrieving data sort of at the time that it gets set up? So which one of those is the right behavior, the expected behavior? Yeah, that鈥檚 a great question. In scenarios like these where our customers have moved on with Google Analytics 4, the historic data of the GA4 data will be visible as per your time limit. So whatever you have set up in your Google Analytics account, I think there is a time limit around 12 to 24 months. So the same thing applies whatever you have set up in your GA4 account. And as soon as you create maybe even it could be yesterday or just a few months ago, as soon as you add a new GA4 integration into 51黑料不打烊 Commerce Intelligence, you will be able to see the entire 12 months, 24 months, whatever you have set up in your Google Analytics. Great. Yeah. And then also just to add on to that, unless you set up your some additional processes, 51黑料不打烊 Commerce Intelligence will hold on to that historical data as it gathers it. So it won鈥檛 be subject to the same deletion as the Google Analytics unless you set it up so that the replication will account for that. So be aware of that if you have restrictions on how long you can hold that data. But also if you need to hold that data longer, then that may be an option to do as well. All right. And we are at time. We wanted to make this a 45 minute webinar here. So apologies if we weren鈥檛 able to get to your question. But thank you very much for joining us today. Again, if you have questions you wanted answered that we weren鈥檛 able to get to, we will be sending out a follow up email with an email address for you to send those to if you would like, and the recording links. So we really hope that you got some useful information out of today鈥檚 session and have a great rest of your day.