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Interest in blockchain continues to be high, but there is still a significant gap between the hype and market reality. Only 11% of CIOs indicated they have deployed or are in short-term planning with blockchain, according to the Gartner, Inc. 2019 CIO Agenda Survey of more than 3,000 CIOs. This may be because the majority of projects fail to get beyond the initial experimentation phase. “Blockchain is currently sliding down toward the Trough of Disillusionment in Gartner’s latest ‘Hype Cycle for Emerging Technologies,’” said Adrian Leow, senior research director at Gartner. “The blockchain platforms and technologies market is still nascent and there is no industry consensus on key components such as product concept, feature set, and core application requirements. We do not expect that there will be a single dominant platform within the next five years.” To successfully conduct a blockchain project, it is necessary to understand the root causes for failure. Here are the seven most common mistakes in blockchain projects and how to avoid them: 1. Misunderstanding or Misusing Blockchain Technology Gartner has found that the majority of blockchain projects are solely used for recording data on blockchain platforms via decentralized ledger technology (DLT), ignoring key features such as decentralized consensus, tokenization, or smart contracts. “DLT is a component of blockchain, not the whole blockchain. The fact that organizations are so infrequently using the complete set of blockchain features prompts the question of whether they even need blockchain,” Leow said. “It is fine to start with DLT, but the priority for CIOs should be to clarify the use cases for blockchain as a whole and move into projects that also utilize other blockchain components.” 2. Assuming the Technology Is Ready for Production Use The blockchain platform market is huge and largely composed of fragmented offerings that try to differentiate themselves in various ways. Some focus on confidentiality, some on tokenization, others on universal computing. Most are too immature for large-scale production work that comes with the accompanying and requisite systems, security, and network management services. However, this will change within the next few years. CIOs should monitor the evolving capabilities of blockchain platforms and align their blockchain project timeline accordingly. 3. Confusing a Protocol With a Business Solution Blockchain is a foundation-level technology that can be used in a variety of industries and scenarios, ranging from supply chain over management to medical information systems. It is not a complete application as it must also include features such as user interface, business logic, data persistence, and interoperability mechanisms. “When it comes to blockchain, there is the implicit assumption that the foundation-level technology is not far removed from a complete application solution. This is not the case. It helps to view blockchain as a protocol to perform a certain task within a full application. No one would assume a protocol can be the sole base for a whole e-commerce system or a social network,” Leow added. 4. Viewing Blockchain Purely as a Database or Storage Mechanism Blockchain technology was designed to provide an authoritative, immutable, trusted record of events arising out of a dynamic collection of untrusted parties. This design model comes at the price of database management capabilities. In its current form, blockchain technology does not implement the full “create, read update, delete” model that is found in conventional database management technology. Instead, only “create” and “read” are supported. ”CIOs should assess the data management requirement of their blockchain project. A conventional data management solution might be the better option in some cases,” Leow said. 5. Assuming That Interoperability Standards Exist While some vendors of blockchain technology platforms talk about interoperability with other blockchains, it is difficult to envision interoperability when most platforms and their underlying protocols are still being designed or developed. Organizations should view vendor discussions regarding interoperability as a marketing strategy. It is supposed to benefit the supplier’s competitive standing but will not necessarily deliver benefits to the end-user organization. “Never select a blockchain platform with the expectation that it will interoperate with next year’s technology from a different vendor,” said Leow. 6. Assuming Smart Contract Technology is a Solved Problem Smart contracts are perhaps the most powerful aspect of blockchain-enabling technologies. They add dynamic behavior to transactions. Conceptually, smart contracts can be understood as stored procedures that are associated with specific transaction records. But unlike a stored procedure in a centralized system, smart contracts are executed by all nodes in the peer-to-peer network, resulting in challenges in scalability and manageability that haven’t been fully addressed yet. Smart contract technology will still undergo significant changes. CIOs should not plan for full adoption yet but run small experiments first. This area of blockchain will continue to mature over the next two or three years. 