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Comparing Top-down and Bottom-up BI



Most organizations don’t realize that they need both top-down and bottom-up BI. Top-down environments are architected to deliver precise  answers to predefined questions, while bottom-up environments are designed  to explore new questions that can’t be anticipated. At a tactical level, reports  generate new questions that require analysis, while analysis generates insights that users want to view on a regular basis through reports. It’s not an  either-or proposition; companies need both (see Figure ).
Both top-down reporting and bottom-up analysis tools take different  approaches to self-service BI. Top-down tools favor semantic layers and  mashups, while analysis tools favor customized publishing. In any case, a BI  environment that supports both provides more options to satisfy the self-service BI needs of all your BI users.

Making a Good Dashboard: Not Simple


By SiliconIndia   |   Friday, 28 December 2012, 16:35 IST 

Bangalore: Business Intelligence is evolving by the day and trends followed in one year will differ radically from the trends followed in the subsequent year. However, one constant function of BI has been the need to report effectively. This has been altered and improved in the past by a variety of ways, for example, Dodge Dart has a novel lighting system and a display which can be customized, as Alan R. Earls notes on SearchBusinessAnalytics.com. The pertinent question remains, do these supposed ‘improvements’ enable usability and boost the overall usage of dashboards.

For BI developers and IT departments, designing dashboards that give information to workers on the business side would offer an interesting crossroads; should one just deliver the bare essentials or should one offer interesting graphics, effects and animations? Would the mentioned graphics get in the way of data analysis? If the end user is experienced with using spreadsheets and advanced analyses, individuals designing dashboards could think about introducing more intricate charts or scatter plots, states David Stodder, Director of BI Research at TDWI. It is always smart to start simple though. The whole point of BI is to increase productivity, especially of business professionals, by cutting out complications and reducing the time required to obtain the right information.

Lately, it has been noted that interactive element of a dashboard is important. What makes a dashboard a unique way of presenting information is the drill down capabilities it could offer. Giving this option is not easy as there is dependence on existing BI systems while on the other hand, not doing so could cause users to return to the age- old spreadsheets. It is easy to get carried away by the presentation which will not matter if ultimately incorrect information is delivered. There is no denying that it is important to present data in an appealing manner, however data quality and consistency are more pressing issues that continue to plague dashboard designers. Dashboards must be regularly updated to ensure that they stay relevant.


Becoming more strategic: Three tips for any executive

You don’t need a formal strategy role to help shape your organization’s strategic direction. Start by moving beyond frameworks and communicating in a more engaging way.

We are entering the age of the strategist. As our colleagues Chris Bradley, Lowell Bryan, and Sven Smit have explained in “Managing the strategy journey,” a powerful means of coping with today’s more volatile environment is increasing the time a company’s top team spends on strategy. Involving more senior leaders in strategic dialogue makes it easier to stay ahead of emerging opportunities, respond quickly to unexpected threats, and make timely decisions.

This is a significant change. At a good number of companies, corporate strategy has long represented the bland aggregation of strategies that individual business unit heads put forward.1 At others, it’s been the domain of a small coterie, perhaps led by a chief strategist who is protective of his or her domain—or the exclusive territory of a CEO.

Rare is the company, though, where all members of the top team have well-developed strategic muscles. Some executives reach the C-suite because of functional expertise, while others, including business unit heads and even some CEOs, are much stronger on execution than on strategic thinking. In some companies, that very issue has given rise to the position of chief strategy officer—yet even a number of executives playing this role disclosed to us, in a series of interviews we conducted over the past year, that they didn’t feel adequately prepared for it.

This article draws on those interviews, as well as our own and our colleagues’ experience working with numerous executives developing strategies, adapting planning approaches, and running strategy capability-building programs. We offer three tips that any executive can act on to become more strategic. They may sound deceptively simple, but our interviews and experience suggest that they represent foundational skills for any strategist and that putting them into practice requires real work. We’ve also tried, through examples, to present practical ways of acting on each suggestion and to show how doing so often means augmenting experience-based instincts with fresh perspectives.

1. Understand what strategy really means in your industry

By the time executives have reached the upper echelons of a company, almost all of them have been exposed to a set of core strategy frameworks, whether in an MBA or executive education program, corporate training sessions, or on the job. Part of the power of these frameworks is that they can be applied to any industry.

