Volta Venture

12/16/2022

Patterns of data-driven business model innovation

The use of data and analytics is an important source of competitive advantage, and companies are well aware of this belief. The majority of companies are actually investing in analytics projects. They use data and analytics to improve decisions in general, to optimize processes and increase efficiency, to design and develop new products and services, to find new customers and delineate market segments, to improve the customer experience, or to develop new business models

There are no data-driven business models per se, but the use of data and analytics opens up a "continuum" of business model transformation opportunities
Representations show a wide range of components or "building blocks" such as key activities, key resources, value proposition, channels, customers, revenue, and cost structure. Because there is no single definition of a business model and a lack of theoretical anchoring, the decomposition of the components varies from author to author. The components are often either a synthesis or unification of other research in the field, or they represent a view motivated by the background of the particular author, leading to the addition of certain components

The business model has become an important source of innovation. Companies can innovate not only by introducing new products or services, but also by developing new ways to create and capture value. Technology can be a key driver of business model innovation, enabling new sources of value to be unlocked while impacting the current business model and challenging the status quo

In their article "Patterns of Data-Infused Business Model Innovation " authors mention three core:
👉 value creation: describing how resources are used in the business to produce and deliver a specific value proposition
👉 value proposition: Describes what can be offered to other parties
👉 value capture: Describes how the value proposition is converted into a (monetary) reward for the company

These core building blocks provide a solid foundation for analyzing the application of data and analytics: data can permeate only certain components or influence all of them at the same time, resulting in a specific permeation pattern ("data infused business model").

12/14/2022

AI IS NOT ALGORITHM

Artificial intelligence encompasses a wide range of technologies that allow machines to act and think more like humans. AI is used for many applications, from personal assistants and virtual agents to speech recognition and image classification. It is also used in machine learning algorithms to make decisions based on data.

AI can help businesses become more efficient by automating previously manual processes, providing better insights into data analysis, and making decisions faster than before.

Artificial intelligence also has the potential to revolutionize healthcare industry by allowing doctors to quickly analyze patient data and make diagnoses faster and with greater accuracy. In the future, AI technology can identify patterns in human behavior and provide even deeper insights into customer needs. This could lead to more personalized services tailored to individual needs and automated recommendations for products or services.

12/12/2022

Metaverse will change our business - no question about it. But how can we stay ahead of the change and build a winning strategy?

There are a few steps you should consider:

👉Companies should establish a comprehensive vision for what they want to accomplish in the Metaverse, including clear definitions of what they should and should not do. A willingness to experiment, fail, learn, and move quickly to the next use case is key.

👉It's important to conduct a technology audit to identify gaps that could hinder the achievement of goals. Create the technical platforms and tools (e.g., a data function) that facilitate the implementation of the strategy

👉A digital twin strategy should be created to incorporate Metaverse use cases into regular operations early on. Look for opportunities to automate or digitize operations and develop IoT use cases in different areas that support current and future business planning. Real-time data from current facilities or operations can be used for management and monitoring, training and development, or design and improvement.

👉The enterprise should continue to research Metaverse customers to demonstrate the value of Metaverse to the enterprise. In-depth research into how customer journeys are evolving will lead to important insights that can help define an organization's Metaverse roadmap.

👉Companies need to develop the ability to continually update their understanding of evolving customer needs to ensure continuous, personalized service. The combination of Metaverse and AI enables segmented offerings that can be powerful differentiators in increasingly competitive sectors.

👉Develop the skills to be competitive - assess internal talent capabilities and identify gaps. Develop a plan to acquire the skills necessary to ensure success.

👉A control office should be established to oversee Metaverse efforts, with a clear mandate and processes. Strong management can help define the target path, highlight interdependencies between teams, and reduce risk to the overall plan.

Read more at:

The metaverse is already a big part of business. It will only become more central.

12/09/2022

How can you create value from data?

By following a solid, strategy-led process, you can explicitly link your strategy to your data, clarify what data you have, and understand how valuable it is and to whom. You can set realistic goals for monetizing your data, and you can prove your right to succeed by testing and overcoming areas of potential failure. You can move from an unfounded assumption about the value of your data to a more informed and business-focused understanding of its value in terms of its use to current and potential customers, its standalone commercialization potential, and most importantly, its potential to improve your current business. This is the true value of data.

👉To monetize data, companies must first understand how they create value today and what their current strategy is.

👉Investment options should be developed based on improving the current business, entering adjacent businesses, or developing new businesses.

👉An inventory of the company's existing data assets should be conducted along with an assessment of any gaps.

👉The next step is to confirm the options by identifying potential customers and determining if the existing data assets adequately meet the strategic options.

👉Monetization aspirations should be realistic, and testing processes should be put in place to assess the ability to execute

👉Finally, commercial considerations as well as data management and support requirements must be taken into account when planning to expand investments.

12/07/2022

What should be considered when selecting a Data Science Unicorn?

