Posted On:February 2017 - AppFerret
While the global smartphone market is as competitive as ever in terms of manufacturers fighting for the consumers’ love (and money), the long-raging platform war appears to be over. According to a recent report by Gartner, Android and iOS now account for more than 99 percent of global smartphone sales, rendering every other platform irrelevant.
As the chart below illustrates that hasn’t always been the case. Back in 2010, Android and iOS devices accounted for less than 40 percent of global smartphone sales. Back then, devices running Nokia’s Symbian and BlackBerry accounted for a significant portion of smartphone sales and Microsoft’s market share stood at 4.2 percent.
While Symbian is long extinct and BlackBerry has started transitioning to Android devices, Microsoft has not yet given up on Windows 10 Mobile as a platform aimed at professional users. Whether Windows, or any other platform for that matter, stands a chance against the dominance of Android and iOS at this point seems highly doubtful though.
Original article here.
Former Amazon executive John Rossman shares his checklist for developing an internet of things strategy for your organization.
The internet of things (IoT) may present the biggest opportunity to enterprises since the dawn of the internet age, and perhaps it will be bigger. Research firm Gartner predicts there will be nearly 20 billion devices on the IoT by 2020, and IoT product and service suppliers will generate $300 billion+ in revenue.
Successfully leveraging that opportunity — bringing together sensors, connectivity, cloud storage, processing, analytics and machine learning to transform business models and processes — requires a plan.
“In the course of my career, I’ve estimated and planned hundreds of projects,” John Rossman, who spent four years launching and then running Amazon’s Marketplace business (which represents more than 50 percent of all Amazon units sold today), writes in his new book, The Amazon Way on IoT: 10 Principles for Every Leader from the World’s Leading Internet of Things Strategies. “I’ve learned that, even before you start seeking answers, it’s imperative to understand the questions. Guiding a team to a successful outcome on a complex project requires understanding of the steps and deliverables, necessary resources, and roles and every inherent risk and dependency.”
Before you start the hardware and software design, and before you figure out how to engage developers, he says, you need to start with a better set of questions.
Rossman says there are three key phases to building a successful IoT strategy. While he presents the steps sequentially, he notes that many steps are actually taken concurrently in practice and can be approached in many different ways.
Part 1. Develop and articulate your strategy
First and foremost, Rossman says, you must narrow and prioritize your options. IoT presents a broad swathe of opportunities. Success depends upon understanding your market, evaluating the opportunities with deliberation and attacking in the right place.
It all begins with a landscape analysis. You need to thoroughly understand your industry and competitors — strengths, weaknesses, opportunities and threats (SWOT). This will help you see the megatrends and forces at play in your market.
“Creating a landscape analysis and value chain of your industry is a very important thing to do,” Rossman tells CIO.com. “Studying the market: What are they saying about IoT in your industry? Truly understanding what is your worst customer moment: Where do customers get frustrated? What data or what event improves that customer experience? What’s the sensor or IoT opportunity that provides that data?”
Value-chain analysis and profit-pool analysis
The next step, Rossman says, is to create a value-chain analysis and profit-pool analysis of your industry. It should be a broad view of the industry, don’t give in to tunnel-vision with a narrow view of your current business. In some cases, this may involve launching a business in one part of the value chain as a way to gain perspective on the rest of the value chain and to identify other business opportunities.
Partner, competitor and vendor analysis
Create a map of other solutions providers in your space to develop a clear understanding of what exactly each one does, who their key clients are and what their IoT use cases are. Rossman says you should even pick a few to interview. Use this process to understand the needs of customers, the smart way those needs are already being met and where the gaps are.
The next step, Rossman says, is to document specific unmet customer needs and identify the key friction points your future customers are currently experiencing.
“Following the path from start to your desired outcome can help you identify details and priorities that might otherwise be dealt with at too high a level or skipped over entirely,” he writes.
Rossman warns that crafting strong customer personas and journeys is hard work, and you may need to start over several times to get it right.
“The biggest mistake you can make on these is to build them for show rather than for work,” he writes. “Don’t worry about the beauty of these deliverables until things are getting baked (if at all). Do worry about getting at insights, talking to customers and validating your findings with others who can bring insights and challenges to your work.”