But that’s also part of the problem. General ideas can be misleading, and as strategy becomes the domain of a broader group of executives, more will also need to learn to think strategically in their particular industry context. It is not enough to do so at the time of a major strategy review. Because strategy is a journey, executives need to study, understand, and internalize the economics, psychology, and laws of their industries, so that context can guide them continually.

For example, being able to think strategically in the high-tech industry involves a nuanced understanding of strategy topics such as network effects, platforms, and standards. In the utilities sector, it involves mastery of the economic implications of (and room for strategic maneuvers afforded by) the regulatory regime. In mining, leaders must understand the strategic implications of cost curves, game theory, and real-options valuation; further, they must know and be sensitive to the stakeholders in their regulatory and societal environment, many of whom can directly influence their opportunities to create value.

There is a rich and specialized literature on strategy in particular industries that many executives will find helpful.2 Tailored executive education courses can also be beneficial. We know organizations that have taken management teams off-site to focus not on setting strategy but on deepening their understanding of how to be a strategist in their industries. For example, one raw-materials player headquartered in Europe took its full leadership team to Asia for a week, in hopes of shaking up the team’s thinking. Executives explored in depth 20 trends that would shape the industry over the next decade, discussing both the trends themselves and their implications for the supply of and demand for the organization’s products.3 They also looked across their industry’s full value chain to understand who was making money and why—and how the trends would change that. A number of the executives in the discussion were surprised by how much value certain specialized intermediaries were capturing and others by how the organization was losing out to competitors that were financing retailers to hold their inventory. The executive team emerged with a clearer appreciation of where the opportunities were in its industry and with ideas to capture them.

Building this kind of industry understanding should be an ongoing process not just because we live in an era of more dynamic management4 but also because of the psychology of the individual. Experience-based instincts about “the way things work” heavily influence all of us, making it hard, without systematic effort, to take advantage of emerging strategic insights or the real lessons of an industry’s history. War games or other experiential exercises are one way executives can help themselves to look at their industry landscape from a new vantage point.5

2. Become expert at identifying potential disrupters

Expanding the group of executives engaged in strategic dialogue should boost the odds of identifying company or industry-disrupting changes that are just over the horizon—the sorts of changes that make or break companies.

But those insights don’t emerge magically. Consider, for example, technological disruption. For many executives, the rise up the corporate ladder requires a deep understanding of industry-specific technologies—those embedded in a company’s products, for example, or in manufacturing techniques—but much less knowledge of cross-cutting technology trends, such as the impact of sensors and the burgeoning “Internet of Things.”6 Moreover, many senior executives are happy to delegate thinking about such technology issues to their company’s chief information officer or chief technology officer. Yet it’s exactly such cross-cutting trends that are most likely to upend value chains, transform industries, and dramatically shift profit pools and competitive advantage.

So what to do? Some executives choose to spend a week or two visiting a technology hub, such as Silicon Valley, to meet companies, investors, and academics. Others ask a more technophile member of the team to keep abreast of the issues and brief them periodically. We know a number of executives who have developed “reverse mentoring” relationships with younger and more junior colleagues (or even their children) that focus on technology and innovation. And of course, there’s no substitute for seeing what your customers are doing with technology: during several store visits, an executive at a baby care retailer saw mothers compare the prices of products on their smartphones at the store and leave if they could get a better deal elsewhere. The store visits brought home how modern mothers research their buying decisions, the interaction between mobile technology and store visits, and the importance of advertising a price-matching scheme to keep tech-savvy customers buying in stores.

Nascent competitors are another easy-to-overlook source of disruption. Senior strategic thinkers are of course well aware of the need to keep an eye on the competition, and many companies have roles or teams focused on competitor intelligence. However, in our experience, often too many resources—including mental energy—are devoted to following the activities of long-standing competitors rather than less conventional ones that may pose an equivalent (or greater) strategic threat.

For example, suppose you are an executive at an oil company with assets in the UK Continental Shelf. It is natural for the competitors that you meet regularly at board meetings of Oil & Gas UK, the regional industry association, to be more top of mind than Asian players that have only just acquired their first positions in the region. And that’s exactly why many long-standing industry leaders were surprised when Korea National Oil Corporation (KNOC), South Korea’s national oil company, clinched a hostile takeover of Dana Petroleum in late 2010, in what was to be the largest oil and gas transaction in the United Kingdom in several years. The transaction was a harbinger of future investments by less traditional players in the North Sea oil and gas industry. Similar dynamics prevail in mining: developed-world majors (such as Anglo American, BHP Billiton, and Rio Tinto), which have long competed with one another globally, now must also take into account players from Brazil, China, India, and elsewhere.

