BIG DATA Governance

BIG DATA Governance How To Avoid The Pitfalls Of Big Data Governance

Table of Contents

The need to provide answers quickly...................................................................................... 3

You can’t measure what you don’t manage ............................................................................ 3

Aligning the overall architecture with the problem ................................................................... 4

Big Data Governance – Managing the difference ................................................................... 5

So, what really changes?........................................................................................................ 6

What is really driving all this change? ..................................................................................... 7

How to build a Big Data Governance solution in an agile way? .............................................. 8

AnalytiX™ Mapping Manager™ Big Data Edition™................................................................ 8

AnalytiX™ Big Data Governance Framework ......................................................................... 9

Addressing Big Data needs with AnalytiX™ Mapping Manager™ Big Data Edition™ ............12

"So once you know what the question actually is, you'll know what the answer means." Deep Thought,

The Hitch Hikers Guide to the Galaxy, Douglas Adams The need to provide answers quickly

Typically we answer questions based upon data and information that is stored in disparate systems, with

completely different access methods and stored in completely different places located inside and outside of the

corporate walls. Just how do you manage the data you need to measure? These answers are required in order for us to decide on overall corporate direction, make product marketing

and pricing decisions (reviewing customer behavior so that we can time

offers to get customers back before our competitors do), commit budget to different projects and to improve

planning and performance. More questions come to mind quickly. Where does the data come from? Was the data vetted? What is the

overall quality of this data? Who owns it? Is it documented? Add to the mix the fact more answers now require data that up until three or four years ago just wasn’t even

planned as being part of the corporate data warehousing landscape, what got termed Big Data (social network

interaction data, voice, video, etc.) and now it is so different from everything that came before it, and you now

have a major mess in your hands. You need to integrate this Big Data in your Business Intelligence (BI) and Corporate Performance Management

(CPM) solutions, and the pressure to deliver it correctly, cheaply and quickly will still be there. What was the question again?

Oh, yes. “Just how do you actually manage the data you need to measure?””

You can’t measure what you don’t manage

Let’s face it, the future of data management is in multi-structured data sets (highly structured, semi-structured

and unstructured data) and extremely flexible data acquisition and integration methods spread across multiple

platforms. In order for you to succeed, you need to examine how Big Data is different from what came before and how it changes how we manage information – how leading companies in many industries use this information to achieve better decisions, and performance; and how this data will change information governance and management.

While some will say this future means the end highly structured data platforms (such as traditional databases

and data warehouse environments), this is really more about the emergence of a new breed of data

management platforms that have been built to support multi- structured data and a flexible approach to applying

schemas and accessing this data – from the ground up.

AnalytiX™ Mapping Manager™ was built, from the beginning to accelerate the successful development and deployment of a successful Business Intelligence solutions, but it can also accelerate the delivery of data (all sorts of data – structured, semi-structured as well as unstructured) into this new breed of data management platform.

The strength of AnalytiX™ Mapping Manager™ lies at the heart of its architecture – highly adaptive and lean, capable of quickly aligning the fluid needs of the business with the structure imposed by Information Technology and accelerating successful Business Intelligence and Analytical solutions.

Aligning the overall architecture with the problem

At the heart of its architecture, AnalytiX™ Mapping Manager™ is a complete requirements management solution which allows analysts to capture data, business and technical requirements and from these requirements, these analysts and data scientist are able to quickly build an initial architecture model that, coupled with the metadata management capabilities native to AnalytiX™ Mapping Manager™ can bring to bear an awesome arsenal into the Big Data battlefield.

This deluge of data we are calling Big Data is a familiar theme to any IT department today. More and more Big

Data initiatives start every day and most of them start without much of a roadmap or a safety net. But, when it comes to Big Data initiatives, however, the challenge isn’t simply the fact they start without a

roadmap or lack a safety net, or even the growing amount of data, but the variety of data that must be

consumed, compiled, governed and analyzed. There are many ways to define Big Data, but the consensus is that the typical definition is based in terms of the

four Vs of Big Data:

• the Volume of information (usually in the billions of interactions);

• the Variety of information (from structured, semi-structured and non-structured data);

• the Velocity of information (typically the speed at which data becomes available and can be analyzed);

and

The Value of information (which can seldom be determined before the data is acquired, loaded, com-piled and

thoroughly analyzed). While these four Vs may drive organizations to deploy new technologies, techniques and methods to cope with these new requirements, these initiatives are usually incomplete and incompatible with their existing business

intelligence and analytics infrastructures, seldom taking in consideration the requirements of information governance in Big Data. Remember: “You can’t measure, what you don’t manage!”

