What is custom analytics?
Packaged analytics solutions, such as Mixpanel, Google Analytics, Localytics and more, provide out-of-the-box dashboards and quick time-to-value for any organization that strives to become more data-driven. But as your organization grows in sophistication, new needs arise that require merging data from multiple disparate sources. Bringing in all your data from all the different silos in which it resides, you can not only own your valuable data, but also leverage it to get a holistic end-to-end understanding of your business not possible historically.
Here at Alooma, we are all very passionate about custom analytics. It's not for everyone, but for our team it has always been the obvious way you go and it's not just us; people all over the internet are writing good informative posts. In this list we have collected 5 resources we feel can help in both planning and using your custom analytics.
Why do custom analytics you ask?
Well, here are 5 good enough reasons:
- Joining multiple data sources
- Data ownership
- Deeper and more custom insights
- Enabling machine learning
- Cutting costs
Now, without further ado:
Data are only as insightful and important as the person (or perhaps machine) who is analyzing them. In the data-centric climate of business decision-making, having a dynamic and reactive solution at one's disposal that provides business intelligence is paramount to success in the marketplace.
Something that always annoyed me of the current state of technology is how easy and pervasive we let tracking become. Tens of connections to 3rd parties carrying Referer and Cookies just to load an article. (We give up our users to social media websites just to show a like button
Since many of our customers store their data on Redshift, we wanted to bring together a panel of people with expertise in using Redshift for business intelligence applications. We assembled a team of panelists from AWS, Clever, and Lumosity who answered our questions on Redshift best practices.
One of the top priorities for any app owner is to keep their users engaged after the first use. Recently, we worked with one of our clients to help them analyze engagement within their portfolio of apps for the purpose of determining which behaviors determined long term engagement.
Posted on May 11 2016 by ClearScale Capturing data and utilizing it for business needs is a relatively easy thing to accomplish if done correctly. Analyzing the data to look for patterns and trends is an entirely different issue. When companies decide to implement deep analytics to drive decision making, often implementing the suite of reports can be a challenge.
You can also keep things exactly as they are. We won't judge :) Good luck.