Evaluation framework and scoring
Design ways to assess the success of your work.
“This includes understanding a project’s feasibility and transition points and how it will tie into other corporate strategies at your company,” Rossman writes. “Sometimes, especially if your organization is new to the field of connected devices, the success of your project should be measured in terms of what you can learn from the project rather than whether or not it can be classically considered a success.”
You might undertake some early IoT initiatives purely to gain experience, with no expected ROI, he says.
Once you have all these analyses under your belt, you need share what you’ve learned with the rest of your team. Rossman says he’s had the most success articulating these learnings by building a flywheel model of business systems and by developing a business model.
Part 2. Build your IoT roadmap
Once you’ve explained your big idea and why your organization should pursue it, you need an IoT roadmap that helps you plan and communicate to others what the journey will be like, what is being built and how it will work.
“In creating your roadmap, embrace on of Amazon’s favorite strategies — think big, but bet small,” Rossman writes.
In other words, you need a big vision, but you don’t want to “bet big.” Make small bets to test your thinking. This can involve creating a prototype, a minimally viable product or jointly developing a project with existing customers and partners.
Rossman suggests four methods that can help you articulate your roadmap:
- The future press release. Develop a simple but specific product announcement. This forces you to clarify your vision, Rossman says.
- A FAQ for your IoT plan. Forecast some of the questions you’re likely to get about your product and create a frequently asked questions (FAQ) document to answer them.
- A user manual. Develop a preliminary user manual for your IoT device. It should address the end user. If the product includes an API, you should also build a user manual for the developer.
- A project charter. Write a project charter. This is a written project overview that outlines the key facets of the project. It should help you understand the resources you need to undertake the project, what the key milestones are and the schedule.
Part 3. Identify and map your IoT requirements
The last step is to identify and map your IoT requirements — the technical capabilities you need to make your solution a success.
“Companies use many different types of approaches, such as use cases, user stories, process flows, personas, architecture specifications and so on to document their requirements,” Rossman writes.
Regardless of the requirements methodology you settle on, Rossman says it’s important to answer questions around insights (data and events), analytics and recommendations, performance and environment and operating costs.
For example, under ‘insights,’ it’s important to answer questions like these:
- What problem, event or insight is the end user solving for?
- What insights would be valuable to the customer?
- What recommendation or optimization using the data would be valuable to a customer?
- What data needs to be collected?
Analytics and recommendations questions might include the following:
- How responsive will “adjustments” or optimizations need to be (specify in time range)?
- How complex will the “math” be? Write the math equation or pseudologic code if you can.
- Will notifications, logic, “math,” or algorithms be consistent and fixed, or will they need to be configurable, updated and managed?
Performance questions might include these:
- Estimate the amount of data transmitted over a period of time (hour, day).
- What are the consequences of data not being collected?
- What are the consequences of data being collected but not transmitted?
Environment and operating requirements questions might include these:
- What operating conditions will the device and sensor be in? Temperature, moisture, pressure, access and vibration are example conditions.
- What device physical security needs or risks are there?
- Will the IoT device or sensors be embedded within another device, or will they be independent and a primary physical device themselves?
Costs questions might include these:
- What is the cost per device target range?
- What is the cost per device for connectivity target range?
- What is the additional operating cost range the business can support for ongoing operating infrastructure?
“As you build your plans, remember that though IoT can provide key pieces to the puzzle, it’s no golden ticket,” Rossman writes. “Simply creating an IoT solution will not bring you success. However, if you focus on providing strong value to your customers through new or updated products and services, improving company operations or creating new or more-efficient business models, you’ll be much more likely to find success.”
Original article here.
Data visualization used to be a nice-to-have skill for specialists, but today data visualization is a key part of business decision-making for every manager, the Harvard Business Review notes. “New software tools mean this type of visualization is easier than ever before: They’re making data analysts of us all,” the journal says.
Members of the IT Central Station community say that the most important factors to consider when choosing a data visualization product include dashboard customization, data analysis capabilities, and ease of use. Five of the top data visualization solutions on the market are Tableau, Sisense, Dundas BI, Qlik Sense, and SAP Lumira, according to online reviews by enterprise users in the IT Central Station community.