7. Ignoring Governance Issues While governance issues in private or permissioned blockchains will usually be handled by the owner of the blockchain, the situation is different with public blockchains. “Governance in public blockchains such as Ethereum and Bitcoin is mostly aimed at technical issues. Human behaviors or motivation are rarely addressed. CIOs must be aware of the risk that blockchain governance issues might pose for the success of their project. Especially larger organizations should think about joining or forming consortia to help define governance models for the public blockchain,” Leow concluded. Upcoming IT Symposium/Xpo Additional analysis on blockchain will be presented during Gartner IT Symposium/Xpo 2019 , a gathering of CIOs and other senior IT executives to gain insight into how their organizations can use IT to overcome business challenges and improve operational efficiency. Upcoming dates and locations include: September 16-18: Cape Town, South Africa October 20-24: Orlando October 28-31: Gold Coast, Australia October 28-31: Sao Paulo, Brazil November 3-7: Barcelona November 11-14: Goa November 12-14: Tokyo Article published by icrunchdata Image credit by Getty Images, DigitalVision, Shana Novak Want more? For Job Seekers | For Employers | For Influencers
The insatiable demand for data continues unabated. We want to gain deeper insights into market trends, customers, competitors and our business performance, but many companies are not making the progress they anticipated. And the promise of big data analytics remains largely out of their reach. Why? Because most companies still don’t take a strategic approach to data integration. It’s laborious and time-consuming. It’s costly. And most cannot see the direct impact it has on driving business objectives while supporting risk management initiatives for governance, regulatory and compliance (GRC) requirements. If anything, data integration has become more complex as the sources of data have exploded. Not only are companies collecting and retaining more data – multinationals have data in many countries that they struggle to integrate, manage and analyze. Moreover, companies are sharing more information with trading and supply chain partners than ever before. Much of this data is beyond the structured transactional variety in conventional systems and databases. In fact, unstructured data – from spreadsheets and documents to Web pages and social shares – is growing exponentially faster. More companies are recognizing that this data represents a trove of knowledge that has largely gone untapped because they have been hidden in user and departmental data silos across the enterprise. Where’s the Time-to-Value? In the software-driven economy, people expect unfettered access to data 24/7. And they are increasingly accessing this data with a mobile device. As the pace of business accelerates, companies are under increasing pressure to ensure that the right users have access to the right data in the right format – at the point of decision. The “3 Vs” of big data are often referred to volume, variety and velocity. However, I believe the true 3 Vs are validity, veracity and value . That’s because all of this data is of little use if it’s not integrated. Inadequate data governance has resulted in data sprawl, with incomplete or inaccurate data sets driving flawed assumptions and multiple versions of models that undermine data-driven decision-making. After all, bad data at the speed of light is still bad data. As much of this data becomes localized, it is more difficult to manage. Equipping users with a desktop data visualization tool and calling it self-service BI/analytics often disappoints both IT and business managers. Users get bogged down trying to integrate data from different sources to prepare for analysis rather than gaining the hoped-for insights. Studies show that despite the panoply of newer technologies, enterprises typically spend up to 80% of their time in business intelligence projects preparing the data for analysis. We also see these data silos exploited by cybercriminals. Sensitive information is exposed and/or stolen, leaving companies to face GRC violations, fines and reputational damage. Commit to Modern Data Integration Part of what’s made data integration so cumbersome and costly is its data warehousing and extract, transform and load (ETL) processes. Moving data is always a challenge, and the old hand-coded cube methodologies that let IT determine the data sets users should be working with are outmoded. Newer integration technologies that support data migration, app consolidation, data quality and profiling, and master and metadata management go beyond the traditional ETL functionality. These tools automate much of the cleansing, matching, error handling and performance monitoring – processes that IT teams often struggle with manually. They allow teams to implement a standardized approach to integrating diverse data sets, including those from SaaS applications and