you get phrases like, “You should meet this guy with the SIO. He’s sort of this kind of social entrepreneur thinking outside of the box in the sustainability space and working on these ideas around sort of web-based social media, and he’s in a round two capital raise in the VP space with the people at SVNP.” This would all be funny if it weren’t true. People just don’t make sense anymore.

A similar story about LinkedIn’s most overused resume words of the year also reminded us of the often careless and sometimes abusive relationships businesspeople seem to have with the English language.

Synergy, paradigm, silo, value-add, net-net, win-win, leverage, most unique, align – these various clichés have all driven me crazy at some point over the years. But, like a lot of people in technology, I’m probably guilty of overusing “solution,” “stack,” “partner,” and a few others. Within BI, “big data” looks to be shooting across the buzzword hype cycle with record speed. When these words are thrown around so loosely, they lose their essential meaning. For example, we’ve tried to break down the meaning of big data.

All of this reminds me that “semantic confusion” in BI remains a serious problem and appears to be getting worse. Consider the great BI (business intelligence) vs. BA (business analytics) debate. The conventional wisdom seems to be that BI is backward-looking, a technology or process that helps organizations understand what happened, while BA is forward-looking and predictive.

Maybe that’s why IBM has positioned its Watson play around analytics. In our view, however, the fundamental difference between BI and BA is basically how the data is processed and presented to the user – what I would characterize as features of the tool or platform.

A greater challenge is the confusion that results when organizations have multiple definitions of key terms like “customers” or “sales.” This is a huge problem at both ends of the BI spectrum – for developers trying to build easy-to-use applications and for analysts and executives trying to make decisions. And it’s a particular problem at large enterprises with complex organizational charts and fragmented IT systems (dare I say siloed?).

Defining core metrics consistently across the organization as part of a common business language is the keystone of effective BI. As we point out here:

Standardizing your business language also enables transparency, accountability, and concurrence. And the process of establishing a common business language helps promote best practices in collaboration and fact‐based decision making.

For workers in any field to collaborate productively, they must be able to communicate clearly. In BI, the need for collaboration between users and BI teams is especially great at the moment, which means there is a premium on communicating clearly. Developers and business users must simply learn to speak each other’s language if they are to bridge the BI Delivery Gap and if investments in data warehousing and BI are to deliver optimal returns. We think continuous engagement is the right model for developers to think about growing and strengthening their relationship with BI users, who, like or not, truly are calling the shots these days.

So that’s why we say let’s lose the lingo, jettison the jargon, and excise the acronyms. Clearer communication can lead to better BI.

Aberdeen’s analysis is based on previous research into development methodologies. The findings showed that 103 out of 237 respondents said their organizations are still using traditional methodologies exclusively. No wonder, in the words of the report’s author, that:

Organizations are struggling to get the right information to the right people at the right time.

The cause is largely due to:

Vast inflow of fresh data and increasingly demanding community of business managers.

So the high-level diagnosis seems on track. But the issues and problems multiply as “decision-making windows” shrink – a phenomenon experienced by 64% of business managers surveyed. Of course, we all know this intuitively; quicker business cycles, intense cost and competitive pressures, and fast-moving, technology-fueled markets mean we need to make decisions faster, which means BI teams must deliver more data faster to support those decisions.

And there is plenty more data go around; Aberdeen’s research says IT has seen 40% increases in data volumes, which bogs down development.

The speed with which “best-in-class” (and the speediest) BI organizations can move really caught our eye. According to Aberdeen, they can “add a column to report in an average of 4.3 hours and create entirely new dashboards in 3.8 days.” There is no way to deliver BI this fast without using Agile BI methodologies to ensure strong interaction and excellent communication between developers and business users. Faster development cycles are just a reality of today’s business.

Further, Aberdeen highlights visual or interactive BI tools as critical to keeping up with the increasing time pressures. How? By enabling greater self-service and more interactive or “drill-down” capabilities.

Self-service BI encapsulates the concept that business managers will be fully empowered to analyze data and discover new business insights without direct help from corporate IT.

This sounds like business-driven BI at its best. We often describe this to our clients as the ability to “ask questions of the data,” a functionality that is built directly into the core of Balanced Insight Consensus.

The bottom line is that IT and the business must collaborate more effectively and frequently if they are to prosper mutually in our fast new world of tighter decision-making cycles. And IT is no longer the gatekeeper, a point we made here. Or, as the report’s author says:

IT must move to a more supportive role – providing analytical tools, education and support, and a rich set of high-quality data – to enable business users to be more self-sufficient … [this] approach to business intelligence takes responsibility for the discovery of relationships in the data away from IT organizations and places it where it can be used to best advantage – with the managers who truly understand the business.

