These 6 Statistics will show you why it's time for a Digital Transformation.<\/p>\n\n\n\n
Silicon Valley Innovation Center<\/strong><\/a> helps financial sector executives experience and connect with the Silicon Valley fintech startup ecosystem through the Leading Digital Transformation executive immersion program<\/strong><\/a>. As Silicon Valley is a hotbed of big data innovation, company executives benefit greatly from visiting the innovation hub and interacting with startups<\/strong> like the ones mentioned in this article. Through this immersive experience, executives also gain deep insights<\/strong> into how partnering with Silicon Valley startups<\/strong> can be a game-changer<\/strong> for their businesses.<\/p>\n\n\n\n Silicon Valley Innovation Center<\/strong><\/a> helps financial sector executives experience and connect with the Silicon Valley fintech startup ecosystem through the Leading Digital Transformation executive immersion program<\/strong><\/a>. As Silicon Valley is a hotbed of big data innovation, company executives benefit greatly from visiting the innovation hub and interacting with startups<\/strong> like the ones mentioned in this article. Through this immersive experience, executives also gain deep insights<\/strong> into how partnering with Silicon Valley startups<\/strong> can be a game-changer<\/strong> for their businesses.<\/p>\n\n\n\n Silicon Valley Innovation Center<\/strong><\/a> helps financial sector executives experience and connect with the Silicon Valley fintech startup ecosystem through the Leading Digital Transformation executive immersion program<\/strong><\/a>. As Silicon Valley is a hotbed of big data innovation, company executives benefit greatly from visiting the innovation hub and interacting with startups<\/strong> like the ones mentioned in this article. Through this immersive experience, executives also gain deep insights<\/strong> into how partnering with Silicon Valley startups<\/strong> can be a game-changer<\/strong> for their businesses.<\/p>\n\n\n\n Silicon Valley Innovation Center<\/strong><\/a> helps financial sector executives experience and connect with the Silicon Valley fintech startup ecosystem through the Leading Digital Transformation executive immersion program<\/strong><\/a>. As Silicon Valley is a hotbed of big data innovation, company executives benefit greatly from visiting the innovation hub and interacting with startups<\/strong> like the ones mentioned in this article. Through this immersive experience, executives also gain deep insights<\/strong> into how partnering with Silicon Valley startups<\/strong> can be a game-changer<\/strong> for their businesses.<\/p>\n\n\n\n Data is emerging as the ultimate competitive advantage as it gives companies near-seer capabilities to understand and anticipate customer needs. For companies keen on experimenting with big data, it is crucial to implement a roadmap to achieve the capabilities outlined above based on the following key structures:<\/p>\n\n\n\n Silicon Valley Innovation Center<\/strong><\/a> helps financial sector executives experience and connect with the Silicon Valley fintech startup ecosystem through the Leading Digital Transformation executive immersion program<\/strong><\/a>. As Silicon Valley is a hotbed of big data innovation, company executives benefit greatly from visiting the innovation hub and interacting with startups<\/strong> like the ones mentioned in this article. Through this immersive experience, executives also gain deep insights<\/strong> into how partnering with Silicon Valley startups<\/strong> can be a game-changer<\/strong> for their businesses.<\/p>\n\n\n\n Data is emerging as the ultimate competitive advantage as it gives companies near-seer capabilities to understand and anticipate customer needs. For companies keen on experimenting with big data, it is crucial to implement a roadmap to achieve the capabilities outlined above based on the following key structures:<\/p>\n\n\n\n Silicon Valley Innovation Center<\/strong><\/a> helps financial sector executives experience and connect with the Silicon Valley fintech startup ecosystem through the Leading Digital Transformation executive immersion program<\/strong><\/a>. As Silicon Valley is a hotbed of big data innovation, company executives benefit greatly from visiting the innovation hub and interacting with startups<\/strong> like the ones mentioned in this article. Through this immersive experience, executives also gain deep insights<\/strong> into how partnering with Silicon Valley startups<\/strong> can be a game-changer<\/strong> for their businesses.<\/p>\n\n\n\n Companies can similarly accelerate their own discovery efforts by analyzing their data either to find improvement opportunities to existing solutions or discover novel ideas for new products and services. The discovery process must, however, be guided by business-level governance models that focus on innovations that either strengthen or build on existing core competencies. Partnering with Silicon Valley startups<\/a> may be a viable option, especially if the company wants to build upon already proven technologies and approaches.