Comment pouvez-vous utiliser la technologie pour améliorer l’analyse des besoins pour la croissance ?

New technologies & innovations have made the data collection relatively easier as compared to the past, if planned well. Today because of proliferation of technologies in daily life, there are plethora of opportunities to collect the data. Data Collection can be 1. Active – where the respondents will provide the data directly as requested. 2. Passive- data collection is way to collect data about the respondent in unobstructive way. Some of the innovative approaches for data collections are: 1. Gamification tools 2. Collect data through visualization and interaction. 3. Computer Aided Personal Interview and Computer Aided Telephonic Interview (CAPI and CATI) 4. Web tracking technologies 5. Interactive Chatbots 6. Social Media analytics

💡 You should also consider the evolving technologies in data collection methodologies, which significantly impact needs analysis for growth. 🌐 Real-Time Data Gathering IoT devices and sensors provide continuous, up-to-minute insights vital for dynamic markets. 🔍 Advanced Web Scraping AI-driven web scraping categorizes and prioritizes data for better quality. 🤖 AI-Driven Customer Interactions Chatbots and virtual assistants gather and analyze real-time customer responses for deeper insights. 📌 Leveraging these advanced technologies in data collection not only broadens the scope of gathered information but also enriches the quality and relevance of the data, essential for informed decision-making and strategic growth.

💭Want to better understand the customers that use your software? Try Hotjar and Intercom. Hotjar will help you better understand where your customers are spending their time in your software with interaction heatmaps. Intercom can help automate interactions with customers while collecting valuable data on what questions your customers are asking about your site and software. There are other options (competitors) as well out there if you want to do more research more on your own. 👍 Like to support this

Technology fuels data collection for growth! These tools are just a tip of the iceberg and depending upon your requirements you can deploy tools: Track user behavior & pain points (Hotjar, Google Analytics). Gather direct feedback & understand trends (SurveyMonkey, Brandwatch). Analyze market reports & competitor strategies (Statista, Buzzsumo). Gain insights from sales & project data (Salesforce, Asana). Prioritize actionable insights & ethical data collection. Combine data with visualization to drive informed decisions. Remember, quality trumps quantity!

If you want to understand your customers and market better - Tech is your secret weapon! Gather insights from everywhere: websites, social media, even sensors! Tools like chatbots and web scrapers give you the full picture, saving you time and effort. Think of it as putting together a puzzle from multiple sources. Each piece adds detail, revealing hidden patterns and trends. This complete picture lets you make smarter decisions for growth.

Healthy Growth strategy based on Real numbers. Real numbers = Real clean Data No doubts about the importance of technology in facilitating our Data accuracy and saving our time. The more right data collected, the more we became stronger in creating our Strategy. At the end, No growth strategy without clear and clean data. In every sector we need more analysis, we refer to the technology to gain more data about we need.

It is definitely beneficial to use technology in collecting data from customers, which can be mentioned using QR Code and creating survey links. But I want to say, have you done the first step of analyzing the needs of your customers? Have you ever called your customers and asked why they chose us? Will you leave us if something happens?

Collecting data is the main goal in order to obtain results. But we must be very careful, because quality in data is the most important thing of all if we wish to obtain a good perspective. Many times we collect a huge quantity of data and we should remember that less is more if less has a high quality.

Utilizing various tools for data collection is an integral way of gathering advanced volumes of data. Make sure to properly organize your data stream with the intent of your business goals intent. A little hint is to focus on the end goal. If you are gathering data for marketing purposes, then focus on the user persona of that particular product or service. This will narrow your search and all aspects of data will be streamlined towards your business goals. If your goal is to penetrate different geo-markets for the same service, examine the persona that uses that service in those different geo-markets. Then gather different segments of information like behavioral, demographics, finance, or other data that aligns with your initial intent.

As a Chief Information Officer (CIO), leveraging information technologies and digital trends for business needs analysis is crucial, Data collection as part of a formal practice of Data Analytics in a company must have two approaches: First: "The business", which allows us to identify the data sources that add the greatest value to the business based on the value chain, and another Second is the technical approach where we define at the #dataArchitecture level the best way to extract that data. according to business needs (time to market or business strategy)

Evolving data points over a period of time is a great way to have a meaningful impact. Some variables might come out differently with time and tech can help us understand how we can better manage the data.

