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FEB 5, 2014 1:22pm ET

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Paxata Gives Back Analysts' Valuable Time

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Paxata, a new data and analytics software provider says it wants to address one of the most pressing challenges facing today’s analyst performing analytics: simplifying data preparation. This trend toward simplification is well aligned with the market’s desire for improving usability, which our benchmark research into Next-Generation Business Intelligence shows is a primary buying consideration in two-thirds (64%) of companies. This trend is driving significant adoption of business-friendly-front-end visual and data discovery tools and is part of my research agenda for 2014.

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Comments (1)
There are two major flaws in this article.

The first is neatly encapsulated in the statement: "In fact, our research shows that analysts consistently spend anywhere from 40 percent to 60 percent of their time in the data preparation phase that precedes actual analysis of the data." This reveals the failure to recognize that the concept that there is an "actual analysis" of data that's somehow a separate realm is fundamentally wrong. There is no "actual analysis" of data that occurs as an end point of business data analysis. Rather, data analysis occurs everywhere there's data to be understood, all the way from the raw source data through to the enterprise-homogenized data that is, apparently, in this traditional framing, the be-all and end-all of business data nalysis.

There are many reasons for the traditional state of affairs. But the present and future need not be locked into the same sad conditions.

Things have changed with the emergence of the modern generation of direct-access immediate-results human-oriented data analytical tools. Tableau, the most visible, and its cousins bring fast, highly effective data analysis to everyone, including the 'analysts' who need to understand the data within their horizons, not just the end-point business consumers. Using modern tools across the spectrum can eliminate the need to build elaborate data cathedrals in many cases, and in those circumstances where data marts and warehouses are still useful they can be built better, faster, and cheaper when the new tools are brought to bear across the activity spectrum. There's no reason why a data warehouse project can't deliver valuable outputs right after initiation, and continue to deliver new value for its lifetime, at a mere fraction of the cost of the traditional ways. Vendors won't generally tell you this, because their revenues are based in selling highly expensive platforms, legions of consultants and their billable hours, or both.

The second flaw is that the spreadsheet is a good or effective mechanism for data analysis. It is not. It is, in fact, very poor at the job; there are much, much better tools available, any of them superior to spreadsheets. Spreadsheets' wide use for analysis is due to historical factors, not to their suitability. Over the past 40 years there have been a fair number of attempts to use a variety of table-based approaches for data analysis and reporting, none of them have succeeded in overcoming the basic fact that there's a fundamental cognitive mismatch between their structure and how people conceive of data and of the analytical relationships between data elements, and the foundational analytical operations.

The landscape is changing, already has changed more than the conservative traditional business environment and media recognize and acknowledge. It won't be business as usual, it will be business done better because the essence of BI-helping everyone understand the data that matters to them, will be better.

Posted by Chris G | Wednesday, February 05 2014 at 6:37PM ET
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