<\/p>\n\n\n\n Data is emerging as the ultimate competitive advantage as it gives companies near-seer capabilities to understand and anticipate customer needs. For companies keen on experimenting with big data, it is crucial to implement a roadmap to achieve the capabilities outlined above based on the following key structures:<\/p>\n\n\n\n Silicon Valley Innovation Center<\/strong><\/a> helps financial sector executives experience and connect with the Silicon Valley fintech startup ecosystem through the Leading Digital Transformation executive immersion program<\/strong><\/a>. As Silicon Valley is a hotbed of big data innovation, company executives benefit greatly from visiting the innovation hub and interacting with startups<\/strong> like the ones mentioned in this article. Through this immersive experience, executives also gain deep insights<\/strong> into how partnering with Silicon Valley startups<\/strong> can be a game-changer<\/strong> for their businesses.<\/p>\n\n\n\n Creating new drugs is often a 10-15-year cycle that involves hundreds of lab experiments, most with a 90% failure rate. IBM is using big data to slash this cycle timeframe. At its Almaden research center in San Jose, California, the company is pulling in data<\/a> from diverse sources including patent applications, university publications, science journals among others and processing this data using Watson Ai to find combinations of data that can offer insights to accelerate development of breakthrough solutions.<\/p>\n\n\n\n Companies can similarly accelerate their own discovery efforts by analyzing their data either to find improvement opportunities to existing solutions or discover novel ideas for new products and services. The discovery process must, however, be guided by business-level governance models that focus on innovations that either strengthen or build on existing core competencies. Partnering with Silicon Valley startups<\/a> may be a viable option, especially if the company wants to build upon already proven technologies and approaches.<\/p>\n\n\n\n Data is emerging as the ultimate competitive advantage as it gives companies near-seer capabilities to understand and anticipate customer needs. For companies keen on experimenting with big data, it is crucial to implement a roadmap to achieve the capabilities outlined above based on the following key structures:<\/p>\n\n\n\n Silicon Valley Innovation Center<\/strong><\/a> helps financial sector executives experience and connect with the Silicon Valley fintech startup ecosystem through the Leading Digital Transformation executive immersion program<\/strong><\/a>. As Silicon Valley is a hotbed of big data innovation, company executives benefit greatly from visiting the innovation hub and interacting with startups<\/strong> like the ones mentioned in this article. Through this immersive experience, executives also gain deep insights<\/strong> into how partnering with Silicon Valley startups<\/strong> can be a game-changer<\/strong> for their businesses.<\/p>\n\n\n\n Creating new drugs is often a 10-15-year cycle that involves hundreds of lab experiments, most with a 90% failure rate. IBM is using big data to slash this cycle timeframe. At its Almaden research center in San Jose, California, the company is pulling in data<\/a> from diverse sources including patent applications, university publications, science journals among others and processing this data using Watson Ai to find combinations of data that can offer insights to accelerate development of breakthrough solutions.<\/p>\n\n\n\n Companies can similarly accelerate their own discovery efforts by analyzing their data either to find improvement opportunities to existing solutions or discover novel ideas for new products and services. The discovery process must, however, be guided by business-level governance models that focus on innovations that either strengthen or build on existing core competencies. Partnering with Silicon Valley startups<\/a> may be a viable option, especially if the company wants to build upon already proven technologies and approaches.<\/p>\n\n\n\n Data is emerging as the ultimate competitive advantage as it gives companies near-seer capabilities to understand and anticipate customer needs. For companies keen on experimenting with big data, it is crucial to implement a roadmap to achieve the capabilities outlined above based on the following key structures:<\/p>\n\n\n\n Silicon Valley Innovation Center<\/strong><\/a> helps financial sector executives experience and connect with the Silicon Valley fintech startup ecosystem through the Leading Digital Transformation executive immersion program<\/strong><\/a>. As Silicon Valley is a hotbed of big data innovation, company executives benefit greatly from visiting the innovation hub and interacting with startups<\/strong> like the ones mentioned in this article. Through this immersive experience, executives also gain deep insights<\/strong> into how partnering with Silicon Valley startups<\/strong> can be a game-changer<\/strong> for their businesses.<\/p>\n\n\n\n Big data, on the other hand, is inherently capable of accomplishing this. For instance, by transcribing support calls and performing sentiment analysis, it is possible to isolate and reward support agents who went above and beyond their mandate when providing support. Also, processing internal corporate communications can help identify gaps in collaboration, information and skill silos and what actions employees can take to address these issues. Such actions can similarly be identified and rewarded as a means of enforcement.<\/p>\n\n\n\n Creating new drugs is often a 10-15-year cycle that involves hundreds of lab experiments, most with a 90% failure rate. IBM is using big data to slash this cycle timeframe. At its Almaden research center in San Jose, California, the company is pulling in data<\/a> from diverse sources including patent applications, university publications, science journals among others and processing this data using Watson Ai to find combinations of data that can offer insights to accelerate development of breakthrough solutions.