Managing & sharing an appropriate business data insights have become really pivotal in terms of predicting & analyzing the consumer's buying behaviour. The effectiveness of systems, goods, and services have drastically improved through technology. Infact market research shows that these are not tech companies only that have found business technology vastly improving their efficiency. 52% of companies worldwide report an increase in worker productivity, 49% better performance, and 46% better customer experience after digital renovation.The use of technology in businesses is a win-to-win solution. You can perform many types of work and roles with technology.

La tecnología ha mejorado los que para mí son dos análisis básicos: Análisis de datos operacionales: análisis de grandes conjuntos de datos de operaciones para identificar patrones y tendencias en el rendimiento de la empresa. Esto permite a las empresas optimizar sus procesos y operaciones, reduciendo costes, aumentando la eficiencia y mejorando la calidad de los productos o servicios. Análisis de ventas: análisis de datos de ventas anteriores, factores estacionales, eventos promocionales y tendencias en línea para prever la demanda futura de productos o servicios. Estas predicciones permiten a las empresas optimizar la producción, el inventario y la cadena de suministro, reduciendo los costes y mejorando la satisfacción del cliente.

Yo creo que en el mundo actual, debemos de utilizar herramientas que ayuden a procesar la data recopilada. Esto ayudará en tiempos sin lugar a dudas. Sin embargo la persona a cargo de analizar, deberá tener la experiencia y capacidad de análisis necesarios para poder darle ese enfoque que se requiere. Nunca perder de vista esto que es parte fundamental, aún las mentes humanas son necesarias!

Artificial Intelligence in Data Analysis. Data analysis using artificial intelligence #AI greatly expands the opportunities for growth.

Imagine a shark using its keen senses not just to hunt but to understand the entire marine ecosystem. Similarly, technology in data analysis acts like these acute senses, dissecting vast oceans of data to provide predictive, prescriptive, and descriptive analytics. It's not just about gathering information; it's about decoding the mysteries beneath the waves. Visualization tools become your sea charts, making complex data as clear as the Caribbean waters. Just as a shark uses every sense to master its environment, use technology to master the depths of your market, identifying trends and opportunities like never before. Dive deep into data analysis with technology, and navigate your growth path with the precision of a great white!

Utilize powerful data analytics tools to derive actionable insights from the collected data. Employ machine learning algorithms to identify patterns, trends, and potential growth opportunities. My experience has shown that real-time analytics dashboards enable quick decision-making by providing up-to-the-minute insights into key performance indicators.

Data analysis is the most Joker card that can win and rapid up the whole process, it gives a strong vision based on numbers. So the strategy home work will have a strong building block that help it to be easily formulated. The more you analyze your data and see the right numbers, the more accurate decisions you will get.

Data analysis, integral to the rapidly advancing tech landscape, exemplified by the growth of Data Analytics and Data Management practices, should catalyze business growth. This necessitates the development and implementation of a robust data strategy, providing the organization with a structured roadmap to harness its data assets in alignment with its maturity level. This strategic approach spans descriptive, predictive, and prescriptive analyses, facilitating the effective management of IT investments in tandem with extracting value from the data.

As I have already mentioned in other articles, a good understanding of the target audience for whom we are developing the dashboard already facilitates 70% of all development, as with a good survey we must develop the most assertive views possible.

I would like to highlight also cybersecurity risks when it comes to data validation. Inadequate data validation poses risks such as injection attacks, data integrity issues, bypassing access controls, and the potential for denial-of-service attacks. Privacy violations may occur if sensitive user data is exposed due to insufficient validation. To address these risks, organizations should implement thorough input validation, data sanitization, and security awareness training for developers and to have robust model testing, adversarial training, and regular checks for anomaly detection to enhance system and model resilience.

