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.
How is it possible for a local company to defeat global giants like Pepsi, Coca-Cola, and Watsons in your market segment and establish market leadership for more than a decade? The answer is given by Nongfu Spring, a Chinese company in manufacturing and retail industries. In my recent report “Case Study: Technology Innovation Enables Nongfu Spring To Strengthen Market Leadership”, I analyzed the key factors behind their success, and provide related best practice from enterprise architecture perspective. These factors include
Business strategy is enterprise architecture's top priority. EA pros often need to be involved in project-level IT activities to resolve issues and help IT teams put out fires. But it's much more important that architects have a vision, clearly understand the business strategy, and thoroughly consider the appropriate road map that will support it in order to be able to address the root causes of challenges.
Agile infrastructure sets up the foundation for scalable business growth. Infrastructure scalability is the basis of business scalability. Infrastructure experts should consider not only the agility that virtualization and IaaS solutions will provide next-generation infrastructure, but also network-level load balancing among multiple telecom carriers. They should also refine the network topology for enterprise security.
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:
Rowan Curran, Research Associate and TechnoPolitics producer, hosts this episode to ask me (your regular host) about The Pragmatic Definition Of Big Data. Listen (5 mins) to hear the genesis of this new definition of big data and why it is pragmatic and actionable for both business and IT professionals.
Podcast: The Pragmatic Definition Of Big Data Explained (5 mins)
As John Brand and I recently wrote, business intelligence (BI) adoption drivers, technology understanding, and organizational process maturity continue to vary widely across Asia Pacific (AP). But there is one constant in this market: the regularity with which BI appears at or near the top of CIOs’ priority lists.
While the gap between global best practices and regional implementations is closing, social, cultural, economic, and underlying technology trends will continue to affect BI adoption in the region for the foreseeable future:
Social. The adoption of social computing is expanding rapidly across all AP markets, but is particularly strong in growth markets like China, Indonesia, and the Philippines. As in North America and Western Europe, this adoption is already having profound effects on how organizations identify, understand, and engage with customers and other market influencers. But the lack of significant BI investments means that organizations in these growth markets are far more likely to consider issues like sentiment analysis, predictive analytics, and near real-time data access when sourcing initial BI projects.
In a recent media interview I was asked about whether the requirements for data visualization had changed. The questions were focused around whether users are still satisfied with dashboards, graphs and charts or do they have new needs, demands and expectations.
Arguably, Ancient Egyptian hieroglyphics were probably the first real "commercial" examples of data visualization (though many people before the Egyptians also used the same approach — but more often as a general communications tool). Since then, visualization of data has certainly always been both a popular and important topic. For example, Florence Nightingale changed the course of healthcare with a single compelling polar area chart on the causes of death during the Crimean War.
In looking at this question of how and why data visualization might be changing, I identified at least 5 major triggers. Namely:
Increasing volumes of data. It's no surprise that we now have to process much larger volumes of data. But this also impacts the ways we need to represent it. The volume of data stimulates new forms of visualization tools. While not all of these tools are new (strictly speaking), they have at least begun to find a much broader audience as we find the need to communicate much more information much more rapidly. Time walling and infographics are just two approaches that are not necessarily all that new but they have attracted much greater usage as a direct result of the increasing volume of data.
Cloud Services Offer New Opportunities For Big Data Solutions
What’s better than writing about one hot topic? Well, writing about two hot topics in one blog post — and here you go:
The State Of BI In The Cloud
Over the past few years, BI business intelligence (BI) was the overlooked stepchild of cloud solutions and market adoption. Sure, some BI software-as-a-service (SaaS) vendors have been pretty successful in this space, but it was success in a niche compared with the four main SaaS applications: customer relationship management (CRM), collaboration, human capital management (HCM), and eProcurement. While those four applications each reached cloud adoption of 25% and more in North America and Western Europe, BI was leading the field of second-tier SaaS solutions used by 17% of all companies in our Forrester Software Survey, Q4 2011. Considering that the main challenges of cloud computing are data security and integration efforts (yes, the story of simply swiping your credit card to get a full operational cloud solution in place is a fairy tale), 17% cloud adoption is actually not bad at all; BI is all about data integration, data analysis, and security. With BI there is of course the flexibility to choose which data a company considers to run in a cloud deployment and what data sources to integrate — a choice that is very limited when implementing, e.g., a CRM or eProcurement cloud solution.
“38% of all companies are planning a BI SaaS project before the end of 2013.”
Earlier this week Dell joined arch-competitor HP in endorsing ARM as a potential platform for scale-out workloads by announcing “Copper,” an ARM-based version of its PowerEdge-C dense server product line. Dell’s announcement and positioning, while a little less high-profile than HP’s February announcement, is intended to serve the same purpose — to enable an ARM ecosystem by providing a platform for exploring ARM workloads and to gain a visible presence in the event that it begins to take off.
Dell’s platform is based on a four-core Marvell ARM V7 SOC implementation, which it claims is somewhat higher performance than the Calxeda part, although drawing more power, at 15W per node (including RAM and local disk). The server uses the PowerEdge-C form factor of 12 vertically mounted server modules in a 3U enclosure, each with four server nodes on them for a total of 48 servers/192 cores in a 3U enclosure. In a departure from other PowerEdge-C products, the Copper server has integrated L2 network connectivity spanning all servers, so that the unit will be able to serve as a low-cost test bed for clustered applications without external switches.
Dell is offering this server to selected customers, not as a GA product, along with open source versions of the LAMP stack, Crowbar, and Hadoop. Currently Cannonical is supplying Ubuntu for ARM servers, and Dell is actively working with other partners. Dell expects to see OpenStack available for demos in May, and there is an active Fedora project underway as well.