We primarily sell Power BI licenses.
Mostly, we focus on the retail industry. Usually, we are using the Power BI reports for dashboards. We are also providing our customers with some data warehouse reporting. Basically, we do long-term categorization of stock and inventory numbers and sales figures so that they can compare the sales and stocks to the inventory numbers.
We tend to deal with physical inventory reports. We tend not to deal with the solution for financial operations.
The solution has different licensing tiers.
The product can scale if you need it to.
Technical support is quite good.
With the cloud deployment, there's no setup required.
The product works well for small or mid-level organizations.
The solution's stability could be improved. In the retail industry, due to issues with information updating and data uploading. Clients may end up pricing items wrong as they could accidentally base their choices on old or wrong data.
The licensing needs improvement. There needs to be a middle option between Pro and Premium versions. It could also be less expensive.
While we have experience with Microsoft tools for the past ten years, I personally have had experience with Microsoft BI for the past two or so years.
The solution is not really stable. I've faced a lot of limitations especially in terms of some data flows updates. We have faced some exceptions. Right now, we have created a case for Microsoft in order to solve some problems we've been having as, right now, there's some sort of system fault.
While the platform is a little more stable, the big problem is the latency issues around customer updates.
In a recent project, in Sweden, we faced an exception in Power BI from the update path. Whenever we refresh the animations and the fact tables, we faced something that said we couldn't upload the data. After 20 minutes we tried to refresh again. We didn't change anything with our reports or platform or anything related to infrastructure or schema. We didn't touch anything. An yet, the data is updated successfully. That leads us to ask the question: what was the problem 20 minutes ago? We still don't know the answer.
That is why we need some stability for the update service. We are deciding some business decisions based on this data. If this data is not updated, we may decide to take the wrong path.
This is very important in the retail industry. For example in the grocery field, sometimes with vegetables or fruits or other products, customers need to decide to change the prices within lock days. They need to decide, based on demand, if they should increase or lower prices. They can't make the correct decisions if the numbers don't update.
The scalability potential actually depends on your licenses. Microsoft provides three licenses. One of them is absolutely free. Another is called Pro. Yet another is per user or Premium. There is a huge difference between the Pro license and the Premium license. There is no need to scale the Pro license or other license models.
When you put the on-premise reports, you can scale out or scale up. It depends on your existing resources. However, in the services as a software (SaaS) version, it actually comes with Microsoft's units or Microsoft resources.
We have an ASFP agreement with Microsoft. As a Gold Partner, Microsoft is pretty good and responding automatically and helping us resolve issues.
If a partner does not have an ASFP agreement, they tend to have to go to forums or try to Google answers to their problems.
We are aware that sometimes when we make changes to BI, the solutions might be complex. There might be SQL involved, which needs to be taken into account, for example. We may have to look into other resources and other tools to see if there are interactions that are the issue. However, Microsoft is quite helpful as we work through issues. We're quite satisfied with their level of support.
We also work with and sell MicroStrategy in Turkey. 95% of the MicroStrategy projects have been completed with our company. It is a direct competitor with Power BI. My company commonly does consulting for some small and medium markets and for that we recommend Power BI. Whenever we talk about enterprise-level solutions, the company we will use is MicroStrategy.
There isn't much of an installation process, as the solution is on the cloud.
There is a free license, however, it is limited. The Premium license and the Pro license functionalities are very different. Whenever we talk with our customers, they ask "Should we chose the Premium?" We say that "No, you don't need to, as it's too expensive. It is much more expensive than the Pro license." However, the Pro license functionality is not enough for some customers. There is no middle between Pro and the Premium. We need something in between.
The Pro license is maybe suitable for SMBs, small and medium-sized businesses. The premium version is ideal for companies that need to scale up and out.
There also may be some additional costs that can drive up the price.
I prefer this product and I suggest Power BI to all customers, however, we know that if Power BI is a new idea, we make sure to show company-specific data or analytics for analyzing the data and how this solution can analyze everything quickly. That said, it's not for everybody. If all companies tried to put all their reporting expectations in the Power BI, it would not fit their expectations.
It's not a standalone solution. You need other items in your toolbelt. You need, for example, something that can handle raw data, you need warehousing, et cetera.
In general, I would rate the solution eight out of ten.
Firstly there was the wow effect, as when I saw the number of connectors which were made for this product/service. It is quite fantastic to have the ability to connect separate services like CRM, NAV, Facebook, Exchange etc. and make data integration and transformation based on these data connections in a few clicks. Also, the ability to download new visuals is quite nice. The effect of new graphics for higher management is magical and this is the good way not only for a pre-sale/sale or up-sale, but also for good impressions. Another great feature is, for example, the collecting of data in concrete folder on disk. If there is, for example, 100 csv files from other information systems, these should be automatically merged, analyzed and transformed into great graphical report.
Because the product is based the ability to quickly produce BI insights and reports, it’s really useful on a daily basis. Another improvement is definitely the possibility of browsing the dashboards on smartphone using an app, and another is the ability to quickly produce reports.
Well definitely the connectors, which is always a huge space for improvement in configurations, especially the amount of connectors etc. Also, the graphic designs of the reports needs work as they are still really strictly defined, and with the amount of output, there isn’t space for such a design realization.
I have used this solution since it was released (a few years ago) and I am using it on a daily basis at work and at university where I work as a researcher and am a PhD student. I use it on mobile, laptop and tablet as well. Also, it has its own app for viewing the reports and dashboards, own app for creating and editing.
There have been no issues with the deployment.
There was no issue with stability.
We have been able to scale it for our needs.
Personally, I used Excel Services/Visio Services.