<\/p>\n\n\n\n Companies can similarly accelerate their own discovery efforts by analyzing their data either to find improvement opportunities to existing solutions or discover novel ideas for new products and services. The discovery process must, however, be guided by business-level governance models that focus on innovations that either strengthen or build on existing core competencies. Partnering with Silicon Valley startups<\/a> may be a viable option, especially if the company wants to build upon already proven technologies and approaches.<\/p>\n\n\n\n Data is emerging as the ultimate competitive advantage as it gives companies near-seer capabilities to understand and anticipate customer needs. For companies keen on experimenting with big data, it is crucial to implement a roadmap to achieve the capabilities outlined above based on the following key structures:<\/p>\n\n\n\n Silicon Valley Innovation Center<\/strong><\/a> helps financial sector executives experience and connect with the Silicon Valley fintech startup ecosystem through the Leading Digital Transformation executive immersion program<\/strong><\/a>. As Silicon Valley is a hotbed of big data innovation, company executives benefit greatly from visiting the innovation hub and interacting with startups<\/strong> like the ones mentioned in this article. Through this immersive experience, executives also gain deep insights<\/strong> into how partnering with Silicon Valley startups<\/strong> can be a game-changer<\/strong> for their businesses.<\/p>\n\n\n\n It is well-known that happy employees make happy customers. This is supported by a Gallup poll that found that increased employee engagement resulted in improved customer relationships and a corresponding 20% jump in sales. One of the major challenges with achieving this happy employee-happy customer matrix is identifying employee efforts and rewarding them accordingly. This is something traditional scorecards, performance reviews, and other KPI-measuring tools cannot effectively do.<\/p>\n\n\n\n Big data, on the other hand, is inherently capable of accomplishing this. For instance, by transcribing support calls and performing sentiment analysis, it is possible to isolate and reward support agents who went above and beyond their mandate when providing support. Also, processing internal corporate communications can help identify gaps in collaboration, information and skill silos and what actions employees can take to address these issues. Such actions can similarly be identified and rewarded as a means of enforcement.<\/p>\n\n\n\n Creating new drugs is often a 10-15-year cycle that involves hundreds of lab experiments, most with a 90% failure rate. IBM is using big data to slash this cycle timeframe. At its Almaden research center in San Jose, California, the company is pulling in data<\/a> from diverse sources including patent applications, university publications, science journals among others and processing this data using Watson Ai to find combinations of data that can offer insights to accelerate development of breakthrough solutions.<\/p>\n\n\n\n Companies can similarly accelerate their own discovery efforts by analyzing their data either to find improvement opportunities to existing solutions or discover novel ideas for new products and services. The discovery process must, however, be guided by business-level governance models that focus on innovations that either strengthen or build on existing core competencies. Partnering with Silicon Valley startups<\/a> may be a viable option, especially if the company wants to build upon already proven technologies and approaches.<\/p>\n\n\n\n Data is emerging as the ultimate competitive advantage as it gives companies near-seer capabilities to understand and anticipate customer needs. For companies keen on experimenting with big data, it is crucial to implement a roadmap to achieve the capabilities outlined above based on the following key structures:<\/p>\n\n\n\n Silicon Valley Innovation Center<\/strong><\/a> helps financial sector executives experience and connect with the Silicon Valley fintech startup ecosystem through the Leading Digital Transformation executive immersion program<\/strong><\/a>. As Silicon Valley is a hotbed of big data innovation, company executives benefit greatly from visiting the innovation hub and interacting with startups<\/strong> like the ones mentioned in this article. Through this immersive experience, executives also gain deep insights<\/strong> into how partnering with Silicon Valley startups<\/strong> can be a game-changer<\/strong> for their businesses.<\/p>\n\n\n\n It is well-known that happy employees make happy customers. This is supported by a Gallup poll that found that increased employee engagement resulted in improved customer relationships and a corresponding 20% jump in sales. One of the major challenges with achieving this happy employee-happy customer matrix is identifying employee efforts and rewarding them accordingly. This is something traditional scorecards, performance reviews, and other KPI-measuring tools cannot effectively do.