Best Practices in Analytics for Big Data

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Best Practices in

Analytics for Big Data

and Liz Kofsky, OpenText

Andy Moore . . . .2

How To Make Big Data Headache Go Away

Let’s start with the good news: Your big data problem is getting smaller every day. The emergence of big data analysis has been described in many ways, from the ridiculous

to the sublime. In some quarters, it’s derided as the next buzzword, a vendor-driven fad that is designed to simply sell more software. Others consider it the defining shift in information management that leaps the chasm and brings data into the useful domain of business and government.

Me? I think it’s a little of both . . . .

Normand Peladeau, . . . .4

Taming Unstructured Data with Text Analytics

It is today widely recognized that the vast majority of information in any business is unstructured data, typically in text format such as reports, filled forms, emails, memos, log entries,

transcripts, etc. Most of the time, this rich source of information remains untapped—sometimes because companies are not fully aware of its potential value, more often because of the tremen-dous effort it takes to sift and dig out information manually from such large volumes. Text mining provides a viable solution. By combining natural language processing, statistical and machine learning techniques, text mining can quickly extract useful information from large

collections of documents . . . .

Pete Rivett, Adaptive, Inc . . . . 5

Are You Creating a Data Swamp?

Organizations have made investments in “small data” for years and many are achieving data governance, or at least understand the gap they need to fill. They know how to work with the relatively small number of technologies in play—including databases (standardized on SQL), ETL, DQ and BI—ideally all linked with modeling tools and/or a business glossary. These organizations are now embracing the promise of big data—a new frontier akin to the wild west or the gold rush, with programmers/data scientists let loose with a daily growing menagerie of languages and technologies outside the normal IT and governance structure. Sometimes this produces genuinely impressive-looking results and insights—especially those supporting marketing. But making decisions based on marketing insights can be low consequence compared to other potential analytical results . . . .

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long time, and have pretty much mastered the analysis of structured AND unstruc-tured content.

Not so fast, bucko. The advent of big data—really big data—and the underlying complexity of it, has changed the game. And more than 70 vendors in the space have rec-ognized the need to address it (each in their own impenetrable ways, of course.)

I love how Timo Elliot at SAP put it recently: “What’s the difference between business analytics and business intelli-gence?” And working for SAP, he should know. “The correct answer,” he says is: “Everybody has an opinion, but nobody knows, and you shouldn’t care.

“At the end of the day,” Timo writes, “the first is the business aspect of BI—the need to get the most value out of informa-tion. This need hasn’t really changed in more than 50 years (although the increasing complexity of the world economy means it’s ever harder to deliver). And the major-ity of real issues that stop us from getting value out of information (information cul-ture, politics, lack of analytic competence, etc.) haven’t changed in decades either.

“The second is the IT aspect of BI— what technology is used to help provide the business need. This obviously does change over time—sometimes radically. The problems in nomenclature typically arise because ‘business intelligence’ is commonly used to refer to both of these according to the context, thus confusing the heck out of everyone.

“In particular, as the IT infrastructure inevitably changes over time, analysts and vendors (especially new entrants) become uncomfortable with what increasingly strikes them as a ‘dated’ term, and want to change it for a newer term that they think will differentiate their coverage/products.

“When people introduce a new term, they inevitably (and deliberately, cynical-ly?) dismiss the old one as ‘just technology driven’ and ‘backward looking,’ while the new term is ‘business oriented’ and ‘ac-tionable.’ This is complete rubbish, and I

encourage you to boo loudly whenever you hear a pundit say it.”

TechTarget’s (WhatIs) writer Margaret Rouse put it well when she said that “Big data analytics is the process of examining large data sets containing a variety of data types—i.e., big data—to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analyt-ical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved opera-tional efficiency, competitive advantages over rival organizations and other busi-ness benefits.”

The effect that big data analytics has on marketing and sales cannot be understated. It seems to have been built for that purpose, and rightly so. Collecting sales informa-tion, consumer feedback, market potentials and trending fashion hits are the bread-and-butter of big data analytics. I don’t know a single CEO or CTO who would ever say, “Learning more about customers? Forgeta-bout it. I got shareholders on the line right now who are trying to talk me down about costs.” In fact, the best business leaders un-derstand the inherent value in big data ana-lytics. It’s a gut feeling for them; they can’t always explain it, but they “just know.”