But what do enterprise users really think about some of these tools? Here, users give a shout-out for some of their favorite features, but also give the vendors a little tough love.
Editor’s note: These reviews of select data visualization tools come from the IT Central Station community. They are the opinions of the users and are based on their own experiences.
“The most valuable feature in Tableau Desktop developer version is the drag-and -drop feature for dimensions and measures. Parameters and action filters are also great.”
— Akarsh A., manager of business intelligence at a tech services company
“The most important and valuable feature is the ability to merge any kind of data with your data set, even cloud-based data. It gives the business user the power to analyze something new with his own data sources.”
— Oscar B., business intelligence specialist at a financial services firm
Room for improvement
“Tableau lacks machine learning algorithms that you can implement using R, SPSS Modeler, and Python. It has clustering and time-series forecasting abilities, which are helpful, but adding machine learning capabilities (like decision trees, CHAID analysis and K-means) would make this product perfect!”
— Yali P., data analysis team leader at an internet service company
“I have difficulty working with many filters on the dashboards, and I’d like to see more options in the Histories section. QlikView makes better use of the dashboard filters.”
— Luiz Henrique F., planning specialist at a communications service provider
“A facility to add custom code to the dashboard would be helpful, and there is no formatting option for individual filters.”
— Sampath P., vice president of strategy, global delivery and operations at a tech services company
You can read more Tableau reviews on IT Central Station.
“We found the ease-of-use to be our primary factor for choosing Sisense. We have a client that changes their mind often and their business is moving very quickly. We needed to bring them a tool that could be learned and then deployed easily, without a lot of technical expertise.”
— J. Matt, section editor of print software at a printing company
“Time to deployment was one of our most critical factors in choosing a BI vendor, and I am not sure you can get to deployment faster than Sisense. On day one, I was able to download it, connect two disparate data sources with multiple tables, and build meaningful dashboards.”
— Richard E., product owner of business intelligence at a software R&D company
“It’s really user-friendly and fast. I can use the product during customer meetings, not only to show dashboards, but to create and process data analytics in real time. It’s perfect for consultant workshops where you can’t work with static dashboards. Also, because it has an HTML5 interface, it now looks very good when compared to BI solutions that haven’t evolved since Windows XP.”
— Romain N., project manager at a manufacturing company
Room for improvement
“The application lacks a control of the exporting function. It gives you the ability to export both the dashboards and the widgets in two formats each, but the format of all the exports are not completely under the control of the administrators. Most of it is select a few options and hope it comes out looking professional. A reporting engine that allows the administrators to format a template used in the exporting options would go a long way.”
— Eric Z., vice president of IT at a manufacturing company
“Better tracking of the targets for our representatives, quick overview of the market by product categories, supplier, customers, states, etc. We’d also like ultra-fast drill-down capabilities that allow us to find answers to ad-hoc business questions in a few seconds during business meetings.”
— Olivier C., business analyst at a marketing services firm
“Although the plug-in support mitigates this, Sisense could use additional out-of-the-box visualizations. The maps feature could also be improved.”
— Jared K., vice president of technology at a tech vendor
You can read more Sisense reviews on IT Central Station.
“Version control and the ability to roll backward and forward on a dashboard. Ability to drag and drop an Excel file onto the page and have it create a data source from which to visualize.”
— Tom L., senior programmer/analyst at a tech services company
“The simplicity involved in generating dashboards has dramatically increased the number of dashboards we can get into the hands of users each month. Using easy drag-and-drop functionality, fast data discovery and powerful dashboard tools is allowing us to quickly give the staff, managers and decision makers the information they need to make informed decisions.”
— Tom M., energy efficiency analyst at an energy company
“Dundas BI is an end-to-end solution. It has ETL, reporting and dashboarding capabilities on one platform. It can manage all ETL procedures on its own. It not only reduces the budget of the project, but it also is easy to collect data from different sources.”