<\/p>\n\n\n\n Big data, on the other hand, is inherently capable of accomplishing this. For instance, by transcribing support calls and performing sentiment analysis, it is possible to isolate and reward support agents who went above and beyond their mandate when providing support. Also, processing internal corporate communications can help identify gaps in collaboration, information and skill silos and what actions employees can take to address these issues. Such actions can similarly be identified and rewarded as a means of enforcement.<\/p>\n\n\n\n Creating new drugs is often a 10-15-year cycle that involves hundreds of lab experiments, most with a 90% failure rate. IBM is using big data to slash this cycle timeframe. At its Almaden research center in San Jose, California, the company is pulling in data<\/a> from diverse sources including patent applications, university publications, science journals among others and processing this data using Watson Ai to find combinations of data that can offer insights to accelerate development of breakthrough solutions.<\/p>\n\n\n\n Companies can similarly accelerate their own discovery efforts by analyzing their data either to find improvement opportunities to existing solutions or discover novel ideas for new products and services. The discovery process must, however, be guided by business-level governance models that focus on innovations that either strengthen or build on existing core competencies. Partnering with Silicon Valley startups<\/a> may be a viable option, especially if the company wants to build upon already proven technologies and approaches.<\/p>\n\n\n\n Data is emerging as the ultimate competitive advantage as it gives companies near-seer capabilities to understand and anticipate customer needs. For companies keen on experimenting with big data, it is crucial to implement a roadmap to achieve the capabilities outlined above based on the following key structures:<\/p>\n\n\n\n Silicon Valley Innovation Center<\/strong><\/a> helps financial sector executives experience and connect with the Silicon Valley fintech startup ecosystem through the Leading Digital Transformation executive immersion program<\/strong><\/a>. As Silicon Valley is a hotbed of big data innovation, company executives benefit greatly from visiting the innovation hub and interacting with startups<\/strong> like the ones mentioned in this article. Through this immersive experience, executives also gain deep insights<\/strong> into how partnering with Silicon Valley startups<\/strong> can be a game-changer<\/strong> for their businesses.<\/p>\n\n\n\n Organizations seeking similar capabilities must optimize their data mining and analytics efforts to identify areas where greater customer-focused productivity can be achieved.<\/p>\n\n\n\n It is well-known that happy employees make happy customers. This is supported by a Gallup poll that found that increased employee engagement resulted in improved customer relationships and a corresponding 20% jump in sales. One of the major challenges with achieving this happy employee-happy customer matrix is identifying employee efforts and rewarding them accordingly. This is something traditional scorecards, performance reviews, and other KPI-measuring tools cannot effectively do.<\/p>\n\n\n\n Big data, on the other hand, is inherently capable of accomplishing this. For instance, by transcribing support calls and performing sentiment analysis, it is possible to isolate and reward support agents who went above and beyond their mandate when providing support. Also, processing internal corporate communications can help identify gaps in collaboration, information and skill silos and what actions employees can take to address these issues. Such actions can similarly be identified and rewarded as a means of enforcement.<\/p>\n\n\n\n Creating new drugs is often a 10-15-year cycle that involves hundreds of lab experiments, most with a 90% failure rate. IBM is using big data to slash this cycle timeframe. At its Almaden research center in San Jose, California, the company is pulling in data<\/a> from diverse sources including patent applications, university publications, science journals among others and processing this data using Watson Ai to find combinations of data that can offer insights to accelerate development of breakthrough solutions.<\/p>\n\n\n\n Companies can similarly accelerate their own discovery efforts by analyzing their data either to find improvement opportunities to existing solutions or discover novel ideas for new products and services. The discovery process must, however, be guided by business-level governance models that focus on innovations that either strengthen or build on existing core competencies. Partnering with Silicon Valley startups<\/a> may be a viable option, especially if the company wants to build upon already proven technologies and approaches.<\/p>\n\n\n\n Data is emerging as the ultimate competitive advantage as it gives companies near-seer capabilities to understand and anticipate customer needs. For companies keen on experimenting with big data, it is crucial to implement a roadmap to achieve the capabilities outlined above based on the following key structures:<\/p>\n\n\n\n Silicon Valley Innovation Center<\/strong><\/a> helps financial sector executives experience and connect with the Silicon Valley fintech startup ecosystem through the Leading Digital Transformation executive immersion program<\/strong><\/a>. As Silicon Valley is a hotbed of big data innovation, company executives benefit greatly from visiting the innovation hub and interacting with startups<\/strong> like the ones mentioned in this article. Through this immersive experience, executives also gain deep insights<\/strong> into how partnering with Silicon Valley startups<\/strong> can be a game-changer<\/strong> for their businesses.