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.
Last week I attended the excellent FutureStack conference. This was the first customer conference by New Relic, the Application Performance Management (APM) and Monitoring company. It was great to see how passionate their customers are and how they realize the strategic importance of monitoring. Well done New Relic! The keynotes and track sessions at this event were great because they did not just focus on technology but addressed the future skills and competencies required for today’s business technology professional.
A couple of weeks ago I wrote a post about the rising number of ‘computer glitch’ articles during 2013 and discussed that our approach to technology monitoring is not good enough for today’s digital economy. Equally I have also seen an increasing number of inquiries in relation to monitoring and management strategies as businesses start to realize the importance of business technology monitoring. This has been good to see but in order to achieve ROI from any monitoring or management solution investment you have to firstly understand the business importance of the IT or digital services that you provide before making any purchasing decisions. While working on Forrester’s TechRadar on Business Technology Monitoring it became evident that the monitoring solution market is evolving at quite a fast pace with a number of developments in infrastructure, application and end user monitoring resulting in new features and new solution approaches.
So if you are responsible for, or are involved in, your company’s technology monitoring or management strategy then here are the major, high level market developments that you need to be aware of:
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.
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