— Zafer M., managing partner at public broadcaster
Room for improvement
“The addition of funnel charts to the visualization options would be great. We have not been able to default a data grid to be collapsed by groups. This would be a big help for some dashboards requiring lots of details on the screen.”
— Andy C., manager of business intelligence and data architecture at a tech services company
“On some of the user interfaces, such as the ‘join’ interface, it is not possible to cancel out of the screen without making any changes.”
— Tom M., energy efficiency analyst at an energy company
You can read more Dundas BI reviews on IT Central Station.
“Its ease of use. No code is necessary to build a nice panel that works for desktops and mobile devices. You can also add customized graphics made by the Github community (branch.qlik.com)!”
— Kleyn G., IT analyst at a government agency
“The possibility of generating in-memory insights using self-service data connections with any database. Also, creating beautiful dashboards and analysis to use in business presentations, gaining value with trusted and solid information generated by the ETL in-memory tool.”
— Arthur K., BI specialist at an educational organizational
Room for improvement
“As of today, QlikView and Qlik Sense are only capable of storing the data results to a proprietary file. No other tool, outside of Qlik, is able to read these files.”
— Nick R., senior programmer analyst at an energy/utilities company
“I miss some of the functions found in their previous product, QlikView, such as dynamic (written by a function) expression or sheet name labels, to create multi-language applications.”
— BIExpert870, BI expert at a tech services company
You can read more Qlik Sense reviews on IT Central Station.
“We like the idea of being able to tell a story about our data and do some ad hoc data mining. We could show the sales across territories and actually see which ones were performing better than other ones. It was kind of an eye-opener.”
— Steve B., business intelligence analyst at a recreational services company
“The dynamic creation of dashboards is the key feature. It provides quick visualizations for reports. As it’s hard to explain our reports, sometimes management misses the point, so the visualization is a lot better than a table that we typically show. It’s the visualization that helps them understand what we report on.”
— SrDirector260, senior director at a tech services company
“What impresses me most are the charts and graphs, maps, and integration with ESRI. What’s more, you can integrate with other third-party APIs and design your own charts.”
— SeniorVP472, senior vice president and head of multicultural insights at a consulting firm
Room for improvement
“I would look at the geospatial part: it’s a little cumbersome, and it’s not as accurate.”
— Steve B., business intelligence analyst at a recreational services company
— Utkarsha K., SAP innovation analyst at a consumer goods company
“The Edge version of SAP Lumira still needs to be more user-friendly. It’s still in the initial stages, so we can expect more features in future releases.”
— BizOpsAnalyst442, business operations analyst at a tech company
You can read more SAP Lumira reviews on IT Central Station.
Original article here.
Amazon’s cloud provider is the biggest player in the rapidly growing cloud infrastructure market, according to new data.
Amazon Web Services (AWS) accounts for one third of the cloud infrastructure market, more than the value generated by its next three biggest rivals combined.
AWS dominates, with a 33.8 percent global market share, while its three nearest competitors — Microsoft, Google, and IBM — together accounted for 30.8 percent of the market, according to calculations by analyst Canalys.
The four leading service providers were followed by Alibaba and Oracle, which made up 2.4 percent and 1.7 percent of the total respectively, with rest of the market made up of a number of smaller players.
According to the researchers, total spending on cloud infrastructure services, which stood at $10.3bn in the fourth quarter of last year (up 49 percent year-on-year) will hit $55.8bn in 2017 — up 46 percent on 2016’s total of $38.1bn.
Continuing demand is leading the cloud companies to accelerate their data centre expansion. Canalys said AWS launched 11 new availability zones globally in 2016, four of which were established in Canada and the UK in the past quarter. IBM also opened its new data centre in the UK, bringing its total cloud data centres to 50 worldwide, while Microsoft also added with new facilities in the UK and Germany.
Google and Oracle set up their first infrastructure in Japan and China respectively, aiming at expanding their footprint in the Asia Pacific region, while Alibaba also unveiled the availability of its four new data centres in Australia, Japan, Germany, and the United Arab Emirates.
Strict data sovereignty laws — under which personal data has to be stored in servers that are physically located within the country — mean cloud service providers have to build data centres in key markets, such as Germany, Canada, Japan, the UK, China, and the Middle East, said Canalys research analyst Daniel Liu.