We certainly share the view of Boris Evelson of Forester, who expects organizations will “learn to live with multiple BI tools” and further movement of BI “into the hands of end users.” We’re all for increasing the user-centricity and business focus of BI – as we point out here and here.

Not surprisingly, there are lots of big claims about tablets, mobility and, of course, the rise of big data for 2012. For example, IDC’s crystal ball says,

Big Data will earn its place as the next “must have” competency in 2012 as the volume of digital content grows to 2.7 zettabytes (ZB), up 48% from 2011.

We broke down what big data means to BI in a few recent posts.

As we simultaneously look back on 2011 and ahead to 2012, we think our resolutions for BI from last year could use reinforcing. They were:

1. Strengthen the delivery of BI essentials.
2. Use rapid prototyping to shrink the report backlog.
3. Engage with business leaders to become the go-to team for answers.

We saw some successes in these areas last year, but plenty room for further improvement remains for 2012. The bottom line is that BI teams will mitigate many common pitfalls and threats to BI success if they focus on these relatively simple steps.

But, since the holidays are a time for optimism, we’ll add a few more of our favorite ideas for how BI teams can rethink BI delivery:

• Take advantage of “continuous engagement” – which is the highly collaborative and iterative process by which BI development teams and end-users (usually business analysts, managers and executives) should work together to develop and refine useful business intelligence apps.
• Get more from your BI budget. In our experience, companies can find real savings – up to 40% – on their BI project expenses, even as they build more quality into their processes and toolsets. For more details on this perennially hot topic, you can check out our recent Webinar here and article here.
• Focus on shrinking the two big gaps in BI. The first is the delivery gap, which occurs when development teams can’t deliver tools fast enough to keep up with ever-shifting business requirements. The second is the “insight gap,” which occurs when companies lack the processes and expertise to understand the data they’re collecting.

So, an early happy new year to all BI pros out there! Here’s hoping your 2012 is filled with satisfied end users, high adoption rates and projects that come in on time and under budget.

The findings were especially interesting because a good balance of IT pros and business stakeholders were surveyed. (We at Balanced Insight admit to user bias when it comes to BI; after all they own BI these days.) The report contains many surprising points, raises a number of critical issues, and outlines the current state of “big data.” Here’s what caught our eye:

Where Big Data Lives & How It’s Processed: TDWI clarifies how big data differs from standard business intelligence and data warehousing practices, in that

big data analytics explores granular details of business operations and customer interactions that seldom find their way into a data warehouse or standard report

Big data is often not “well-understood, auditable, and squeaky clean.” The report also made clear that leading-edge platforms are capable of processing huge amounts of data in record times (translation: it’s not about the technology). Organizationally, the BI/DW teams are the most common owner of “big data,” according to 41% of survey respondents.

What to Call Analysis of Big Data: “Advanced analytics” may not be the optimal name for what analysts will do with big data, according to survey respondents, who thought “exploratory analytics” or “discovery analytics” were more accurate. I like the emphasis here on interaction with the data, with users able to “explore” and “discover.” This will be key to success with big data analytics (or whatever it ends up being called).

In other words, with big data analytics, the user is typically a business analyst who is trying to discover new business facts that no one in the enterprise knew before.

To our ears, this sounds suspiciously like effective business intelligence, though perhaps “BI on steroids” would be a fitting term provided users could actually ask questions of the data. I couldn’t help but chuckle at the survey respondent who wanted to name this new type of analysis “we-need-to-buy-more-hardware analytics.”

Impact on BI & Challenges to Adoption: Significant percentages of survey respondents believed the impact of big data on BI would be mostly beneficial, thanks to:

more numerous and accurate business insights (45%), an understanding of business change (30%), better planning and forecasting (29%), and the identification of root causes of cost (29%)

All that sounds good, but compare these benefits to the barriers to adoption cited by survey respondents. Resource scarcity is the top concern, highlighted by 46%. The “skill set is not quite the same as that for business intelligence and data warehousing,” the report warns. “Lack of business sponsorship” was the third most frequently mentioned barrier at 38%, while “cannot make big data usable for end users” was cited by 22% of respondents.

