The Pursuit of Analytics: Zeno's Data Management Strategy

Tuesday, March 1, 2016 - 09:15
Enterprise BI is undergoing a fundamental shift. The conventional, IT-driven, centralized analytics and reporting platform is giving way to a variety of self-service analytics technologies featuring the ideas and enthusiasm of business analysts and data scientists. This new analytics philosophy promotes increased data awareness and accelerated data driven decision making across the Enterprise. We believe this shift is the rise of Enterprise Analytics.
 
One of the trends supporting the transition from Enterprise BI to Enterprise Analytics is the volume, variety, and velocity of data flooding the IT infrastructure. The conventional task of centrally managing every single data source within a single, large enterprise repository is increasingly becoming a near impossible task. Enterprise Analytics will cause an expansion of the centralized data strategy requiring a confederation of data repositories with some more tightly governed than others.  
 

Volume, Variety, and Velocity in the Enterprise

Data is increasingly collected on every object and interaction that touches our lives and businesses. Gartner estimates that 6.4 billion connected things will be in use in 2016, and will surpass 20 billion by 2020(1).  The use of connected things in the enterprise will account for $667 billion in spending in 2016 and will reach $911 billion in five years(2).  The “connected everything” is becoming more of a reality every day - and it’s just the beginning of the shift towards using technology to live, explore, and experience life more fully.
 
Being in tune with and understanding your data has always been the first step in developing business intelligence solutions. But with so many sources of data required by analysts - a vibrant, if chaotic, array - it’s difficult for the enterprise to keep everything under one roof. A centralized data authority faces the daunting task of vetting, cleansing, maintaining, and providing an ever-increasing number of data sources and tools to the analysts and business users of the enterprise. 
 

The Metaphysics of Business Analytics

If achieving the traditionally-understood ideal of a central, monolithic data store wasn’t intimidating already, in the age of “connected everything” and the increasing pace of data-driven decision making, it seems nearly impossible. 
 
However, having a centralized data management system is critical to confident data reporting, reliable data sharing, and data-driven decision making. Indeed, Gartner predicts that in 2016, a mere 10% of self-service BI initiatives will be adequately prepared to prevent data inconsistencies which will adversely affect the business(3).
 
On the other hand, prioritizing a strict, tight-fisted data centralization strategy can stall analytic breakthroughs with consequences for the business. Not ideal. The conventional centralization strategy alone will no longer meet the needs of the modern enterprise.  A solution must be created to enable the enterprise to use data in the most efficient, safe, and accessible way possible. 

 

Zeno’s Paradox and Enterprise Analytics

The information desired for this new age of analytics is highly distributed. How can a single central authority manage, monitor, review, and ensure data quality? Though it may seem to be an impossibly infinite task, it is more like Zeno’s Paradox of the Dichotomy.
 
Zeno of Elea was a pre-Socratic Greek philosopher whose arguments were recorded by Aristotle.  His most well-known paradox concerns the manner in which the hero Achilles can finish a race. In order to complete the race, Achilles must first run half the distance from the starting point to the finish line. Then, he must cover half of the remaining distance. Then, half of what’s left, and again and again (ad infinitum). The distance he must cover to finish the race is finite, but it includes an infinite number of tasks. There will always be another division to reach(4).
 
While there will always be a virtually infinite number of possible data sources to incorporate into the centralized architecture, the key components can be addressed first for greatest impact, and as additional sources become commonly referenced and shared, they can be folded in. 
 
Like Zeno’s Achilles, in a world of expanding data, a centralized data architecture may never be achievable.  However, this does not mean IT should throw up its arms and quit the race.  Rather, ingesting, cataloging, and platform job data must always be actively worked on, but with a priority for actual analytic use cases, not the perceived needs of business users. Instead of focusing on the finish line that can seemingly never be reached because of other requirements, Enterprise Analytics focuses on achieving the next largest step possible towards the end goal of a secure, centralized, and flexible data ecosystem.
 

Prioritizing Analytical Use Cases Over Perceived Requirements

Prioritizing actual analytical use cases over perceived requirements will accelerate the enterprise’s learning curve through rapid data discovery in a standardized, yet dynamic data ecosystem. We’ve seen the fruits of this strategy firsthand in many client opportunities. The myriad of unorganized and unreliable data sources instilling suspicions of data and metric inconsistencies can be resolved by enabling analysts to do their jobs better.
 
One of our logistics clients was being suffocated by siloed Microsoft Access databases. In interviews with business users throughout the organization, overwhelmingly we heard fears that the data wasn’t consistent or reliable. Some were upset that the source data was not in a consumable format, others were concerned that their attempts to clean the data were inadequate, still others pointed to discrepancies in calculating the same metric. All concerns pointed to the need for a governed enterprise data warehouse. While it was tempting to bring all of the data sources this client used in every aspect of their business, we saw that the most clear path to success was to first focus on the frequently referenced primary sources of data before folding in marketing and service platforms that were rarely referenced.
 
