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Half of startups have no women on their leadership team, according to Silicon Valley Bank's Women in Technology Leadership 2019 report based on survey responses from technology and healthcare executives in major innovation hubs. While the annual report finds that there is some progress, a lack of gender parity persists. Just 56 percent of startups have at least one woman in an executive position, and only 40 percent have at least one woman on the board of directors.  It was found that 59 percent of startups have some type of program in place designed to increase the number of women in leadership — and that the founding team’s gender often determines which executive roles women hold at startups. "We have measured gender parity in startup leadership since 2014 and the numbers continue to be concerning. We must do better," said Greg Becker, CEO of Silicon Valley Bank. "There is, however, a bright spot in that startups are recognizing the pressing need to be more proactive; 59 percent now have programs in place to help close the gender gap. While there is still a great deal of work to be done, we believe that the innovation economy is making progress and sees that increasing gender diversity as an important way to attract skilled talent, one of the biggest challenges facing startups," said Becker. The report measures the percentage of women in leadership positions and compares startups' views of the innovation economy based on the gender of their founders. Survey findings include: 28 percent of startups have at least one woman on the founding team. Founder gender often determines women's roles. Just five percent of startups with only men on the founding team have a female CEO, and they are much more likely to have women leading HR and marketing. 59 percent of startups have programs in place designed to support gender diversity, the highest percentage we have seen since the report's inception in 2014. Raising capital is hard for all startups, and even more challenging for companies with at least one female founder. 87 percent of companies with at least one female founder describe the fundraising environment as somewhat or extremely challenging compared to 78 percent of all-male founding teams. Startups with at least one female founder are more likely to engage with small investors. When it comes to gender-based hiring goals, 24 percent of startups have company-wide hiring and promotion goals, seven percent have goals for C-level positions only, and 17 percent have goals to add female board members. Startups were also asked to describe the programs they have in place to support gender diversity. The most common programs include creating a flexible work environment, recruiting/interview techniques, and leadership development. "Over the past couple of years at Gapsquare, we have seen tech companies using data to narrow gaps," said Dr. Zara Nanu, CEO and Co-Founder of Gapsquare, a UK-based big data company using cloud-based software to analyze and narrow the gender pay gap, as well as building equality and diversity into company practices. "There are a few initiatives that are already providing results. Some of the most successful initiatives we have seen focus on providing flexible working for all employees, and finding new and innovative ways to think about hiring new talent as well as promoting employees within the company. We have also seen progress in companies which analyze all of the reward and compensation elements and restructure them to ensure they benefit everyone," said Nanu. About the Women in Technology Leadership 2019 report This report is part of the 10th anniversary edition of SVB's Startup Outlook Report, which is based on a survey of 1,400 technology and healthcare startup founders and executives primarily in the US, the UK, China, and for the first time, Canada. Follow the conversation on Twitter at @SVB_Financial and with #StartupOutlook.  Download and read the full report . Article published by icrunchdata Image credit by Silicon Valley Bank Want more? For Job Seekers | For Employers | For Influencers
Augmented analytics, continuous intelligence, and explainable artificial intelligence (AI) are among the top trends in data and analytics technology that have significant disruptive potential over the next three to five years, according to Gartner, Inc. Rita Sallam, research vice president at Gartner, said data and analytics leaders must examine the potential business impact of these trends and adjust business models and operations accordingly, or risk losing competitive advantage to those who do. “The story of data and analytics keeps evolving, from supporting internal decision making to continuous intelligence, information products, and appointing chief data officers,” she said. “It’s critical to gain a deeper understanding of the technology trends fueling that evolving story and prioritize them based on business value.” According to Donald Feinberg, vice president and distinguished analyst at Gartner, the very challenge created by digital disruption — too much data — has also created an unprecedented opportunity. The vast amount of data, together with increasingly powerful processing capabilities enabled by the cloud, means it is now possible to train and execute algorithms at the large scale necessary to finally realize the full potential of AI. “The size, complexity, distributed nature of data, speed of action, and the continuous intelligence required by digital business means that rigid and centralized architectures and tools break down,” Mr. Feinberg said. “The continued survival of any business will depend upon an agile, data-centric