On October 14, I attended Big Data & Business Insights 2014 in Bangkok — the first public big data event in Thailand. I spoke about how to use big data to increase customer value in the age of the customer — a topic that seemed a bit distant from the audience’s daily reality. Most of them use traditional data warehouse and business intelligence tools and are new to big data solutions like Hadoop platforms, big data visualization, and predictive solutions. Here’s what I came away with:
Big data is still new to Thai businesses. Most big data projects in Thailand are still at the testing stages, and these trials are taking place in university labs rather than commercial environments. Dr. Putchong Uthayopas of the Department of Computer Engineering at Kasetsart University noted that big data projects in Thailand are now moving from pilot projects to actual usage.
Organizations need more details of real big data solutions. Thai businesses have held off investing in big data solutions because they felt uncertainty about the outcomes of big data projects. Attendees showed a lot of interest when I talked about big data usage in traditional industries, such as John Deere’s “Farm Forward” use case, which helped farmers make better decisions on what, when, and how to plant.
Digitally empowered customers — both businesses and consumers — wield a huge influence on enterprise strategies, policies, and customer-facing and internal processes. With mobile devices, the Internet, and all-but-unlimited access to information about products, services, prices, and deals, customers are now well informed about companies and their products, and are able to quickly find alternatives and use peer pressure to drive change. But not all organizations have readily embraced this new paradigm shift, desperately clinging to rigid policies and inflexible business processes. A common thread running through the profile of most of the companies that are not succeeding in this new day and age is an inability to manage change successfully. Business agility — reacting to fast-changing business needs — is what enables businesses to thrive amid ever-accelerating market changes and dynamics.
Chinese people are hypersocial in their lifestyle and daily work, and Forrester forecasts that 681 million of them will be using social media by 2019. Online Chinese are actively engaging with brands and companies on social media: 29 brands or companies on Sina Weibo and 32 brands or companies on WeChat on average. Chinese businesses have realized the importance of social for customer life-cycle management. While they’ve started using social to increase brand awareness — such as broadcasting on Sina Weibo — they can’t recognize potential customers in this one-way communication. They use public WeChat accounts to shorten response times to client service requests — but they can’t predict these requests in advance. To address these challenges, businesses in China are starting to use enterprise-class analytics tools for Chinese social platforms.
Here at Forrester we are busy planning our upcoming Forum For CIOs And CMOs. With a theme of “Building A Customer-Obsessed Enterprise” the event explores the partnership between marketing and technology leaders. But what about our government clients? The role of marketing is associated with the private sector. Companies employ marketers to identify their target markets and the opportunities for providing goods and services to them. Public-sector organizations don't typically have the luxury of choosing their target market or their products and services. Or at least that’s what most organizations think. But even if that is the case, it doesn't mean that these organizations shouldn't get to know their "customers" and understand how best to meet their needs. While the service might be prescribed by legislation or regulation, public organizations can influence the customer experience, and the rising focus on citizen engagement mandates they do so.
This Forum will help you identify brand new software opportunities and run with them. It will hit on the must-have competencies that will empower application development and delivery leaders to execute on their company’s engagement strategies. This includes accelerating development processes, creating digital experiences, reaching mobile customers, and exploiting analytics and big data. Forrester analysts will deliver forward-thinking content while industry specialists – from companies such as McDonald’s, Mastercard, and GE Capital - will provide insight into some real and revolutionary new business approaches that are relevant to you right now.
Behavior tracking data is the new black. It is a type of big data that can help you better understand your target consumers — everything from the amount of time they spend on each social media outlet to their most popular time of day to visit shopping websites. Compared with other data sources, it allows you to capture actions at a very detailed level with precision, eliminating measurement errors by analyzing usage of what consumers do, not what they say they do.
