Marc Teerlink

We’re on the verge of the AI Gold Rush. And like the infamous historical gold rush, only a precious few with plans, and backup plans, will become the success stories that panned for gold and struck it rich. Real economic growth will be achieved by those companies which are selling the equivalent of picks, food, supplies, shovels, and jeans for Artificial Intelligence & Machine Learning.

Computers used to fill a room. Now they fit in a salt shaker. How will ever-shrinking technology create the next business disruption?

Data Is an Asset, and It Must Have Values; Already, 22% of U.S. companies have attributed part of their profits to AI and advanced cases of (AI infused) predictive analytics. According to a recent study SAP conducted in conjunction with the Economist’s Intelligent Unit, organizations doing the most with machine learning have experienced 43% more growth on average versus those who aren’t using AI and ML at all — or not using AI well.

One of their secrets: They treat data as an asset. The… Meer weergeven

Data Is an Asset, and It Must Have Values; Already, 22% of U.S. companies have attributed part of their profits to AI and advanced cases of (AI infused) predictive analytics. According to a recent study SAP conducted in conjunction with the Economist’s Intelligent Unit, organizations doing the most with machine learning have experienced 43% more growth on average versus those who aren’t using AI and ML at all — or not using AI well.

One of their secrets: They treat data as an asset. The same way organizations treat inventory, fleet, and manufacturing assets. Minder weergeven

In the DC, Maryland and Virginia area, a single mother with two children has an average six dollars left for their daily dinner. Ironically, buying processed food will cost her less than creating a healthy meal.

At the same time, within the US, one of the richest nations in the world, on average, we throw away 40% of the food we buy. That’s almost $400 per person, per year.

To reflect on these numbers at a global scale, consider that the United Nations notes that annually 1/3 of… Meer weergeven

In the DC, Maryland and Virginia area, a single mother with two children has an average six dollars left for their daily dinner. Ironically, buying processed food will cost her less than creating a healthy meal.

At the same time, within the US, one of the richest nations in the world, on average, we throw away 40% of the food we buy. That’s almost $400 per person, per year.

To reflect on these numbers at a global scale, consider that the United Nations notes that annually 1/3 of all food produced for human consumption is lost or wasted. Minder weergeven

Social psychologist and Harvard professor Shoshana Zuboff said, “Everything that can be automated should be automated” Zuboff’s viewpoint is particularly true for what we call “knowledge worker” tasks. Professionals and Knowledge workers typically make tactical decisions such as reconciliating invoices, extending a warranty period, determining replenishment levels when stock ratios suddenly drop, and focusing on the opportunities that are most likely to lead to winning a new customer.

When AI offers the ability to offload the routine actions and support and extend ongoing communication between brands and customers, the businesses that are investing heavily in AI will be the ones who can sell through, and to, to AI assistants. It’s critical for businesses to stay ahead of this, as AI assistants are bound to have significant purchasing power in the near future.

So, what will the future of IoT in 2020–2025 (and beyond) look like? Research firms like Statista project more than 30 billion devices by 2020 and 75 billion by 2025 will be connected.
The bottom line is that we’re talking about a significant amount of ‘things’ that present a lot of data capture/information. And, with that, many opportunities for those who see the potential value and (candidly) profit that can be derived from various approaches to IoT use cases.
Accordingly, a realistic… Meer weergeven

So, what will the future of IoT in 2020–2025 (and beyond) look like? Research firms like Statista project more than 30 billion devices by 2020 and 75 billion by 2025 will be connected.
The bottom line is that we’re talking about a significant amount of ‘things’ that present a lot of data capture/information. And, with that, many opportunities for those who see the potential value and (candidly) profit that can be derived from various approaches to IoT use cases.
Accordingly, a realistic view is needed on what your organization defines as ‘return on investment’ when compared to business strategy.08/05/2019\ Minder weergeven

Today, organizations sit in an opportunity to dive into AI and beat the rush and avoid playing catch up. Since we are on the verge of artificial intelligence (AI) taking off, we need to prepare as only a few leading organizations will be able to harness the impact successfully. Those that do will realize real economic growth after leveraging AI in tools such as, training data, governance tools, consulting and integration services, and most critical, the creation of new sustainable revenue… Meer weergeven

Today, organizations sit in an opportunity to dive into AI and beat the rush and avoid playing catch up. Since we are on the verge of artificial intelligence (AI) taking off, we need to prepare as only a few leading organizations will be able to harness the impact successfully. Those that do will realize real economic growth after leveraging AI in tools such as, training data, governance tools, consulting and integration services, and most critical, the creation of new sustainable revenue models. Over the next two-to-five years, we can expect a profound transformation for knowledge workers and professionals as their daily tasks are infused, thanks to AI and machine learning (ML). AI will reduce repetitive work for humans, and force (or assist) humans to make decisions faster, easier making the process simple. Minder weergeven

n the second of a two-part series, you’ll learn how companies are successfully using Artificial Intelligence and how to take advantage of these new capabilities.