Making sure your data is reliable helps you figure out what your business needs to grow. Technology helps by checking if your data is accurate, consistent, and makes sense. It gets rid of mistakes, weird stuff, and things counted twice, making your data strong and healthy. Checking data from different places, like surveys with website visits or reviews with sales numbers, makes sure it's real. This makes your understanding of what's needed clearer. Using technology not only cleans up your data, but also makes it strong enough to base important decisions on, so your business can grow in the right way

Just as a wise shark double-checks its hunting grounds for the best catch, technology in data validation ensures you're basing decisions on the freshest, most accurate data. Employing tech to scrutinize data quality and consistency is like the shark's keen eye for detail, sifting through the sea to avoid deceptive pufferfish or elusive squids. Cross-referencing tools act as your oceanic network, confirming every data point's authenticity. With technology, make your data as reliable as a shark's instincts, ensuring your needs analysis is built on a foundation as solid as coral reefs. Dive into data validation with the precision of a predator, and let your strategies evolve with the certainty of the tides! #divineintervention #gabenfreude

Verification of data is #mandatory. Verify authenticity, #quality, #consistency, accuracy. Data protection. #Cloud storage provides powerful tools to protect data. #Azure tools.

Validating your data should not be a yearly activity. There is constant effort, planning and preparation that goes into validating data. Companies today process huge amounts of data daily and this makes it paramount that there are fail safes and checks in place to ensure its authenticity. Technology can be your best friend to process and validate big data and you should look to leverage the workflow functionality within systems that will flag data which is not in a desired format.

Implement robust data validation processes to ensure the accuracy and reliability of the collected information. Employ validation algorithms and automated checks to minimize errors. Additionally, incorporating user feedback loops for data validation helps in refining and enhancing the quality of the data over time.

Pour valider les données et améliorer l'analyse des besoins de croissance, il est essentiel de vérifier la précision, la cohérence et la fiabilité des données en utilisant des techniques telles que la vérification croisée, la comparaison avec des sources fiables, la détection et la correction des erreurs, ainsi que l'application de filtres pour éliminer les données incorrectes ou incohérentes.

As I stated in a previous article, the GIGO effect has to be always a concern to be aware of, no matter how advanced the technology gets. That's why my recommendation to perform an EDA by a SME because for someone without the proper business acumen data may not uncover important initial insights, either to see patterns, black swans or that data may need to be improved for whatever reason.

Technology serves as a powerful tool to validate and enhance the reliability of data used in needs analysis. Through various technological solutions, you can verify the quality, accuracy, and consistency of data while identifying and rectifying errors, outliers, or duplicates. Additionally, technology facilitates cross-referencing data with multiple sources, ensuring its authenticity and validity. For instance, employing data validation tools enables comparisons between survey results and web analytics or validates customer reviews against sales data, ensuring a more robust and credible foundation for needs analysis.

Para que a tecnologia ajude a melhorar resultados, os dados analisados precisam ser confiáveis e corretos, a verificação da autenticidade dos dados é fundamental para validar as informações.

Like a shark masterfully navigating currents, technology in data integration gives you a complete, unified picture for your needs analysis. It merges diverse data streams into a single source of truth, much like a shark's integrated senses. This creates a coherent view, aligning your data with strategic goals. Imagine merging customer feedback with product features or linking market research with your growth strategy—this is the power of data integration. Harness these tools to move through the market as deftly as a shark through the ocean, ensuring your brand's strategy is as sharp and focused as the predator's hunt. #divineintervention #gabenfreude

In my experience data collection without the ability to consolidate it into the required framework that can be analyzed and applied versus specific business needs is useless. And the amount of data required to be processed in most cases is so large that it simply cannot be performed without modern technology and AI

Yes, that's right. We use technology to combine& consolidate data from different sources&formats, and to create a single source of truth to analyze a company's needs. But we also need to understand that data must be properly processed, stored, and delivered. Choosing #cloud storage for optimal data management 7/24/365

Integrate diverse data sources within your organization to create a unified and holistic view. Utilize technologies like Application Programming Interfaces (APIs) and data integration platforms to connect disparate systems seamlessly. Personally, I've seen significant improvements in decision-making when there's a comprehensive view of data from various departments.

