Category: Business Intelligence

Melissa Coates disambiguates “data flows” from “dataflows” because those are two totally different things:

It’s another terminology post! Earlier this week I was having a delightful lunch with Angela Henry, Kevin Feasel, Javier Guillen, and Jason Thomas. We were chatting about various new things. Partway thru our conversation Jason stops me because he thought I was talking about Power BI Dataflows when I was really talking about Azure Data Factory Data Flows. It was kind of a funny moment actually but it did illustrate that we have some overlapping terminology coming into our world.

So, with that inspiration, let’s have a chat about some of the new data flow capabilities in the Microsoft world, shall we?

Melissa clarifies the term “data flow” (or “dataflow” as the case may be) across several products in Microsoft’s BI stack.  Worth the read.

Jen Underwood explains the value behind Gartner Critical Capabilities reports, specifically the one for analytics and BI platforms:

Notably, the three Magic Quadrant Leaders except Tableau were ranked near the middle in all use cases. MicroStrategy, Birst, Sisense, TIBCO, YellowFin, Salesforce, SAS and a few other players excelled above the rest with high scores on this report. These results are a bit refreshing to see. Gartner Critical Capabilities scores seem to better align with Forrester’s rankings of Analytics and Business Intelligence Platforms and also my own understanding of several top offerings. I admit that I was surprised by these results. I was rarely – if ever – asked about several of the top scoring vendors over the past three years.

Read the whole thing, and then read the report.

Bruno Aziza looks at the new Gartner magic quadrant for business intelligence solutions:

For the first time in 3 years, Gartner dropped a significant amount of vendors off its quadrant.  There were 24 vendors in the firm’s quadrant in 2016 and 2017.  This year, the Magic Quadrant only lists 20 vendors…that’s a 16% quadrant reduction.  Has the market shrunk?!

Not exactly: the market has evolved….and in a pretty predictable way actually.  Take a look at our 3-year-movement analysis table below: we see a pretty consistent story, e.g. the big are getting bigger, some of the visionaries got absorbed (or disappeared) and few ‘trend-setters’ graduated up.

Read on for more.  The leader quadrant pretty much fits my expectations in terms of the major vendors and their rank ordering.

Buxing Jiang argues that there are reporting scenarios in which building a data computing layer is critical:

In previous articles, we mentioned that most reporting performance issues need to be addressed during the data preparation stage, but many scenarios can’t be handled within the data source. For example, parallel data retrieval should be performed outside of the data source because its purpose is to increase I/O performance. To achieve the controllable buffer, the buffer information needs to be written to an external storage device, which can’t be handled within a data source. The asynchronous data buffering and loading data by random page number in building a list report can’t be handled by a data source. Even for an associative query over multiple datasets that a data source can deal with, it would be necessary to get it done outside the data source when multiple databases or a non-database source is involved and when the database load needs to be reduced. Obviously, these scenarios that are not able to be handled within a data source also can’t be handled by a reporting tool.

I would be concerned about implementation details overwhelming the general value of a data computing layer.