IaaS or PaaS clouds. Data integration is not a one-size-fits-all approach. It’s important for IT teams to make sure they’re using the right tool for the job. For example, bulk processes may be effective for a modeler working with large data sets that lack update times. In contrast, data virtualization may be appropriate for high-availability latency-sensitive transactional systems such as high-frequency trading environments. Modern data integration tools can handle batch projects or interoperate with real-time analytics applications. And newer tools allow this integration to occur in a data lake, eliminating the need to move the data. Some refer to this process as extract, load and transform (ELT). For manageability, it’s important to keep the number of integration tools to a minimum. This will largely be a factor of user profiles, project criteria and the types of data they are working with. At the same time, it’s critical that these tools interoperate seamlessly to achieve the desired data efficiency. Modern data integration enables IT to be more responsive to business users and strategic initiatives. These tools help IT ensure that the data users access are complete, current, consistent and accurate. Additionally, modern data integration allows IT teams to manage data more effectively at reduced costs. They become more productive by spending less time on writing specialized scripts and more time on getting people the information they need – where and when they need it. It also makes it easier for data teams to collaborate with compliance and security teams to ensure policy adherence and resilience to in the event of a cyberattack. Article written by Gabriel Lowy Image credit by Getty Images, Corbis, C.J. Burton Want more? For Job Seekers | For Employers | For Influencers
“Leadership is the capacity to translate vision into reality.” — Warren Bennis The quest for leadership Competition is increasing everywhere. At school, at the office, in business, in politics, in the international community of nations, in all fields, all over. If you want to tackle competition and be a leader in your own right you need inevitably to gain competitive advantages. Otherwise, you are bound to be just one more in the bunch of those left behind. So the question is: how to become a leader and, if possible, an awesome leader? By this, I mean the kind of front-runner that sees what the others don’t; the kind of strategist that anticipates his competitor’s actions; the resilient believer that doesn’t hesitate and goes staunchly the extra mile before the others. Altogether, the driving force that irradiates a huge resolve that convinces the others around that there is an upcoming light at the end of the tunnel. In other words, the person who has the ability to translate visions into reality; the person with the capacity to sense what may happen in the future; the person that leads the decision-making power to take the right anticipatory steps to gain advantage from future outcomes. Guessing the future; generating visions The future is extremely hard to predict. There are so many variables and unexpected interlinked events that predictions usually fall foul of reality. Nevertheless, some people seem to have an innate ability to anticipate the future. They have a special ability to generate new ideas, new thoughts; most are business achievers that move before the others into unknown and unexplored markets. Steve Jobs excelled in this regard. He predicted, for instance, that millions of people would find convenience in having a small and handy computer to carry around without the traditional keyboard. So he introduced the iPad, amongst many other innovative products. It was Jobs’ intuition that ultimately led Apple into its huge success. Jobs had visions that he skillfully managed to translate into reality. He was an exceptional and unique visionary leader. How to become a visionary leader? Until a few years ago, future visions for strategic purposes were basically a by-product of collecting and reading information, calculation, guessing and intuition. Experts in strategy resorted to mathematics (mostly statistics and risk/uncertainties computation) and various techniques of scenario generation to envisage possible alternate future outcomes. In the footsteps of Herman Kahn, the founder of the Hudson Institute, and especially after the 1960s, the so-called futurists, or specialists in futures research, have bettered methods to produce visions of the future. Delphi surveys and scenarios workshops have been widely used for foresight purposes aiming at generating future trajectories for an event or a set of events. Some, like Philip Tetlock, from the Good Judgment Project, tap into what’s often called the “wisdom of crowds”, pooling large groups of people and asking each individual to deliver forecasts for specific events. In some cases the results are amazing. Some people seem to have a curious ability far above the others to deliver impressive forecasts (Tetlock calls them “superforecasters”). The late Pierre Wack, at Royal Dutch Shell, is a remarkable example. He anticipated the two oil shocks of the 1970s. His