<\/p>\n\n\n\n For instance, it is conceivable that processing returns quickly translates into customers resuming purchasing faster. This could be the main reason the company has fine-tuned return processing at a customer care level, which points to customer-focused productivity, as data shows this is a path to ensuring uninterrupted purchasing flows.<\/p>\n\n\n\n Organizations seeking similar capabilities must optimize their data mining and analytics efforts to identify areas where greater customer-focused productivity can be achieved.<\/p>\n\n\n\n It is well-known that happy employees make happy customers. This is supported by a Gallup poll that found that increased employee engagement resulted in improved customer relationships and a corresponding 20% jump in sales. One of the major challenges with achieving this happy employee-happy customer matrix is identifying employee efforts and rewarding them accordingly. This is something traditional scorecards, performance reviews, and other KPI-measuring tools cannot effectively do.<\/p>\n\n\n\n Big data, on the other hand, is inherently capable of accomplishing this. For instance, by transcribing support calls and performing sentiment analysis, it is possible to isolate and reward support agents who went above and beyond their mandate when providing support. Also, processing internal corporate communications can help identify gaps in collaboration, information and skill silos and what actions employees can take to address these issues. Such actions can similarly be identified and rewarded as a means of enforcement.<\/p>\n\n\n\n Creating new drugs is often a 10-15-year cycle that involves hundreds of lab experiments, most with a 90% failure rate. IBM is using big data to slash this cycle timeframe. At its Almaden research center in San Jose, California, the company is pulling in data<\/a> from diverse sources including patent applications, university publications, science journals among others and processing this data using Watson Ai to find combinations of data that can offer insights to accelerate development of breakthrough solutions.<\/p>\n\n\n\n Companies can similarly accelerate their own discovery efforts by analyzing their data either to find improvement opportunities to existing solutions or discover novel ideas for new products and services. The discovery process must, however, be guided by business-level governance models that focus on innovations that either strengthen or build on existing core competencies. Partnering with Silicon Valley startups<\/a> may be a viable option, especially if the company wants to build upon already proven technologies and approaches.<\/p>\n\n\n\n Data is emerging as the ultimate competitive advantage as it gives companies near-seer capabilities to understand and anticipate customer needs. For companies keen on experimenting with big data, it is crucial to implement a roadmap to achieve the capabilities outlined above based on the following key structures:<\/p>\n\n\n\n Silicon Valley Innovation Center<\/strong><\/a> helps financial sector executives experience and connect with the Silicon Valley fintech startup ecosystem through the Leading Digital Transformation executive immersion program<\/strong><\/a>. As Silicon Valley is a hotbed of big data innovation, company executives benefit greatly from visiting the innovation hub and interacting with startups<\/strong> like the ones mentioned in this article. Through this immersive experience, executives also gain deep insights<\/strong> into how partnering with Silicon Valley startups<\/strong> can be a game-changer<\/strong> for their businesses.<\/p>\n\n\n\n Amazon is well-known as a customer-centric company. What drives this culture is not just goodwill, but technology. The company is maniacal about optimizing everything through technology. By using data collected from millions of purchases, returns, customer care inquiries, and other data points, the company has managed to implement customer-focused processes that have clearly identifiable outcomes.<\/p>\n\n\n\n For instance, it is conceivable that processing returns quickly translates into customers resuming purchasing faster. This could be the main reason the company has fine-tuned return processing at a customer care level, which points to customer-focused productivity, as data shows this is a path to ensuring uninterrupted purchasing flows.<\/p>\n\n\n\n Organizations seeking similar capabilities must optimize their data mining and analytics efforts to identify areas where greater customer-focused productivity can be achieved.<\/p>\n\n\n\n It is well-known that happy employees make happy customers. This is supported by a Gallup poll that found that increased employee engagement resulted in improved customer relationships and a corresponding 20% jump in sales. One of the major challenges with achieving this happy employee-happy customer matrix is identifying employee efforts and rewarding them accordingly. This is something traditional scorecards, performance reviews, and other KPI-measuring tools cannot effectively do.<\/p>\n\n\n\n Big data, on the other hand, is inherently capable of accomplishing this. For instance, by transcribing support calls and performing sentiment analysis, it is possible to isolate and reward support agents who went above and beyond their mandate when providing support. Also, processing internal corporate communications can help identify gaps in collaboration, information and skill silos and what actions employees can take to address these issues. Such actions can similarly be identified and rewarded as a means of enforcement.