It’s not hard to see how the last issue could become a much greater challenge as big data grows more prevalent. And while big data has additional technical challenges in this regard, I want to point out that few organizations have been successful making normal volumes of data usable for end users.

It seems that big data has a lot in common with BI and that much future overlap is likely. The business benefits are similarly compelling, and similar challenges present similar difficulties. Process and organizational factors (perhaps including development methodologies) will have a lot to say about ultimate big data success, just as they do with BI. It will be interesting to watch the evolution.

The bottom line is that big data is here, it’s real and it’s not going anywhere – even though the industry is still struggling to define it precisely or calculate its impacts. In the meantime, we’re thankful that TDWI has contributed useful research that will help the BI industry begin to sort the potential innovations and advancements from the considerable hype.

Titled “Big Data: the Next Frontier for Innovation, Competition and Productivity,” the report was full of fun facts (30 billion pieces of content are shared on Facebook every month and all the world’s music could be stored on a $600 disk drive) and bold claims. For instance,

The use of big data will underpin new waves of productivity growth and consumer surplus. For example, we estimate that a retailer using big data to the full has the potential to increase its operating margin by more than 60 percent.

Elsewhere, the authors described annual savings of $300 billion in the U.S. healthcare market from the effective usage of big data.

Those eye-popping numbers raised a few skeptical eyebrows (mine included). Sure, retailers have come a long way in using data to make targeted offers to consumers, but even advanced users – like Amazon – still have fairly primitive recommendation engines. Obviously, there is great potential value from huge data, but harvesting it will require the same tools, skills and processes most companies are still searching for in order to use plain ol’ business intelligence “to the full.”

Further, the authors never quite specify what “using big data to the full” really means. In our view, it would entail enabling business analysts and users with easy-to-use tools that allow them to interact with core data, creatively and interactively. That means posing lots of “what if” questions and probing even deeper based on the initial answers the data provides. Sounds like highly effective BI to us.

Gartner’s take is that the volume and storage issues are a red herring, because:

While big data is a significant issue, … the more important one is making sense of big data and finding patterns in it that help organizations make better business decisions.

In other words, the high hurdles of using big data “to the full” are the same high hurdles of using normal volumes of data “to the full.” So, if these big dreams about 60% operating margin increases from big data are to come true, effective BI principles are going to be a big part of the story.

If you list the top five reasons that companies have not used BI “to the full,” nowhere in that list will you find “too much data to process.” That problem only exists with unstructured data (e.g., when the web is a data source). So how does the advent of parallel data processing solve the problems that BI has been struggling with for years? A very good argument can be made that big data is about to make some of BI’s core challenges worse, since parallel data processing requires a far lower-level programming language to work with as opposed to BI.

Maybe McKinsey’s next report will be about “agile data.” In the meantime, we expect we’ll be breaking down the idea and implications of big data in future posts.

continuous processes of applying quality control — small pieces of effort, applied frequently. Continuous integration aims to improve the quality of software, and to reduce the time taken to deliver it, by replacing the traditional practice of applying quality control after completing all development.

As powerful as the notion of continuous integration is (especially from a technical standpoint), we think continuous engagement may be even more so.

What is continuous engagement and why do we think it matters so much to overall BI success?

In our view, continuous engagement is the process by which BI development teams and end-users (usually business analysts, managers and executives) should work together to develop and refine useful business intelligence apps.

In the past, BI developers often viewed this engagement as an initial first step, after which the project becomes a wholly technical project. At best, they met with users a few times during an entire development cycle – once during initial requirements definition, later during prototype reviews. Getting additional time from busy business stakeholders was difficult, because requirements document reviews and similar meetings are unproductive and painful. The problem is compounded by the fact that business users and developers don’t speak the same language. The end result was costly changes late in the game, high user frustration with needed adjustments pushed to the illusory phase N+1, and low user adoption of the apps that were developed.

Continuous engagement – by which rapid prototyping enables business user feedback early and often – also helps overcome the BI delivery gap. This common problem occurs when requirements change in the period of time between the first meeting about a BI project and the delivery of the initial prototypes or working apps.

As quality is to continuous integration, so user adoption is to continuous engagement. In other words, user adoption is a goal to be addressed and facilitated throughout development – not a separate step that maybe happens after development is complete, with a little training and some quickie documentation. Our view is that user adoption is not separate from development.

Continuous engagement simply reflects the idea that BI in the 21^st century is a lot more about the users – and less about the BI platforms or IT, which has taken a back seat to users.

