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Trends and Outliers

TIBCO Spotfire's Business Intelligence Blog

Category Archives: Collaborative BI


Collaborative BI: Trends & Mobility Factors

Although face-to-face meetings, email and phone calls are the top “mechanisms for collaborating with BI insights in 2013,” those mechanisms are not necessarily the future for mobile collaboration on big data projects.

Collaborative BIDresner post 150x150 Collaborative BI: Trends & Mobility FactorsThat’s according to a recent survey on the collaborative BI market by Howard Dresner, founder and president of Dresner Advisory Services.

Let’s look at some of the findings of Dresner’s “2013 Wisdom of Crowds Collaborative Business Intelligence Market Study.”

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Insights into “Big Opportunities with Big Data & Analytics for Consumer Packaged Goods Manufacturers”

Packaging goods is no secret to the consumer goods market, but unpacking the insights from the deluge of data this sector brings is a challenge for many companies.

shutterstock 69081949 300x201 Insights into “Big Opportunities with Big Data & Analytics for Consumer Packaged Goods Manufacturers”However, big data analytics can help consumer goods organizations derive value from past, present and future data and solve big business problems such as how to:

  • Manufacture products with fewer steps.
  • Deliver new products to market with more efficiency and lower startup costs.
  • Perform customer service more effectively.

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Top 5 Overlooked Best Practices for BI Success

Everyone’s buzzing about big data and the vast opportunities available for companies to make use of data analysis and data discovery tools and techniques.

However, even though many business leaders are eager to exploit big data for their businesses, many also believe that they face sizeable obstacles to achieving success with business intelligence (BI).

Business Intelligence BI Solutions to improve business processes Top 5 Overlooked Best Practices for BI SuccessA whopping 85% of respondents cite significant impediments to managing and analyzing data, according to a recent survey of 569 C-level executives, business unit leaders, and IT executives. The challenges range from being overwhelmed by the sheer volume of data to not having enough dedicated staff to analyze it.

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Wielding Analytics to Conquer the Customer Experience Battle

customer service is not star trek 150x150 Wielding Analytics to Conquer the Customer Experience BattleIn a time when consumers have unprecedented access to reams of data about an organization – from Facebook posts to forum reviews – companies must be more prepared than their empowered customers when they dial up call centers or access other corporate touch points.

Bolstering the customer experience across multiple channels is one of the most often mentioned benefits of mining the treasure trove of information buried within big data.

Take AT&T, for example, which is aiming for an effortless customer experience, according to Phil Bienert, senior vice president of digital experience at AT&T. One of the biggest challenges to delivering a positive customer experience is staying ahead of customer expectations in a market mired in one of the largest changes in consumer behavior in history, he adds.

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Forrester: The Era of Self-Service BI is Here

business intelligence 150x150 Forrester: The Era of Self Service BI is Here Forrester Research Inc. has just released its Q2: 2012 Forrester Wave: Self-Service Business Intelligence (BI) Platforms report and we’re pleased to announce that the research firm has ranked TIBCO Spotfire as “a leader in self-service BI platforms with the highest ‘Current Offering’ score.”

While that’s pretty cool, we’re really excited about the findings of the report – a move toward business-led, self-service BI. Here are a few highlights from the report and our takes on them.

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Why Analytics Teams Don’t Collaborate and How to Fix It

collaboration 150x150 Why Analytics Teams Dont Collaborate and How to Fix ItOne of the best things about contemporary analytics tools is that they enable teams of data scientists and other decision makers to brainstorm about business challenges that companies face and identify approaches for solving them.

For example, maybe a big box retailer has experienced a drop in sales for a particular set of stores or across specific categories. Well, data scientists and business leaders can use analytics tools to identify the likely causes for the drop-off as well as approaches for improving sales.

The beauty of collaboration is that individuals offer different perspectives and ways of attacking problems. Sometimes internal debates can lead to vicious arguments when people feel passionately about particular positions. Still, collaboration can bring great results when intelligent, even-handed executives are charged with making the best decisions based on the input that’s been presented to them.