Big Data forces organizations to take a different look at how you integrate this data into the existing

Business Intelligence and Analytics landscape. Big Data Governance forces these organizations to rethink

how you manage all this data. What was the question again?

Oh, yes. “Just how do you actually manage the data you need to measure?””

Big Data Governance – Managing the difference Big Data comes into play from the need to take Business Intelligence and Analytics well beyond the great systems of record (yes, we need this to too!), which are focused on efficiency to a whole new breed of solutions centered around effectiveness – what is now being termed the systems of engagement (the systems that hold the data from all other corporate systems).

With future solutions clearly evolving from data that comes from highly efficient and well behaved systems to data that comes from the extended enterprise, on where data is not necessarily so well structured and behaved, forcing the organi-zation into a more collaborative mode of operation, with its core infrastructure being adapted from the consumer space.

Is Big Data Governance really that different from traditional data governance initiatives?

While the needs, reasons and benefits of governance remain the same whether you are dealing with Big Data or with small data, to see and understand what really changes we must go back to the four Vs of Big Data (Volume, Variety, Velocity and Value) and understand what really changes with Big Data Governance:

Volume. With extremely large data volumes, the need for highly sophisticated tools to assess and profile data be-comes a necessity as traditional approaches of profiling and managing data primarily through observation be-comes unfeasible;

Variety. The different types of data associated with Big Data (video, voice, sensor readings, images, social network feeds, etc.), definitely require us to look at storing all of this in something other than a traditional relational database and govern this data according to the types of data we are needing to measure

Velocity. The different latency requirements for intake and consumption, analysis, integration and publication or promo-

tion of the information make it necessary for the raw data to be available for analysis in real-time, with data quality and

database load schedules pretty much having to take a back seat to pressures of getting to market quickly.

Value. Typically, with Big Data, the Value of information can only be determined once the data can has been acquired,

loaded, compiled and thoroughly analyzed. This goes directly against what we, for so long, held as one of the tenets for

(small) data governance – that we would have a significant investment in time and effort, spending a considerable

amount of time designing data models and building out the data integration architecture before the first row of data is

acquired.

So, what really changes? Just as the navigational, pointer-driven data structures lost out to the current standard, with highly structured, well behaved data in the relational data model, the current requirements for flexibility of structure and access are moving Business Intelligence and Analytics solutions to a world on where data will have to be consumed in a very dynamic way. And that forces changes to the approach with which we govern Big Data.

This approach demanded by Big Data Governance requires you to be flexible and as adaptive as the data you

are consuming, but that doesn’t mean you have a “get out of jail free” card – quite the contrary, you have to be

just as diligent as you have always been, you just have to be quicker and more flexible than you have ever

been. Data will continue to grow at staggering rates, and you will need new methods and tools that are capable of dealing with this unbridled growth. There is growing awareness of the need for data governance, but there is also an even bigger challenge in defining who owns the data and identifying all the various siloes in which the data is contained. It will now become increasingly more important to manage and govern all the siloed data stores both inside and outside of the enterprise and, more importantly, govern them so it is understandable how new answers to business questions are being answered in this rapidly changing world of Big Data.

Fig 1 – AnalytiX™ Mapping Manager ™ end to end Big Data Governance

Consider the image above which depicts a typical corporate architecture in a BIG Data Governance environ-

ment. Each of the Data Sources (internal & external) need to be managed and the data federation processes

which move data between environments and outside partners must be managed and governed.

What is really driving all this change? The simple answer would depend on what you do. But, while the needs tend to be different, the driver behind it

tends to converge to the need to analyze data in support of the extended enterprise. Just as Government has different needs than those found in private industry, organizations in the same indus-

try segment might have needs that are as different and completely divergent form one another – but they all point to the need to evolve and support the “extended enterprise” and how this need is forcing organizations everywhere to increase their competitiveness or effectiveness or suffer commoditiza-tion of the goods and services they provide.