The Urge for Big Data Analytics

And I haven’t even mentioned BI or the open source tools that are dominating the market yet. Big data analytics is the one place on Earth that open-source tools have made a difference. You can’t swing a cat at a big data meeting without hitting Hadoop, for instance.

Hadoop is a technology that is open to anyone to improve or at least comment on. Here’s the part I stole from Wikipedia (be-cause my feeble mind can’t seem to grasp it): “To process the data, Hadoop Map/Re-duce transfers code (specifically Jar files)

How To Make Big Data

Headache Go Away

L

et’s start with the good news: Your big data problem is getting smaller every day.

The emergence of big data analysis has been described in many ways, from the ridiculous to the sublime. In some quar-ters, it’s derided as the next buzzword, a vendor-driven fad that is designed to sim-ply sell more software. Others consider it the defining shift in information man-agement that leaps the chasm and brings data into the useful domain of business and government.

Me? I think it’s a little of both. But what I think barely matters—in fact, doesn’t matter at all. In this worldwide economy of information trading and access, all that really matters is the ability to find and use information hidden away in those vast stor-age repositories.

So why would you consider reading any more of this belly-gazing pondering? Partly because I have unique access to the best minds in the business. Mine is not one of them, I assure you. But I am fortunate to have the phone numbers of those minds that are. I’ll get to those in a minute.

Let’s get some dictionary work out of the way first. Big data is not only big. It’s complex. It’s full of (as they say) not just volume, but also variety (rich data, media forms, structured and unstructured content), plus it’s riddled with duplicative files, junk files and mistakes. Let’s not even TALK about the stuff that people take home on their I-phones and laptops. So big data is more than just big.

Let’s also mention the other “Vs.” The analysts like to narrow the issue down to acronyms, and I guess to be honest, I do too. Besides the great volume and variety, there’s also the velocity in which data and content enters your house, and then there’s the other “Vs”: veracity (can they rely on the truth of the content?) and most impor-tantly, the value of the information. Much of it’s junk, let’s face it. But there are many elements hidden within content that can make or break today’s deal.

I know what you’re thinking. Business intelligence tools have been around for a

By Andy Moore,

Editorial Director, KMWorld Specialty Publishing Group

Andy Moore is the publisher of KMWorld Magazine. In addition, as the editorial direc-tor of the KMWorld Specialty Publishing Group, Andy Moore oversees the content of the monthly “KM-World Best Practices White Paper series,” in print and online, as well as assisting with the creation and content of several single-spon-sored “positioning papers” per year. He is also the host and moderator of the popular KMWorld Web event online broadcast series.

Moore is based in Camden, Maine, and can be reached at [email protected]

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to nodes that have the required data, which the nodes then process in parallel. This ap-proach takes advantage of data locality to allow the data to be processed faster and more efficiently via distributed process-ing than by usprocess-ing a more conventional supercomputer architecture that relies on a parallel file system where computation and data are connected via high-speed net-working.”

Whew. I hope you got that. I need to spend more time at the Hadoop spa, that’s for sure.

But unlike typical business intelligence tools, Hadoop has the ability to “divide and conquer” large amounts of data in a unique way. As I have written many times on these pages, business intelligence (BI) is not my favorite topic. I have always thought it to be cumbersome, elitist and typically use-less. But that’s just me.

I’ll start with what Doug Henschen, ex-ecutive editor of InformationWeek (and co-incidentally a former colleague of mine in another life) had to say about big data ana-lytics: Analytics and BI standardization is

waning. “A decade ago,” Doug writes, “the

BI market was consolidating as companies tried to standardize on fewer BI products that could be deployed throughout the or-ganization. Times are changing. Cloud and mobile innovation and big-data exploration favor experimentation with a new genera-tion of tools.”

Well, Doug might find an argument with the participants in this month’s white paper. We found a couple of vendors in the space who might have another view:

“It is today widely recognized that the vast majority of information in any business is unstructured data, typically in text format such as reports, filled forms, emails, memos, log entries, transcripts, etc.” writes Normand Peladeau, CEO of Provalis Research. “Most of the time, this rich source of information remains un-tapped—sometimes because companies are not fully aware of its potential value, more often because of the tremendous

effort it takes to sift and dig out informa-tion manually from such large volumes.

“Text mining provides a viable solution. By combining natural lan-guage processing, statistical and ma-chine learning techniques, text mining can quickly extract useful information from large collections of documents. A text mining tool will typically process a million words in a few seconds to au-tomatically extract topics and discov-er unknown relationships and pattdiscov-erns. Companies see the real power behind text analytics when they combine text mining results with structured data.”

