There is no easy way to convert or replace BusinessObjects which is why most BusinessObjects customers today are in a state of co-existence (BO-Tableau being the most popular followed by BO-PowerBI). Since neither Tableau nor PowerBI are great at BI reporting, customers keep BO for the reporting and use Tableau/PowerBI for data discovery and visualization. You should look at right-sizing your BO deployment to save on costs. For the DWH, if you are looking at Cloud then Snowflake or Azure are the most popular with BO customers

So, operating with a segment of our client base, mostly commercial stage startups where the staff comes from larger, national firms, we often get a directive: We want new infrastructure software and it can be anything as long as it's not Tier One (SAP/Oracle and similar). I guess the $5m upgrades were getting old at their previous companies. 

Also, with the Covid issue last year, many companies had to pivot very quickly to stay alive. Looking at a SAP 18-24 month rollout was looking company death in the eye, so again, please perform the impossible with the unlikely were the marching orders. 

Fortunately, around 2014 we'd started what we called 'component software' which relied on made for cloud backends (NetSuite/Acumatica/Intaact). Mostly because these were recent (in relative terms) developments but more because of being built to integrate with other external systems, along with an open development toolsets  - what we couldn't find to integrate, we could develop ('we'- being the developers we could find to handle the job). 

That approach has helped a lot of the early stage startups through the IPO process and launched into initial 5 year growth stages. We've also implemented NetSuite sub systems so we could launch eCommerce or other pivot options quickly and they're feeding financials to backend SAP/Oracle. 

In any case, we have some relatively large clients who've had substantial success with this approach, and if your management is serious about parachuting out of Tier One, perhaps you could set up your backend data in a more easily reportable structure (just a wild stab - not knowing the real issues). 

I'd also point you toward the FP&A side instead of BI if it's truly reporting that's needed. Anaplan/Host/Hyperion - (I think Hyperion has undergone rewrites, the verision we mostly use is the NetSuite Planning Budgeting and Analytics - which the users say, "Oh look, it's Hyperion").

Hope some of that is relevant to what you're looking for. 

Hi,

You cannot build a DWH by using PowerBI, Qlik or Tableau as they are mainly data visualization platforms. Qlik does have its own tool but it creates proprietary datasets that cannot be accessed from outside Qlik. It seems you want a data mart tool instead of a DWH as you want users to be able to create reports. Please look into my review of SAS Data Integration studio for a better idea.

I'd second looking into Snowflake, Big Query or Redshift for your DW (and would lean toward Snowflake). I'd also recommend doing some research on Looker. For us, and likely for some others, it has been a viable alternative to Business Objects (and Data Cubes). Tableau, while arguably the strongest data viz tool, doesn't lend itself well to business user, drag and drop report authoring and/or report scheduling and distribution. I believe Qlik was a little stronger here with their acquisition of nPrinting a ways back, but it's been a long time since I've worked with Qlik in any capacity.   

Hello, 

It would be great to know a little more about your architecture and problems to be more helpful.

But as a general guide, something like BigQuery (Redshift/Snowflake) would work as your "DWH" and Qlik/Tableau/Looker/PowerBI/Datastudio would work as your visualization tools.

If I understand your question correctly, then you are looking for a replacement for SAP and Oracle, since both are too expensive. 

You can look at the issue of costs from different perspectives. The easiest way would be to look at the license costs, then the implementation and operating costs of your solution. I would not overestimate the construction costs, because they are one-time, it is much more expensive to keep a solution alive. 

An unsuitable architecture causes the highest costs. For me, architecture has nothing to do with a software product. So when you know what you are going to deliver to your users (your product), then you can design your architecture, and only then do you look for the right software. When you try to solve problems with software, in the end, it is like having just a hammer: every problem becomes a nail. 

In summary, I mean, first, describe your problem, then think about your architecture, and at the very end, the question of the tools for implementation arises.

Managements want to get rid of a lot of things but at the end of the day some other guys are responsible for the seamless replacement and migration of those things :)..

BO lets you set your reporting layer(Universe in BO World) on ANY data model with any complexity, literally on ANY! You don't have this option in Tableau. So how complex is your environment?

DWH: you want to replace Oracle with SQL Server. SQL Server may not perform so well compared to Oracle. Ok Oracle ist costly, but once it has the data in memory, it is lightning fast for any selects.

So if you stick to BO-Oracle the only thing left is the need of replicating the data to DWH/Oracle. And there are some replication solutions in the markt..

