How do you innovate and experiment with big data analytics to create new digital products and services?

Prototyping and testing are essential for your product’s success. But what’s more important at this stage is the involvement of an end user, your client’s team, in this process. My point is that when you test your product, it’s a completely different story than when actual users try to complete the intended tasks. You’ll see the difference! And before they start, teach them how to use these tools. You might not even notice what you do and how because you’re the developer. Keep it in mind, and your clients will say, “Thank you!”

Generally, data can be impactful in three ways: 1. Bottoms up: delivers insights into the existing products/services. Sometimes, the best ideas for new products/services come from how your existing products/services are being used. Look for insights into how, when, where, and who of usage. 2. Top down: Validate assumptions or hypothesis regarding new markets or customer segments, 3. Data itself: Opportunities for the data itself to be a product.

What are the problems or challenges that you want to solve or address with your new digital product or service? What are the assumptions or hypotheses that you have about your customers, market, competitors, or internal processes? How will you test and validate your hypotheses with data? Defining your goals and hypotheses will help you focus your data analysis and guide your innovation and experimentation process.

I agree, but some sets of data lend themselves to visual exploration and that can inform the next steps with existing data, expansion paths and hints at where to go next. So your starting point may be 'let's visualise a simple piece of data with a graph of some type (or varying types), and let's look for visual cues and patterns'. It's OK to explore what you have using simple means before taking the plunge with bigger tasks.

1. Identify the opportunities 2. Have clear goals and objectives 3. Understand your data 4. Select the right tools and technologies 5. Cross functional Collaboration 6. Build a skilled team 7. Experiment and pilot project 8. Implement advanced analytics 9. Data integration 10. Focus on user experience 11. Scalability and sustainability

Be careful about what you anchor as you choose your data and sources. Whatever you build around and what your data eventually tells you is very different depending on what you anchor your analysis on. A very simplistic example is if say, you check sales data and realize you sold many hats on a Monday and decide that Mondays are good to have sales on hats. You may completely miss that particular Mondays may have been sunnier than usual leading to the increased sales. In this case, data on weather patterns may be better to anchor for insights on when best to have a hat sale.

you will need to select the most relevant and reliable data sources and tools for your big data analytics project. You may need to use internal data, such as customer records, transactions, feedback, or operational metrics, or external data, such as social media, web analytics, market research, or public data sets. You may also need to use different tools and methods for data collection, storage, processing, analysis, and visualization, such as cloud platforms, databases, frameworks, languages, libraries, or software. Choosing the right data sources and tools will help you ensure the quality, accuracy, and usability of your data analysis.

Selecting relevant data sources and appropriate analytical tools is crucial. For the aforementioned project, we combined internal production data with external supply chain data. Choosing tools involved a balance between advanced capabilities and ease of use for the team. We settled on a tool that was less intimidating for the less tech-savvy team members, humorously referred to as the "Fisher-Price" of analytics tools due to its user-friendly interface.

Diving into big data for digital innovation feels like I'm a kid again playing a game with a treasure hunts. You've got your map—goals and hypotheses—and your toolkit is chosen: a blend of internal and external data sources, plus a variety of analytical tools. Adjust your compass as you go, integrating AI for smarter insights and teaming up with experts for guidance, ensuring the journey stays on course. Be prepared to pivot with each new discovery. This adventure isn't just about collecting data; it's about uncovering hidden gems of opportunity, charting new territories in the digital landscape, and creating services that captivate and inspire. Seems a bit like land navigation training during my time in the military as well!

Selecting the appropriate data sources and tools is critical to the success of your big data analytics project. Remember, the goal is to strike a balance between the capabilities of the tools and the needs of your project. Adapt your choices based on your specific objectives, resources, and team skillsets. By selecting the right data sources and tools, you'll set the stage for successful data analysis and actionable insights.

tart exploring and experimenting with your data to discover patterns, trends, correlations, anomalies, or insights that can inform your innovation and experimentation process. You can use different techniques and approaches for data exploration and experimentation, such as descriptive analytics, predictive analytics, prescriptive analytics, or machine learning. You can also use different formats and styles for data visualization,

The process of exploring and experimenting with your data is where the magic of uncovering insights and making informed decisions happens. By immersing yourself in your data, exploring its nuances, and experimenting with various analytical techniques, you'll gain a deeper understanding of your domain, refine your hypotheses, and pave the way for data-driven innovation.

