SCALING THE TRANSFORMATION TO BIG ANALYSIS
Democratization Is Not Anarchy
A central challenge in transforming a ponderous Big Data headache into an agile Big Analysis win is finding a way to scale (often spelled S-U-P-P-O-R-T) Tableau users with the data they seek to visualize. Like most coins, there are two images we can observe – one from the perspective of the data analyst visualizing in Tableau and one from the data manager who structures and provisions data tables worth visualizing. My last post took the former perspective – today, I’ll look at the latter.
Data security, governance, and management are essential elements – non-negotiable elements – in traditional data warehousing or contemporary Big Data initiatives. And, as table stakes, they can effectively limit the degree of freedom for deploying new solutions or tools.
The current wave of adoption for data visualization tools, such as Tableau, rivals the rate of adoption of early desktop tools for word processing, database, and spreadsheet tasks – for comparison, Lotus sold roughly 5 million copies of 1-2-3 in its first seven and one-half years. Unlike that past, today’s IT teams have a fundamental role in accelerating or slowing new adoption. Security and governance, data storage, preparation and support costs – all factor into the resistance to or embrace of new user tools.
Rapid adoption – like we have seen with Tableau – has democratized both analysis and the access to data for that analysis. That trend creates a nearly unstoppable demand in today’s data-driven economy. Opposing that force are the existential requirements for security and governance. That opposition is the fundamental dynamic in most organizations – determining how rapidly any enterprise will emerge as data-driven.
Order Amidst Chaos
Scaling analytic literacy by several orders of magnitude – without a corresponding increase in the cost of supplying or managing the data required for analysis – and without disrupting or complicating existing security and governance controls has been difficult-to-impossible task for most organizations.
It. Is. Possible. – Now. And, security and governance are maintained.
Attivio’s semantic data catalog starts with a simple requirement – existing security, governance, and management protocols and configurations must remain – in-force, without change. With its implementation of ‘virtual data marts’, combining tables discovered through a self-service interface, appropriately joined from automatic/advanced database sampling and profiling, and provisioned using industry standard SQL and ODBC connectivity – Attivio delivers a DIY data discovery capability that’s safe and easily self-supported.
And because the tool works with minimally/moderately-governed data, e.g. spreadsheets and databases resident on user desktops, it offers IT the opportunity to secure and govern those sources as users add them into their solutions. Implemented as a server-side, browser-based application, the semantic data catalog profiles data from any source – warehouse-to desktop – creating a rich catalog to support a Tableau user’s analysis and visualization.
Empowering Data Managers
Attivio’s semantic data catalog offering will also transform the productivity of IT data managers charged with managing the development and delivery of Tableau-ready data to a community of analysts. Using the exact same tools described above, IT professionals can create and provision data from ‘virtual data marts’ they create – without moving any data or altering existing data management infrastructure. Their access to an integrated data source catalog affords them the opportunity to define, save, and share the data marts they create - with unprecedented agility.
In particular, the semantic data catalog offers the potential to organize and manage the proliferation of spreadsheet and database-resident tables on user desktops (so-called spreadmarts) – effectively and efficiently while adding security and governance to their widespread use. And the same solution that handles desktop-resident data works just as effectively with traditional EDW and modern Hadoop repositories, as well.
“Start Your Engines” …
Here’s a quick (2:32) introduction to data discovery – illustrating how easily ‘virtual data marts’ can deploy to support Tableau usage. Saving time for analysts and for data managers is what it’s all about – eliminating the bottleneck between Big Data and Big Analysis.
- Analysts will arrive at insights sooner, with greater certainty and confidence,
- Data managers and analysts will transform their Tableau productivity, and
- Your project deadlines will be crushed like never before.
Manage. Tableau. Effectively.