While this kind of teamwork sounds great in theory, unfortunately, it doesn’t always work out in the real world. As Forbes contributor Ron Ashkenas points out in a recent blog post, one of the ways that teams typically react to group challenges is through “cooperation.”

Under this scenario, each person develops and implements his own plan and then shares what he’s doing with the group. While there’s some degree of joint dialogue, “the focus is still on individual actions rather than a collective strategy,” Ashkenas says. Each member of the team – or subgroups if the teams are large enough – often takes a different approach to solving a shared challenge.

Let’s say an automaker is looking to increase sales by 5% for a particular quarter. Through the use of analytics, one dealership might determine that it makes sense to create a set of sales incentives for recent college graduates or members of the military. Another dealership, however, might see the benefit of creating trade-in offers for owners or lessees of vehicles of rival car makers. Each approach is aimed at achieving a similar result – lifting sales – but the dealerships use different tactics. This is hardly a collaborative effort.

While different approaches to fostering collaboration tend to work well depending on the organization’s cultural fit, here are four styles that can help any company:

  1. Champion collaboration. It’s critical to have senior leadership regularly and openly communicate the value and importance of collaboration. This includes highlighting how constructive debate can yield fresh ideas and more effective ways to solve problems. Tangible examples and storytelling are great ways to emphasize this.
  2. Agree to disagree. It isn’t collaboration if team members all nod their heads in agreement when a senior executive presents an idea. Even if it’s a solid idea, it should be dissected and turned on its side to explore possible cracks and ensure that it’s the best available method.
  3. Disregard chain of command. To truly promote a culture of open dialogue, people must believe they can speak freely without fear of retribution. Company leaders aren’t looking for “yes” men or women. They want people who stand behind their convictions and offer the organization alternative views to help shape multi-dimensional decision making.
  4. Make it fun. Strategic projects are serious stuff. Still, it’s important to inject some humor into project meetings and keep the mood loose. A bit of levity will go a long way to keeping the project team fresh and help strengthen the bond among its members.

Next Steps: Download this complimentary “5-Minute Guide to Business Analytics,” and learn how user-driven “analytic” or “data discovery” technologies help business and technology users more quickly uncover insights and speed action.

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Making the Most of Embedded BI

embedded BI 150x150 Making the Most of Embedded BI  Data, once organized and presented correctly, is a source of information. Information shared across an organization offers insight. Insight delivered quickly becomes actionable.

In today’s business climate staying ahead requires a system to deliver actionable insight to decision makers in a format that’s easy to understand and easy to access. The delivery of a quality, universally accepted business intelligence (BI) system that integrates well with existing systems is the challenge for a number of organizations today.

The Aberdeen Group has conducted research on the subject and published its findings describing how companies successfully develop and deploy embedded BI systems.

The Aberdeen study surveys 420 respondents who reveal that deploying embedded BI systems leads to quicker adoption, longer engagements, and a fostering of BI system familiarity when compared to standalone BI systems. A customer relationship management system (CRM) and a human resource planning (HRP) system augmented with analytics are perfect examples of an embedded BI implementation.

Of the 420 respondents, 174 (about 41%) indicate that they employ embedded BI approaches by augmenting/integrating with corporate systems used to conduct day-to-day operations. Aberdeen categorizes and groups the 174 respondents embedding BI in three performance areas:

  • How actively involved the users of the business organization are with the BI system;
  • How well the user accesses the data from the BI system without a great deal of assistance from the information technology department; and
  • If the organization has experienced significant cycle time improvements when evaluating the business processes and conducting a comparative analysis from year to year.

Aberdeen classifes organizations measuring in the top 20% as “Best-in-Class;” those in the middle were “Industry Average” and the bottom 30% were classified as “Laggard.”

Best-in-Class organizations generally start the BI embedding process as a result of clear business justifications; the business user drives the analytical process. Additionally, Best-in-Class organizations share data across their organizations (where security permits) to facilitate best practices and functional collaboration – clearly articulating performance measures to promote cultures of accountability.

Balancing the cost of building, purchasing/licensing or a combination of the two can be a challenge. Aberdeen indentifies ease-of-use, functionality, and linkage to existing systems as additional challenges to consider before embedding analytics.