<\/p>\n\n\n\n Creating new drugs is often a 10-15-year cycle that involves hundreds of lab experiments, most with a 90% failure rate. IBM is using big data to slash this cycle timeframe. At its Almaden research center in San Jose, California, the company is pulling in data<\/a> from diverse sources including patent applications, university publications, science journals among others and processing this data using Watson Ai to find combinations of data that can offer insights to accelerate development of breakthrough solutions.<\/p>\n\n\n\n Companies can similarly accelerate their own discovery efforts by analyzing their data either to find improvement opportunities to existing solutions or discover novel ideas for new products and services. The discovery process must, however, be guided by business-level governance models that focus on innovations that either strengthen or build on existing core competencies. Partnering with Silicon Valley startups<\/a> may be a viable option, especially if the company wants to build upon already proven technologies and approaches.<\/p>\n\n\n\n Data is emerging as the ultimate competitive advantage as it gives companies near-seer capabilities to understand and anticipate customer needs. For companies keen on experimenting with big data, it is crucial to implement a roadmap to achieve the capabilities outlined above based on the following key structures:<\/p>\n\n\n\n Silicon Valley Innovation Center<\/strong><\/a> helps financial sector executives experience and connect with the Silicon Valley fintech startup ecosystem through the Leading Digital Transformation executive immersion program<\/strong><\/a>. As Silicon Valley is a hotbed of big data innovation, company executives benefit greatly from visiting the innovation hub and interacting with startups<\/strong> like the ones mentioned in this article. Through this immersive experience, executives also gain deep insights<\/strong> into how partnering with Silicon Valley startups<\/strong> can be a game-changer<\/strong> for their businesses.<\/p>\n\n\n\n Customer-centricity is a growing trend among leading global companies. Driven by the rise in social media and the resultant brand transparency this has forced on companies, today, customer centricity is as much an essential business strategy as any other. But making that transition is often fraught with corporate culture challenges, especially for large legacy businesses that have been focused inwards for decades. This shift can be made easier through big data.<\/p>\n\n\n\n Amazon is well-known as a customer-centric company. What drives this culture is not just goodwill, but technology. The company is maniacal about optimizing everything through technology. By using data collected from millions of purchases, returns, customer care inquiries, and other data points, the company has managed to implement customer-focused processes that have clearly identifiable outcomes.<\/p>\n\n\n\n For instance, it is conceivable that processing returns quickly translates into customers resuming purchasing faster. This could be the main reason the company has fine-tuned return processing at a customer care level, which points to customer-focused productivity, as data shows this is a path to ensuring uninterrupted purchasing flows.<\/p>\n\n\n\n Organizations seeking similar capabilities must optimize their data mining and analytics efforts to identify areas where greater customer-focused productivity can be achieved.<\/p>\n\n\n\n It is well-known that happy employees make happy customers. This is supported by a Gallup poll that found that increased employee engagement resulted in improved customer relationships and a corresponding 20% jump in sales. One of the major challenges with achieving this happy employee-happy customer matrix is identifying employee efforts and rewarding them accordingly. This is something traditional scorecards, performance reviews, and other KPI-measuring tools cannot effectively do.<\/p>\n\n\n\n Big data, on the other hand, is inherently capable of accomplishing this. For instance, by transcribing support calls and performing sentiment analysis, it is possible to isolate and reward support agents who went above and beyond their mandate when providing support. Also, processing internal corporate communications can help identify gaps in collaboration, information and skill silos and what actions employees can take to address these issues. Such actions can similarly be identified and rewarded as a means of enforcement.<\/p>\n\n\n\n Creating new drugs is often a 10-15-year cycle that involves hundreds of lab experiments, most with a 90% failure rate. IBM is using big data to slash this cycle timeframe. At its Almaden research center in San Jose, California, the company is pulling in data<\/a> from diverse sources including patent applications, university publications, science journals among others and processing this data using Watson Ai to find combinations of data that can offer insights to accelerate development of breakthrough solutions.<\/p>\n\n\n\n Companies can similarly accelerate their own discovery efforts by analyzing their data either to find improvement opportunities to existing solutions or discover novel ideas for new products and services. The discovery process must, however, be guided by business-level governance models that focus on innovations that either strengthen or build on existing core competencies. Partnering with Silicon Valley startups<\/a> may be a viable option, especially if the company wants to build upon already proven technologies and approaches.