Initial business intelligence (BI) ployment efforts are often difficult to predict and may dwarf the investment you made in BI platform software. The effort and costs associated with professional services, whether you use internal staff or hire contractors, depend not only on the complexity of business requirements like metrics, measures, reports, dashboards, and alerts, but also on the number of data sources you are integrating, the complexity of your data integration processes, and logical and physical data modeling. At the very least Forrester recommends considering the following components and their complexity to estimate development, system integration and deployment effort:
We recently attended Amdocs' customer event in Singapore. Amdocs is gradually adjusting its strategy to reflect one of the most fundamental changes in the ICT industry today: Increasingly, business line managers, think the marketing or sales officer, are the ones influencing sourcing decisions. Traditional decision-makers, CTOs and CIOs, are no longer the sole ICT decision-makers. Amdocs is addressing this shift by:
Strengthening its customer experience portfolio.Successful telcos will try to regain lost relevance through improved customer experience. Marketing, portfolio product development, and sales are therefore growing in importance for telcos. Amdocs’ integrated customer experience offering, CES 9, provides telcos with a multichannel experience; proactive care; and self-service tools.
Betting big on big data/analytics.Amdocs is leveraging big data/analytics to provide real-time, predictive, and prescriptive insights to telcos about their customers’ behaviour. Communications-industry-specific converged charging and billing solutions as well as other catalogue solutions give Amdocs the opportunity to provide more value to telcos than some of the other players.
I attended Google’s annual atmosphere road show recently, an event aimed at presenting solutions for business customers. The main points I took away were:
Google’s “mosaic” approach to portfolio development offers tremendous potential. Google has comprehensive offerings covering communications and collaboration solutions (Gmail, Google Plus), contextualized services (Maps, Compute Engine), application development (App Engine), discovery and archiving (Search, Vault), and access tools to information and entertainment (Nexus range, Chromebook/Chromebox).
Google’s approach to innovation sets an industry benchmark. Google is going for 10x innovation, rather than the typical industry approach of pursuing 10% incremental improvements. Compared with its peers, this “moonshot” approach is unorthodox. However, moonshot innovation constitutes a cornerstone of Google’s competitive advantage. It requires Google’s team to think outside established norms. One part of its innovation drive encourages staff to spend 20% of their work time outside their day-to-day tasks. Google is a rare species of company in that it does not see failure if experiments don’t work out. Google cuts the losses, looks at the lessons learned — and employees move on to new projects.
The IT services industry is being challenged on two opposite fronts. At one end, IT organizations need efficient, reliable operations; at the other, business stakeholders increasingly demand new, innovative systems of engagement that enable better customer and partner interactions.
My colleagues Andy Bartels and Craig Le Clair recently published thought provoking reports on an emerging class of software — smart process apps — that enable systems of engagement. In his report, Craig explains that “Smart process apps will package enterprise social platforms, mobility, and dynamic case management (DCM) to serve goals of innovation, collaboration, and workforce productivity.” In other words, smart process apps play a critical role in filling gaping process holes between traditional systems of records and systems of engagement.
Where customer experience and analytics meet, in real time
For a while now, I’ve been using Hailo as a European poster child for innovation in the context of big data analytics. Due to the level of interest generated by this example, and the number of questions I’ve received along the way about Hailo, its technology and business model, etc., I decided to put together this blog post rather than write loads of separate emails.
Ironically, I’ve not actually been able to use Hailo myself (much as I would like to), as I have neither an iOS or Android-based smartphone. I have, however, met lots of people who’re using Hailo as customers, and I’ve also spoken to taxi drivers about it. I have yet to meet anybody who isn’t a fan.
For those of you who don’t know Hailo, it’s an app that allows you to hail a registered cab from your smartphone; as it was started in London, it’s often also called “the black cab app.” With the company founders being three London cabbies (black cab drivers), the entire service has been uniquely focused around the needs of the two main participants in a taxi ride: the customer and the driver.
Notes from the TechAmerica Europe seminar in Brussels, March 27, 2013
This may not be the most timely event write-up ever produced, but in light of all the discussions I’ve had on the same themes during the past few weeks, I thought I’d share my notes anyway.