Aberdeen makes the following recommendations based on the behaviors exhibited by Best-in-Class organizations:

  • Start with the software most widely used by the organization;
  • Ensure that the BI functionality integrates well with the software that will serve as the host;
  • Gather user requirements to ascertain and understand analytical needs; and
  • Develop polices for sharing data, ensuring compliance with the organization’s security and governance policy.

Next Steps:

  • Download our five-minute guide to business analytics to learn how user-driven “analytic” or “data discovery” technologies help business and technology users more quickly uncover insights and speed action.
  • Sign up to listen to our complimentary webcast on collaborative BI with Boris Evelson (@bevelson), Vice President and Principal Analyst at Forrester Research
  • Subscribe to our blog to stay abreast of insights and trends in collaborative BI.

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Taking a Dimension-Free View of Data

data exploration 150x150 Taking a Dimension Free View of DataOne of the biggest challenges facing businesses today is the intimidating nature of big data.

Many decision makers suffer from the so-called “paralysis by analysis” as they struggle to decide how best to sample and act on the petabytes of structured and unstructured customer data that’s available to them today.

Companies need to be able to cut through the chaff and enable decision makers to view key business and customer trends in near real time so they can act quickly and decisively.

Dimension-free data exploration enables end users to utilize contextual and social analytics capabilities without having to request assistance from their IT departments. This approach blends the strengths of visual data discovery with collaborative analytics for improved and more rapid decision making and borderless analytical capabilities.

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Collaborative Analytics and the Benefits of Local Language Support

local language support for collaborative analytics 300x243 Collaborative Analytics and the Benefits of Local Language SupportEnterprise companies rely on data scientists from a range of geographies and backgrounds. To help keep analytics teams on the same page, it’s useful for analytics team members to use the same analytics tools. This can and should include tools that offer local language support  for data scientists who speak Chinese, Russian, Japanese, etc.

The ability for companies to build and draw upon geographically-dispersed collaborative analytics teams has become essential to business success, if not survival. A Frost & Sullivan report reveals that collaboration is a cornerstone of business performance. For instance, 36% of a company’s business performance is tied to its “collaboration index,” according to the report. By comparison, this is more than two times the impact of a company’s strategic direction (16%) and more than five times the impact of market and technological turbulence influences (7%).

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Top BI Technology Trends in 2012

Top BI Technology Trends 2012 Top BI Technology Trends in 2012 According to Information Week’s 2012 Business Intelligence, Analytics, and Information Management Survey, if there’s one dominant trend in BI and information management, “it’s the meteoric rise of analytics, particularly advanced statistical and predictive analytics.” For the third consecutive year, “survey respondents rate advanced analytics as the most compelling among a dozen leading-edge technologies.”

The report speculates that the high-priority status of analytics is closely related to the rising interest in big data as a tool for mitigating risk, predicting customer behavior, and developing new product or service offerings. So it’s not surprising that “advanced data visualization capabilities” (e.g., sparklines and heat maps) and “embedded BI” closely followed “advanced analytics” in the survey ranking. After all – easy access and effective delivery are key factors for getting the most out of analytics.

Trends and Outliers follows these top topics regularly – so if you want to catch up quickly, get a recap, or spark some new ideas, try these recent posts:

Risk and Decision Making Put Predictive Analytics Front and Center

How Does Predictive Analytics Work?

Executive Analytics:  The Path to Success

The ABCs of Enterprise Analytics

How to Speak Like a Data Scientist

The Power of Data Visualization in Four Minutes

There’s also an informative webcast on predictive analytics with Spotfire. (And if you want to stay on top of these top topics, subscribe to our blog!)

Two other interesting trends from the Information Week report:

“This year’s survey shows that resistance is ebbing and IT professionals are giving cloud-based BI, analysis and information management serious consideration.”

A new category added to the Information Week survey this year made a solid entrance: “analysis of big data, particularly unstructured/nonrelational data” debuted in a number three slot, garnering the same level of respondent enthusiasm as “collaborative BI.”

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