Businesses and government agencies need to be much more cooperative today than ever before in order to make the chains of extended enterprise partnerships perform as expected. Supply and delivery chains, social media sites, customer service and support centers, government agencies and the like all need to tap and manage this “unstructured data” from these new sources. While this is a lot more difficult to organize and tag than structured data from transactional databases, it is not an impossible task.

Recent research indicates this unstructured data to be growing at alarming rates (greater than

65% per year in most cases), and the places this growth is coming from are not necessarily new, and while most

Data Governance Methods, Architectures and Tools focus on the structured

data kept in databases of business applications, reality is that up to 90% of the relevant corporate information is contained in semi-structured and unstructured data stores (pdfs, contracts, weblogs, product manuals, instructional videos, images, spreadsheets and the backbone of the personal archive system: the mail system). And not all of it is inside the corporate walls anymore.

As users demand even more access to data, mobile devices (notebooks, PDA's, Smartphones, tablets, etc.) now store up to 60% of the typical organization’s data, creating a compliance and governance nightmare. Issues range from having a large number of copies of a single document around stored around in primary, secondary, archive, backup and mobile environments (making it almost impossible to tell what version was the last version is – time stamps are not adequate anymore) to allowing serious privacy and security breaches to occur.

How to build a Big Data Governance solution in an agile way? The rise in the use of non-traditional data sources and the changes to the way Big Data is being used in solutions to increase not efficiency, but effectiveness reaffirms the need for Big Data to be managed and governed using a formal data management discipline within a formal enterprise architecture program, using tools that are specialized, flexible and well adept to working in a

highly fluid environment as the Big Data environment. On our initial white paper we focused around the use of AnalytiX™ Mapping Manager™ as an enabler for the data governance program. Today we will focus on using AnalytiX™ Mapping Manager™ as an enabler for an end to end Enterprise Governance solution which addresses all facets of the enterprise, including the need to govern Big Data along with traditional structured data.

AnalytiX™ Mapping Manager™ is much more than what the name implies: a data mapping tool or a mapping

management tool. AnalytiX™ Mapping Manager™ is an enterprise-level framework and source to target mapping

tool that allows the management of all metadata related to sources, targets and business rules associated with

the data and information reporting needs of the enterprise as well as a centralized metadata repository capable

of improving the speed with which an organization deploys a Business Intelligence solution as well as a very

capable data governance tool. One that allows any data governance program to

define ownership, standards, definitions, processes, policies, security requirements not only around well

behaved, highly structured data, but also around semi-structured and non- structured data commonly found in

what we today term: Big Data.

AnalytiX™ Mapping Manager™ Big Data Edition™ What is AnalytiX™ Mapping Manager™ Big Data Edition™ and how can it help deploy a complex

governance solution that deals with both Traditional and Big Data Governance problems? AnalytiX™ Mapping Manager™ Big Data Edition™ integrates well into the Big Data governance strategy of any

organization by extending its already powerful centralized repository to support current leading Big Data

technologies and supporting a very flexible and highly scalable structure to support Big Data constructs. One of the key benefits of AnalytiX™ Mapping Manager™ Big Data Edition™ is the ability to support the acquisition of unstructured data file definitions without having to create a schema first or acquiring the entire data set first – you simply point over to the right data file, acquire the technical metadata and take in the data you are interested in, evaluate and make changes to the data requirements. Once you are satisfied with your data set, you can simply acquire your data into Hadoop and allow the consuming programs to determine and apply structure when necessary. AnalytiX™ Mapping Manager™ Big Data Edition™ will track and document these new structures from a centralized metadata repository which is available to all consuming

applications through a simple Application Program Interface which exposes the central metadata

repository.. Using AnalytiX™ Mapping Manager™ Big Data Edition™ you will be able to manage all operational system

metadata including the unstructured data stores and silos inside and outside the organization as well as manage

the business and transformation rules in order to

understand how this data is mapped and analyzed to support the corporate decision making process. AnalytiX™

Mapping Manager™ Big Data Edition™ also features advanced impact analysis and visual data lineage analyzer

to analyze how data is federated inside and outside of the organization.