Of course, having the development approach, right mindset in your BI teams and the right toolset are all critical to enabling continuous engagement. We see Agile BI as the right approach and believe Balanced Insight Consensus is uniquely designed to enable such steady collaboration and frequent communication.

Check out the video here and supporting article here.

Consumer empowerment and the “Apple-ization” of IT have made this traditional view unsustainable. Usability has become too important. We’ve shared our thoughts before on the “why” and “how” and of usability. Basically, our point of view is this: forget fancy graphics, beautiful colors and slick interfaces; for developers and BI teams, usability is a strategy to ensure BI apps are adopted across the organization. It also helps eliminate rework. BI apps with major usability issues are difficult to fix after the fact – like trying to build a ranch house on top of a foundation designed for a two-story Colonial.

BI teams can no longer afford to think about “usability” as something separate from development, or as a follow-on “nice to have” activity. Rather, usability issues should be addressed as an intrinsic part of the development process and focused on by all members of the team. Why? Because, today, BI belongs to users. The goal of BI is not simply to store data on a disk (a relatively common view in the industry), but rather to answer business questions, which are asked by users.

That’s why user engagement should happen early and often in development cycles. That’s why prototypes based on existing platforms and built with real data should be shared with users as soon as possible in development cycles. That why documentation should be generated in automated fashion as new functionality is added. This is how many of the barriers to user adoption can be “designed away” during development.

While user engagement is the enabler in terms of development methodologies, the right development and productivity tools should not be overlooked. We designed Balanced Insight Consensus with usability in mind – that is, it helps overcome communication gaps and semantic differences during the BI development process that so often result in poor user adoption. That’s how it gives development teams a huge head start on usability, provided they engage with users.

The good news is that more BI pros recognize the “usability imperative.” We are asked more often about how to ensure users adopt the BI solutions under development. It’s the right question to ask, and we’re hopeful that more BI delivery teams will begin asking it earlier in their project planning and development cycles.

Melinda Sue Gordon/Columbia Pictures

Now that the hype around the latest Brad Pitt movie has died down a bit, we wanted to revisit our previous post, which outlined what BI teams can learn from the Moneyball approach to baseball management – like the importance of clear semantics, prioritization and consistency in metrics. We are impressed by the movie’s solid box office and mostly positive reviews. It shows how “the data guys have won,” as this story put it, and what happens when executives have the skills, knowledge and freedom to make “creative use of data.”

Among baseball pundits, the movie has generated considerable debate about what led to the success of the Oakland A’s in the mid-2000s. A strong case can be made that it wasn’t so much about superior player evaluation and asking the right questions of data, but rather just about having the right personnel – specifically a few great young pitchers. Or maybe it was just that the A’s innovated by going against standard industry practice; they decided to zig when everyone else was still zagging.

This is not the right debate to have. And it’s similar to the fallacy that BI success is “all about the technology.”  In our view, the Moneyball story is how baseball evolved from running mainly on the “gut feel” of scouts to a more data-driven approach. In the old days, baseball executives were using some data, but mostly it was their intuition and what conventional wisdom said.

The A’s seized competitive advantage with a philosophy that dared to insist on statistical proof and math over intuition. Intuition and conventional wisdom says you need a power hitter, who will hit X home runs. The data says it would be more beneficial to have someone who can get on base Y percent of the time.

There are companies out there who have gone through similar evolution. They relied on visionary leaders to make the tough call or focused only on a few metrics, until later they realized there were far more important metrics that better aligned to company performance and long-term objectives. Such companies have a high threshold for gut-feel arguments winning out over good data.

This is not to say that data is all you need to find the right answers. You still need smart people to ask the right questions of the data. Of course skilled resources (whether it’s business analysts, BI developers or starting pitchers) will help any organization meet its goals (whether it’s identifying new sales opportunities or wining baseball games). It’s not a question of either data or talent but rather a demonstration of the value of having both.

That’s one reason we stress engagement between BI teams and developers and business stakeholders – it brings the talent together. When the folks who know how to build applications designed to find answers can connect and collaborate with the folks who know the most important questions to ask, very good things can happen.

1. Build it right the first time.
2. Make business users the owners of BI apps.
3. Start with core requirements and re-use components.
4. Automate everything.
5. Use what you have and don’t move sideways.

While some of these tips may seem self-evident, it’s important to remember that not following them often leads to most of the common BI delivery issues and challenges. To find out how to apply the tips, check out the full article here.

And don’t miss our BI Knowledge Exchange Webinar on this topic November 3rd.