Our data analytics team happened to try IBM Analytics just to get acquainted with it & it turned out that this tool fits our business requirement better than the one which we were using in terms of the features along with the level of support that they provide. so, choosing the …
IBM Analytics is a great tool and a welcome addition to your overall IBM strategy. I think in cases of tools like this, you either go with what your platform works best with or you go completely different with a 3rd party, like Snowflake. We are an Azure shop and just happened …
We initially wanted to go with Google BigQuery, mainly for the name recognition. However, the pricing and support structure led us to seek alternatives, which pointed us to IBM. Apache Spark was also in the running, but here IBM's domination in the industry made the choice a …
We did an evaluation of Google Analytics and Microsoft Azure Stream Analytics in comparison to the IBM Analytics Engine product. We choose the product offering from IBM because we felt that for our company, this product offered a more complete and comprehensive package to …
I have been using Azure for my previous analysis, I had a difficult time in understanding the Analytics engine rather IBM provided step by step tutorial for setup.
Also turning off a machine was not an option in Azure for some of the services so I had to pay for the service …
Our professor has worked with IBM And many major tech companies. He’d recommend us which tools to use. And comparing to Azure, IBM is more convenient to use.
The main reason why Looker Studio was selected is because it was already integrated into the google ecosystem. But it actually once you start working with it, is really easy to get into. For really complex reports you will have to focus in building a good model in the database …
I liked Tableau, but it is a bit pricey, also there is no option to share the report online unless you add the team members. If your research is not really big and difficult, using Looker Studio is more practical and easy. I think it is very important to estimate how big and …
Looker Studio is far easier to implement, stand up, and learn. The interface is simpler and user-friendly for various levels of data visualization/analysis knowledge and experience. The biggest benefit of Looker Studio, however, is its ease of connection to GA data and speed. …
The free version of Looker Studio is still better than the leading enterprise-embedded BI tools, despite its weaknesses. The leading embedded BI platforms have terrible visualizations that can be spotted a mile away. They are also primarily locked to a grid, making it very hard …
Google Data Studio integrates data into visually appealing reports and can constantly update based on the linked data source (i.e., Google Analytics), a feature that neither of the platforms listed can do. However, sometimes Google Data Studio can't generate graphs based on my …
We selected Google Data Studio because it is easier to make connections with third-party data sources. Also Google Data Studio can understand connections between different tables and databases better that its competitors such as Power BI. Compared to Power BI you do not need to …
In comparison to Kibana , its much easier and being free its worth a lot. Apart from that it has flexibility of connecting to more than 300 connectors, which is not there in Kibana. Apart from that no doubt its much better that in terms of visualization of Data, analytics etc.
Data Studio is the
first step in your data visualization journey, as your data gets bigger and
your need for information grows you will have to move to something more powerful,
Google holds it's own against these competitors as they each have their own strengths and weaknesses. While Tableau provides user-interactive reporting, its formatting options can be quickly rigid and frustrating.
Google Data Studio provides a great feature set considering its price point, especially when compared to commercial options from Microsoft and Tableau. While it may not be as versatile when it comes to working with and developing complex datasets, there is enough charm in its …
Google Data Studio is a Google product and many other companies also use Google Suite (as Gmail), so it was easier to share reports to clients using it than Zoho Analytics (which needs a paid account to give access to any other user).
Google Analytics and Google Data studio work well with each other. They don't necessarily stack against each other, Google Data studio just makes interpreting the data more visual and concise. I recommend both and not one over the other. Both are free tools provided by Google, …
Google Data Studio is free, easily integrates with the Google Marketing Platform, and is simple to use. It's a better choice for most basic marketing reporting. Tableau is better for more BI and exploratory data analysis.
Obviously, Google Data Studio is an improvement over Google Charts, which I believe powers the chart modules used in Data Studio. I think of Data Studio as a convenient way to combine multiple Google Charts in one, easy-to-read report.
