Grow, from Epicor since the March 2022 acquisition, is a business intelligence software that is designed to empower businesses to become data-driven and accelerate growth by aligning team objectives and inspiring strategic decisions.
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Looker
Score 8.2 out of 10
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Looker is a BI application with an analytics-oriented application server that sits on top of relational data stores. It includes an end-user interface for exploring data, a reusable development paradigm for data discovery, and an API for supporting data in other systems.
Grow.com is well suited if you have a lot of data or client data to manage. If you do not have tons of data, there are other options out there that are a bit more simple and easy to use and a better price.
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).
I love the fact that you have a team of developers who can help me build metrics or fix metrics that I do not create properly. I had to learn SQL in order to really build the metrics that mattered, which was a lot of fun, but resulted in many broken metrics. The vendor's team was great in responding to me requests for help!
Really, really appreciate the fact that Grow has SQL formulas to reference when building metrics. Again, as a novice, this accelerated my ability to learn and not have to leave your site and resources and stay within the Grow web properties.
Great account managers who check in regularly and are willing to help out whenever needed.
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
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
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.
Grow.com is structured in [...] such a way that it is easy to get started for individuals that known SQL. For individuals that are not as well versed at SQL, the learning curve is much steeper. In such cases, it would be better for users to learn SQL and then go for data visualization with Grow.com.
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.
If there is one area that Grow.com excel[s] upon much better than other data visualization tools, it is Grow.com's customer/tech support. Grow.com responds much quicker and does a better job of getting to a solution [than] any other business intelligence tool on the market. The support staff gets the big picture [...] and knows how to help.
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.
Grow doesn't have as many advanced analytics features and other capabilities as some other 'dashboard' platforms on the market. Simply, if you want a tool for internal use only that has high-level analytics capabilities (regressions, completely custom visualizations, modeling), Grow probably isn't the platform for you. To put it another way, if you have a team of data engineers and scientists doing complex analysis, Grow might not make the most sense. However, if you understand the value of data/visualization/dashboards, but lack some of those skillsets in your company, Grow might be the perfect fit. Not only is it incredibly scalable and reasonably priced compared to other market solutions, but their transform capabilities really stand out. They have many native data transformations that mimic SQL coding of a dataset, without requiring any knowledge of SQL. They've converted these transformations to easy-to-use tools in the platform, which are perfect for the less-technical user.
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.
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.