Picking up weak competitive signals is more often than not a result of careful practice: a systematic updating of competitive insights as an ongoing part of existing strategic processes.7 Executives with diverse backgrounds can boost the quality of dialogue by contributing to—and insisting on—issue-based competitive analyses. Who is well-positioned to play in emerging business areas? If new technologies are involved, what are they, and who else might master them? Who seems poorly positioned, and what does that mean for competitive balance in the industry or for acquisition opportunities? Focusing competitive reviews on questions like these often yields insights of significantly greater value than would be possible through the more common practice of periodically examining competitors’ financial and operating results. It also helps push the senior team away from linear, deterministic thinking and toward a more contingent, scenario-based mind-set that’s better suited to today’s fast-moving strategy environment.

3. Develop communications that can break through

A more adaptive strategy-development process places a premium on effective communications from all the executives participating. The strategy journey model described by our colleagues, for example, involves meeting for two to four hours every week or two to discuss strategy topics and requires each executive taking part to flag issues and lead the discussion about them.

In such an environment, time spent looking for better, more innovative ways to communicate strategy—to make strategic insights cut through the day-to-day morass of information that any executive receives—is rarely wasted. This requires discipline, as it is always tempting to invest in further analysis so that the executive has a deeper grasp of the issues rather than in communications design to ensure that everybody has a good grasp of them. It also may require building new skills; indeed, developing messages that can break through the clutter is becoming a required skill for the modern strategist.8

Experiential exercises are one way of boosting the effectiveness of strategic communications within a top team. A strategist we know at a shoe manufacturer wanted to illustrate the point that many of his company’s products were both unattractive and expensive. He started with a two-by-two matrix. So far, so predictable. But his matrix was built using masking tape on the floor of the executive suite, and the shoes were real ones from the company and its competitors. His colleagues had to classify the shoes right there and then—and he made his point. Similarly, we know another strategist who spent an afternoon cutting the labels off samples of men’s boxer shorts. She wanted the board members to put them in order of price so they could see how their perceptions of quality were driven by brands and not manufacturing standards.

We would add that as strategy becomes more of a real-time journey, it’s important to figure out ways to support discussions with data that’s engaging and easy to manipulate. To the extent possible, executives need to be able to push on data and its implications “in the moment,” instead of raising questions and then waiting two weeks for a team of analysts to come back with answers. Ideally, in fact, anyone in a room could drill into thoughtfully visualized data with the flick of a finger on a tablet computer. The proliferation of tactile mobile devices and new software tools that help make spreadsheets more visual and interactive should facilitate more dynamic, data-driven dialogue.

Executives hoping to become more strategic should look for opportunities to innovate in their communication of data, while prodding their organizations to institutionalize such capabilities. Breakthroughs abound—look no further than the interactive visualizations in the New York Timesin the United States or the Guardian in the United Kingdom; the 2006 TED.com video “Hans Rosling shows the best stats you’ve ever seen”; Generation Grownup’s interactive tool Name Voyager, which examines the popularity of baby names over time (see babynamewizard.com/voyager); or Kiva.org’s Intercontinental Ballistic Microfinance visualization of loan-funding and repayment flows. But few companies have kept up.

It’s not enough to increase the number and diversity of executives engaged in setting strategy. Many of those leaders also must enhance their own strategic capabilities. We hope these three tips help them get started.

About the Authors

Michael Birshan is a principal in McKinsey’s London office, where Jayanti Kar is a consultant.

The authors wish to thank Emma Parry for her contribution to the development of this article.


1In a McKinsey Global Survey of more than 2,000 global executives, only one-third agreed that their corporate strategy approach represented “a distinct exercise that specifically addresses corporate-level strategy, portfolio composition issues.” For details, see “Creating more value with corporate strategy: McKinsey Global Survey results,” mckinseyquarterly.com, January 2011.

2 See, for example, Carl Shapiro and Hal R. Varian, Information Rules: A Strategic Guide to the Network Economy (Harvard Business Review Press, November 1998), which focuses on information businesses, such as software.

3 For more on trend analysis, see Peter Bisson, Elizabeth Stephenson, and S. Patrick Viguerie, “Global forces: An introduction,” mckinseyquarterly.com, June 2010; and Filipe Barbosa, Damian Hattingh, and Michael Kloss, “Applying global trends: A look at China’s auto industry,” mckinseyquarterly.com, July 2010.

