analytic - Blog - Global Risk Community2024-03-29T11:10:49Zhttps://globalriskcommunity.com/profiles/blogs/feed/tag/analyticA Startling Fact about Analytics-driven Organizationshttps://globalriskcommunity.com/profiles/blogs/a-startling-fact-about-analytics-driven-organizations2020-03-20T06:30:00.000Z2020-03-20T06:30:00.000ZJoseph Robinsonhttps://globalriskcommunity.com/members/JosephRobinson808<div><p>Enterprises invest in <a href="https://flevy.com/business-toolkit/analytics">Analytics</a> to improve <a href="https://flevy.com/business-toolkit/decision-making">Decision Making</a> and outcomes across the business. This is from <a href="https://flevy.com/business-toolkit/product-strategy-prod">Product Strategy</a> and <a href="https://flevy.com/browse/stream/innovation">Innovation</a> to <a href="https://flevy.com/business-toolkit/supply-chain-management">Supply Chain Management</a>, <a href="https://flevy.com/business-toolkit/customer-experience">Customer Experience</a>, and <a href="https://flevy.com/business-toolkit/risk-management">Risk Management</a>. Yet, many <a href="{{#staticFileLink}}8028316454,original{{/staticFileLink}}" target="_blank"><img src="{{#staticFileLink}}8028316454,original{{/staticFileLink}}" class="align-right" alt="8028316454?profile=original" /></a>executives are not yet seeing the results of their Analytics initiatives and investments.</p><p>Every organization putting on investment in Analytics has experienced several stumbling blocks. This differentiates the leaders from the laggards. <a href="https://flevy.com/browse/flevypro/analytics-driven-organization-4019">Analytics-driven Organizations</a> have clearly established processes, practices, and organizational conditions to achieve <a href="https://flevy.com/operational-excellence">Operational Excellence</a>. Their commitment to Analytics is creating a major payoff from their investments and a competitive edge.</p><h3>What It Takes to Be Analytics-driven</h3><p>The <a href="https://hbr.org/hbr-analytic-services">Harvard Business Review Analytic Services</a> conducted a survey of 744 business executives around the world and across a variety of industries. Their focus was on the performance gap between companies that have struggled to get a return on their Analytics investment and those that have effectively leveraged their investment.</p><p>The survey showed that Analytics-driven Organizations get sufficient return on investment in Analytics. In fact, they have been highly successful in gaining a return on Analytics investment. This is gainfully achieved as organizations use Analytics consistently in strategic decision making. Executives of Analytics-driven Organizations rely on Analytics insights when it contradicted their gut feel.</p><p>Essentially, Analytics-driven Organizations have reduced costs and risks, increased Productivity, Revenue, and Innovation, and have successfully executed their Strategy. Yet, in evolving the organization’s Analytics approach, there can be 4 core obstacles that can affect their drive to getting a greater return on investment in Analytics.</p><h3>The Core Obstacles to Finding Return on Analytics Investment</h3><p>There are <a href="https://flevy.com/browse/flevypro/analytics-driven-organization-4019">4 core obstacles to being an Analytics-driven Organization</a>.</p><p><a href="https://flevy.com/browse/flevypro/analytics-driven-organization-4019" target="_blank"><img src="http://flevy.com/blog/wp-content/uploads/2020/01/pic-2-Analytics-driven-Organization.png?profile=RESIZE_710x" width="750" class="align-full" alt="pic-2-Analytics-driven-Organization.png?profile=RESIZE_710x" /></a></p><p>Let's briefly take a look at the first 2 obstacles:</p><ol><li><strong>Communication and Decision-making Integration</strong>. The lack of Communication and Decision-making Integration limits the integration of Analytics into workflows and decision processes do not reach decision-makers. As a result of these core obstacles, the use of Analytics is limited in specific areas.</li></ol><ol start="2"><li><strong> Skills to Interpret and Apply Analytics</strong>. A second core obstacle is the inadequate skills of business staff to interpret and use Analytics. In fact, the survey showed that only one-quarter of frontline employees use Analytics with only 7% using Analytics regularly.</li></ol><p>The other two core obstacles are siloed and fragmented Analytics and time delay. These are two equally important core obstacles that can hinder the use of Analytics to maximize return on investment. Further, the 4 core obstacles are barriers to analytic success.</p><h3>Are You Ready to Be an Analytics Leader?</h3><p>Leaders use Analytics consistently in decision making. In fact, based on the survey, 83% of executives use it in business planning and forecasting. On the other hand, laggards only use it 67% of the time. Even in various aspects of the organization such as Marketing, Operations, <a href="https://flevy.com/browse/stream/strategy-development">Strategy Development</a>, Sales, Supply Chain, Pricing and Revenue Management, and Information Technology, laggards use Analytics only half the time compared to Analytics Leaders.