Understanding Governed Data

Discovery (GDD) BI Platforms

Ofer Averny

VP of Product Marketing at Pyramid Analytics

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Overview ....................................................................................................................................................................................... 3

Divergent demands .................................................................................................................................................................... 3

Technologies that optimized either control or agility ....................................................................................................... 3

Today’s problem: scaling BI for the enterprise ................................................................................................................... 3

New solutions: GDD BI platforms ........................................................................................................................................... 4

Unblock broad adoption and practical results .................................................................................................................... 5

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Overview overned Data Discovery (GDD) is a trending buzzword in today’s Business Intelligence (BI) and Analytics field. It sounds

important but what, exactly, does it mean? My idea for this article is to give you a grasp of GDD basics. Briefly, GDD has to

do with BI software that aligns interests and brings together business users of data analytics and the IT department,

responsible for data management. When analysts talk about the “data discovery” they are referring to the ability of an entity to

find valuable insights from their data. For example, a manufacturer who has produced a defective part might employ data

discovery methods and tools on many different data sets to find a previously hidden root cause of the defect.

Divergent demands Over the last several years, the IT people who manage data and the business people who conduct data discovery have lacked tools

to conciliate their different goals and expectations. Business users want unfettered data discovery, with immediate, self-service,

and highly flexible access to BI. In contrast, IT typically wants to ensure that the data being used by the business is of high quality

and represents “one version of the truth.” GDD seeks to find a collaborative solution to these differing needs.

The problems solved by GDD are not novel, but are more intense than ever. While a desire for more flexible data reporting and

analysis probably dates back to the days of ENIAC, in recent years the very capabilities of advanced data analysis and visualization

tools have put business users and IT somewhat at odds. Gartner characterizes the GDD solution by referring to its “…ability to

meet the dual demands of enterprise IT and business users.”1 The dual demands are divergent, but they can now be resolved.

Technologies that optimized either control or agility At issue is the following: Business and IT both want to contribute to the success of the business. Previously, though, the tooling

available for analytics made it hard to achieve the kind of agile collaboration that was needed. The mega-vendors provided

powerful, scalable, and secure platforms for highly skilled IT professionals and business analysts to understand and operate. The

constraints of database technology and software development simply limited the creation of analytics and data visualization to

specialists in the IT department. In some cases, companies would set up dedicated data management teams for the same

purpose, but the result was the same.

Then BI evolved, with more knowledge worker end users getting direct access to data discovery. This has been achieved through

data discovery solutions that were easy-to-understand and operate, as well as capable of delivering immediate visualization of

departmental data and trends. With data discovery solutions, a knowledge worker who knew the questions to ask could extract

needed information to gain insights and take appropriate actions to improve their department’s performance. What was once the

province of centralized corporate BI groups is expected to be available to knowledge workers at their desks and on the move.

Today’s problem: scaling BI for the enterprise

These BI data discovery tools are easy to set up, and tend to land in the organization without even making to its radar – the so-

called “Shadow BI”. But over time, such tools fizzle or fail to meet evolving needs of the business users. New users join. New

content emerges in the system. More and different data are added to the backend. Enterprises seek to make BI more entrenched

in their operations and decision making. Unfortunately, many end user-oriented data discovery tools are not built to manage

these changes.

1 Magic Quadrant for Business Intelligence and Analytics Platforms

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The results of this ungoverned data discovery can be frustrating to manage:

Multiple “versions of the truth” – When multiple localized data analytics tools each have their own data set to work

with, a situation can easily arise where different end users develop multiple versions of the same data set. For instance,

if two teams analyze the same Accounts Receivable aging using different cost of capital figures to estimate future value

of cash flows, they will arrive at two different results. This is sometimes referred to as “islands of data.”

Data disaggregation – If the results from multiple distributed data analytics tools are re-inserted into databases of

record, there will be disaggregation of those data sources. For example, if one of the sets of future cash flow values is

uploaded to the accounts receivable database, it may cause data conflicts with other systems that use a different cost of

capital to estimate cash flow. The IT department is tasked with maintaining data integrity, so they want to avoid this kind

of ungoverned data modification that can become a costly and time-consuming situation to unwind.

Friction in collaboration/Inefficient reporting processes - Data analysis, visualization, and reporting are usually group

activities. If there are multiple analytics tools in use, each creating its own version of the truth, collaboration on reports

can be an inefficient, stressful process.

Skillset shortages – Multiple types of data analytics tools can lead to shortages of tool-specific skills across an

organization. As people come and go and teams evolve, skills deficits can inhibit timely analysis of data that’s necessary

for the business.

Barriers to scalability – Not all data analytics tools scale equally well. The inability to scale a data analytics solution

across a large organization can compound the “islands of data” phenomenon.

New solutions: GDD BI platforms

A new class of data analytics tools has emerged that addresses the issues presented by both the locked down, less flexible

approach of the earlier generation of BI and the more recent distributed, desktop BI tools. Governed Data Discovery tools, such as

Pyramid Analytics BI Office, give businesses a new way of replacing their disparate systems for managing their Business

Intelligence. To be a true Governed Data Discovery solution, the BI platform needs to offer four main components:

1. Self-Service Central Control - A built-in, self-service, and centralized administrative toolset to govern an organization’s BI.

This robust and extensive administrative backend is required to provide the capability for managing every user’s experience,

security, content, and data access from a singular, intuitive interface.

2. Data Governance - For one version of truth for data, it is important that data models or mashups not be isolated on a desktop

machine. The data needs to remain centralized – easily shareable, securable, and consistently updated. A Data Lineage

capability that tracks the life-cycle of the model is also highly recommended. This allows the user to see the data model’s

different versions over time and across different platforms. By tracking the implementation of how the data models (and

elements) are being used downstream, one can gain insights into resource allocation and optimization.

3. The Content Lifecycle - Content repositories need to exist in a centralized, shared paradigm that also tracks the content life-

cycle. This ensures content integrity and makes it easy to find and implement any changes or upgrades. Content can be then

shared by groups or kept private.

4. Secure and Protected - A strong security model is vital so that the data is not only secure from the external public, but also

kept confidential internally. Specific capabilities include: Integrated Security, Role Based Security, Content Security, Data

Security, Multi-Tenancy, Social Networking Security, User Profiles, User Licensing, Auto Provisioning, and Authentication.

These are the four overarching components that contribute to a powerful Governed Data Discovery platform. There are many

more elements within each topic to discuss and further explore.

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Unblock broad adoption and practical results In conclusion, GDD reconciles seemingly asymmetric needs from IT and business units, offering both control and agility. That is key

for modern enterprises that want to drive further adoption and treat BI as an asset for competitive advantage and customer

intimacy.

I invite you to check Pyramid Analytics’ BI Office at http://www.pyramidanalytics.com/pages/trial-download/. Our approach to

governance, security, content lifecycle, and central control is helping our users orchestrate successful and scalable BI programs.