Here's how you can effectively utilize big data for informed business decisions in business strategy.

En este caso el Cubo OLAP es esencial y nosotros lo utilizamos para nuestros clientes porque permite la consolidación de datos de múltiples fuentes para análisis rápidos y multidimensionales. Esta herramienta transforma grandes volúmenes de datos en visualizaciones claras, facilitando decisiones estratégicas informadas al identificar tendencias y áreas de mejora. El Cubo OLAP optimiza el uso del big data para impulsar decisiones empresariales inteligentes y oportunas.

Effectively utilize big data for informed business decisions by integrating vast datasets from diverse sources to gain comprehensive insights. Analyze this data to identify patterns, trends, and correlations that inform strategic decisions, optimize operations, and uncover new opportunities. Employ advanced analytics tools and techniques to translate raw data into actionable intelligence, allowing you to make evidence-based decisions that enhance your business strategy and drive success.

Effectively utilizing big data for informed business decisions involves leveraging advanced analytics to extract actionable insights from vast datasets, enabling businesses to identify trends, predict customer behavior, and optimize operations. Companies can gain a comprehensive understanding of their business environment by integrating data from various sources, such as customer interactions, market trends, and internal processes.This data-driven approach supports strategic planning, enhances decision-making accuracy, and drives innovation by uncovering opportunities for growth and improvement. Also,real-time data analysis allows businesses to respond swiftly to market changes, maintaining a competitive edge and fostering long-term success

Ensure a well-defined strategy with clear goals and objectives.gathering data from(diff data sources structured,unstructured and semi-structured).Choose appropriate tools for data collection and processing,it could be Batch Processing,Real-Time Processing,and Database Connectors.Implement effective data management practices and select suitable storage solutions such as data warehouses,data lakes (e.g.,Amazon S3,Azure Data Lake),or NoSQL databases (e.g.,MongoDB).Use data integration tools (e.g.,Talend,Informatica) for seamless data preparation,ensuring data quality through cleansing and formatting.Finally,Using advanced analytics techniques to generate actionable insights supporting strategic decision-making aligned with business objectives.

1. Data Integration: Consolidate data from diverse sources like CRM systems, social media platforms, and IoT devices to create a unified dataset for comprehensive analysis. 2. Quality Assurance: Implement protocols to maintain data cleanliness and accuracy, ensuring that insights derived are reliable for strategic decision-making. 3. Advanced Analytics: Utilize tools like predictive modeling and machine learning to uncover patterns, trends, and correlations within big data, facilitating proactive and data-driven business strategies.

Já trabalhei com as principais ferramentas de análise de dados, e o que eu presenciei nas empresas é um grande misto entre as principais do quadrante mágico, em especial Power BI, Tableau e Qlik, ambas oferecem análises poderosas que combinados ao conhecimento de negócio norteam o dia a dias das pessoas

Utilizing analytical tools to enhance business decision-making is vital for any modern enterprise. The increasing adoption of data science and analytics tools gives these businesses a significant advantage over traditional, non-data-driven approaches. In the long run, employing machine learning models like ARIMA to predict price changes enables businesses make informed, data-driven decisions.

Essa sem dúvida é a parte mais complexa de se trabalhar com dados, é muito complexo trazer a cultura de dados para muitos lugares onde a cultura está engessada e não se tem apoio do board e de outros níveis, é realmente triste construir soluções em dados onde não é utilizado 1/3 do que é feito.

Historically, many companies introduced new features based on what THEY thought would work. This can work well at the inception of an organization, but as time goes on, you must take customer feedback and usage data into account. By doing this, you're leveraging the data you have to show what the best way to move forward is and then actually being able to *prove it*. Without this, you're essentially moving forward blind without a sense of direction

🗹 Optimise Operations: Use big data insights to identify and address inefficiencies. Enhance supply chain efficiency to save costs and improve delivery times. 🗹 Enhance Customer Experiences: Leverage data to understand customer behaviour. Tailor services to meet changing preferences and boost satisfaction. 🗹 Innovate Products and Services: Analyse trends and customer feedback. Develop new products or refine existing ones to stay competitive. 🗹 Drive Strategic Decisions: Base decisions on data-driven insights. Ensure strategies align with business goals and market demands. Maximising big data insights transforms operations, enhances customer satisfaction, and drives innovation.

In this largely data driven world it is crucial to be able to leverage the amount of data we have at hand to derive useful insights and design or modify the planned strategy rather than have a static strategic approach that isn't evolving according to trends

There's an infinite number of ways you can present a finite amount of data. The more data you collect, the more difficult this can become. By identifying organizational goals, technical capabilities, and customer desires, you should be able to put together a version of a roadmap that aligns all of these things together. Using data to prioritize what to do and when to do it will help set you apart from the competition as you look towards what the market wants rather than what you want

Big data analytics can be a great factor in risk reduction especially when macro economic variable are involved and not much is in the control of firm it's crucial to opt for wait and watch strategy rather than jump into acting upon it ,so to identify and opt for the best strategy based on exogenous factors ,big data is rather a boon

Big data transforms risk management: Early Risk Detection: Analyze vast datasets to spot risks before they escalate. Strategic Adaptation: Adjust business models based on data-driven insights. Predictive Power: Anticipate economic shifts, regulations, and tech disruptions. Harness data for resilience!

Big data transforms strategy: Continuous Learning: Insights drive ongoing improvements. Iterative Approach: Regularly review data for relevance. Agility & Adaptation: Stay competitive by refining strategies. Data fuels progress!

Nada é tão bom que não possa melhorar! Devido a prazos apertados acabamos entregando o que está no escopo inicial dos projetos em dados e nem sempre conseguimos aprimorar nossas construções, entretanto você pode ter a oportunidade de melhorar indicadores do passado, uma vez que seu conhecimento sobre o negócio e tecnologia so aumenta a medida que o tempo passa.

I learned that one of the practical aspects of continuous improvement with big data management included keeping the data up to date. A way of doing this is through automatic updates that can be programmed through the server manager which detects changes as they occur/periodically. Using the most updated data will enable more accuracy in recommendations and decision making.

Data Quality and Governance - Implement data quality and governance frameworks to maintain the accuracy, consistency, and security of your data. Establish data stewardship roles and use data cleansing tools to ensure that the data remains reliable and actionable. High-quality data is accurate, consistent, and reliable, which is essential for making sound business decisions. Define and monitor data quality metrics to measure the effectiveness of your data governance efforts.