Original article here.
The market for artificial intelligence (AI) technologies is flourishing. Beyond the hype and the heightened media attention, the numerous startups and the internet giants racing to acquire them, there is a significant increase in investment and adoption by enterprises. A Narrative Science survey found last year that 38% of enterprises are already using AI, growing to 62% by 2018. Forrester Research predicted a greater than 300% increase in investment in artificial intelligence in 2017 compared with 2016. IDC estimated that the AI market will grow from $8 billion in 2016 to more than $47 billion in 2020.
Coined in 1955 to describe a new computer science sub-discipline, “Artificial Intelligence” today includes a variety of technologies and tools, some time-tested, others relatively new. To help make sense of what’s hot and what’s not, Forrester just published a TechRadar report on Artificial Intelligence (for application development professionals), a detailed analysis of 13 technologies enterprises should consider adopting to support human decision-making.
Based on Forrester’s analysis, here’s my list of the 10 hottest AI technologies:
- Natural Language Generation: Producing text from computer data. Currently used in customer service, report generation, and summarizing business intelligence insights. Sample vendors: Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, Yseop.
- Speech Recognition: Transcribe and transform human speech into format useful for computer applications. Currently used in interactive voice response systems and mobile applications. Sample vendors: NICE, Nuance Communications, OpenText, Verint Systems.
- Virtual Agents: “The current darling of the media,” says Forrester (I believe they refer to my evolving relationships with Alexa), from simple chatbots to advanced systems that can network with humans. Currently used in customer service and support and as a smart home manager. Sample vendors: Amazon, Apple, Artificial Solutions, Assist AI, Creative Virtual, Google, IBM, IPsoft, Microsoft, Satisfi.
- Machine Learning Platforms: Providing algorithms, APIs, development and training toolkits, data, as well as computing power to design, train, and deploy models into applications, processes, and other machines. Currently used in a wide range of enterprise applications, mostly `involving prediction or classification. Sample vendors: Amazon, Fractal Analytics, Google, H2O.ai, Microsoft, SAS, Skytree.
- AI-optimized Hardware: Graphics processing units (GPU) and appliances specifically designed and architected to efficiently run AI-oriented computational jobs. Currently primarily making a difference in deep learning applications. Sample vendors: Alluviate, Cray, Google, IBM, Intel, Nvidia.
- Decision Management: Engines that insert rules and logic into AI systems and used for initial setup/training and ongoing maintenance and tuning. A mature technology, it is used in a wide variety of enterprise applications, assisting in or performing automated decision-making. Sample vendors: Advanced Systems Concepts, Informatica, Maana, Pegasystems, UiPath.
- Deep Learning Platforms: A special type of machine learning consisting of artificial neural networks with multiple abstraction layers. Currently primarily used in pattern recognition and classification applications supported by very large data sets. Sample vendors: Deep Instinct, Ersatz Labs, Fluid AI, MathWorks, Peltarion, Saffron Technology, Sentient Technologies.
- Biometrics: Enable more natural interactions between humans and machines, including but not limited to image and touch recognition, speech, and body language. Currently used primarily in market research. Sample vendors: 3VR, Affectiva, Agnitio, FaceFirst, Sensory, Synqera, Tahzoo.
- Robotic Process Automation: Using scripts and other methods to automate human action to support efficient business processes. Currently used where it’s too expensive or inefficient for humans to execute a task or a process. Sample vendors: Advanced Systems Concepts, Automation Anywhere, Blue Prism, UiPath, WorkFusion.
- Text Analytics and NLP: Natural language processing (NLP) uses and supports text analytics by facilitating the understanding of sentence structure and meaning, sentiment, and intent through statistical and machine learning methods. Currently used in fraud detection and security, a wide range of automated assistants, and applications for mining unstructured data. Sample vendors: Basis Technology, Coveo, Expert System, Indico, Knime, Lexalytics, Linguamatics, Mindbreeze, Sinequa, Stratifyd, Synapsify.