architecture that responds to the constant rate of change.” Gartner recommends that data and analytics leaders talk with senior business leaders about their critical business priorities and explore how the following top trends can enable them. Trend No. 1: Augmented Analytics Augmented analytics is the next wave of disruption in the data and analytics market. It uses machine learning (ML) and AI techniques to transform how analytics content is developed, consumed, and shared. By 2020, augmented analytics will be a dominant driver of new purchases of analytics and BI, as well as data science and ML platforms, and of embedded analytics. Data and analytics leaders should plan to adopt augmented analytics as platform capabilities mature. Trend No. 2: Augmented Data Management Augmented data management leverages ML capabilities and AI engines to make enterprise information management categories including data quality, metadata management, master data management, data integration, as well as database management systems (DBMSs) self-configuring and self-tuning. It is automating many of the manual tasks and allows less technically skilled users to be more autonomous using data. It also allows highly skilled technical resources to focus on higher value tasks. Augmented data management converts metadata from being used for audit, lineage, and reporting only, to powering dynamic systems. Metadata is changing from passive to active and is becoming the primary driver for all AI/ML. Through to the end of 2022, data management manual tasks will be reduced by 45 percent through the addition of ML and automated service-level management. Trend No. 3: Continuous Intelligence By 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions. Continuous intelligence is a design pattern in which real-time analytics are integrated within a business operation, processing current and historical data to prescribe actions in response to events. It provides decision automation or decision support. Continuous intelligence leverages multiple technologies such as augmented analytics, event stream processing, optimization, business rule management, and ML. “Continuous intelligence represents a major change in the job of the data and analytics team,” said Ms. Sallam. “It’s a grand challenge — and a grand opportunity — for analytics and BI (business intelligence) teams to help businesses make smarter real-time decisions in 2019. It could be seen as the ultimate in operational BI.” Trend No. 4: Explainable AI AI models are increasingly deployed to augment and replace human decision making. However, in some scenarios, businesses must justify how these models arrive at their decisions. To build trust with users and stakeholders, application leaders must make these models more interpretable and explainable. Unfortunately, most of these advanced AI models are complex black boxes that are not able to explain why they reached a specific recommendation or a decision. Explainable AI in data science and ML platforms, for example, auto-generates an explanation of models in terms of accuracy, attributes, model statistics, and features in natural language. Trend No. 5: Graph Graph analytics is a set of analytic techniques that allows for the exploration of relationships between entities of interest such as organizations, people and transactions. The application of graph processing and graph DBMSs will grow at 100 percent annually through 2022 to continuously accelerate data preparation and enable more complex and adaptive data science. Graph data stores can efficiently model, explore, and query data with complex interrelationships across data silos, but the need for specialized skills has limited their adoption to date, according to Gartner. Graph analytics will grow in the next few years due to the need to ask complex questions across complex data, which is not always practical or even possible at scale using SQL queries. Trend No. 6: Data Fabric Data fabric enables frictionless access and sharing of data in a distributed data environment. It enables a single and consistent data management framework, which allows seamless data access and processing by design across otherwise siloed storage. Through 2022, bespoke data fabric designs will be deployed primarily as a static infrastructure, forcing organizations into a new wave of cost to completely re-design for more dynamic data mesh approaches. Trend No. 7: NLP/ Conversational Analytics By 2020, 50 percent of analytical queries will be generated via search, natural language processing (NLP) or voice, or will be automatically generated. The need to analyze complex combinations of data and to make analytics accessible to everyone in the organization will drive broader adoption, allowing analytics tools to be as easy as a search interface or a conversation with a virtual assistant. Trend No. 8: Commercial AI and ML It is predicted that by 2022, 75 percent of new end-user solutions leveraging AI and ML techniques will be built with commercial solutions rather than open source platforms. Commercial vendors have now built connectors into the Open Source ecosystem and they provide the enterprise features necessary to scale and democratize AI and ML, such as project and model management, reuse, transparency, data lineage, and platform cohesiveness and integration that Open Source technologies lack. Trend No. 9: Blockchain The core value proposition of blockchain, and distributed ledger technologies, is providing decentralized trust across a network of untrusted participants. The potential ramifications for analytics use cases are significant, especially those leveraging participant relationships and interactions. However, it