Many of the companies I talk with boast of freeing business analysts from reliance on IT. This, to put it mildly, is not a unique value proposition. As I wrote in 2012, when I went on a history of analytics posting kick,
- Most interesting analytic software has been adopted first and foremost at the departmental level.
- People seem to be forgetting that fact.
In particular, I would argue that the following analytic technologies started and prospered largely through departmental adoption:
- Fourth-generation languages (the analytically-focused ones, which in fact started out being consumed on a remote/time-sharing basis)
- Electronic spreadsheets
- 1990s-era business intelligence
- Fancy-visualization business intelligence
- Predictive analytics
- Text analytics
- Rules engines
What brings me back to the topic is conversations I had this week with Paxata and Metanautix. The Paxata story starts:
- Paxata is offering easy — and hopefully in the future comprehensive — “data preparation” tools …
- … that are meant to be used by business analysts rather than ETL (Extract/Transform/Load) specialists or other IT professionals …
- … where what Paxata means by “data preparation” is not specifically what a statistician would mean by the term, but rather generally refers to getting data ready for business intelligence or other analytics.
Metanautix seems to aspire to a more complete full-analytic-stack-without-IT kind of story, but clearly sees the data preparation part as a big part of its value.
If there’s anything new about such stories, it has to be on the transformation side; BI tools have been helping with data extraction since — well, since the dawn of BI. The data movement tool I used personally in the 1990s was Q+E, an early BI tool that also had some update capabilities.* And this use of BI has never stopped; for example, in 2011, Stephen Groschupf gave me the impression that a significant fraction of Datameer’s usage was for lightweight ETL.
*Q+E came from Pioneer Software, the original predecessor of Progress DataDirect, which first came to fame in association with Microsoft Excel and the invention of ODBC.
More generally, I’d say that there are several good ways for IT to give out data access, the two most obvious of which are:
- “Semantic layers” in BI tools.
- Data copies in departmental data marts.
If neither of those works for you, then most likely either:
- Your problem isn’t technology.
- Your problem isn’t data access.
And so we’ve circled back to what I wrote last month:
Data transformation is a better business to enter than data movement. Differentiated value in data movement comes in areas such as performance, reliability and maturity, where established players have major advantages. But differentiated value in data transformation can come from “intelligence”, which is easier to excel in as a start-up.
What remains to be seen is whether and to what extent any of these startups (the ones I mentioned above, or Trifacta, or Tamr, or whoever) can overcome what I wrote in the same post:
When I talk with data integration startups, I ask questions such as “What fraction of Informatica’s revenue are you shooting for?” and, as a follow-up, “Why would that be grounds for excitement?”
It will be interesting to see what happens.
- Analytics for everybody! (March, 2014)