Exploring and experimenting with data is essential for driving innovation in digital products. By employing techniques like predictive analytics, businesses can forecast trends and behaviors, while prescriptive analytics suggests actionable steps based on those predictions. For instance, using machine learning algorithms can help identify unexpected correlations or hidden patterns within vast data sets. Complementing these analyses with sophisticated visualization tools like Power BI or Tableau allows teams to present complex data in an accessible and compelling format, fostering clearer decision-making and more creative solutions for new product development.

The excitement of exploring data for innovative insights is akin to unraveling a mystery. Once, while delving into customer preferences for a streaming service, we discovered a niche audience interested in vintage movie genres. Advice: Foster a culture that encourages curiosity and data exploration. A challenge might be data quality; my solution involved employing cleansing algorithms and cross-validating with multiple sources. Something beneficial was establishing a multidisciplinary team, blending data scientists with industry experts, to enrich the analytical perspective

This stage involves diving into the data to uncover patterns and insights. In one instance, a Mittelstand client in the retail sector was surprised to find that weather data significantly impacted sales patterns. This led to experiments incorporating weather forecasts into their analytics models, a concept initially met with skepticism.

A prototype is a simple and low-cost version of your product or service that you can use to get feedback from your customers, stakeholders, or users. A test is a way of evaluating the performance, usability, and value of your prototype in a real or simulated environment. You can use different methods and tools for prototyping and testing, such as sketches, wireframes, mockups, prototypes, or MVPs (minimum viable products), and surveys, interviews, focus groups, usability tests, or A/B tests.

When innovating with big data analytics, the journey from concept to prototype is all about learning and adapting. To start, craft a simple prototype of your digital product and introduce it to a controlled group of users. Use big data tools to collect and analyze their interactions...this feedback is gold! Pay close attention to how users navigate your prototype and what stumbling blocks they encounter. Employ predictive analytics to forecast how changes might play out.

Absolutely, prototyping and testing are crucial steps in the process of bringing your data-driven innovation to life. By prototyping and testing, you're not only validating your hypotheses and ensuring the usability of your product but also gathering invaluable insights for continuous improvement. This iterative process allows you to refine your digital product based on real-world feedback, leading to a more robust and user-centric final product.

As you move to prototype your groundbreaking idea, envision a tangible manifestation. In my journey, developing a beta version of a personalized financial advisory app was pivotal. Advice: Embrace rapid prototyping to gather user feedback early and refine iteratively. Challenges may arise in balancing speed with accuracy, but I found that employing agile methodologies kept us on track. One valuable lesson was embracing a fail-fast mentality, learning from each iteration

Prototyping and testing are crucial steps in the innovation process, especially when leveraging big data analytics. As an AI and transformation expert, I've observed that incorporating machine learning algorithms during the prototyping phase can significantly enhance the predictive capabilities and personalization of digital products or services. This approach not only refines the user experience but also accelerates the validation process of hypotheses, ensuring that the final product is both innovative and aligned with market needs. Utilizing AI in this manner can transform raw data into actionable insights, driving strategic decisions and fostering a culture of continuous improvement and adaptation.

You've captured a crucial phase in the development and deployment of a digital product or service. Iteration and scaling are essential for refining your offering and maximizing its impact. By combining a commitment to iterating based on user feedback and scaling strategically, you'll not only enhance your product's value proposition but also establish a foundation for long-term growth and success in the digital marketplace.

Iterating means making changes and improvements to your product or service based on the data and feedback that you have collected. Scaling means expanding and growing your product or service to reach more customers, markets, or regions. You can use different strategies and techniques for iterating and scaling, such as agile development, lean startup, design thinking, or growth hacking. Iterating and scaling your new digital product or service will help you increase your value proposition, customer satisfaction, and competitive advantage.