The purpose of the event was to peel away some of the hype layers around the “big data” discussion, and — from a European perspective — take a look at the opportunities as well as challenges brought by the increasing amounts of data that is available, and the technologies that enable its exploitation. As was to be expected, an ever-present subtext was the potential of having laws and regulations put in place which — while well-intentioned — can ultimately stifle innovation and even act against consumer interests. And speaking of innovation: Another theme running through several of the discussions was the seeming lack of technology-driven innovation in Europe, in particular when considered in the context of an economic environment in dire need of every stimulus it can get.
The scene was set by John Boswell, senior VP, chief legal officer, and corporate secretary at SAS, who provided a neat summary of the technology developments (cheap storage, unprecedented access to compute power, pervasive connectivity) giving rise to countless opportunities related to the availability, sharing and exploitation of ever-increasing amounts of data. He also outlined the threats posed to companies, governments, and individuals by those who with more sinister intent when it comes to data exploitation, be it for ideological, financial, or political reasons. Clearly, those threats require mitigation, but John also made the point that “regulatory overlays” can also hinder progress, through limiting or even preventing altogether the free flow of data.
IBM has just announced that one of Australia’s “big four” banks, the ANZ, will adopt the IBM Watson technology in their wealth management division for customer service and engagement. Australia has always been an early adopter of new technologies but I’d also like to think that we’re a little smarter and savvier than your average geek back in high school in 1982.
IBM’s Watson announcement is significant, not necessarily because of the sophistication of the Watson technology, but because of IBM's ability to successfully market the Watson concept.
To take us all back a little, the term ‘cognitive computing’ emerged in response to the failings of what was once termed ‘artificial intelligence’. Though the underlying concepts have been around for 50 years or more, AI remains a niche and specialist market with limited applications and a significant trail of failed or aborted projects. That’s not to say that we haven’t seen some sophisticated algorithmic based systems evolve. There’s already a good portfolio of large scale, deep analytic systems developed in the areas of fraud, risk, forensics, medicine, physics and more.
BI professionals spend a significant portion of their time trying to instill the discipline of datadriven performance management into their business partners. However, isn’t there something wrong with teaching someone else to fly when you’re still learning to walk? Few BI pros have a way to measure their BI performance quantitatively (46% do not measure BI performance efficiencies and 55% do not measure effectiveness). Everyone collects statistics on the database and BI application server performance, and many conduct periodic surveys to gauge business users’ level of satisfaction. But how do you really know if you have a high-performing, widely used, popular BI environment? For example, you should know BI performance
Efficiency metrics such as number of times a report is used or a number of duplicate/similar reports, etc
Effectiveness metrics such as average number of clicks to find a report and clicks within a report to find an answer to a question and many others
Metric attributes/dimensions such as users, roles, departments, LOBs, regions and others
The deluge of customer data shows no signs of abating. The perpetually-connected customer leaves data footprints in every interaction with a brand. This presents tremendous opportunities for customer insights professionals and analytics practitioners tasked with analyzing this data, to not only get smarter about customers but ensure that the insights get appropriately used at the point of customer interaction.
When we asked customer analytics users about the challenges and drivers of customer analytics adoption, we found that data integration and data quality continue to inhibit better adoption of customer analytics while users still want to use analytics to improve the data-driven focus of the organization and drive satisfaction and customer retention.
Forrester’s Customer Analytics Playbook guides customer insights professionals, marketing scientists and customer analytics practitioners into this new reality of customer data and helps discover analytics opportunities, plan for greater sophistication, take steps towards building a customer analytics capability and continually monitor progress of analytics initiatives. It will include 12 chapters (and an executive overview) that cover different aspects of customer analytics.
Whether you are just starting on your BI journey or are continuing to improve on past successes, a shortage of skilled and experienced BI resources is going to be one of your top challenges. You are definitely not alone in this quest. Here are some scary statistics:
“By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills, as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.” (Source: May 2012 McKinsey Global Institute report on Big Data)
“… trigger a talent shortage, with up to 190,000 skilled professionals needed to cope with demand in the US alone over the next five years.” (Source: 2012 Deloitte report on technology trends)
“Fewer than 25% of the survey respondents worldwide said they have the skills and resources to analyze unstructured data, such as text, voice, and sensor data.” (Source: 2012 research report by IBM and the Saïd Business School at the University of Oxford)