Really straightforward. The most complex part is defining the data gateways between the cloud part and the on premises part of the infrastructure. Another bigger task is to define security model of all the datasets and reports with correct audience, data refresh etc.
It's too soon to calculate. I think, that for the correct ROI value, you need to have it in place for more than three years. But, it saves a lot of time and not only developers time, but also management time etc. Things are easier when there are functionalities like “quick insight” for auto-creating of data based on machine learning algorithms, or Q&A for using natural query language.
I played with Tableau. I see many similarities in Power BI to other products, so there is no reason to combine many different vendors/third parties to build such a complex BI solution.
It's a tool for a new kind of business intelligence from Microsoft. Tool for quick modeling of data structures and for visualizing almost everything you have in mind at the moment. There is still a lot of room for improvement and there is also a huge space for new functionalities. But it’s a simple and great tool for everyday use.
You should get it and implement it. However, you should get a trial version, and contact a partner who can provide some sales presentation with the live session (CIE for example) and show, what the possibilities are of Power BI.
Discretization buckets lets you group numerical attributes into ranges. Say you have a customer dimension including the age of the customer you can use this feature to group them into age clusters such as 0-5, 6-10 and so on. While you can tweak how the algorithm creates groups and even provide naming templates for the groups you still have relatively limited control over them. Worst case scenario: A grouping is removed / changed by the algorithm which is referenced in a report. A better way of grouping these attributes is by doing it yourself either in the data source view or a view in the database (there will be a separate tip on this). This way you have complete control over the distribution of values into groups and the naming of the groups.
SSAS has a couple of features that enable it to source data directly from a normalized data model typically found in business applications such as ERP systems. For instance you can “fake” a star schema through queries in the data source view. You can also utilize proactive caching to eliminate any ETL to populate your cube with data. This all sounds very tempting but unfortunatly I have never seen this work in reality. Unless you are working with a very small source system with impeccable data quality and few simultanous users you should avoid the temptation for all the usual reasons: Proactive caching will stress your source system, data quality will most likely be an issue, integrating new data sources will be nearly impossible,etc. There is a reason BI projects spend 70-80% of their time working with modelling and integrating data.
If you are working with multiple environments (dev/test/prod) do not use the deployment functionality of visual studio to deploy to another environment. This will overwrite partitions and roles that may be different between the environments. Use the deployment wizard.
Keep in mind that most client tools do not cope well with changes to SSAS data models. Any renames or removals you do in the model will most likely cause clients that reference those entities to fail. Make sure you test all your reports against the changed model before deploying it to production. Also, if you allow ad-hoc access to your SSAS solution be aware that users may have created reports that you do not know about. Query logging may help you a little here (it gives you an indication of which attribute hierarchies are in use). The best way to avoid all of this is to thoughtfully design your cube and the naming of your SSAS objects so that there is no need to change or remove anything in the first place.
“Real time” means different things to different people. Some interpret it as “simultaneous to an event occurring” while others have more leeway and have various levels of tolerance for delays. I prefer the term “latency”: How old can the data in the BI solution get before it needs to be refreshed?. The lowest latency I have ever implemented is two hours. That is hours not minutes. I know this does not sound very impressive but that is honestly the best I have been able to do at a reasonable cost. When doing “real time” you need to consider a lot of factors: Partitioning, changes to dimensions, ROLAP vs MOLAP / direct query vs xVelocity, source system access, how to administer it, etc., etc. These things add up quickly to a point where the value simply does not justify the cost.
Hi Peter !
Let's discuss from point 6 to 10 in here;
#6: I totally agree with you, never trust or undermine the fact that data will be coming in the format as suggested by the ETL Team. There is always a possibility of wrong data types, bad data, switched data, all kind of data to be appear as source data, so as a ETL developer you need to make sure you put data validation checks for each and every case you have in mind. Still you might miss out some cases. The good thing about MS SQL Server 2012 is now they have provided the TRY_CAST function which can be used to avoid casting errors. A craftily designed framework would be handy to have where ETL developers need to know about the framework, so invest on building a framework which can be used across multiple ETL projects. I strongly agreed with your point that data is evil and sometimes is such hard to load single files which have all kind of these bad data validation errors.
#7: Definitely, by having a framework you can save time by not spending your time on writing same piece of code again and again. While designing your ETL, please beware of the data types which you are using, for some people there is slight difference between Float, Decimal & Numeric data type but if you have been writing ETL solutions you know what kind of a mess it would create if you don\t pick up the right data type, same for Date & DateTime data types.
#8: MDX calculation needs to be tested again and again which is called regression testing. All these years i have been building end to end BI solutions, which involves writing complex ETL's, it is like impossible for QA agents to identify the problem in calculations, so while you assign someone task of verifying MDX calculation or just verifying the BI Dashboard output, make sure he has enough knowledge of Data Analysis. He would be proficient enough to query the database and be able to browse the Cube and also perform cross Data Verification. As a BI Consultant I invest much time in training my QA agents to be able to perform this regression testing.
#9: Partition is always a good practice when you are sure that data influx might going to be run into billions of rows. But if you are designing a BI Solution for an organization which might not have this big amount of data under Analysis then you may avoid partitioning.
#10: Strongly recommended, built in Excel provider is going to make you crazy really soon by having it own data type sensing ability, although you can try to turn it off by setting the property of Type Guess = 0, but there are so many problem with excel provider it always sense the data types for each source column.
One thing I need to mention, is carefully designed ETL with customized logging process can save you tons of time while analyzing the cause of data failure. And it's always good to have the ETL logging process which can be shared with your client as well.
Regards,
Hasham Niaz