Más que integración de datos lo denominaría agregación de datos en un solo site, que permita consolidarlos y analizarlos en un solo sitio. Hoy en día existen númerosas herramientas que te permiten consolodiarlo y analizarlo al mismo tiempo!

Data integration is the most challenging piece of the puzzle that lives at the center of data management. Often, businesses and organizations struggle bringing data from different sources and creating a master data structure that unifies how data is used across all systems. It is a cumbersome and time consuming process (even with technology) and hence most companies live with data in disparate systems. This further drives manual effort on employees end and they spend countless hours fixing and authenticating data before they even use data for analytics. Data Warehouses and Data Lakes (snowflake) are commonly used to aggregate and integrate data. There are numerous middle warehouse tools as well like Dell Boomi to integrate data.

A melhor forma de analisar os dados é com a integração de dados diferentes, são com estes dados que será possível analisar todo o contexto.

Na minha experiência como gestor empresarial. É fundamental a integração dos dados coletados e disponíveis. Posto que, por exemplo permitirá ajustes e investimentos em determinados setores.

Great technology platforms - like Clari, PowerBI, Tableau, Domo - paired with solid data sources can allow you to identify and analyze new growth trends in realtime. When communicating your analysis to stakeholders, think about whether you want to demonstrate the comparison, distribution, composition, or relationships between different data points. Even better if you can give your colleagues direct access to the interactive dashboards or reports themselves, so they can pivot between the high-level trends and the exact datapoints.

Besides the importance of a company to have tools to share data across departments, there is another great oportunity which is to develop the capability of analysing data and take action on it. Most of companies are good on giving people dashboards but they suffer on generating insights that are useful to salespeople, for example, improve sales actions to customers. There are a green field to Business Intelligence departments to make data as a cultural element of the organizational rather than face it as a single-department skill.

The presentation of data to #stakeholders should only be in the form of #processeddata. It is unacceptable to provide unverified, unprocessed (#raw) data. Data for decision-making by #leaders should be presented briefly, clearly, in the form of graphs and brief tables.

Think of sharks as the communicators of the sea, sharing insights for a thriving ecosystem. In the business world, technology in data sharing does this for your needs analysis, sending ripples of information across your organization. It fosters collaboration, much like marine life working together, ensuring your data and insights reach all stakeholders effectively. Tailor this shared knowledge for various audiences, enhancing understanding and engagement. Use data sharing tools to craft interactive reports and presentations, making your insights as engaging as the ocean's mysteries. Let your strategies spread far and wide, ensuring every part of your organization moves in harmony, informed and ready!

Foster a culture of data sharing across departments. Implement collaborative platforms that facilitate easy sharing of insights and information. By promoting transparency and open communication, teams can align their efforts towards common growth objectives. In my experience, fostering a data-sharing culture accelerates innovation and problem-solving.

Un punto crítico en todos los sectores y que con los años continúa siendo un área de oportunidad en las empresas es que una vez teniendo los resultados, los hallazgos encontrados, se compartan con todas las áreas. Esto es algo que a la fecha les cuesta mucho trabajo llevar a cabo y los resultados se quedan solo con 1 ó 2 áreas de las empresas. Y no se diga, darse el tiempo de poder internamente comprender y trabajar en talleres creativos con todas las áreas para poder hacer más Accionables los resultados. Sería ideal llevar a cabo estos talleres con un día de trabajo, se podría mejorar en todas las áreas, sin embargo todas las empresas están enfrascadas en las labores diarias que pocas deciden llegar a este punto.

O Compartilhamento de dados é fundamental para a melhoria continua. Exemplo: O setor de vendas precisa saber do feedback dos clientes para poder corrigir possíveis inconsistências no processo de vendas.

While Data collection and analysis can help you understand your customer better. The real test of strategies come from the actions that we take on these information. We had a wonderful case where we were able to significantly broaden the funnel by placing customized notifications based on realtime analytics of customer on the website and mobile app.

