Looker vs. Oracle Analytics Server

Quick dashboards from Google Sheets - Easier to do the graphs than in Google Sheets - Operational dashboards to be used in the day-to-day work - It is good both for retrospective data and to do a pulse check of the current status - Better for not giant amounts of data and not multiple data sources. - If you need a lot of graphs to be displayed on the same page, it can be a bit glitchy during configuration (then the use works fine).

Incentivized

The entire deployment and configuration stage was easy and it was accessible across the organization in a matter of days. Oracle Analytics Server being a cloud-based solution helped right from Input of Bulk Data to Info preparation, Data cleaning, and finally Data Modeling. Everything was visual and the Help Wizard was very intuitive.

Incentivized

• Filtering - you can filter across different dimensions and metrics to get a more specific "cut" of data
• Refreshing - data automatically ingests into Looker which allows reports to be updated and backfilled in real time
• Conditional Reporting - you can leverage Looker's reporting features to flag when a given metric or KPI falls below or above a specified threshold. For example, if you had a daily sales benchmark in a SAAS organization, you could use Looker to flag whenever daily sales falls above or below the benchmark

Incentivized

• Oracle helps with easy customization of reports.
• It helps with the creation of dashboards and the integration of different data sources.
• Oracle BI was a package used for multiple users across our organization - publishing and distributing reports was a breeze.

Incentivized

• Looker is less graphical or pictorial which makes it less attractive
• Consumes a lot of memory when there are multiple rows and columns, impacts performance too
• At times when we download huge chunks of raw data from Looker dashbords, the time taken to prepare the file is enormous - The user fails to understand if Looker has frozen or if the data is getting prepared in the background for downloading. In turn, user ends up triggering multiple downloads

Incentivized

• More AI but with human back up.
• Less mumbo jumbo so even IT guys/gals can more easily work.
• More statistics (math) and orientation for those not familiar with it.
• More live as well as virtual (live) show casing of real situations in business.

Incentivized

We've been very happy with Looker so far, and all teams in the organization are starting to see its value, and use it on a frequent basis. It has quickly become our accessible "source of truth" for all data/metrics.

Incentivized

Scalability and rich integration capabilities. In the future, if we go with Hyperion for the Financial Consolidation and planning purposes -BI integration with Hyperion is going to be much simpler as it has native interface connectivity and even integration capabilities with well known CRM products (Siebel) and ERP Products (Oracle EBS, Peoplesoft, SAP) is going to be easy and straight forward.

Looker is relatively easy to use, even as it is set up. The customers for the front-end only have issues with the initial setup for looker ml creations. Other "looks" are relatively easy to set up, depending on the ETL and the data which is coming into Looker on a regular basis.

Incentivized

For me or a staff memeber that uses it frequently, its usability is sufficient. However newbies could have a few issues but find their workflow

Incentivized

No problems, it's that simple.

Incentivized

Other than anomalies or expected quirks, there were never any earth-shattering delays.

Incentivized

Never had to work with support for issues. Any questions we had, they would respond promptly and clearly. The one-time setup was easy, by reading documentation. If the feature is not supported, they will add a feature request. In this case, LDAP support was requested over OKTA. They are looking into it.

Incentivized

It's fast, efficient and easy to work with. Scheduling could help when planning out a day however overall it's a big plus when rolling out and supporting Oracle.

Incentivized

Oracle University offers very in-depth training.

It was a private training site.

Incentivized

A properly implemented Endeca solution performs extremely well on the largest of datasets and it positions your organization to immediately achieve your ROI.

Looker is an off-the-shelf, free tool for Google business users. Other than the internal cost of time to build, we had no costs to set up what we needed to do. Knowledge sharing internally and using templates greatly reduced this cost, making the overall cost very low.

Incentivized

Oracle BI works pretty well and has been well acknowledged and appreciated by the business users and power users who develop reports and dashboards. Compared to other tools, Oracle BI object development is easier and has a quick turnout. Using Oracle BI with Essbase and Oracle Exadata db, we believe the performance rendering data is quite good. We are using MicroStrategy as well almost equally, but for critical reporting needs considering the back end is Oracle and Oracle Financial Services Analytical Applications (OFSAA) we have chosen OBIEE.

Incentivized

We have seen the results of this in our initial research and are not surprised that Oracle does this like it does soo many other things in this area, so well.

Incentivized

• Allowing others to self-serve their own analytics and connect it to Looker simply and easily has helped unblock the central data team so they can instead focus on validated dashboards whilst stakeholders manage their day-to-day analysis themselves. Countless engineering hours have been freed up by not having to manage every user permission for each BI tool; we have a BYOBI approach; Bring Your Own BI
• Creation and management of a semantic layer (LookML =Looker Modeling Language ) allows peoples sandboxes and production databases to become clutter free. Minor adjustments, conditional fields, and even some modelling can all be done in LookML which doesn't need oversight or governance from the central data team.
• LookML, specifying drilldown fields and their sub-queries, as well as generally creating dynamic parameters with Liquid are all great features, but can have a steep learning curve. it may take some time to understand how to create this middle layer correctly, or even pose a risk of inheriting complex code from another source which can be unmaintainable if it becomes too big. Some level of governance is recommended if Looker is used by a large number of editors.

Incentivized

• We've used OBIEE (or it's previous named product) for over 13 years and it's still the most used tool for BI by the business.
• We moved our largest business system off of Business Object into OBI so we could gain improved performance, reliability, and easier management of metadata.