4 See Lowell Bryan, “Dynamic management: Better decisions in uncertain times,” mckinseyquarterly.com, December 2010.

5 See John Horn, “Playing war games to win,” mckinseyquarterly.com, March 2011.

6 See Michael Chui, Markus Löffler, and Roger Roberts, “The Internet of Things,” mckinseyquarterly.com, March 2010.

7 See Hugh Courtney, John T. Horn, and Jayanti Kar, “Getting into your competitor’s head,” mckinseyquarterly.com, February 2009.

8 Stanford University business school professor Chip Heath and his coauthor and brother, Dan Heath, describe such messages as “sticky ideas” that people understand and remember “and that change something about the way they think or act.” Sticky ideas have at least some of these six characteristics: simplicity, unexpectedness, concreteness, credibility, emotion, and the ability to tell a story. For more, see Lenny T. Mendonca and Matt Miller, “Crafting a message that sticks: An interview with Chip Heath,” mckinseyquarterly.com, November 2007; and Chip Heath and Dan Heath, Made to Stick: Why Some Ideas Survive and Others Die, New York, NY: Random House, January 2007.


Seven Techniques for Gathering Requirements


Understanding requirements is one of the key elements for project success. Elicitation is the part of the requirements gathering process where you actually capture the client needs. There are a number of techniques for eliciting requirements. Your project may need to use multiple techniques depending on the circumstances. 

1. One-on-one interviews
The most common technique for gathering requirements is to sit down with the clients and ask them what they need. 

2. Group interviews
These are similar to the one-on-one interview except that there is more than one person being interviewed. Group interviews require more preparation and more formality to get the information you want from all the participants.

3. Facilitated sessions
In a facilitated session, you bring a larger group together for a common purpose. In this case, you are trying to gather a set of common requirements from the group in a faster manner than if you were to interview each of them separately. 

4. JAD sessions
Joint Application Development (JAD) sessions are similar to general facilitated sessions. However, the group typically stays in the session until a complete set of requirements is documented and agree to. 

5. Questionnaires
These are good tools to gather requirements from stakeholders in remote locations or those that will have only minor input into the overall requirements. It can also be the only practical way to gather requirements from dozens, hundreds or thousands of people.

6. Prototyping
Prototyping is a way to build an initial version of the solution – a prototype. You show this to the client, who then gives you additional requirements.

7. Following people around
This is especially helpful when gathering information on current processes. You may need to watch people perform their job before you can understand the entire picture. In some cases, you might also like to participate in the actual work process to get a hands-on feel for how the business function works today.

Knowing your audience will help you determine the right techniques to utilize to best meet your needs. You should select techniques that get you the most relative information and are best suited for the audience.


Hadoop framework, big data systems will coexist with relational data in 2013

Big data edged up in interest in 2011, but in 2012 it skyrocketed, potentially changing aspects of data management in a dramatic way. Big data systems spawned changes for managing and handling machine data, continuous extract, transform and load functions, operational business intelligence, big data in motion, cloud-based data warehousing and more.


Still, as big data enters 2013, no big data systems technologies are more active than NoSQL databases and the Hadoop framework, and it appears they have more room to grow. The Hadoop-MapReduce market alone is forecasted to grow at a compound annual growth rate of 58%, reaching $2.2 billion in 2018, according to an August 2012 MarketAnalysis.com report.

Now, things that were not practical are becoming practical. This has taken data out of its comfort zone.

Judith Hurwitz,
independent analyst

NoSQL and Hadoop appear to be major means for coping with unstructured data such as text and Web logs. Like Apache Hadoop, these technologies often have open source roots, and are still new as commercial products.

According to Judith Hurwitz, president and CEO of Needham, Mass.-based Hurwitz and Associates Inc., big data architecture and massively parallel processing are dramatically transforming the data landscape. “Previously, even though data was really important to companies, they didn’t really have the capability of grabbing great amounts of data and analyzing it in real time,” Hurwitz said.

“Now, things that were not practical are becoming practical. This has taken data out of its comfort zone,” she said.

SQL takes a hit, punches back

As seen in the pages of SearchDataManagment.com, 2012 began with predictions of trouble for mainstay relational databases. The criticism proved partially prophetic. After battling many would-be replacements over the years, the SQL relational database seems to have met serious competition for handling massive amounts of data now — or soon to be — filtering through the enterprise.