<\/p>\n\n\n\n Data is emerging as the ultimate competitive advantage as it gives companies near-seer capabilities to understand and anticipate customer needs. For companies keen on experimenting with big data, it is crucial to implement a roadmap to achieve the capabilities outlined above based on the following key structures:<\/p>\n\n\n\n Silicon Valley Innovation Center<\/strong><\/a> helps financial sector executives experience and connect with the Silicon Valley fintech startup ecosystem through the Leading Digital Transformation executive immersion program<\/strong><\/a>. As Silicon Valley is a hotbed of big data innovation, company executives benefit greatly from visiting the innovation hub and interacting with startups<\/strong> like the ones mentioned in this article. Through this immersive experience, executives also gain deep insights<\/strong> into how partnering with Silicon Valley startups<\/strong> can be a game-changer<\/strong> for their businesses.<\/p>\n\n\n\n Customer-centricity is a growing trend among leading global companies. Driven by the rise in social media and the resultant brand transparency this has forced on companies, today, customer centricity is as much an essential business strategy as any other. But making that transition is often fraught with corporate culture challenges, especially for large legacy businesses that have been focused inwards for decades. This shift can be made easier through big data.<\/p>\n\n\n\n Amazon is well-known as a customer-centric company. What drives this culture is not just goodwill, but technology. The company is maniacal about optimizing everything through technology. By using data collected from millions of purchases, returns, customer care inquiries, and other data points, the company has managed to implement customer-focused processes that have clearly identifiable outcomes.<\/p>\n\n\n\n For instance, it is conceivable that processing returns quickly translates into customers resuming purchasing faster. This could be the main reason the company has fine-tuned return processing at a customer care level, which points to customer-focused productivity, as data shows this is a path to ensuring uninterrupted purchasing flows.<\/p>\n\n\n\n Organizations seeking similar capabilities must optimize their data mining and analytics efforts to identify areas where greater customer-focused productivity can be achieved.<\/p>\n\n\n\n It is well-known that happy employees make happy customers. This is supported by a Gallup poll that found that increased employee engagement resulted in improved customer relationships and a corresponding 20% jump in sales. One of the major challenges with achieving this happy employee-happy customer matrix is identifying employee efforts and rewarding them accordingly. This is something traditional scorecards, performance reviews, and other KPI-measuring tools cannot effectively do.<\/p>\n\n\n\n Big data, on the other hand, is inherently capable of accomplishing this. For instance, by transcribing support calls and performing sentiment analysis, it is possible to isolate and reward support agents who went above and beyond their mandate when providing support. Also, processing internal corporate communications can help identify gaps in collaboration, information and skill silos and what actions employees can take to address these issues. Such actions can similarly be identified and rewarded as a means of enforcement.<\/p>\n\n\n\n Creating new drugs is often a 10-15-year cycle that involves hundreds of lab experiments, most with a 90% failure rate. IBM is using big data to slash this cycle timeframe. At its Almaden research center in San Jose, California, the company is pulling in data<\/a> from diverse sources including patent applications, university publications, science journals among others and processing this data using Watson Ai to find combinations of data that can offer insights to accelerate development of breakthrough solutions.<\/p>\n\n\n\n Companies can similarly accelerate their own discovery efforts by analyzing their data either to find improvement opportunities to existing solutions or discover novel ideas for new products and services. The discovery process must, however, be guided by business-level governance models that focus on innovations that either strengthen or build on existing core competencies. Partnering with Silicon Valley startups<\/a> may be a viable option, especially if the company wants to build upon already proven technologies and approaches.<\/p>\n\n\n\n Data is emerging as the ultimate competitive advantage as it gives companies near-seer capabilities to understand and anticipate customer needs. For companies keen on experimenting with big data, it is crucial to implement a roadmap to achieve the capabilities outlined above based on the following key structures:<\/p>\n\n\n\n Silicon Valley Innovation Center<\/strong><\/a> helps financial sector executives experience and connect with the Silicon Valley fintech startup ecosystem through the Leading Digital Transformation executive immersion program<\/strong><\/a>. As Silicon Valley is a hotbed of big data innovation, company executives benefit greatly from visiting the innovation hub and interacting with startups<\/strong> like the ones mentioned in this article. Through this immersive experience, executives also gain deep insights<\/strong> into how partnering with Silicon Valley startups<\/strong> can be a game-changer<\/strong> for their businesses.