Iterative refinement is the heartbeat of successful digital products. Scaling up, imagine optimizing an AI-driven healthcare app after initial success. Advice: Establish a robust feedback loop and iterative development cycle. A challenge is managing scalability without compromising performance; leveraging cloud-based solutions provided a scalable infrastructure. One useful tactic was conducting regular retrospectives to fine-tune processes and identify scaling bottlenecks proactively

Durch iterative Verbesserungen können Unternehmen auf Kundenfeedback reagieren und das Produkt kontinuierlich optimieren. Skalierung ermöglicht es, das Produkt oder die Dienstleistung auf einen größeren Markt auszuweiten und das Potenzial für Wachstum zu maximieren. Dabei ist es wichtig, die Infrastruktur und Ressourcen entsprechend anzupassen, um eine reibungslose Skalierung zu gewährleisten. Big-Data-Analysen spielen eine wichtige Rolle bei der Iteration und Skalierung neuer digitaler Produkte und Dienstleistungen. Durch die kontinuierliche Analyse großer Datenmengen können Unternehmen wertvolle Einblicke gewinnen und fundierte Entscheidungen treffen, um das Produkt weiter zu verbessern und erfolgreich zu skalieren.

To strategically scale, it's imperative to anchor your decisions in data-driven insights. Leverage the intelligence gleaned from your analytics to discern which elements of your service are delivering the most value. This enables you to allocate resources effectively, concentrating on areas that propel growth and enhance customer satisfaction.

To innovate with big data analytics and create new digital products and services, it's crucial to learn and adapt to changing needs and expectations. Continuously analyze user behavior and market trends to identify shifts. Use this data to refine your offerings, test new ideas, and adjust strategies. Staying flexible and responsive , these all are the things i found useful to ensure our products remain relevant and valuable to customers.

In the dynamic digital landscape, adaptability is non-negotiable. Reflect on a scenario where evolving customer expectations prompted a swift pivot in service offerings. Advice: Instill a continuous learning mindset; regularly reassess industry trends and user feedback. A challenge might be resistance to change; addressing this involves effective communication and showcasing the positive impact of adaptation. Something I found useful was establishing cross-functional teams for ongoing monitoring and swift adaptations

Der Einsatz von Big Data-Analysen ermöglicht es Unternehmen, die sich entwickelnden Bedürfnisse und Erwartungen der Kunden besser zu verstehen und darauf zu reagieren. Dies kann dazu beitragen, neue Chancen zu identifizieren und innovative Lösungen zu entwickeln, die einen echten Mehrwert bieten. Ein agiler Ansatz, der auf kontinuierlichem Lernen und Anpassen basiert, ist entscheidend, um im dynamischen Umfeld des digitalen Wandels erfolgreich zu sein. Unternehmen sollten flexibel sein und bereit sein, ihre Strategien und Produkte entsprechend den sich ändernden Bedürfnissen und Erwartungen anzupassen.

This is a great point - nothing about all of these projects is one time. It's supposed to live and expand as you grow. Think about it - you probably do not know all of the things that you need at the beginning - as you get more insights your appetite grows and the customers' as well.

Si bien tenemos un road map de producto para poder establecer presupuestos, necesidades, arquitectura y equipo de trabajo, en un entorno cambiante debemos estar abiertos a cambiar alguna prioridad y a tener la capacidad de atenderla si esto significa que los objetivos de producto van a estar beneficiados y sobre todo el cliente.

Innovating and experimenting with big data analytics to create new digital products and services can indeed provide your business with a competitive edge and enhanced value proposition. Innovating and experimenting with big data analytics to create new digital products and services involves a combination of data-driven insights, creativity, user-centric design, and a willingness to learn and adapt. By following these best practices, you can leverage the power of big data to transform your business and meet the evolving needs of your customers and stakeholders.

Amidst this transformative journey, remember the importance of human-centricity. Beyond the technical facets, consider the human experience in every stage. My advice: Regularly engage with end-users to understand their evolving needs. Challenges may emerge in aligning technical advancements with user-friendly interfaces; dedicating time to user experience testing mitigates this. One thing I found useful was incorporating user stories in the development process, fostering empathy and refining our products to genuinely meet user expectations

Data privacy is a crucial challenge for Product Analytics. Especially with strict data privacy rules such as the GDPR and CCPA, failure or a data breach can result in significant fines and reputation damage. Handling this needs adopting a privacy-first strategy for product analytics. This, on the one hand, necessitates obtaining explicit user consent for data collection and, on the other hand, anonymizing data to protect individual identities and diligently using data to get insights. Establishing a data governance framework is necessary here; also, having a Data Protection Officer (DPO) can help ensure ongoing compliance.

Desde mi experiencia: 1. Diseñar el producto con todo el equipo necesario 2. Establecer MVP y road map de producto 3. Datos desde el momento cero 4. Siempre medir journey del cliente