The push behind the trend is the enterprise’s desire to take on more unstructured data at a faster rate in order to become more data-driven in decision making. Customary approaches are being reworked to encompass the best of the new techniques.

Just a few of the many moves by established data management vendors in 2012 show the impact big data and Hadoop are having on the relational data status quo:

  • IBM continued to add boutique data and analytics companies to its portfolio, though it happened with less frequency than in 2011. Big Blue’s efforts ranged from small enhancements such as a NoSQL Graph Store for DB2 10 and InfoSphere Warehouse 10, to a very large PureData System appliance intended to “tame big data” in the enterprise.
  • Oracle rolled out its big data appliance at the start of the year. The announcement was followed up later with Oracle NoSQL Database 2.0, which has automatic rebalancing, new application programming interfaces for handling large objects, and tighter integration with the Oracle database, allowing querying of Oracle NoSQL database records directly from SQL.
  • Microsoft showed previews of Hadoop support for Windows Azure and Windows Server; Teradata Corp. released its Aster Big Analytics Appliance; and Informatica Corp. launched a Big Data Edition of its PowerCenter suite that was said to remove the need for Hadoop hand coding by bringing the programming task into the Informatica development environment.

SQL may have taken a punch or two in 2012, but it refused to go down for the count. Companies specializing in the alternative NoSQL and Hadoop side of things brushed up their SQL credentials this year. A prime example was Hadoop startup Cloudera Inc. It looked to enhance its SQL standing with Impala, a Hadoop software offering that supports interactive queries done in standard SQL.

Big data shift

Moves like this may indicate a bit of momentum — one that sees SQL and NoSQL being mentioned together more often. In a way, SQL was downplayed in the early big data buzz.

“In the last couple of years, because of the big data movement, SQL has not been on everyone’s lips,” said Ronnie Beggs, vice president of marketing at San Francisco-based SQLstream, a maker of streaming databases. Meanwhile, he continued, “Big data and NoSQL [have] taken off as a topic, and hit the mainstream.”

In 2013, we should see evidence of change there, he indicated. There has been of lot of effort in recent years to better enable NoSQL databases for SQL-style development, he said.

“It’s simply evolving. What we see for next year is the return of SQL as an interface for all the big data platforms,” Beggs said.


Big data is the voluminous amount of structured, unstructured and semi-structured data a company creates — data that in many cases would take too much time and cost too much money to load into a conventional relational database for analysis.

Read more from the Whatis.com definition of big data

This evolution toward coexistence of the Hadoop framework, NoSQL and SQL approaches could mark a new step in big data’s maturation. As 2013 approaches, there is the possibility that big data may move from hot topic to practical reality.

“I think people are trying to get through the hype of big data and really understand where the business value is,” said Colin White, president and founder of Ashland, Ore.-based BI Research. “In 2013, I think we will see good examples of people getting business value from big data. It’s not about big data, it’s what you do with the data that matters.”

While there is wide interest in the new technologies, not all companies will move to full-fledged big data systems at the same rate. This was borne out as an integration services manager at a major bank spoke recently with SearchDataManagment.com.

He marked banking as an area where some, but not all, of the basics of big data are in play. Banking and other fields see data that is big in volume, but not big in unstructured data. At least that is the case today.

“When you look at the tenets of big data, there are two parts. One, there is lots of it, and two, it is unstructured. The banks have the first part,” he claimed. “But we are not collecting ‘tweets,’ at least not yet. We are in wait-and-see mode, looking to see how the financial data service market can handle it.”

Social Science Pushing Data Frontiers



I’ve noted in several blogs over recent months the proximity of the business-led discipline data science with academically-focused quantitative social science. From my perspective, DS obsesses on data, statistical and computational methodologies to study social behavior, much the same as QSS.

Addressing the pressing need to recruit suitable university graduates for careers in data science, the Nokia mother-daughter data science team of Amy O’Connor and Danielle Dean made the case at Strata-Hadoop World 2012 for college grads with math/stats, bioengineering and social science educational backgrounds.

The two authors of O’Reilly’s excellent “Machine Learning for Hackers” are both practicing data scientists pursuing Ph.D.’s – one in psychology and the other in political science.

Rachel Schutt, originator of the Introduction to Data Science course at Columbia University, has a Ph.D. in political science. Schutt’s course objective and take on data science? “This course is an introduction to the interdisciplinary and emerging field of data science, which lies at the intersection of statistics, computer science, data visualization and the social sciences.”

