Accelo vs. Hive

Accelo is a system that can do it all. It's extremely sophisticated. Depending on the sophistication of your organization, or specifically the abilities of the people you put in charge of the implementation, it can almost be too sophisticated. It all just depends on your situation. The more you use it, the more data you give it, the more you'll be able to see and do. It's incredibly powerful. But it's definitely not a tool that would be appropriate for smaller boutique type organizations that have only basic needs.

Incentivized

Hive is a powerful tool for data analysis and management that is well-suited for a wide range of scenarios. Here are some specific examples of scenarios where Hive might be particularly well-suited: Data warehousing: Hive is often used as a data warehousing platform, allowing users to store and analyze large amounts of structured and semi-structured data. It is especially good at handling data that is too large to be stored and analyzed on a single machine, and supports a wide variety of data formats. Batch processing: Hive is designed for batch processing of large datasets, making it well-suited for tasks such as data ETL (extract, transform, load), data cleansing, and data aggregation.Simple queries on large datasets: Hive is optimized for simple queries on large datasets, making it a good choice for tasks such as data exploration and summary statistics. Data transformation: Hive allows users to perform data transformations and manipulations using custom scripts written in Java, Python, or other programming languages. This can be useful for tasks such as data cleansing, data aggregation, and data transformation. On the other hand, here are some specific examples of scenarios where Hive might be less appropriate: Real-time queries: Hive is a batch-oriented system, which means that it is designed to process large amounts of data in a batch mode rather than in real-time. While it is possible to use Hive for real-time queries, it may not be the most efficient choice for this type of workload. Complex queries: Hive is optimized for simple queries on large datasets, but may struggle with more complex queries or queries that require multiple joins or subqueries.Very large datasets: While Hive is designed to scale horizontally and can handle large amounts of data, it may not scale as well as some other tools for very large datasets or complex workloads.

Incentivized

• Timing Client Work: You are able to better manage your time on your clients.
• Housing Client Info in one Area: You can view all you need for any given client in one area.

Incentivized

• Simplicity, it offers a clean environment without risking the outcome. An example of this are the timesheets that allow a fast way to keep track of progress
• Interaction, the different options make it faster and easier to interact and collaborate in the development of a product. An example of this would be Hive Notes for meetings
• The different visualisations it offers allow to explore the best ways to affront your projects. I really like the Gantt mappings view to understand who can be contacted at each point

Incentivized

• Accelo is excellent on the adaptation to its deep functionalities.
• Managing multiple project is very challenging.
• Not easy to collect multiple reports across platforms.

Incentivized

• Organizing tasks by assignees could be better. It's a little cumbersome to check off each person you want. Can you group these?
• I don't really use any view besides task view. Is there something better I could be using?
• It would be nice if attachments showed up in a nicer format, maybe with a preview?

Incentivized

Looking forward to seeing more refinement of features provided and upcoming features promised :)

Its a easy tool, the best way to organize the workflow but has room for more improvements.

From the beginning the Accelo team has been very invested in ensuring our success and overall happiness with the platform. The initial implementation specialists and trainers did a fairly good job of learning our company and needs, and tried to tailor the trainings accordingly. However, once we made the formal switch to the platform, meaning we no longer were using our legacy systems, we found that we had a lot of questions....and a lot of ideas and recommendations. The support team is extremely responsive and seemingly happy to receive our continual feedback. And if we encounter an issue that seems to be a system issue, they work diligently to fix it (we've actually had an engineer join a call with us to learn of the issue - and subsequently fix it)

Incentivized

Our CSR is easily accessible and they have support built into the app itself. They also have a pretty robust support site. We also took advantage of the free trial and learned so much by putting Hive through the paces and figuring out the best way to mold it to our needs.

Incentivized

Accelo doesn't really exceed any competitors in any one area of their product, but in 2015 when we made the move this was the best option that included all the tools we wanted. However, user adoption was low in some areas due to the UI, so we scaled back Accelo and added other tools.

Incentivized

Hive is a bit different than Jira and Monday, which I used mostly. Overall does a great job managing project and helps with team communication. Removes dependency of asking team members for updates by going to conference rooms. With Hive, the team updates the status, and we can easily track it.

Incentivized

• It substantially reduced the number of systems we use to run our business.
• It reduced the need to train our staff on a variety of platforms.
• It gave us the ability to have a client-facing portal that directly integrates with our backend systems.

Incentivized

• Workflow Management will help you better move your projects along which saves time and money.
• Time tracking will allow you to better manage the hours and keep your contractors accountable.
• Overall visibility of projects allow you to keep your margins down and combat "bleeding" and hidden costs or surprises.

Incentivized