As last since you hardware is fast, you can virtualize(DB Views) your modelling/cleansing needs.

So it means you can survive without ETL's in your old solution! against additional costs for BO/Oracle. (virtualize(DB Views) your modelling/cleansing needs if you don't need to historize the data)

 I would look at two things in your place: How complex is my reporting layer, what are some complex queries hitting the db's? to ease answering the question: Can Tableau/SQL Server pair can master the tasks I have?

2. I would look at the most complex use case(universe) I have : copy the relevant data to Sql Server and try to implement the reports in Tableau what you originally had at BO on Oracle. It is a costly effort but just look at the future: you migrated to Tableau/SQL Server and things are not going as you dreamed of! ;)

Looker, Domo, Sisense, Tableau, and Power BI are tools are good for enterprise. If you are looking for SMB (Small Medium Business), where you have 5-10 users who use BI for portfolio, then you should look at small Startup (ConverSight.ai, ToughtSpot etc.) for a per-user license, which is comparatively low in the market.

This is a common question for many businesses that have gone down the SaaS route. SaaS Applications provide a critical application in the cloud based on a subscription model, which is great for companies looking to add software or shift capital expenses to operational expenses. However, the problem is that most of these applications are getting your data out of the SaaS and into a reporting data warehouse or another third party application.

The most common means of accessing your data is normally via an ODBC/OLE connection that is purchased at an additional monthly cost from the SaaS. This is normally a read-only connection but does provide a limited degree of access to your data. Don’t expect the SaaS Company to provide you with ER diagrams or table structures, nor should you assume the data model or schemas will follow any of the best practices or common design methods. They won’t. You must remember that SaaS-based applications try to fit everyone’s needs into a standardized set of tables. So expect odd columns and table joins throughout the application.

Once you figure out where your data is, you need to decide if you're just going to query the data from the SaaS database when the reports are run or if you're going to extract your data into a data warehouse nightly and then run your reports. This also means you now have to have a database server either on-premise or in the cloud.

I prefer the latter of the two options however it does introduce a gap in the data since the extracted data won’t be real-time. The benefit is that you can query the data as often as you want without impacting the live application. The most common tools normally used for this are MS SSIS and SAP Data Services. Both applications perform similar functions however there are many other ETL tools on the market.

Once you have the data extracted, your reporting software can run it reports. Gartner provides in-depth studies on the various applications however it really comes down to the following tools:
Microsoft Power BI
SAP Business Intelligence
Qlikview/Qliksense

These are the top reporting applications. Each has their pros and cons when compared however each can do basically the same thing as the others.

Here’s what makes them stand out from each other:

Microsoft Power Bi - Easy for end-users to create Adhoc reports from almost any dataset out there.
SAP Business Intelligence- Global standard for reporting, established tools and community
Qlikview/Qliksense- Top-rated dashboard and visualization tool.

Power BI, Tableau, and Qlik have the capability to do calculations for KPIs, are easy to create visualizations, and also have the capability to get the source from any data sources. Qlik and Tableau have more advantage than Power BI. Qlik and Power BI provide the desktop for personal use. Tableau only provides trials. For the Enterprise version, it has a feature to share and collaborate with the team. Now, only Tableau can deploy the tools on Linux environments (Qlik and Power BI only for windows).

Oracle EPM is more robust and provides comprehensive coverage for calculating KPIs. The products follow a global standardized pattern which has ease of adoption and implementation.

TIBCO Jaspersoft

I think Qlik is strong at data processing and has a high performance within a large scale of data with its unique in-memory database.

I have been doing data science by Python, now looking for a tools that can easily scale up the projects at low cost. KNIME seems to be a good one so I had tried it for a few months, but find difficult to copy my concepts in Python to KNIME.

Take 07_Customer_prediction_with_H2O as an example, I can code it in Python and well understand the concepts and codes, but when it comes to "nodes" in KNIME, I can't easily understand the relationship between nodes. I copied the sample process and modify a bit according to my data and understand the propose and concept, but I still don't understand why "Parameter Optimization Loop Start --> H2O Cross Validation Loop Start --> ......" in the Random Forest Metanode for instance, now I just copy to make it work but don't understand what reasons to put the nodes in that way.

Having said that I like the concept to make data science in nodes, as I am quite experience in playing around with Node-Red.

Domo or Pentaho and the differences between the startup version and enterprise version are the tools that you can use to pull out or get in the data and it is important to have the flexibility to get the information in different ways.