We paired the new EDW with Tableau, providing analysts a centralized repository for the exploration and analysis that was previously being juggled in Access. By paying attention to what role the Access databases were intended to fill for the analysts, but had failed for lack of governance or availability, we were able to build a centralized and standardized data ecosystem, enabling business users to share and discover with more reliability, consistency, and continuity. 
 

Data Engagement as Second Nature will Ease the Transition

That is not to say the road to Enterprise Analytics will be easy. Gartner estimates that by 2017, 75% of IT organizations will be managing a bimodal conventional and self-service data model. Only half of these organizations will find success, as the others will either suffocate under the pressure of rigid conformity or succumb to the risks of agile methods(5).
 
As daily life becomes integrated with technology, consumers increasingly have opportunities to engage with their self-generated data, regardless of their tech expertise. This trend will give business users with little to no technical expertise, as well as analysts and data scientists,  the encouragement to reach out - literally in some cases - to touch their enterprise datasets. They will want to engage with the data of the business just as they can engage with their personal fitness data. 
 
The pace of technology and the sheer volume of data will encourage analysts and other business users to no longer passively receive internal data, but to actively pursue both internal and external sources for new insights and opportunities. A recent blog on Information Management cites a powerful example of self-service analytics working within a centralized governance model. An ER doctor at Mount Sinai Health System was able to identify revenue leakage, and a nurse practitioner improved patient satisfaction(6).  These insights were derived from business users who were encouraged and empowered to dig into the data.
 
In the logistics example above, we were able to prioritize the problems that forced analysts to depend on clunky Access databases in the first place. A centrally governed EDW paired with a data discovery platform gave our client the freedom and confidence to actively engage the data in ways that were not possible, or were at least dismissed as suspicious and unreliable, before. They will have the opportunity to blend in other reliable data sources as the need arises, and eventually those sources may be plugged into the EDW. Zeno’s dichotomy is resolved with this flexibility.
 

IT Becomes an Enabler

By embracing a paradigm shift in the understanding of how data is consumed by this and future generations, IT leaders will become enablers of the business analyst’s desires to incorporate new data sources and analysis techniques. 
 
Everyone can agree that data centralization is necessary and good for the health of an organization. Indeed, data centralization and management is key to an enterprise’s safety and success. However, the enterprises of 2016 and beyond should not be data management-centric. They should be analytic-centric. IT plays a key role in empowering this analytic-centric approach by supporting centralization, quality, and access of data, both new and existing. The enterprise BI culture must transform from IT-produced to IT-enabled(7).
 
While Enterprise Analytics requires an investment in technology, it more importantly requires an investment in people. A team that understands the business will be able to work together to create a culture that values data management and quality while pursuing powerful analytic and data science opportunities.
 
Building a team who recognizes this future begins by ensuring that all participants in the analytic lifecycle play a part to help the team thrive. Analysts are exploring. With the right Enterprise Analytics framework in place, technology teams will be able to chart the territory.  
 
This post is the first in a series exploring the five trends that Inquidia sees in the business intelligence marketplace. 
  1. Data is expanding, and the “connected everything” encourages users to engage their data.
  2. Inviting and powerful UIs enable business users to be more comfortable with less IT experience.
  3. Data scientists are empowered by faster and stronger algorithms and techniques to test hypotheses.
  4. The age of search and the intelligent web conditions users to expect instant access to data for exploration and development of hypotheses.
  5. Enterprises will use storytelling, data drilling, and peer-powered collaboration to generate hypotheses and make decisions. 

 

Endnotes

(1) "Gartner Says 6.4 Billion Connected "Things" Will Be in Use in 2016, Up 30 Percent From 2015." November 10, 2015. http://www.gartner.com/newsroom/id/3165317.

(2) Ibid
(3) Howson, Cindi. "Embrace Self-Service Data Preparation Tools for Agility, but Govern to Avoid Data Chaos." Gartner, March 17, 2015.
(4) "Zeno's Paradoxes, Dichotomy Paradox." Wikipedia, The Free Encyclopedia. Accessed February 25, 2016. https://en.wikipedia.org/wiki/Zeno's_paradoxes#Dichotomy_paradox.
(5) Howson, Cindi. "Embrace Self-Service Data Preparation Tools for Agility, but Govern to Avoid Data Chaos." Gartner, March 17, 2015.
(6) De, Andy. "3 Top Business Intelligence, Analytics Healthcare Trends for 2016." Information Management. January 14, 2016. http://www.information-management.com/news/big-data-analytics/3-top-business-intelligence-analytics-healthcare-trends-for-2016-10028053-1.html.
(7) Parenteau, Josh, Rita L. Sallam, Cindi Howson, Joao Tapadinhas, Thomas W. Oestreich, and Kurt Schlegel. "Technology Insight for Modern Business Intelligence and Analytics Platforms." Gartner, October 23, 2015.
 

 

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