will be several years before four or five major blockchain technologies become dominant. Until that happens, technology end users will be forced to integrate with the blockchain technologies and standards dictated by their dominant customers or networks. This includes integration with existing data and analytics infrastructure. The costs of integration may outweigh any potential benefit. Blockchains are a data source, not a database, and will not replace existing data management technologies. Trend No. 10: Persistent Memory Servers New persistent-memory technologies will help reduce costs and complexity of adopting in-memory computing (IMC)-enabled architectures. Persistent memory represents a new memory tier between DRAM and NAND flash memory that can provide cost-effective mass memory for high-performance workloads. It has the potential to improve application performance, availability, boot times, clustering methods and security practices, while keeping costs under control. It will also help organizations reduce the complexity of their application and data architectures by decreasing the need for data duplication. “The amount of data is growing quickly and the urgency of transforming data into value in real-time is growing at an equally rapid pace,” Feinberg said. “New server workloads are demanding not just faster CPU performance, but massive memory and faster storage.” Upcoming Data & Analytics Summits Gartner Data & Analytics Summits 2019 will take place March 18-21 in Orlando, May 29-30 in Sao Paulo, June 10-11 in Mumbai, September 11-12 in Mexico City and October 19-20 in Frankfurt. Follow news and updates from the events on Twitter using #GartnerDA. Article published by icrunchdata Image credit by Getty Images, Moment, Juhari Muhade Want more? For Job Seekers | For Employers | For Influencers
Whether monitoring for competitive intelligence, strategic innovation, or asset protection, the real-time monitoring of open information sources or streams, the so-called Open Source Intelligence (OSINT), is quickly becoming an important concept and core, strategic activity in any organization. Having poor information-monitoring practices is an expensive oversight, one that many organizations learn the hard way. Why has OSINT become so important? Take the example of the 1975 film Three Days of the Condor . It is the original OSINT adventure: books, magazines in foreign languages (aka “open source intelligence” by today’s standards) – read and monitored by humans (in this case, Robert Redford as Joe Turner, the Condor) and converted from analog to digital by powerful computers that could search for hidden messages or codes. Alone, a single piece of information may have little value. But when combined with hundreds of other pieces of information, that one little piece could be the essential element that ties the whole picture together. With today’s technology, and diversity of sources, analysts can do even more than the Condor could have imagined. Building out the big picture and making important connections between even disparate pieces of information, in a variety of languages, may mean collecting hundreds of pieces of information from as many sources. Today’s OSINT is being used in a number of capacities, from investigating corruption and other criminal activity, to tracking terrorists or stolen works of art, or protecting people and companies from cyber attacks but also for more common activities like monitoring the competition, the market trends. When the discussion progresses from “if” to “how” such activities should be implemented, not surprisingly, there are many different approaches. Although traditionally outsourced reports such as social media monitoring are increasingly being done in-house, other activities are still being outsourced to “experts”. A standard criteria for outsourcing best practices, like those mentioned in “Linking Outsourcing to Business Strategy” by Richard C. Insinga and Michael J. Werle ( Academy of Management Executive , Vol. 14, Nov.4, November 2000), tasks organizations to evaluate “the likelihood a task will bring competitive advantage” against their own internal capability for doing it effectively. Following these guidelines, a task that could bring competitive advantage would be better outsourced if the organization is unable to optimally manage it in-house. OSINT analysis often falls into this category. In fact, few organizations, mainly in selected industries, have the internal capabilities for effectively running OSINT in house. However, I would argue that, given the value of such information, not bringing OSINT in house could itself compromise the organization’s viability in the long term (and more likely, even sooner). A better strategy: outsource now, but start investing in the people, processes and software infrastructure to progressively bring this activity in house. Start with a few selected activities (reputation management, supplier analysis, country risk, etc.) and continue to expand the scope of your corporate OSINT strategy. If you don’t have a strategy in place to monitor what’s important, when it’s of most importance, you’re ignoring one of the most basic elements of operational risk mitigation. Stronger skills in this area together with the knowledge of your business environment will allow you to become much better and more effective for your business than any external experts. No matter how effective they may be, no one knows how important a specific piece of information can be to your business like you do. Article written by Luca Scagliarini Want more? For Job Seekers | For Employers | For Influencers
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