There are certainly many business benefits gained from AI technologies today, but according to a survey Forrester conducted last year, there are also obstacles to AI adoption as expressed by companies with no plans of investing in AI:
There is no defined business case 42%
Not clear what AI can be used for 39%
Don’t have the required skills 33%
Need first to invest in modernizing data mgt platform 29%
Don’t have the budget 23%
Not certain what is needed for implementing an AI system 19%
AI systems are not proven 14%
Do not have the right processes or governance 13%
AI is a lot of hype with little substance 11%
Don’t own or have access to the required data 8%
Not sure what AI means 3%
Once enterprises overcome these obstacles, Forrester concludes, they stand to gain from AI driving accelerated transformation in customer-facing applications and developing an interconnected web of enterprise intelligence.
Original article here.
Mithril’s website features a comparison to Angular, React, and Vue. Mithril, for example, offers much quicker library load times and update performance than React, and it has a better learning curve and update performance than Angular. Compared to Vue, Mithril supposedly offers better library load times and update performance. Vue and Mithril both use virtual DOM and lifecycle methods, and both Angular and Mithril supporting componentization.
The framework hopes to make the learning curve for modern web development as low as possible with its API and tooling requirements. Plans for Mithril call for a continued focus on simplifying development workflow. “The v1.0.0 release is actually smaller in size than the previous releases, but I feel it’s still possible to simplify things more than the current status quo on the tooling side,” Horie said.
Original article here.
Amazon Web Services, or AWS, the current king of Infrastructure as a Service (IaaS), has once again proved that their leadership position remains undisputed. AWS continued its tradition of posting quarterly sequential growth during the fourth quarter of 2016, reaching $3.546 billion in revenues, representing growth of 47% compared to Q4-15 and $305 million more than their Q3-16 numbers.
Just a quick look at the last three quarters will reveal that their sequential sales expansion increased in 2016 compared to 2015. Despite reaching a run rate of more than $14 billion, Amazon Web Services continues to expand at an extremely fast pace, posting nearly 50% growth year over year.
The fact that they are able to add +$300 million in sales sequentially (from one quarter to the next) shows that the growth momentum is still very much intact and that a slowdown in the short term is out of the question.
If Amazon Web Services continues the current trajectory, an annual revenue figure close to or more than $20 billion will be possible in the next four to six quarters. That will be a monumental achievement for a hard core retailer competing with top tech companies of the world.
During the recent quarter, AWS’s competitor Microsoft announced that annualized commercial cloud run rate has exceeded $14 billion. Amazon possible makes a little bit more from cloud than Microsoft, but the latter makes a hefty sum from its SaaS product line-up led by Office 365. The fact that Amazon is still a little ahead of Microsoft without a significant SaaS portfolio is testament to the kind of strength AWS has in the IaaS space.
AWS continues to be the most profitable unit for Amazon with an operating income of $926 million, compared to $816 million from Amazon’s North America retail unit. During the fourth quarter earnings call Amazon CFO Brian Olsavsky told analysts that Amazon had cut prices seven times during fourth quarter, and added more than 1000 services and features in 2016 compared to over 700 in 2015.
The process of cutting prices and adding more services helped AWS operating numbers nicely, with the segment reporting an operating margin of 31.3%.
At these levels Amazon does remains the company to beat because, with such fat margins, engaging Amazon in a price war is not going to work for any competitor. The unit has enough bandwidth to withstand any price onslaught.
The only way to compete with Amazon is to have a better offering and add more value to your services. But even here, Amazon is leaving no wiggle room. How does a competitor match up to a company that has added thousands of services, is laser-focused on cloud infrastructure, keeps cutting prices without waiting for someone else to do it first, keeps slowly but steadily expanding its data center footprint and has $14 billion to show for it.
The relentless Amazon is possibly the best thing that happened to the cloud industry because they will keep everyone on their toes, including themselves.
You can access Amazon’s fourth quarter 2016 earnings report here.
Original article here.
Blockchain is the new buzzword on the block; and while many business leaders, managers, developers and IT departments are Googling it and left scratching their heads, others are wising up to it, are realising how brilliant it is, and are recognising the opportunity it’s going to bring and the potential impact it will have.
