The majority of large organizations have either already shifted away from using BI as just another back-office process and toward competing on BI-enabled information or are in the process of doing so. Businesses can no longer compete just on the cost, margins, or quality of their products and services in an increasingly commoditized global economy. Two kinds of companies will ultimately be more successful, prosperous, and profitable: 1) those with richer, more accurate information about their customers and products than their competitors and 2) those that have the same quality of information as their competitors but get it sooner. Forrester's Forrsights Strategy Spotlight: Business Intelligence And Big Data, Q4 2012 (we are currently fielding a 2014 update, stay tuned for the results) survey showed that enterprises that invest more in BI have higher growth.
The software industry recognized this trend decades ago, resulting in a market swarming with startups that appeared and (very often) found success faster than large vendors could acquire them. The market is still jam-packed and includes multiple dynamics such as (see more details here):
All ERP and software stack vendors offer leading BI platforms
. . . but there's also plenty of room for independent BI vendors
Departmental desktop BI tools aimed at business users are scaling up
Enterprise BI platform vendors are going after self-service use cases.
Cloud offers options to organizations that would rather not deal with BI stack complexity.
Hadoop is breathing new life into open source BI.
The line between BI software and services is blurring
Rather than going with the usual, ubiquitous, and often (yawn) repetitive “top 10 BI predictions” for the next year, we thought we’d try something different. After all, didn’t the cult movie Highlander prove beyond the shadow of a doubt that “in the end there will be only one”? And didn’t the Lord Of The Rings saga convince us that we need one prediction “to rule them all”? The proposed top BI prediction for 2014 rests on the following indisputable facts:
Business and IT are not aligned. Business and IT stakeholders still have a huge BI disconnect (after all these years — what a shocker!). This is not surprising. Business users mostly care about their requirements, which are driven by their roles and responsibilities, daily tasks, internal processes, and dealings with customers (who have neither patience nor interest in enterprises’ internal rules, policies, and processes). These requirements often trump IT goals and objectives to manage risk and security and be frugal and budget minded by standardizing, consolidating, and rationalizing platforms. Alas, these goals and objective often take business and IT in different directions.
Requirements are often lost in translation. Business and IT speak different languages. Business speaks in terms of customer satisfaction, improved top and bottom lines, whereas IT speaks in metrics (on a good day), star schemas, facts, and dimensions. Another consideration is that it’s human nature to say what we think others want to hear (yes, we all want our yearly bonus) versus what we really mean. My father, a retired psychiatrist, always taught me to pay less attention to what people say and pay more attention to what people actually do — quite handy and wise fatherly advice that often helps navigate corporate politics.
I regularly hear CIOs and IT suppliers discussing the “four pillars” of cloud, social, mobile, and big data as if they’re an end in themselves, creating plenty of buzz around all four. But really, they’re just a means to an end: Cloud, social, mobile, and big data are the tools we use to reach the ultimate goal of providing a great customer experience. Most CIOs in Australia do understand that digital disruption and customer obsession are the factors that are changing their world, and that the only way to succeed is to embrace this change.
Over the past few years, IBM has certainly copped its fair share of criticism in the Asian media, particularly in Australia. Whether this criticism is deserved or not is beside the point. Perception is reality — and it’s led some companies and governments to exclude IBM from project bids and longer-term sourcing deals. On top of this, the firm’s recent earnings in Asia Pacific have disappointed.
But I’ve had the chance to spend some quality time with IBM at analyst events across Asia Pacific over the past 12 months, and it’s clear that the company does some things well — in fact, IBM is sometimes years ahead of the pack. For this reason, I advise clients that it would be detrimental to exclude IBM from a deal that may play to one of these strengths.
IBM’s value lies in the innovation and global best practices it can bring to deals; the capabilities coming out of IBM Labs and the resulting products, services, and capabilities continue to lead the industry. IBM is one of the few IT vendors whose R&D has struck the right balance between shorter-term business returns and longer-term big bets.
The end of a quarter forces me to reflect on what I learned in regards to my coverage area: measurement and attribution. From customer insights (CI) pros and marketers, I saw an increased interest in advancing their measurement approaches. On the attribution front, there is an appetite to learn about specific methodologies, use cases, ongoing attribution management strategies, and attribution applications to marketing/media buys. On the vendor side, I saw more advancement in tools, approaches, and offline and mobile data integration. I predict attribution — and general consumer and marketing measurement — will continue to be a hot topic for marketers and CI professionals well into 2014. Specifically, I expect to see more attribution adoption and usage of attribution to measure customer purchase paths and to learn more about customer behaviors and motivations.