In our recent publication, Mobile Behavioral Data: UK, we analyzed Forrester's UK Consumer Technographics® Behavioral Study, November 2013 to March 2014, and found that:
WhatsAppkeeps UK smartphone owners engaged the longest. Forty-one percent of UK adults use the app for just over 8 hours per month (or about 2 hours per week). That is longer user engagement than for any of the other top 10 most popular apps that UK consumers use on their smartphone; this includes Facebook, the most popular app, which keeps smartphone users engaged for a little over 90 minutes per week.
Young UK smartphone owners are most likely to use finance/banking apps. More than half of 18- to 24-year-old UK smartphone owners use finance/banking apps, like the Lloyds Bank app and the NatWest app. These youngsters show the highest adoption of finance/banking apps in the UK, and rates decline with age; about 40% of 25- to 44-year-olds and 34% of 45- to 54-year-olds use finance/banking apps.
Day one of the first Cognitive Computing Forum in San Jose, hosted by Dataversity, gave a great perspective on the state of cognitive computing; promising, but early. I am here this week with my research director Leslie Owens and analyst colleague Diego LoGudice. Gathering research for a series of reports for our cognitive engagement coverage, we were able to debrief tonight on what we heard and the questions these insights raise. Here are some key take-aways:
1) Big data mind shift to explore and accept failure is a heightened principle. Chris Welty, formerly at IBM and a key developer of Watson and it's Jeoapardy winning solution, preached restraint. Analytic pursuit of perfect answers delivers no business value. Keep your eye on the prize and move the needle on what matters, even if your batting average is only .300 (30%). The objective is a holistic pursuit of optimization.
2) The algorithms aren't new, the platform capabilities and greater access to data allow us to realize cognitive for production uses. Every speaker from academic, vendor, and expert was in agreement that the algorithms created decades ago are the same. Hardware and the volume of available data have made neural networks and other machine learning algorithms both possible and more effective.
When it comes to data technology, are you lost in translation? What's the difference between data federation, virtualization, and data or information-as-a-service? Are columnar databases also relational? Does one use the same or different tools for BAM (Business Activity Monitoring) and for CEP (Complex Event Processing)? These questions are just the tip of the iceberg of a plethora of terms and definitions in the rich and complex world of enterprise data and information. Enterprise application developers, data, and information architects manage multiple challenges on a daily basis already, and the last thing they need to deal with are misunderstandings of the various data technology component definitions.
The tide is turning on privacy. Since the earliest days of the World Wide Web, there has been an increasing sense that the Internet would effectively kill privacy – and in the wake of the NSA PRISM program revelations, that sentiment was stronger than ever. However, by using our Forrester’s Technographics 360 methodology, which blends multiple qualitative and quantitative data sources, we found that attitudes on privacy are evolving: Consumers are beginning to shift from a state of apathy and resignation to caution and empowerment.
China faces a growing air pollution problem — one of the consequences of its significant economic growth over the past two decades. Surrounded by a large number of coal-burning factories in Hebei province, Beijing faces ever-worsening smog. To tackle this problem, city government has implemented new policies and laws, such as the Beijing Air Pollution Control Regulations, that provide guidance to technology vendors developing smog control solutions.
Optimized Energy Management Is The Key To Reducing Air Pollution
Beijing’s government is focusing on air quality monitoring and has invited tech vendors like Baidu, IZP Technologies, and Yonyou to develop solutions. The city wants to show the source of pollutants and how they will disperse across Beijing a couple of days in advance — but that doesn’t do anything to reduce the smog itself. Rather, the key to reducing air pollution is changing how China consumes energy. For example, the government could use big data analytics to:
Optimize factories’ energy consumption. Asset-intensive industries like steel, cement, and chemicals face challenges in analyzing the vast amounts of data generated by energy-monitoring sensors and devices. Tech vendors like Cisco and IBM could leverage their Internet of Things data analysis technology to help customers turn this data into actionable insights. For example, one steel factory in Hebei province is considering technology that identifies when an oxygen furnace is wasting energy because the temperature of the output smoke is too high.