Normand continues, quite accurately, that “analyzing human language is a very complex task, and text mining is still, in many respects, in its infancy. Newcom-ers to text mining expecting their tools to readily provide comprehensive and pre-cise answers to their questions may very well be disappointed. Moving beyond the most obvious to achieve greater details and precision often requires some efforts on the part of the text analyst. It involves building a custom dictionary composed of keywords, key phrases and rules. Such a crucial task may take days, weeks, in some cases months. Yet it still represents a tiny fraction of the time it would take to do manually. Once developed and val-idated, such taxonomy becomes invalu-able, allowing one to fully automate the analysis of newly obtained text data or process incoming streams of text data in real-time.

“Text mining regularly turns up pre-viously hidden gems, which companies quickly respond to positively. Such in-sights give them the competitive advan-tage they are looking for, hidden this whole time in their very own ‘backyard’ data,” says Normand.

Adaptive puts an even finer point on it. They refer to a “data lake” as often resem-bling a “data swamp.” “To get desired in-formation, someone needs to have a basic understanding of where the data resides

to extract it. In a data lake, (as Adaptive likes to call it) mass amounts of data are ‘thrown’ into the lake, with little con-textual information. No one knows what the files are for, how up-to-date they are, who’s responsible for them, whether they can be used, etc. Likewise, any ‘marts’ formed out of the data lake need to have a detailed level of provenance back to the original data source. Analysts already have that for traditional ETL tools—it’s critical that the data lake provides the same capability. For any serious decisions made from the analysis, that same level of provenance is again needed, and in fact le-gally required by regulators.”

They move on to the crux of it: “Organi-zations have made investments in ‘small data’ for years and many are achieving data gover-nance, or at least understand the gap they need to fill. They know how to work with the rela-tively small number of technologies in play— including databases (standardized on SQL), ETL, DQ and BI—ideally all linked with modeling tools and/or a business glossary. These organizations are now embracing the promise of big data—a new frontier akin to the wild west or the gold rush—with pro-grammers/data scientists let loose with a daily growing menagerie of languages and technol-ogies outside the normal IT and governance structure. Sometimes this produces genuinely impressive-looking results and insights—es-pecially those supporting marketing.

“But making decisions based on mar-keting insights can be low consequence compared to other potential analytical re-sults, such as risk analysis and pricing in-formation.”

So there’s no question that big data an-alytics and text mining is a huge task, but many prospective partners and trustful ad-visors are on hand to help. There are some in the pages to follow, and I hope you find a way through this maze with their help. Please read on and gather your own con-clusions about the value of big data in your organizations. I know I have. ❚

“I know what you’re thinking. Business intelligence tools have been

around for a long time, and have pretty much mastered the analysis

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text mining can quickly extract useful infor-mation from large collections of documents. A text mining tool will typically process a mil-lion words in a few seconds to automatically extract topics and discover unknown relation-ships and patterns.

Companies see the real power behind text analytics when they combine text mining results with structured data. For example, a manufacturing company can mine hundreds of thousands of warranty claims, maintenance reports or incident reports to identify the most costly defects. A services company can analyze customer

comments alongside satisfaction or NPS scores to develop strategies for improving product offerings. The creation of numeri-cal indices derived from text can also be in-tegrated into predictive models, generating improved forecasting or prediction results.

Beyond Sentiment Analysis and

Social Media

In recent years, there has been a growing interest in text analytics, along with an in-crease in new vendors offering text analytics solutions. Most of these focus almost exclu-sively on sentiment analysis and social media, to the extent that this application has become, in the minds of many, the very definition of text analytics. This is unfortunate, as it does not do justice to the wide range of business applications of text analytics. One could also raise serious questions about whether social media really represent the most useful source of information.

We suggest that companies who will ben-efit the most from the implementation of text analytics will be those recognizing the oppor-tunities offered by unstructured data already available or easily accessible within their company. The tremendous hype around sen-timent analysis should also be tempered by a careful assessment of its usefulness and its ac-curacy (which is often much lower than what vendors would like you to believe).

More Detailed, More Accurate Text

Analytics

Analyzing human language is a very com-plex task, and text mining is still, in many respects, in its infancy. Newcomers to text mining expecting their tools to readily pro-vide comprehensive and precise answers to their questions may very well be disappoint-ed. Moving beyond the obvious to achieve greater details and precision often requires some efforts on the part of the text analyst. It involves building a custom dictionary com-posed of keywords, key phrases and rules. Such a crucial task may take days, weeks, in some cases months. Yet it still represents a tiny fraction of the time it would take to do manu-ally. Once developed and validated, such tax-onomy becomes invaluable, allowing one to fully automate the analysis of newly obtained text data or process incoming streams of text data in real-time.