In the meantime, let me recap the Q3 2013 measurement takeaways:
Business intelligence (BI) is an evergreen that simply refuses to give up and get commoditized. Even though very few vendors try to differentiate these days on commodity features like point and click, drag and drop, report grouping, ranking, and sorting filtering (for those that still do: Get with the program!), there are still plenty of innovative and differentiated features to master. We categorize these capabilities under the aegis of Forrester agile BI; they include:
Making BI more automated: suggestive BI, automatic information discovery, contextual BI, integrated and full BI life cycle, BI on BI.
Making BI more pervasive: embedding BI within applications and processes, within the information workplace, and collaborative, self-service, mobile, and cloud-based BI.
Making BI more unified: unifying structured data and unstructured content, batch and streaming BI, historical and predictive, and handling complex nonrelational data structures.
“Figuring out how to think about the problem.” That’s what Albert Einstein said when asked what single event was most helpful in developing the Theory of Relativity. Application integration is a problem. A big problem. Not to mention data, B2B, and other domains of integration. As an industry analyst and solution architect, what I’m most interested in first is how to think about the problem.
Pop Quiz: The Goal of Integration
Which of the following statements best articulates the goal of integration strategy?
The goal of integration is to keep data in sync across two or more siloed applications.
The goal of integration is to improve business outcomes by achieving consistent, coherent, effective business operations.
The correct answer is B. Was that too easy? Apparently not, because most of the integration strategies I see are framed as if the answer were A. Most, but not all — and it’s the ones framed around B that I’m most interested in. Here’s the difference:
A-style integration centers on technology. It begins with data and business logic fractured across application silos, and then asks, “How can integration technologies make it easier to live with this siloed mess?”
B-style integration centers on business design. It begins with a businessperson’s view of well-oiled business operations: streamlined processes, consistent transactions, unified tools for each user role, purpose-built views of data, and the like. It designs these first — that is, it centers on business design — and then asks, “How can integration technologies give us coherent business operations despite our application silos?”
Buy analytics software, hire marketing scientists, and engage analytics consultants. Now wait for the magic of customer analytics to happen. Right?
Wrong. Building a successful customer analytics capability involves careful orchestration of several capabilities and requires customer insights (CI) professionals to answer some key questions about their current state of customer analytics:
What is the level of importance given to customer analytics in your organization?
Have you clearly defined where you will use the output of customer analytics?
How is your analytics team structured and supported?
How do you manage and process your customer data?
Do you have clear line of sight between analytics efforts and business outcomes?
What is the process of sharing insights from analytics projects?
What type of technology do you need to produce, consume and activate analytics?
Too little data, too much data, inaccessible data, reports and dashboard that take too long to produce and often aren’t fit for purpose, analytics tools that can only be used by a handful of trained specialists – the list of complaints about business intelligence (BI) delivery is long, and IT is often seen as part of the problem. At the same time, BI has been a top implementation priority for organizations for a number of years now, as firms clearly recognize the value of data and analytics when it comes to improving decisions and outcomes.
So what can you do to make sure that your BI initiative doesn't end up on the scrap heap of failed projects? Seeking answers to this question isn't unique to BI projects — but there is an added sense of urgency in the BI context, given that BI-related endeavors are typically difficult to get off the ground, and there are horror stories aplenty of big-ticket BI investments that haven’t yielded the desired benefit.
In a recent research project, we set out to discover what sets apart successful BI projects from those that struggle. The best practices we identified may seem obvious, but they are what differentiates those whose BI projects fail to meet business needs (or fail altogether) from those whose projects are successful. Overall, it’s about finding the right balance between business and IT when it comes to responsibilities and tasks – neither party can go it alone. The six key best practices are:
· Put the business into business intelligence.
· Be agile, and aim to deliver self-service.
· Establish a solid foundation for your data as well your BI initiative.
Forrester is launching new research looking at how firms and companies can better use data and analytics. Please help us make this research better by taking our survey. We want to hear from you whether you use data extensively or not, and your responses will be extremely valuable. Plus you get a free Forrester report (not to mention the warm glow you'll get from helping out).
In addition, we appreciate any efforts to spread the word: Forward this to anyone who uses - or could use - data as part of their job.
On behalf of the Forrester team, thank you very much!