Evaluating BI Services In Asia Pacific

Michael Barnes

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.

In response, Fred Giron and I have just published The Forrester Wave™: Business Intelligence Service Providers In Asia Pacific, Q4 2013. In it, we identified eight companies that offer strong capabilities and services for AP-based organizations seeking BI service support.

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.
Read more

SAP Takes Another Step Towards Agile BI With KXEN Acquisition

Boris Evelson

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.
Read more

Will Privacy Concerns Stop Or Stunt The Power Of Predictive Analytics

Mike Gualtieri

The power of predictive analytics in the age of Big Data is super-cool, but will privacy concerns stop or stunt it's adoption? Watch this episode of Forrester TechnoPolitics with Eric Siegel, author of Predictive Analytics: The Power to Predict Who Will Click, Lie, Buy, or Die to find out. 

About Forrester TechnoPolitics

Read more

Maximize Your Chances Of Business Intelligence Success

Martha Bennett

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.

Read more

Get ready for BI change

Boris Evelson

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:
  • People acquisitions
  • Technology acquisitions 
  • Business process changes 
  • New technology implementations 
  • Organizational transformations
  • Leadership changes
  • Changes to business process outsourcing or IT sourcing 
Read more

How to estimate cost of BI deployment

Boris Evelson

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:

  • Top down business requirements such number of 
    • Goals and objectives
    • Metrics, Measures
    • Attributes and dimensions
Read more

How The Obama Campaign Used Predictive Analytics To Influence Voters

Mike Gualtieri

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)


About Forrester TechnoPolitics

Read more

Q&A With Greg Swimer, VP IT, Business Intelligence, Unilever

Kyle McNabb

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 employeesGreg 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?

Read more

Are You a Data Hoarder? We’re Betting So.

Fatemeh Khatibloo

As an analyst on Forrester's Customer Insight's team, I spend a lot of time counseling clients on best-practice customer data usage strategies. And if there's one thing I've learned, it's that there is no such thing as a 360-degree view of the customer.

Here's the cold, hard truth: you can't possibly expect to know your customer, no matter how much data you have, if all of that data 1) is about her transactions with YOU and you 2) is hoarded away from your partners. And this isn't just about customer data either -- it's about product data, operational data, and even cultural-environmental data. As our customers become more sophisticated and collaborative with each other ("perpetually connected"), so organizations must do the same. That means sharing data, creating collaborative insight, and becoming willing participants in open data marketplaces. 

Now, why should you care? Isn't it kind of risky to share your hard-won data? And isn't the data you have enough to delight your customers today? Sure, it might be. But I'd put money on the fact that it won't be for long, because digital disruptors are out there shaking up the foundations of insight and analytics, customer experience, and process improvement in big ways. Let me give you a couple of examples:

Read more

BI on BI Or How BI Pros Must Eat Their Own Dog Food

Boris Evelson


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
Read more