Text mining regularly turns up pre-viously hidden gems, which companies quickly respond to positively. Such insights give them the competitive advantage they are looking for, hidden this whole time in their very own “backyard” data. ❚

Provalis Research is a world-leading developer of text analytics tools used by more than 3,000 institutions worldwide. Visit provalisresearch.com or call 855-355-5252 for more information.

Taming Unstructured

Data with Text Analytics

I

t is today widely recognized that the vast majority of information in any business is unstructured data, typically in text format such as reports, filled forms, emails, mem-os, log entries, transcripts, etc. Most of the time, this rich source of information remains untapped—sometimes because companies are not fully aware of its potential value, more often because of the tremendous effort it takes to sift and dig out information manu-ally from such large volumes.

Text mining provides a viable solution. By combining natural language processing, statistical and machine learning techniques,

By Normand Peladeau,

CEO, Provalis Research

Montreal-headquartered software compa-ny eXplorance provides tools for authoring and distributing survey and evaluation forms, and reporting on them. Their all-in-one assessment system helps organizations assess skills, knowledge, competencies, needs and expectations, and to develop a culture of continuous improvement. Their assessment tools provide online, stream-lined forms, which can be completed on desktop computers or mobile devices. The system collects data on close-ended questions, ratings, as well as open-ended questions. When respondents start-ed to receive the more user-friendly online forms, they soon responded with prolific open-ended feedback.

“As organizations focus more on meeting employee and customer expectations, they need a way to make sense of the qualitative feedback provided by these stakeholders,” said Samer Saab, CEO of eXplorance. “This kind of feedback is free form, and represents an un-biased, unguided expression of expectations.”

eXplorance decided to seek out a provider for an advanced text analytics tool. “In feedback culture, sentiment analysis is not enough. That information was more or less available in the ratings and numerical scores from our reports,” said Saab. “We needed something more so-phisticated. What characterized unsatisfied from satisfied respondents? What are some aspects they like? What needs to be improved?”

eXplorance collaborated with Provalis Re-search to develop a text analytics solution. They used the WordStat desktop application to develop a custom dictionary and their SDK to integrate this categorization dictionary into their learning experience management system. “Provalis Research has a rigorous approach to text analytics, using theme-based interpreta-tions,” said Saab. “We find their mixed-method approach to analysis has brought even greater insights to the data gathered by our system.”

eXplorance has customers on almost every continent. Their text analytics must manage not just typical misspellings, but regional spelling and word meanings, as well as cultural context. To address this, they use a teaching and learn-ing dictionary based on 1.8 million open-ended responses from diverse sources. The current dictionary comprises more than 10,300 entries, consisting of word, word patterns, phrases, rules and more than 4,000 misspellings classi-fied in 151 categories, including 22 positive and 23 negative attributes, and various topics and potential issues.

“Several clients have already adopted the text analysis tool, and we continue to expand our analytics set,” Saab said. “We look forward to further enhancing our offering, to provide the data organizations need to foster strategic insights for future innovation. We believe text analytics will play an important role in this.”

Software Company Uses Text Analytics

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where the data resides in order to extract it. In a data lake, mass amounts of data are “thrown” into the lake, with little contex-tual information. No one knows what the files are for, how up-to-date they are, who’s responsible for them, whether they can be used, etc. Likewise, any “marts” formed out of the data lake need to have a detailed level of provenance back to the original

data source. Analysts already have that for traditional ETL tools—it’s critical that the data lake provides the same capability. For any serious decisions made from the anal-ysis, that same level of provenance is again needed, and in fact legally required by regulators.

Adding a context/governance layer can provide the following benefits and answer critical questions:

Catalog. What information is out there

and what can I use? Who do I contact if the information looks wrong or if I need more?

Governance. Who owns/supports

what? (e.g. files, technologies, analytic programs, results, data coming into data lake.) How up to date is the information and who is using it?

Context/impact. Does the data have

consistent meaning (name matches are not sufficient)? What are the technology dependencies and risks? ❚

Are You Creating a

Data Swamp?

