You can't bring up semantics without someone inserting an apology for the geekiness of the discussion. If you're a data person like me, geek away! But for everyone else, it's a topic best left alone. Well, like every geek, the semantic geeks now have their day — and may just rule the data world.
It begins with a seemingly innocent set of questions:
"Is there a better way to master my data?"
"Is there a better way to understand the data I have?"
"Is there a better way to bring data and content together?"
"Is there a better way to personalize data and insight to be relevant?"
Semantics discussions today are born out of the data chaos that our traditional data management and governance capabilities are struggling under. They're born out of the fact that even with the best big data technology and analytics being adopted, business stakeholder satisfaction with analytics has decreased by 21% from 2014 to 2015, according to Forrester's Global Business Technographics® Data And Analytics Survey, 2015. Innovative data architects and vendors realize that semantics is the key to bringing context and meaning to our information so we can extract those much-needed business insights, at scale, and more importantly, personalized.
I recently attended IBM BusinessConnect 2015 in Germany. I had great discussions regarding industrial Internet of Things (IoT) and Industrie 4.0 solutions as well as digital transformation in the B2B segment. One issue that particularly caught my attention: edge computing in the context of the mobile IoT.
Mobility in the IoT context raises the question when to use a central computing approach versus when to use edge computing. The CIO must decide whether solution intelligence should primarily reside in a central location or at the edge of the network and therefore closer to (or even inside) mobile IoT devices like cars, smart watches, or smart meters. At least three factors should guide this decision:
Data transmission costs. The costs of data transmission can quickly undermine any mobile IoT business case. For instance, aircraft engine sensors collect massive amounts of data during a flight but send only a small fraction of that data in real time via satellite connectivity to a central data monitoring center while the plane is in the air. All other data is sent via Wi-Fi or traditional mobile broadband connectivity like UMTS or LTE once the plane is on the ground.
Mobile bandwidth, latency, and speed. The available bandwidth limits the amount of data that can be transmitted at any given time, limiting the use cases for mobile IoT. For instance, sharing large volumes of data about the turbines of a large container ship and detailed inventory measurements of each container on board is completely impractical unless the ship is close to a coastal area with high mobile broadband connectivity.
Get ready for AWS business intelligence (BI): it's real and it packs a punch!
Today’s BI market is like a perpetual motion machine — an unstoppable engine that never seems to run out of steam. Forrester currently tracks more than 50 BI vendors, and not a month goes by without a software vendor or startup with tangential BI capabilities trying to take advantage of the craze for BI, analytics, and big data. This month is no exception: On October 7, Amazon crashed the party by announcing QuickSight, a new BI and analytics data management platform. BI pros will need to pay close attention, because this new platform is inexpensive, highly scalable, and has the potential to disrupt the BI vendor landscape. QuickSight is based on AWS’s cloud infrastructure, so it shares AWS characteristics like elasticity, abstracted complexity, and a pay-per-use consumption model. Specifically, the new QuickSight platform provides
New ways to get terabytes of data into AWS
Automatic enrichment of AWS metadata for more effective BI
An in-memory accelerator (SPICE) to speed up big data analytics
An industrial grade data analysis and visualization platform (QuickSight), including mobile clients
Tomorrow Forrester will host our Geneva-based clients for a breakfast meeting and discussion on “Powering Innovation Strategies with Insights.” My colleague, Luca Paderni, will kick off the morning with a presentation on digital disruption in the age of the customer, specifically looking at how to take a pragmatic approach to innovation with the “adjacent possible.” Then I will lead a discussion on how to build an action-oriented approach to data and analytics, exploring examples of companies that have successfully turned their data into new business opportunities – into data-derived innovation.
Thanks to Forrester’s Business Technographics, we know that business and technology leaders prioritize initiatives that secure their position in the age of the customer – to improve customer experience, address rising customer expectations, and improve their products and services (kind of all the same thing, or very closely related). It’s all about the customer. But when we ask about these priorities, the one that comes next – right after the customer-focused initiatives – is innovation: “improving our ability to innovate.” They know that the disruptions they face in the age of the customer won’t be addressed with business as usual (BAU as one of my clients referred to it yesterday; I learned a new TLA). Innovation has been elevated to an initiative, which means that executives are focused on it and likely someone is in-charge of it – we’ll come back to that one.
Consumers (and B2B customers) are more and more empowered with mobile devices and cloud-based, all but unlimited access to information about products, services, and prices. Customer stickiness is increasingly difficult to achieve as they demand instant gratification for their ever changing tastes and requirements. Switching product and service providers is now just a matter of clicking a few keys on a mobile phone. Forrester calls this the age of the customer, which elevates business and technology priorities to achieve:
Business agility.Business agility often equals the ability to adopt, react, and succeed in the midst of an unending fountain of customer driven requirements. Agile organizations make decisions differently by embracing a new, more grass-roots-based management approach. Employees down in the trenches, in individual business units, are the ones who are in close touch with customer problems, market shifts, and process inefficiencies. These workers are often in the best position to understand challenges and opportunities and to make decisions to improve the business. It is only when responses to change come from these highly aware and empowered employees, that enterprises become agile, competitive, and successful.