The new masters in Computational Science and Engineering at Harvard is being touted as a cross-disciplinary program “with the aim of training new leaders for a future where large-scale computation and advanced mathematical modeling will propel discovery and innovation in fields from psychology to photonics…. The Harvard program will offer a curriculum broader than typical for master’s degrees in computational science, anchored by core courses in both computer science and applied mathematics and embracing a wide range of applications, including the social sciences in particular.”

Finally, the pioneering research work of sociologist and now-Microsoft researcher Duncan Watts is the archetype for the amalgam of data and social science, bringing methodological and computational tools to bear on knotty business social science questions. Watts’ keys are “agility, field experiments, open innovation, crowdsourcing, data and analytics.” The author lauds the science of business manifesto outlined by MIT professors Erik Brynjolfsson and Michael Schrage for its potential of bringing hypotheses, experiments and analytics to all facets of business.

Watts is at it again with his informative Harvard Business Review blog, “The Importance of Studying the Obvious.” The problem with “obvious” and “common sense” explanations of social behavior is that they’re often as not incorrect – quick, gut, intuitive, simplistic – and wrong. Think of Watts’ common sense as Nobel prize-winning psychologist Daniel Kahneman’s “Thinking Fast” which serves us well for simple decisions but can lead astray for complex ones. Watts’ scientific “uncommon sense” in contrast, looks a lot like Kahneman’s more deliberate and evidence-based “thinking slow,” which should be the foundation of complex business decision-making.

The Watts’ antidote for the limitations of intuition/common sense? “One interesting possibility is raised by the arrival of “big data,” increasingly derived from digital communications, social media, mobile apps, and e-commerce sites … For this reason, companies like Facebook, Google and Microsoft, where I now work, are beefing up their research labs both with computer scientists, who have the technical skills to handle huge datasets, and social scientists, whose job it is to ask the right questions. In fact, the emerging intersection of computer and social science – what some people are calling computational social science – is one of the hottest areas of research today.”

While CSS is in its infancy, there’s a growing cadre of quantitative social science luminaries busy promoting its inevitable growth. “The capacity to collect and analyze massive amounts of data has unambiguously transformed such fields as biology and physics. The emergence of such a data-driven ‘computational social science’ has been much slower, largely spearheaded by a few intrepid computer scientists, physicists, and social scientists. If one were to look at the leading disciplinary journals in economics, sociology, and political science, there would be minimal evidence of an emerging computational social science engaged in quantitative modeling of these new kinds of digital traces. However, computational social science is occurring, and on a large scale, in places like Google, Yahoo and the National Security Agency.”

In contrast to the limited, traditional social science data acquisition methods like the self-reporting survey, “[n]ew technologies, such as video surveillance, e-mail and ‘smart’ name badges offer a remarkable, second-by-second picture of interactions over extended periods of time, providing information about both the structure and content of relationships.” Interestingly, the academics are unsure which discipline will ultimately own CSS. “In the longer run, the question will be: should academia be building computational social scientists, or teams of computationally literate social scientists and socially literate computer scientists?”

I like the computational social science coinage a lot. It connotes science, big data, statistics and computation. Look for the computational social scientists of the academic world to push the frontiers of data science to the benefit of business – and for data scientists to simultaneously elevate CSS to the benefit of academia.


Realizing true value from customer analytics investments

Organizations across the globe are using advanced data analytics to maximize the value of their every customer. Customer data and analytics has finally hit that tipping point where it is serving such an important business need that it can’t be ignored anymore. 

We have compiled a free briefing titled: Customer Analytics – Realizing True Customer Value, full of practical insight, recommendations and predictions from analytics experts from eBay, Telus, RingCentral and more. 

Download your free copy of the report here

You’ll learn: 

  • Tips and tricks for nurturing customers in order to optimize their long term profitability 
  • Why real-time data should be at the core of your business strategy from a former SVP at Telus
  • The keys to tracking social media feedback to spot trends and insights that can amplify your business – straight from Social Media Strategist at RingCentral, Baochi Nguyen
  • A practical Q&A with eBay’s own Sudha Jamthe shedding light on creating a data-friendly culture, personalizing relationships and much more

As always, don’t hesitate to reach out with any questions. Our team would love to hear from you! 


Brian Smith | Vice President USA | Text Analytics News |             201-234-4764       | bsmith@textanalyticsnews.com 

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