O

rganizations have made investments in “small data” for years and many are achieving data governance, or at least un-derstand the gap they need to fill. They know how to work with the relatively small number of technologies in play—in-cluding databases (standardized on SQL), ETL, DQ and BI—ideally all linked with modeling tools and/or a business glossary. These organizations are now embracing the promise of big data—a new frontier akin to the wild west or the gold rush, with programmers/data scientists let loose with a daily growing menagerie of languages and technologies outside the normal IT and governance structure. Sometimes this produces genuinely impressive-looking results and insights—especially those sup-porting marketing.

But making decisions based on marketing insights can be low consequence compared to other potential analytical results, such as risk analysis and pricing information. Today, there are more than 70 vendors in the big data space alone, and growing. Not to mention a large number of open source technologies, many of which are repackaged by those same vendors. Not only is the system complex, it’s getting more complex each day.

The usage of big data technology (such as Hadoop) can vary significantly:

Analysis. Which in turn can be

subdi-vided into:

Batch (using algorithms such as

map-re-duce applied to relatively static data);

Dynamic (applied to dynamic event

streams such as website clicks, system logs); or

Hybrid (dynamic assisted by static data

such as customer information).

Data management (typically called

a “data lake”). Providing a very scalable “schema-less” holding place for all source data (structured or unstructured) in its native form without having to pre-design one spe-cific format/schema such as a traditional data warehouse. The idea is to dynamically create schemas for particular purposes for analysis, reporting, or use of traditional tools.

Is My Data Lake Really

a Data Swamp?

To get desired information, someone needs to have a basic understanding of

By Pete Rivett,

CTO, Adaptive, Inc.

To help prepare organizations for big data initia-tives, Adaptive has developed a software solution that ensures consistency and traceability of data across an organization. They can help organizations build the necessary foundation to enable governance and control over the data flowing into traditional data warehouses and big data environments to drive big data analytics. Adaptive’s solution provides support for three critical capabilities:

Business glossary. A cloud-based enterprise-wide glossary of an organization’s terms, defini-tions and ontologies, providing a single point of truth for governance and knowledge transfer. Us-ers can add stewardship and govern the changes of any item in the repository.

Metadata capture. Using Adaptive’s 75+ bridges, users can extract metadata from data-bases, data modeling tools, ETL tools, BI tools and Hadoop ecosystems. These extracts can be versioned and audited, allowing users to docu-ment the full history of an object for regulatory reporting.

Alignment. A comprehensive capability to support traceability and lineage analytics. Even if a piece of data is stored in 42 databases and is being processed 2,000 times, the solution can be used to follow the thread and understand the impact, change and use of data throughout the enterprise.

Adaptive’s offerings are built on industry stan-dards—providing easier interoperability with different types of technologies. “Adaptive can discover data structures from a variety of technology landscapes,” says Jeff Goins, president and CEO of Adaptive. “Many organizations are silo-centric in nature, and Adaptive’s expertise connects all the silos,

providing enterprise-wide transparency and gover-nance management practices that are critical to Big Data initiatives.”

A Client Success Story

Adaptive’s solutions are being utilized by organizations in the banking, pharmaceutical, healthcare, retail, government and energy sec-tors. Although the business drivers across the cli-ent base vary, each firm is striving to ensure the accuracy and consistency of disparate data sourc-es to support their big data initiativsourc-es. For exam-ple, a large financial services organization was investing more than $10 million in a new data lake in order to streamline the reporting process and provide more “BI on demand” capabilities to their business stakeholders. The organization was importing thousands of artifacts into the lake and needed a solution to provide governance and provenance to the data within the lake. Adaptive was selected to provide three critical capabilities:

Catalog. An analyst first uses Adaptive to understand what data is available for reporting;

Governance. Analysts can confirm the data they are about to use for reporting is under change control and can verify that all data in the report has a steward assigned; and

Lineage/impact. Users can trace their report-ing data all the way back to its source, ensurreport-ing they are using the correct data in their analysis.

The client can now analyze governed data faster and more reliably than ever before. They have the best of both worlds—a data lake for faster, on-de-mand reporting; and the governance required by the business for precise decision making and adherence to compliance regulations.

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2015 WHITE PAPER CALENDAR

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ANALYTICS FOR BIG DATA

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BI/Data Mining/Hadoop

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Provalis Research 1255 University St, Suite #1202 Montreal, QC. H3B 3W9 PH: 514. 899.1672 FAX: 514.899.1750 Contact: [email protected] Web: www.provalsresearch.com Adaptive, Inc.

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