We are heavily within the Google ecosystem and therefore didn't really consider alternatives to Google Data Studio since it met our somewhat limited needs at the time of implementation. For outside presentations, we would probably lean towards something that allows us to more …
Data studio gives a more robust set of metrics that can be added to the reports. Personalization of the information you want to show and analyze is much better with Data Studio. Website and campaign performance reporting all in one place are very useful and practical. Creating …
Compared with Tableau and Power BI, I would say Google Data Studio is fairly placed or a pretty decent tool. We need to understand, this is a free tool and it will have its own limitations - apart from that this is a pretty decent tool compared to the biggies in the market.
At this point I am unable to have Facebook ads data in the Agorapulse reports (although I can customize them now). If at any point I am able to include this data I'm my Agorapulse reports I will switch to that so that I. Not having to use several different software programs.
Google Data Studio is an all in one package, like Adobe and Mapp. It is perfect if companies using Google AdWords and Google Analytics. The integrated dashboard is easy to configure and has a really nice visualization.
We are at present utilizing IBM Analytics Engine and it works incredible. Following are the things that I like the most about this product is:- - Simple to Utilize - Reasonable Cost - With only a couple seconds you can ready to fabricate and convey groups - you can without much of a stretch break down information through different applications
Does great at open canvas editing and letting you fully customize without the need for a grid. It is democratizing self-service no-code analytics. You do not need to be a data or analytics engineer to get started, and you can go very far based on how intuitive and straightforward the UI is. Some of the biggest challenges with Looker Studio relate to user management/security, embedding options, and issue support. For a long time, every user needed to have a Gmail to invite them to view a dashboard via login, not sure if that has been improved yet. You can let any user view without logging in, but that is not always recommended due to security reasons. In terms of embedding, you can only iframe dashboards. More sophisticated BI tools let you embed elements via API or Javascript. Iframing dashboards also make drill downs and dashboard to dashboard navigation tricky/near impossible. There is also no ability to contact Google for support when bugs or outages happen. They point everyone to the Data Studio community. There is some ability to get in contact with Google if you have an enterprise-level contract with Google Cloud, but the path for support is very ad hoc and not always fruitful.
It is the simplest and least expensive way for us to automate our reporting at this time. I like the ability to customize literally everything about each report, and the ability to send out reports automatically in emails. The only issue we have been having recently is a technical glitch in the automatic email report. Sadly, there is almost no support for this tool from Google, but is also free, so that is important to take into consideration
It is not ideal and requires time and dedication to understand how to work with it. Also, it has a lot of limitations around data it can accept. But in most cases, this tool is sufficient for everyday tasks of product and marketing departments. I wouldn't say that the interface is very user-friendly, but for people who regularly work with analytical tools, it must be ok.
I give it a lower support rating because it seems like our Dev team hasn't gotten the support they need to set up our database to connect. Seems like we hit a roadblock and the project got put on pause for dev. That sucks for me because it is harder to get the dev team to focus on it if they don't get the help they need to set it up.
I have been using Azure for my previous analysis, I had a difficult time in understanding the Analytics engine rather IBM provided step by step tutorial for setup.
Also turning off a machine was not an option in Azure for some of the services so I had to pay for the service whether I use it or not
Looker Studio is far easier to implement, stand up, and learn. The interface is simpler and user-friendly for various levels of data visualization/analysis knowledge and experience. The biggest benefit of Looker Studio, however, is its ease of connection to GA data and speed. Furthermore, since it is an online program/tool, it requires less CPU/battery/storage on the user's device.
It has saved us quite a bit of time managing our catalog of clusters and keeping things organized.
Since we had a division we acquired running IBM Cloud, it was easy to get it running and try it out, but we found we prefer our Azure configuration better simply to keep our technology in alignment across corporate functions.
I definitely see some cost savings by separating out the storage and compute. It helps you start to put an appropriate price tag on certain instances of big data.