Business decision-makers in Asia Pacific (AP) are increasingly aware of the importance of business intelligence (BI) and broader analytics to business strategy and execution. However, lack of internal expertise remains a significant barrier to BI project success.
To succeed in the region, BI service providers must provide guidance on how to translate data access into actual insight and information into business value. This requires a strong understanding of local cultures, business practices, regulatory frameworks, and market dynamics. When evaluating providers, understand how their capabilities are likely to evolve across five categories:
People. To minimize project risks, understand who will be the on-site business and technical leads on BI projects and how many successful implementations this staff has led in a similar industry and similar technical environment within the region.
Technical expertise. Service providers need to demonstrate region-specific knowledge of the technical characteristics of various BI tools, platforms, architectures, and applications. Most companies will not have all of the necessary skills on site, so closely evaluate ease of access to remote staff from the service provider as well.
There’s no doubt that, to consumer marketing professionals, data about the users of mobile network are highly valuable. But AT&T is finding that enterprise application designers, corporate security & risk professionals, corporate trainers and CFOs are very interested in this data as well - so much so that the US-based network operator is turning access to and collaboration on its data into a new business service.
Under the guidance of Laura Merling, VP of Ecosystem Development & Platform Services (and formerly of Mashery), AT&T Business Solutions is embarking on an ambitious plan for sharing its data in a secure programmatic fashion leveraging RESTful APIs. It had previously shared it data in a more informal fashion with selected partners and customers but found this approach difficult to standardize and repeat on a larger scale. It also has participated in data collaboration efforts such as the well-known hackathon with American Airlines at South by Southwest earlier this year.
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.
There is a reason the phrase, “beauty is in the eye of the beholder,” has held significance and power in our society for so many generations. And in that phrase is a lesson for all of us about business analysis. The power of different points of view examining a given set of inputs is key to truly understanding what lies before us and seeing the new possibilities and different threats looming.
Sit silently in a museum listening to the patrons take in just a single painting and within a day you will hear a hundred different insights, many of which you didn’t see before. Insights that show you things in that artwork you never would have seen, such as the way greens and reds are mixed to create hues that don’t invoke their origins, the style of brushstrokes used that convey depth and how a pattern viewed up close can be very different than the whole. So much insight doesn’t stem from the painting but from the varied experiences, backgrounds, cultures and histories the observers bring with them. The same is true in data analysis. It’s through different points of view that something can be fully analyzed. Each person brings their varied experiences (their data) to the analysis.
As businesses we tend not to sit silently and take in what others see about ourselves and our data. We tend not to expose our data at all to our partners, trusted third parties or potential collaborators (like our customers) and by not doing so, they cannot combine their data with ours and uncover things we cannot see. As a result, we cannot see the broader picture. And this leads to bad business decisions based on a myopic point of view.
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.
Market conditions are changing quickly; firms need to make the best possible business decisions at the right time and base them on timely, accurate, and relevant information from business intelligence (BI) solutions. The repercussions of not handling BI change well are especially painful and may include lost revenue, lower staff morale and productivity, continued proliferation of shadow IT BI applications, and unwanted employee departures. Ineffective change management often lies in the process of preparing the people affected by change rather than in planning the technology implementation. Firms that fail to prepare employees for enterprise BI change early enough or well enough will be left behind. They need to implement a multifaceted series of activities ranging from management communication about why change is needed to in-depth, role-appropriate employee training.
Why change management is so critical? Most strategic business events, like mergers, are high-risk initiatives involving major changes over two or more years; others, such as restructuring, must be implemented in six months. In the case of BI, some changes might need to happen within a few weeks or even days. All changes will lead to either achieving or failing to achieve a business result. There are seven major categories of business and organizational change:
Business process changes
New technology implementations
Changes to business process outsourcing or IT sourcing
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:
The Obama 2012 campaign famously used big data predictive analytics to influence individual voters. They hired more than 50 analytics experts, including data scientists, to predict which voters will be positively persuaded by political campaign contact such as a call, door knock, flyer, or TV ad. Uplift modeling (aka persuasion modeling) is one of the hottest forms of predictive analytics, for obvious reasons — most organizations wish to persuade people to to do something such as buy! In this special episode of Forrester TechnoPolitics, Mike interviews Eric Siegel, Ph.D., author of Predictive Analytics, to find out: 1) What exactly is uplift modeling? and 2) How did the Obama 2012 campaign use it to persuade voters? (< 4 minutes)
In advance of next week’s Forrester’s European Business Technology Forums in London on June 10 and 11, we had an opportunity to speak with Greg Swimer about information management and how Unilever delivers real-time data to its employees. Greg Swimer is a global IT leader at Unilever, responsible for delivering new information management, business intelligence, reporting, consolidation, analytics, and master data solutions to more than 20,000 users across all of Unilever’s businesses globally.
1) What are the two forces you and the Unilever team are balancing with your “Data At Your Fingertips” vision?
Putting the data at Unilever’s fingertips means working on two complementary aspects of information management. One aspect is to build an analytics powerhouse with the capacity to handle big data, providing users with the technological power to analyse that data in order to gain greater insight and drive better decision-making. The other aspect is the importance of simplifying and standardizing that data so that it’s accessible enough to understand and act upon. We want to create a simplified landscape, one that allows better decisions, in real time, where there is a common language and a great experience for users.
2) What keys to success have you uncovered in your efforts?