DataHero vs. IBM Analytics Engine

DataHero ensures every business user in my organization is up to date on key performance metrics and insights through robust data analytics and a configurable data dashboard.

Incentivized

• Well suited for my big data related project or a static data set analysis especially for uploading huge dataset to the cluster.
• But had some issues with connecting IoT real-time data and feeding to Power BI. It might be my understanding please take it as a mere comment rather than a suggestion.

Incentivized

• Collecting data from on-premise and cloud data repositories.
• Availability of drag and drop chart creation improves data visualization.
• It makes it effective to share insights with KPI dashboards.

Incentivized

• Jobs with Spark, Hadoop, or Hive queries are rapidly attained
• Can collect, organize and analyze your data accurately
• You can customize, for example, Spark or Hadoop configuration settings, or Python, R, Scala, or Java libraries.

Incentivized

• Collecting data from on-premise tools like Concur Invoice was overwhelming, but we were able to partner with external service providers.

Incentivized

• Easier pricing and plug-and-play like you see with AWS and Azure, it would be nice from a budgeting and billing standpoint, as well as better support for the administration.
• Bundling of the Cloud Object Storage should be included with the Analytics Engine.
• The inability to add your own Hadoop stack components has made some transfers a little more complex.

Incentivized

DataHero has intuitive and easy to use user interface making it easy to navigate features.

Incentivized

I haven't interacted with DataHero support, but they must be knowledgeable and responsive regarding how they have documented the features on this tool.

Incentivized

Both DataHero and Excel4apps are great business intelligence tools, but the latter don't offer room for growth due to unavailability of large and global enterprises BI tools.

Incentivized

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 no-brainer. As previously stated, the support received was not quite what we expected, but was adequate.

Incentivized

• Seamless insight sharing across teams in different departments increase productivity.
• DataHero keep us updated by getting insights across all datasets.
• No stale data or manual procedures when reporting with DataHer.

Incentivized

• This product has allowed us to gather analytics data across multiple platforms so we can view and analyze the data from different workflows, all in one place.
• IBM Analytics has allowed us to scale on demand which allows us to capture more and more data, thus increasing our ROI.
• The convenience of the ability to access and administer the product via multiple interfaces has allowed our administrators to ensure that the application is making a positive ROI for our business users and partners.

Incentivized