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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
Your security strategy should begin with the premise that you’re already compromised. Get your head around this, and you can shift your focus from trying to prevent attacks from occurring to resilience when they inevitably do. You’ll be better prepared to quickly identify an attack, contain it from spreading and recover from any fallout – minimizing risk exposure for the business. In this sense, security strategy is similar to disaster recovery and business continuity. The main difference is that the probability that you’ll suffer a breach of some type is much higher than a disaster happening. But the consequences – business interruption, loss of private information or intellectual property, compliance violations and damaged reputation – can be just as devastating. A “contain and respond” strategy takes a holistic approach to prioritizing risks. If you prioritize your risks – those that can have the most negative impact on operations – then you can defend the critical data assets that map to those risks. A Multi-Layered Approach to Security Governance Figure 1. Securing Data that Underlies Risks. Source: Tech-Tonics Advisors Once you’ve prioritized your risks, you can implement a multilayered, data-centric approach that establishes a secure perimeter around the data associated with risks, locks down the data, removes risk from privileged users and provides the information that identifies malicious insiders and possibly compromised accounts. This strategy is more proactive and intelligence-based, enabling you to better secure your most valuable data assets, respond to and remediate incidents in a timely fashion and meet GRC (governance, regulatory, compliance) requirements. It also lets you test your risk defenses to better identify and close potential vulnerabilities. A contain and respond strategy will also help you manage security and compliance costs – which your CFO will love you for. It lets your team focus on priorities. You can relegate lower-level risks and data to “best efforts” protection using automation. Automating more of the functionality makes integration easier by reducing data silos and false positive overload. Finally, it lifts team productivity and morale by evolving their skill set toward greater intelligence, such as threat analytics, incident response and forensics. People Are Your Biggest Vulnerability; Include Them in Strategy Cybercriminals target and exploit an organization’s weakest link – its people. Employees who open an infected attachment, click on a link that takes them to a dodgy site or whose devices get infected while working remotely are most vulnerable. They expose you to your greatest risks and the data that map to them. In fact, almost all of your security risks are caused by people. Most are just careless and ambivalent about your security strategy. The malicious insiders are not. Either way, your team is held accountable for breaches – despite the fact that most attacks are not of your own making. Include your people in security strategy – just as they are in effective data governance and disaster recovery and business continuity initiatives. Make them aware of their vulnerabilities. Train them to be more vigilant. Create incentives for them to adhere to policies and penalize them for transgressions. As a formal data governance program defines how data should be handled to improve quality and accuracy for analysis and decision-making, security governance should define similar best practices to protect against risks and defend strategic data assets. In fact, I recommend that security governance be linked with data governance. Security is everyone’s business; but responsibility still rests with the security team. Security governance should be considered as much a business initiative as data governance or disaster recovery and business continuity. But to be truly effective, it must be endorsed and practiced by senior management and board members. That is the only way common business objectives can be achieved more securely. I believe that a company’s ability to demonstrate stronger security governance relative to peers will become viewed as a competitive advantage. This includes how it responds to a breach. How a company informs customers, regulators and investors that an attack has occurred and what they are doing/have done to contain it is critical to maintaining security governance and preserving company reputation. Article written by Gabriel Lowy Want more? For Job Seekers | For Employers | For Influencers
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