Fig 2 – AnalytiX™ Mapping Manager™ provides an end to end Enterprise Governance solution

The need for customization, the Mapping Manager Framework features an open metadata repository for customized inte-

gration and analysis of metadata and data mappings. The framework supports a set of out of the box plug-in modules as

well as the ability for customers

to create custom “in-house” application that integrate with the open repository to further analyze, integrate data or create

customized views of the repository data.

AnalytiX™ Big Data Governance Framework

A strong Data Governance program framework is crucial to the success of any big data initiative and the management of that

data. If the program is implemented early on and uses the right

organizational, technical, metadata and business processes, enterprises will be able to realize

the promises of big data processing and reduce the risk associated with such a program from a cost and time perspective.

This is just the beginning, in order to succeed in this, businesses need to follow a known path and use the proper tools to

bring order and governance to Big Data. These include addressing security, privacy, consistency, reliability and repeatabil-

ity from a single tool.

Fig 3 – AnalytiX™ DIAL-M™ Framework for Big Data Governance

The highly simplified AnalytiX™ DIAL-M™ for Big Data Governance framework model above demonstrates how a team of architects, analysts, developers and testers can bring a Business Intelligence Solution to market. In order to bring agility to the Business Intelligence solution, AnalytiX™ Mapping Manager™ allows the alignment of business and IT needs, the quick deployment of solutions that incorporate both traditional small and Big data, at the same time, addressing key enterprise needs, such as

• Documentation of data, business and technical requirements and automatic generation of a logical data

model from these requirements

• Documentation and regulatory compliance (such as Basel, PHI,SPI, Healthcare, SOX, Solvency II etc )of

data mappings, aggregations and transformation rules that have to be highly adaptive as requirements

change;

• Definition, documentation and creation of a central repository of business terms, rules,

data elements, KPI’s and metrics;

Establishment of data and information requirements that span multiple data sources (including non-traditional data sources), mixed latency and extreme data volumes;

Creation of detailed physical data model for Business Intelligence and Business Analytics Solutions

AnalytiX™ Mapping Manager™ Big Data Edition™ can easily provide, by virtue of its architecture, significant

improvement over traditional methods that use spreadsheets,

documents or decoupled metadata management solutions by coordinating and governing all aspects of Big

Data Governance. AnalytiX™ Mapping Manager™ Big Data Edition™ improves efficiency in processes, people and solution

deployment - not only in the analysis, design and development phases of integration, but also in downstream

work flow management and regulatory compliance and reporting as we have seen in previous articles. Typically, all metadata management tools provide some sort of repository and can import and export data

definitions from sources and targets. What they typically lack is a set of well defined processes to accelerate

development and deployment and the ability to manage change. AnalytiX™ Mapping Manager™ Big Data Edition™ bridges the gap between leveraging metadata, and making the data definition and acquisition processes automated, disciplined and predictable to all data enterprise stakeholders via its web enabled portal and customized business oriented views. For those customers demanding more flexibility and customization the framework allows for customers to create custom modules which interact directly with the open repository and plug-in directly to the user interface. An ecosphere of free and third party plug-ins are available to extend the Mapping Manager’s out of the box governance framework.

Fig 4 – AnalytiX™ Custom Application Plug-In Framework

Addressing Big Data needs with AnalytiX™ Mapping Manager™ Big Data Edition™ If you recall, we started this White Paper by quoting Deep Thought – "So once you know what the question actually

is, you'll know what the answer means." What was the question again? Oh, yes. “Just how do you actually manage the data you need to measure?””

First you have to acquire your data, be that big data or small data and bring it into the fold, then you have to

create a governance program which addresses all needs and provides the benefits that align business and IT,

promote the right architecture (both flexible and scalable) and accelerate delivery by reducing the time to market

requirements and increasing the quality of the overall solution. There are varied benefits from using AnalytiX™ Mapping Manager™ to accelerate deployment of a Business

Intelligence solution, by providing the following:

the ability to run quickly build conceptual, logical and physical data models from captured

requirements,

the ability to create, maintain and consolidate data dictionaries for all enterprise systems,the ability to quickly map source to target maps(STM) and use these STM documents to automatically generate data integration processes.

the ability to quickly analyze data lineage and evaluate impact and thus manage and communicate change

efficiently and effectively. Especially in high regulated industries such as healthcare and finance where

regulatory compliance is required surrounding data and how it is governed

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