forecasting work was instrumental for the strategy thinking of the oil Dutch giant in those days. Thanks to him and many others, future studies have crossed the boundary from a somewhat metaphysical aura into a more mundane role poised to enlighten decision makers about available options and future scenarios. The advent of Big Data and its contribution to predicting the future Before Big Data, available information of past events was mostly limited to written/graphical/audio-visual sources, human observation and a relatively small amount of digital data (relatively small compared to Big Data). The advent of Big Data and the consequent emergence of predictive analytics software are impacting big time on the ability to generate scenarios extrapolated from past events. Terabytes of data collected from an explosion of multiple sources (e.g.: sensors, IoT, Listening 2.0 and social media) can now be pooled and crunched quickly to derive insights as never before. The unstoppable development of more computational machine power together with the rise of machine learning and artificial intelligence are elevating the capacity to analyse information to previously unthinkable levels. The ongoing development of more sophisticated algorithms for data crunching is expanding enormously the comprehension of the environment and thus advancing big time the analytical power for prediction purposes (algorithms perform data mining and statistical analysis in order to detect trends and patterns in data). Nowadays, far-sighted leaders and their teams tap into predictive analytics software to generate visions based upon solid and objective material. They also resort to prescriptive analytics software for suggestions of courses of action based on predictive metrics. The awesome leader needs data-driven Competitive Intelligence Predictive and prescriptive analytics have also become a boon to Competitive Intelligence (CI). CI aims at gaining crucial information from your competitors in order to better understand the environment. Looking outside your window is not enough. You need to get into your competitors’ shoes and find out about their views and strategies. And to do this, you need to invest in CI. Big Data mining and predictive analytics applied to CI is now the way to go. You can legally dig up almost anything that is not confidential about your competitors. Data-driven CI offers a huge source of knowledge about the players in the market and its functioning. Such knowledge will subsequently enrich the forecasting of future events related to your competitors, in particular events that may impact upon you. And this is critical for your strategic thinking and planning. Prescriptive analytics applied to CI will then determine the right course of action vis-à-vis your adversaries. This will empower your organization to be one step ahead of competition. In sum, the goal is to develop visions about the outcomes of your competitors to inform your strategy. Indeed, if you want to be an awesome leader, you have to move outside your traditional box. You have to invest seriously in CI to be aware of your competitor’s visions, prospects and future actions. After all, you’re all operating in the same environment. You’re all inevitably connected to some extent; each of your steps may impact them and vice-versa. Intuition as the endgame Steve Jobs, as above-mentioned, excelled in intuition. Refined intuition seems to be a common trait in great leaders. Intuition relates to any kind of unconscious reasoning that individuals develop to a greater or lesser extent. Basically, it is a process that enables humans to realize something, usually spontaneously, without analytic reasoning, linking the unconscious and conscious parts of the brain, thus connecting instinct and reason. Predictive and prescriptive analytics may greatly enlighten the decision-making process, but in the end, human intuition will prevail as the leading factor influencing the choice between the various predictions and courses of action suggested by planners, futurists, strategists and data scientists. Besides, artificial intuition doesn’t seem to be in the pipeline of artificial intelligence development, at least for the foreseeable future. Therefore, human intuition will continue for many years to be the main determinant in the decision-making process. Summing up, to become an awesome leader you have to excel in developing visions and in gaining competitive advantages, in particular through Competitive Intelligence. Nowadays, data science is able to provide you with most of the clues for that. But then you have to rely on your intuition to select the most appropriate vision. Subsequently, you have to convince your staff and bring them all on board to face the challenge that your vision represents. Altogether it seems evident that awesome leadership is, more than ever, a balanced combination of Big Data driven intelligence, refined instinct and great communication. Article written by Manuel Gomes Samuel Image credit by Getty Images, Corbis, Gregor Schuster Want more? For Job Seekers | For Employers | For Influencers
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