Alan Hylands, in his article "Generalists Are The Real Data Science Unicorn," has taken a closer look at this problem and described his view.

Data Engineering. Data Analysis. Data Transformation. Machine Learning. Artificial Intelligence etc...

If we look at any job posting, a Data Science Unicorn must be proficient in all areas of data science to be truly interesting. They also need to have a Ph.D., maybe even a couple of master's degrees, and lots of side projects on Github. Not to forget, of course, the relevant internships and work experience. And most of the time, the list does not end there.

However, it's worth asking yourself one important question: Is it worth looking for a specialist in a specific area, such as machine learning or data engineering, or finding someone who has general knowledge—do they approach the topics broadly and superficially, but have the germ of knowledge on each topic?

Finding a specialist seems like a promising option, especially when it comes to working for a large company. The problem for Data Science Unicorns, however, might be that early in their careers it's difficult to learn about different areas of data science to really know which direction to focus on.

But what if you are running a smaller company, such as a small business or early-stage startup? With a small data team, you are more likely to be looking for new hires with skills that cover multiple bases at once. This might lead you to choose Option 2—Generalist.

So make sure you build the right infrastructure to get the most out of your data/analytics/data science. A machine learning engineer, regardless of talent and intelligence, is simply not the right person for the job. You need a generalist.

12/05/2022

"Predictive maintenance" technology is on the rise, with Pepsi, Colgate, and other companies equipping their plants with Augury sensors that use artificial intelligence to "listen" for problems in machines. Founded in 2011, Augury develops wireless sensors that attach to factory equipment and pick up the sounds they emit. The data is uploaded to a cloud-based platform and analyzed by artificial intelligence software trained to recognize more than 80,000 sounds from industrial machines in different life cycles.

Anna Farberov, CEO of PepsiCo Labs, the technology division of PepsiCo Inc, said that in the past year so-called predictive maintenance systems at four Frito-Lay plants have reduced unexpected breakdowns, downtime, and additional costs for spare parts, among other things.

The technology, developed by New York-based startup Augury Inc. has helped Frito-Lay increase its production capacity by about 4,000 hours a year.

According to analyst firm Research and Markets, the global market for predictive maintenance technology, also known as machine health technology, is expected to reach $18.6 billion by 2027, growing at just over 26% annually.

In May, Augury was acquired by Seebo, an AI-based process intelligence startup, in a deal valued at more than $100 million. Yoskovitz, Augury's chief executive, said his long-term goal for the company is not to be acquired by a major manufacturer or IT supplier, but to go public "when the time is right."

Photo source: VentureBeat

12/02/2022

Life has the most meaning when it focuses on helping others. It has the most meaning when work gives you clear value, peace, rest, and support.

Let's take a look at consulting. It can be based on creating slides that are difficult to understand and therefore of no use to anyone. However, as a consultant, if I focus on what we want to change and improve, the slides become a help in translating the strategy.

Everything takes on a different meaning when the help is the intention. The intention "I will do the task because I promised" has nothing to do with the desire to help, but rather focuses on oneself, on "pleasing" one's ego.

11/30/2022

Scaling up: How founder CEOs and teams can go beyond aspiration to ascent

For many startups, the challenge is no longer raising capital, but learning how to restructure as quickly as their products or organizations can evolve. In the attached article, McKinsey has highlighted some factors that are critical to the success of companies in their rapid growth expeditions:

A structure specifically designed for growth
A company's structure is key to its success, especially as it grows larger. Founders and CEOs need to pay attention to how their company is growing and adapt accordingly. The right operating model can help a company hyper-grow.

Effective ways of working
Business decisions, performance management processes and overall governance can become more difficult for companies as they grow. Startups may need to implement more processes as they grow to better manage complexity. Cross-functional governance approaches can help hyperscaling companies foster knowledge sharing, identify dependencies and vulnerabilities, and quickly improve performance. Leaders in hyperscaling organizations should be clear about who has the final say when it comes to decision-making.

A distinct culture
In startups, culture shows up in everything from interactions to design decisions. As companies scale, the culture must change as well. Founder CEOs need to be aware of how the culture needs to change as the company grows. They can use surveys and benchmarks to decide which parts of the current culture should be retained and which should be eliminated. Founding CEOs need to communicate why the company is growing and changing its culture. This can be done through town hall meetings, forums, or even during the onboarding process of new employees. Employees who exemplify the cultural values can be rewarded in a variety of ways, such as financial perks or extra days off."

Build leadership skills
As a company grows, a deliberate approach to leadership development is needed throughout the organization. Team sprints can help leaders and teams collaborate to develop an operating model that balances the needs of "growth leaders" and "operational leaders" An Indian online meal delivery platform has developed a leadership development program targeting the company's top 140 members that includes 360-degree feedback and one-on-one coaching. Through this program, the company was able to build agreement among managers at various levels on priorities for growth, and participants reported significant personal growth.

Read more at: https://pos.li/2mhvnj

For many start-ups, the challenge is no longer about securing capital--it’s about learning how to restructure themselves as fast as their products or organizations can evolve.