If we put aside the tech behind it and focus on what it can do, it’s actually capable of disrupting many industries and bringing new innovations not only into finance, but also property, automotive, music, trading and healthcare.
To make it easier to understand what blockchain can bring to businesses, think about how a Google Doc enables people to access and make updates in real time. No need to save over and send new files to all and sundry, as the next time someone opens the doc it will be the most up to date as the file automatically keeps a record of who made which changes and when, as that digital address is native to the cloud, not the local hard drive. Google Docs is to Microsoft Word what blockchain is to a traditional ledger system.
Startups and large corporations are working together to figure out how this ‘shared ledger’ concept can benefit their businesses. And this concept of data retention is at the heart of cloud-based technology.
Cloud technologies are the forerunners to blockchain and developers and designers who are creating new innovations in this space, should keep an eye on blockchain opportunities too. Private blockchain networks can run in secure cloud environments and we have witnessed test collaborations between Google’s cloud services, IBM, Microsoft and Amazon and if successful, these cloud services could play a role in blockchain deployments.
Applying blockchain to business
Let’s take a look at some use cases and how blockchain can be implement in different industry sectors to speed up processes, guarantee security, trust and transparency and keep accurate records that can be accessed by stakeholders, no matter where they are in the world.
Property: You’re buying a house and want to know when the last repairs and updates were carried out, which companies provided them and when. Blockchain could help homeowners and estate agents keep a record of information relating to a property, which would be centrally located for anyone in the house buying and selling process to access – reducing hours of paper pushing and phone calls and create transparent information on the status and maintenance of the house before putting in an offer.
Automotive: In a similar way to housing, tracking the value of second hand vehicles through blockchain would make purchases a lot easier for buyers and traders. Information on the car’s mileage, services, and driving history would be accurate, and if the car was ever written off the information could be accessed digitally to salvage the new gearbox that was installed only two months ago.
Music: There has already been massive disruption in the music industry but in the age of streaming services, blockchain could show musicians, creators, fans, marketers and labels the data and dialogue involved in listening to their songs and albums. Artists would be much closer to their fans and over time they could influence and reward them. A truly democratic and commercially viable way of promoting music. Thanks to blockchain.
Banking: Most big banks have a headline piece highlighting how they are working with blockchain especially within security. The technology promotes security and trust and allows all parties to work with one single reference point, which can cut manpower and middlemen costs.
As with any new technology, there are stumbling blocks. Commercial banks may not want all that information to be managed by developers so private blockchains may need to be created. It’s important to take a collaborative approach so banking organisations can pool their resources, identify and share hurdles and resolutions.
Trading stocks and shares: Nasdaq has successfully completed a blockchain test in Estonia to run proxy voting on its exchange and is now assessing whether to implement the new system as it has streamlined a process that was highly manual and time consuming. Nasdaq is one of the early adopters and a supporter of the technology in the exchange industry and already uses it to power its market for share of private companies It is also launching a marketplace powered by blockchain for pre-IPO private securities exchange in the USA.
Healthcare: Within healthcare, blockchain promises to address security and data integrity issues relating to patient information within healthcare providers, hospitals, insurance companies and clinical trials. IBM Watson teamed up with the US FDA to trial a data sharing initiative to keep track of patients involved in a particular trial and they are going on to explore how a blockchain framework could potentially provide benefits to public health.
Blockchain as a service: Blockchain as a service is the most viable way for the technology to scale. Start-ups like Chain.com are making blockchain applications much more accessible to big corporations. It is probably the most recognised ‘blockchain as a service’ platform startup as it lets enterprises use blockchain technology in a variety of network infrastructures.
Where to next
To put into perspective how big it could become, the World Economic Forum predicts that by about 2027 about 10% of the global GDP would be stored on blockchains so companies looking to get their piece of the action should start investigating now.
Silicon Valley investor Marc Andreessen cites blockchain as “one of the most fundamental inventions in the history of computer science” and we’d agree. 2017 is going to be the year it is tested, trialled and iterated to suit individual market and business requirements.
All that without even mentioning Bitcoin – we’ll save that for another day.
Original article here.