Data needs a leader. As the new natural resource of the twenty-first century, data has the power to transform industries and business models; but it equally has the power to overwhelm systems and stymie growth. As executives witness data’s proven impact on performance and innovation and recognize its strategic significance, they also realize the growing need for a leader whose primary role is to understand and advocate on behalf of data. As a result, leading organizations across
industries… Meer weergeven

Data needs a leader. As the new natural resource of the twenty-first century, data has the power to transform industries and business models; but it equally has the power to overwhelm systems and stymie growth. As executives witness data’s proven impact on performance and innovation and recognize its strategic significance, they also realize the growing need for a leader whose primary role is to understand and advocate on behalf of data. As a result, leading organizations across
industries around the globe are appointing Chief Data Officers to deliver data-driven growth and innovation that matters. Minder weergeven

The sheer scale of information now available to CEOs can be overwhelming. Marc Teerlink, Global Strategist at IBM, explains why properly trained data scientists can help provide some much needed clarity and drive #innovation

Are Data Scientists: tech's New Rock Stars?

The Movie Moneyball tells the story of the Oakland Athletics baseball team and its general manager Billy Beane. Its narrative depicts the unveiling of a radical new approach to baseball, derived from the management's analytical fact-based and sabermetric approach to assembling a competitive baseball team, despite Oakland's disadvantaged revenue situation. To scout for possible new players and build up a team to compete in the World Series… Meer weergeven

Are Data Scientists: tech's New Rock Stars?

The Movie Moneyball tells the story of the Oakland Athletics baseball team and its general manager Billy Beane. Its narrative depicts the unveiling of a radical new approach to baseball, derived from the management's analytical fact-based and sabermetric approach to assembling a competitive baseball team, despite Oakland's disadvantaged revenue situation. To scout for possible new players and build up a team to compete in the World Series, Beane applies revolutionary ideas about how to assess players' value through an unorthodox strategy for achieving results based on analytic computer models, created by Beane ́s economics graduate assistant Peter Brand. The analytical approach helped winning 20 consecutive games, an American League record. Since then, this idea has spread to all other sporting disciplines, with similar success. During the 2012 US presidential elections, Barack Obama’s campaign was run as the political equivalent of Moneyball: A numerical analysis preceded every decision made by the campaign, right down to the most insignificant phone call, with the analyses conducted by a small team headed by data scientist Harper Reed.

In a recent interview, top data scientist Jake Porway was asked during an interview to share his thoughts on why data scientists are being referred to as the new rock stars of the technology world. “This analogy actually goes deeper than data nerds' desire to be considered ultracool. Just like a rock star, a data scientist's career includes diversity, artistic freedom and adaptability. And like the rock stars of the entertainment world, the best data scientists tend to gain quite a following of people from all walks of the data and technology industry”. Nate Silver, another well known data scientist, is an American statistician who gained fame after accurately predicting the outcomes of the 2008 & 2012 presidential elections. Silver launched his data blog www.fivethirtyeight.com in 2008. Minder weergeven

Organizations are increasingly inundated by data: more sources of data, more types of data, and more detailed data. Yet while the volume, depth, and diversity of data continue to increase exponentially, the need to cut through the growing noise becomes more challenging and ever more urgent. Organizations will need a new type of decision maker to address this need.

当前的企业 拥有来源广泛的大量客户数据。尽管大多数企业认为这些 数据可带来潜在的收益,不过,在将信息有效地转化为可 行的洞察力方面,许多企业面临着困难。然而,有效的客户分析战略有助于推动收 入增加,避免不必要的成本支出,并且提高客户满意度。为了帮助企业寻求更深入的客户洞察力,我们确定了组织能力的四个阶段及相关的客户分析战略。

Businesses today have an abundance of customer data available from an increasing number of sources, but many organizations struggle to turn this information into actionable insights. An effective customer analytics strategy, however, can help drive top-line growth, avoid unnecessary costs and increase customer satisfaction.

To help organizations in their pursuit for deeper customer insight, we have identified four stages of organizational capabilities, which are enabled by four… Meer weergeven

Businesses today have an abundance of customer data available from an increasing number of sources, but many organizations struggle to turn this information into actionable insights. An effective customer analytics strategy, however, can help drive top-line growth, avoid unnecessary costs and increase customer satisfaction.

To help organizations in their pursuit for deeper customer insight, we have identified four stages of organizational capabilities, which are enabled by four associated customer analytics strategies.

In the first stage, organizations apply a customer analytics strategy to gain information cost reduction. They do so by honing their capability to gain insight from the information explosion and develop a deeper understanding of the customer.

The second stage requires the capability to share information internally and across the value chain. Organizations in this stage embrace a customer analytics strategy that enables information sharing to create a consistent customer experience over multiple channels.

In the third stage, organizations shift toward enabling information responsiveness. Able to move from reaction to prediction, these organizations focus on identifying the questions that – if answered – will impact their business the most.

In the fourth and last stage, the most successful marketing organizations execute a strategy that enables information on demand and an analytics-driven approach called multichannel next-base action. These organizations have the capability to adapt business models that enable faster creation of value.

The analytics-driven transformation is an ongoing journey with a series of destinations – each a staging post for the next. Top-performers differentiate themselves through a deep understanding of their customers. They treat information as a business asset with business managers accountable for customer data and customer communication.

Minder weergeven