Translate insights into actionable strategies. Use technology to automate and streamline the implementation of growth initiatives. Implement project management tools, task automation, and communication platforms to ensure efficient execution. Personally, I've found that creating a clear roadmap with defined action items and timelines enhances accountability and accelerates the pace of implementation.

At the end of the day, without translating the Data insights and analytics into actions, all the efforts will be on the shelf. Operational plans that reflect our strategy which is built on real data, is the main driver for any Growth.

Leverage technology for needs analysis by utilizing data analytics tools to gather and analyze relevant information. Conduct surveys or use online platforms to collect feedback. Implement customer relationship management (CRM) systems to track customer interactions and preferences. AI and machine learning can help predict future needs. Regularly review analytics to identify trends and areas for growth, ensuring a dynamic and data-driven approach.

"Technology is the driver to take information from Data." Itself sufficient to make out the importance of Technology. We could add more factors like - Speed, Accuracy, Availability, Ease, and other. These all made the modern world we all are enjoying.

La donné ou data est l'or de demain. Aujourd'hui c'est la donnée qui sert de carburant à l'ensemble des intelligences artificielles. Les GAFAM l'ont bien compris et compilent à eux la quasi totalité des données mondiale. Autre chose à prendre en compte est de s'assurer de l'authenticité des données. Il est courant de tomber sur desfichiers de données corrompus

One of the most overlooked items is the quality of the data. Is the data accurate, reliable and relevant? It’s so critical to verify the sources of data, checking for any errors or inconsistencies, and validating the data against known benchmarks or industry standards. Ignoring data quality can lead to flawed analysis and decision-making, which can ultimately have a negative impact on the effectiveness of any strategy.

Na minha experiência "dados" são informações essenciais para o desenvolvimento e otimização da Empresa. Como Diretor Executivo tive a oportunidade de desenvolver ferramentas de coleta, análise e integração de dados. Tal fato, permitiu não só um salto de qualidade nos serviços prestados, bem como me auxiliou na tomada de decisões e alocação de recursos.

Needs analysis for B2B growth efforts? There's a great AI use case. (More below) First, define 'needs analysis for growth efforts.' Just what it sounds like: You assess what your org needs more of to accelerate growth. The best place to start? Your clients needs. Serve your clients' needs better than anyone else and growth will follow. Now, your clients' needs? Where to start? Study their investor call transcripts. Want to streamline that? Use AI...specifically Finchat.io. Think ChatGPT, but trained upon & accessing → Earnings Call Transcripts → Revenue & Profit statements → Other Business KPIs You'll learn quick about what your customers' need to win. And in doing so, you learn about what you need to do for them (to grow).

CRMs can track and analyze customer interactions, preferences, and feedback. Advanced CRMs use AI to provide insights that help tailor offerings and improve customer experience. AI algorithms can forecast market changes and consumer needs by processing vast quantities of data more effectively than humans. Machine learning can continuously improve predictions over time, making the business more adaptable. Cloud-based solutions enable scalable storage and computation, which is necessary for processing large datasets used in business needs analysis, ensuring that resources are available as business grows.

Technology/AI is an awesome tool for data analysis but always remember it has its own biases especially predictive analysis. I will place interacting with respective the stakeholders to understand the needs analysis while and leverage using technology/AI for validation.

The utilization of technology helps to gather deep insights. These technologies enable businesses to analyze large datasets, identify trends and patterns, and forecast future needs accurately. For example, AI can predict customer behavior, helping to tailor products and services more effectively. Machine learning algorithms can analyze feedback and social media trends to pinpoint areas for improvement or expansion. Moreover, big data analytics can uncover hidden opportunities in existing markets or suggest new ones. This technology-driven approach allows for data-informed decisions, ensuring that growth strategies are aligned with actual market needs and trends, thereby enhancing the likelihood of successful expansion and innovation.

Think of data as a tool or resource rather than the destination and just one piece of the puzzle. If we rely exclusively on the data, it's likely everyone comes to the same conclusion and no edge is created. Keep in mind, if the data was right all the time, everyone would know what stock goes up tomorrow and the favorite in horse racing would always win (they only win 35% of the time).