</p><p><a href="https://flevy.com/browse/flevypro/analytics-driven-organization-4019">Analytics Leaders</a> always ensure that they establish the processes and organizational conditions to allow them to successfully deploy Analytics. In fact, to increase return on Analytics, organizations must undertake the use of four interrelated initiatives that will drive greater return on investment Analytics. These are four initiatives essential to building an Analytics-driven Organization.</p><p>One is building an organizational culture around Analytics. To achieve this the organization must have clear, strategic, and operational objectives that are set for Analytics. Second is deploying Analytics throughout all core functions of the business.</p><blockquote><p>Starting with an Analytics-driven Culture can greatly facilitate cross-functional deployment of Analytics.</p></blockquote><p>Interested in gaining more understanding of <a href="https://flevy.com/browse/flevypro/analytics-driven-organization-4019">Analytics-driven Organization</a>? You can learn more and download an <a href="https://flevy.com/browse/flevypro/analytics-driven-organization-4019">editable PowerPoint about <strong>Analytics-driven Organization</strong> here</a> on the <a href="https://flevy.com/browse">Flevy documents marketplace</a>.</p><p><strong>Are you a management consultant?</strong></p><p>You can download this and hundreds of other <a href="http://flevy.com/pro/library/frameworks">consulting frameworks</a> and <a href="http://flevy.com/pro/library/consulting">consulting training guides</a> from the <a href="http://flevy.com/pro/library">FlevyPro library</a>.</p></div>Looking for partners with CFAR-m (risk analytic)https://globalriskcommunity.com/profiles/blogs/looking-for-partners-with-cfar-m-risk-analytic2012-07-22T01:52:34.000Z2012-07-22T01:52:34.000ZRemi Molliconehttps://globalriskcommunity.com/members/RemiMollicone<div><p>CFAR-m main features (unique algo and features) </p><p><br />Aggregation is a way to combine several single indicators representing different components (dimensions) <br />of the same concept to form a single aggregate. The result leads to a single score, called a composite <br />indicator, which has the ability to summarize a large amount of information in a comprehensible form .<br />Aggregation requires the determination of a weighting scheme of the different components. This task is <br />extremely difficult and is one of the central problems in the construction of composite indicators. <br />Weights must take into account all existing forms of interaction between the components aggregated and <br />have a significant effect on the result. However, there is no universally agreed methodology and the <br />arbitrary nature of the weighting process by which components are combined constitutes the main <br />weakness of composite indicators which CFAR-m overcomes.<br />CFAR-m OVERCOMES THIS PROBLEM:<br /> CFAR-m is an original method of aggregation based on neural networks which can summarize <br />with great objectivity the information contained in a large number of variables emanating from <br />many different fields.<br /> Its contribution lies in determining, from the database itself, a wei ghting scheme of variables <br />specific to each individual. CFAR-m solves the major problem of fixing the subjective importance <br />of each variable in the aggregation.<br /> It avoids the adoption of an equal weighting or a weighting based on exogenous criteria. Th e <br />weightings for CFAR-m emanate only from the information content of variables themselves and <br />their own internal dynamics.<br />THE RANKING PROVIDED BY CFAR-m HAS THE FOLLOWING ENABLES THE FOLLOWING <br />ADVANTAGES:<br /> Objectivity: No handling of weightings - the weighting is resolutely objective and it emanates from <br />the informational content of the variables themselves of their research and internal dynamics.<br /> Specificity: a specific equation for each individual piece of data to is used calculate the indicator<br /> Decision support: ability to run simulations and propose to the decision makers plans of action <br />and optimal sequences of reforms.<br />In addition:<br /> It can provides the contribution of the variables to the ranking<br /> It keeps all the variables during the calculus and so it is helpful for extracting what is happening <br />within the noise. This is very interesting for predicitve model</p><p></p><p>-- <br />Remi Mollicone<br />remi@cfar-m.com<br />Innovation, Alliances/Partnerships, Business Development<br />Tel: +33 6 30 72 90 13<br />Tel: +33 6 27 70 56 76<br />Fax: +33 9 59 12 01 82<br />Skype: remimollicone7<br /><a href="http://www.cfar-m.com">www.cfar-m.com</a></p></div>