Microsoft officially launched Cortana Suites — a key part of Windows Azure Intelligent Cloud — in China last week, together with MySQL Database on Azure. Windows Azure Intelligent Cloud provides real-time analytics and open source database services to Chinese customers in nationwide data centers operated by 21Vianet.
To give Chinese customers a better idea of how to use cloud-based analytics, Windows Azure demonstrated customer usage scenarios involving big data analytics on cloud. The China Meteorological Administration partnered with AccuWeather, using Windows Azure to monitor and analyze air quality data from meteorological satellites and local air monitoring stations in real time.
Chinese manufacturers face challenges from digital service providers that better understand customers and shorten the distance from product design to the end user. After implementing real-time analytics on sensor data and customer behavior, manufacturers can improve their business models via:
Product innovation. Chinese manufacturers have started tracking operational data from sensors embedded in their products to manage and predict product upgrade and maintenance cycles. Customers prefer to pay for the time they actually use the equipment — so mechanical manufacturers use cloud analytics to support this sales model. The recent rash of elevator accidents in China primarily involved elevators whose manufacturers had limited labor resources for post-sales services — a common complaint of Chinese elevator manufacturers.
Ah, the good old days. The world used to be simple. ETL vendors provided data integration functionality, DBMS vendors data warehouse platforms and BI vendors concentrated on reporting, analysis and data visualization. And they all lived happily ever after without stepping on each others’ toes and benefiting from lucrative partnerships. Alas, the modern world of BI and data integration is infinitely more complex with multiple, often overlapping offerings from data integration and BI vendors. I see the following three major segments in the market of preparing data for BI:
Fully functional and highly scalable ETL platforms that are used for integrating analytical data as well as moving, synchronizing and replicating operational, transactional data. This is still the realm of tech professionals who use ETL products from Informatica, AbInitio, IBM, Oracle, Microsoft and others.
An emerging market of data preparation technologies that specialize mostly in integrating data for BI use cases and mostly run by business users. Notable vendors in the space include Alteryx, Paxata, Trifecta, Datawatch, Birst, and a few others.
Data preparation features built right into BI platforms. Most leading BI vendors today provide such capabilities to a varying degree.
You’ve heard it before but we said it again – this time in our recent webinar. There's a new kid in town: the chief data officer. Why the new role? Because of an increasing awareness of the value of data and the painful recognition of an inability to take advantage of the opportunities that it provides — due to technology, business, or basic cultural barriers. That was the topic of our webinar presented to a full house a few days ago; we discussed our recent report, Top Performers Appoint Chief Data Officers. Fortunately for those who weren’t there, the presentation – Chief Data Officers Cross The Chasm – is available (to clients) for download.
As the title suggests, chief data officers are no longer just for the early adopters – those enthusiasts and visionaries on the forefront of new technology trends. With 45% of global companies having appointed a chief data officer (not to be confused with a chief digital officer, as we specifically asked about “data”) and another 16% planning to make an appointment in the next 12 months – according to Forrester's Business Technographics surveys, the role of the chief data officer really has move into the mainstream.
However, there remain many companies who are not sure of whether they need a CDO or not. Many of those in our audience fell into that category. We asked two questions of the audience to gauge their interest and their actions to improve their data maturity:
Are you making organizational changes specifically to improve your data capabilities?
In chaos theory, the butterfly effect posits that seemingly small changes at one moment in time can result in large, dramatic changes at another. The subtle flap of a butterfly’s wing can trigger a violent hurricane that occurs miles away or days later. Rationally, the idea may seem like a stretch, but in a digital sense, we are witnesses to – and victims of – the butterfly effect every day through social media. A few individuals’ posts online can escalate into a chorus of voices that mobilizes communities and creates new standards. We saw this last year after a homeless man in Boston turned in a backpack and, more recently, when Cecil the lion was killed in Zimbabwe.
Social media has always been a catalyst for bringing people together as well as an outlet where consumers can vent. But when a surge of voices results in change, social media posts are more than ephemeral cybertext. And, according to Forrester’s Consumer Technographics® data, consumers around the world leverage social media to generate buzz about current events, although members of some countries are more vocal than others:
In the past three decades, management information systems, data integration, data warehouses (DWs), BI, and other relevant technologies and processes only scratched the surface of turning data into useful information and actionable insights:
Organizations leverage less than half of their structured data for insights. The latest Forrester data and analytics survey finds that organizations use on average only 40% of their structured data for strategic decision-making.
Unstructured data remains largely untapped. Organizations are even less mature in their use of unstructured data. They tap only about a third of their unstructured data sources (28% of semistructured and 31% of unstructured) for strategic decision-making. And these percentages don’t include more recent components of a 360-degree view of the customer, such as voice of the customer (VoC), social media, and the Internet of Things.
BI architectures continue to become more complex. The intricacies of earlier-generation and many current business intelligence (BI) architectural stacks, which usually require the integration of dozens of components from different vendors, are just one reason it takes so long and costs so much to deliver a single version of the truth with a seamlessly integrated, centralized enterprise BI environment.
Existing BI architectures are not flexible enough. Most organizations take too long to get to the ultimate goal of a centralized BI environment, and by the time they think they are done, there are new data sources, new regulations, and new customer needs, which all require more changes to the BI environment.