<\/p>\n\n\n\n Zooming in to smaller organizations, the same capabilities can be harnessed albeit at a smaller scale. To get started, organizations must first identify and tag their sources of data. This could be clickstreams, social media, mobile apps, or other sources. Next, organizations must process this (often unstructured) data into structured data. This can be accomplished either in-house using off-the-shelf tools, or by partnering with big data analytics companies. From this data, patterns identified may include spikes in traffic at certain times of the year, app usage at certain times of day, per-customer-segment purchasing trends, and others. Such insights may point to either operational inefficiencies or new market opportunities for exploitation.<\/p>\n\n\n\n Customer-centricity is a growing trend among leading global companies. Driven by the rise in social media and the resultant brand transparency this has forced on companies, today, customer centricity is as much an essential business strategy as any other. But making that transition is often fraught with corporate culture challenges, especially for large legacy businesses that have been focused inwards for decades. This shift can be made easier through big data.<\/p>\n\n\n\n Amazon is well-known as a customer-centric company. What drives this culture is not just goodwill, but technology. The company is maniacal about optimizing everything through technology. By using data collected from millions of purchases, returns, customer care inquiries, and other data points, the company has managed to implement customer-focused processes that have clearly identifiable outcomes.<\/p>\n\n\n\n For instance, it is conceivable that processing returns quickly translates into customers resuming purchasing faster. This could be the main reason the company has fine-tuned return processing at a customer care level, which points to customer-focused productivity, as data shows this is a path to ensuring uninterrupted purchasing flows.<\/p>\n\n\n\n Organizations seeking similar capabilities must optimize their data mining and analytics efforts to identify areas where greater customer-focused productivity can be achieved.<\/p>\n\n\n\n It is well-known that happy employees make happy customers. This is supported by a Gallup poll that found that increased employee engagement resulted in improved customer relationships and a corresponding 20% jump in sales. One of the major challenges with achieving this happy employee-happy customer matrix is identifying employee efforts and rewarding them accordingly. This is something traditional scorecards, performance reviews, and other KPI-measuring tools cannot effectively do.<\/p>\n\n\n\n Big data, on the other hand, is inherently capable of accomplishing this. For instance, by transcribing support calls and performing sentiment analysis, it is possible to isolate and reward support agents who went above and beyond their mandate when providing support. Also, processing internal corporate communications can help identify gaps in collaboration, information and skill silos and what actions employees can take to address these issues. Such actions can similarly be identified and rewarded as a means of enforcement.<\/p>\n\n\n\n Creating new drugs is often a 10-15-year cycle that involves hundreds of lab experiments, most with a 90% failure rate. IBM is using big data to slash this cycle timeframe. At its Almaden research center in San Jose, California, the company is pulling in data<\/a> from diverse sources including patent applications, university publications, science journals among others and processing this data using Watson Ai to find combinations of data that can offer insights to accelerate development of breakthrough solutions.<\/p>\n\n\n\n Companies can similarly accelerate their own discovery efforts by analyzing their data either to find improvement opportunities to existing solutions or discover novel ideas for new products and services. The discovery process must, however, be guided by business-level governance models that focus on innovations that either strengthen or build on existing core competencies. Partnering with Silicon Valley startups<\/a> may be a viable option, especially if the company wants to build upon already proven technologies and approaches.<\/p>\n\n\n\n Data is emerging as the ultimate competitive advantage as it gives companies near-seer capabilities to understand and anticipate customer needs. For companies keen on experimenting with big data, it is crucial to implement a roadmap to achieve the capabilities outlined above based on the following key structures:<\/p>\n\n\n\n Silicon Valley Innovation Center<\/strong><\/a> helps financial sector executives experience and connect with the Silicon Valley fintech startup ecosystem through the Leading Digital Transformation executive immersion program<\/strong><\/a>. As Silicon Valley is a hotbed of big data innovation, company executives benefit greatly from visiting the innovation hub and interacting with startups<\/strong> like the ones mentioned in this article. Through this immersive experience, executives also gain deep insights<\/strong> into how partnering with Silicon Valley startups<\/strong> can be a game-changer<\/strong> for their businesses.<\/p>\n\n\n\nVisit Silicon Valley Big Data Startups<\/h2>\n\n\n\n
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