11/28/2022

The use and monetization of data can be a source of competitive advantage for companies in the digital economy.

As an emerging phenomenon driven by current technological trends in the context of Big Data, data monetization is gaining momentum in both research and practice. Najjar and Kettinger, in their paper "Business Strategies for Data Monetization: Deriving Insights from Practice," define data monetization as transforming the intangible value of data into real value. They emphasize that real and quantifiable value can occur both as monetary value and as any other quantifiable economic benefit. Value then becomes real through quantifiable, subsequent effects or returns, such as reduced costs, repeat purchases, and/or increased market share. On this basis, a data monetization strategy is considered to be a path followed by an organization to leverage data for quantifiable economic benefit.

How do companies monetize data?
Najjar and Kettinger's study provides an in-depth analysis of case studies from different industries, company types, and company sizes to deepen theoretical and practical understanding of data monetization. To answer this question, they created a set of 102 case studies from which they derived a comprehensive set of general monetization strategies

They extract 12 data monetization strategies, 3 of which are direct monetization:

Asset Sale
to established findings, the case analysis shows that companies often sold data exclusively as an asset (example: data was sold directly to customers, giving them full control of the Verizon Wireless asset). The economic benefit is the creation of new revenue streams and the expansion of the customer base.

Data Insights Sale
Selling information/knowledge gained through any processing step of Insights. The economic benefit is the creation of new revenue streams and business lines.

Data Bartering
Data bartering occurs when a company exchanges data for valuable assets. Data is thus leveraged for quantifiable benefits by trading it for other data, insights, tools, services, or special offerings. Examples include data and analytics providers offering customized discounts based on contributions to the company's database or through the direct exchange of data with financial business partners. In addition, data trading often takes place in the retail sector when point-of-sale data is exchanged for demographic information or analytics software, for example. The economic benefit consists of the exchanged value in the form of tools, services, or data, for example.

11/23/2022

MLOps platform Galileo lands $18 million to launch free service

Galileo startup has announced that it raised $18 million in a Series A round. The new cash will be used to expand the company's engineering and go-to-market teams, as well as enhance its core platform to support new data modalities.

As AI becomes more prevalent in enterprises, so does the demand for products that make it easier to inspect, detect and fix critical AI errors is increasing. Galileo's platform aims to systematize AI development pipelines across teams by using "auto-loggers" and algorithms that highlight system-critical issues.

Customers are using Galileo to develop machine learning applications such as hate speech detection, caller intent detection in contact centers, and improving the customer experience through conversational AI.

It is expected that the launch of Galileo's free offering - Galileo Community Edition - will boost sign-ups further. The Community Edition allows data scientists working on natural language processing to build machine learning models using some of the tools included in the paid version

Galileo Community Edition is a powerful platform that allows you to inspect, find and fix data errors or select the right data for the next labels using the powerful Galileo UI. With Galileo Community Edition , you can sign up for free and add a few lines of code while training your model with labeled data or during an inference run with unlabeled data.

If you're interested in details, I encourage you to read this article:
https://pos.li/2mgq85

Galileo, a startup developing MLOps tools for data scientists, has raised $18 million in a venture funding round.

11/21/2022

AI is creating enormous wealth and every month will continue to increase it. So why shouldn't you and your company take advantage of this opportunity and start implementing AI in your business? It's time to democratize access to AI!

Of course, AI projects are expensive to build- they require highly skilled engineers, and it can cost millions of dollars to develop a one-size-fits-all AI system, such as one that improves web search. For this reason, only large tech companies can afford such innovations and make huge profits from them. But what about smaller companies and individuals for whom one-size-fits-all AI systems do not work due to very specific and individual data?

Ng explained in his TED talk, "How AI Could Empower Any Business," that we are getting closer every day to a widely available AI system. And we don't have to be experts in coding! There are new AI development platforms that shift the focus from asking people to write a lot of code to providing data. This way any small business can use these platforms to make their work easier or increase revenue by leveraging their specific data.

Andrew shared an amazing example of a platform his team developed that helps detect defects in fabric. You can easily show the AI what tears or discoloration look like in the fabric by drawing rectangles on provided images - helping the AI get smarter and eventually detect such defects itself.

" Building AI systems has been out of reach for most people, but that does not have to be the case. In the coming era for AI, we'll empower everyone to build AI systems for themselves, and I think that will be incredibly exciting future." Andrew Ng.

Listen to Andrew Ng TED talk here:
https://pos.li/2mgq6y

Expensive to build and often needing highly skilled engineers to maintain, artificial intelligence systems generally only pay off for large tech companies wi...

11/16/2022

The power of APIs lies in their ability to provide data at all times and from multiple sources within one system. This allows SaaS companies like Shopify with API access to contribute not only to financials but also to customer satisfaction levels! By pulling together information on sales performance across different marketplaces into just one place, businesses can make better decisions for future strategic planning because it gives the entire picture possible about how things are going.