What emerging trends in business intelligence can you use to align your strategy?

Augmented analytics leverages AI and ML to automate data tasks, empowering users to delve into insights effortlessly. Imagine a marketing team using augmented analytics to sift through vast datasets, uncovering hidden trends without manual analysis. This approach streamlines decision-making, enabling swift adjustments to strategies based on real-time insights. Augmented analytics democratizes data, making it accessible to non-technical users, fostering a culture of data-driven decision-making across the organization.

Few Emerging trends in BI I can think of are 1) Using AI to build BI dashboard, automate processes, predict trends, and gain deeper insights from data 2) Focus on ensuring data quality, security, and compliance with regulations with massively growing data and AI 3) Availability and regular use of Machine Learning within dashboards to predict and build models 4) Cloud based enhancements within dashboards

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Aligning your business intelligence (BI) strategy with emerging trends can help you stay competitive and maximize the value of your data assets. Here are some emerging trends in BI that you can consider incorporating into your strategy: Augmented Analytics: Embedded BI: Data Democratization: Real-time Analytics: AI and Machine Learning: Data Governance and Privacy: Edge Analytics: Predictive and Prescriptive Analytics:

Machine Learning can help not only building models but can also point out features that are of importance so that you can deep dive into them. If you for example have to payment solutions in parallell, and the modeling suggest that the choice of vendor is a heavy weighing feature in predicting the results... this can encourage to deep-dive into their differences.

Consider utilizing the recent AI trends such as : Augmented Analytics ,Predictive Analytics ,AI-driven Data Visualization tools and the AI driven Tools for enhancing data governance and security within the BI inviroment

Data storytelling is the fusion of data visualization, narrative, and context to convey insights in a compelling manner. It aligns strategies by influencing stakeholders, customers, and partners. Through storytelling, we showcase value propositions, highlight achievements, and explain challenges, making data relatable and driving action. It's an essential skill for any data-driven professional, transforming numbers into impactful narratives.

Effective data storytelling transforms complex data into compelling stories that influence decisions. For instance, when the pandemic impacted a restaurant chain's growth projections, interactive charts showed that some locations experienced increased demand through online orders. An analyst combined these insights with local trends, customer demographics, and potential new sites to create a persuasive expansion plan. By breaking down assumptions and presenting data in a relatable manner, the story convinced stakeholders to approve targeted growth goals. Exceptional storytelling involves seamlessly blending clear visualization, logical flow, and accessibility. Relate data to events or concepts that resonate with the audience.

Data democratization breaks down technical barriers and unlocks the potential of data analysis for everyone in the organization. For example, a factory that once relied on slow, centralized analytics processes transformed its operations by providing production and quality teams with accessible dashboards using collaborative platforms and low-code tools. However, to truly empower workers on the frontlines, the company invested in data fluency training. This training ensured that employees could confidently interpret data and take action, such as implementing quality improvement measures without waiting for approvals. Data democratization involves not only providing access to data but also fostering the skills and understanding.

La democratización de los datos puede ser una estrategia poderosa para impulsar la toma de decisiones informadas, fomentando la innovación en toda organización. Al facilitar el acceso y la manipulación de los datos por sí mismos, sin depender de equipos de TI o analistas de datos, permitiendo la visualización y análisis personalizado y brindando la transparencia en el uso de información fiabilidad, pemitirá la colaboración entre diferentes equipos y áreas de la organización.

As the industry leader BI solutions software are moving to cloud-based reporting, one needs to be cognizant while migrating of the downstream and upstream impacts.

Cloud-based solutions offer many scalability advantages. I have seen myself how we could for example crank up processing power for a weekend to do some execution, without having to carry the burden of such hardware for a prolonged time. This instant ability to scale up and down is really interesting.

Cloud-based BI revolutionizes how businesses harness data. Imagine a multinational corporation utilizing cloud-based BI to streamline operations. With scalable resources, they effortlessly handle fluctuating data loads, optimizing costs. Flexibility allows teams to access insights from anywhere, fostering collaboration across continents. Enhanced security protocols ensure data integrity, crucial for compliance in various markets. Utilizing cloud-native technologies, they deploy agile solutions, leveraging serverless computing for efficient processing. Microservices architecture enables modular development, enhancing scalability and maintenance. Containers streamline deployment, ensuring consistency across environments.

Moving Business Intelligence (BI) to the cloud enhances alignment with business needs through adaptability and innovation. A rapidly expanding startup overcame platform obstacles that hindered advanced analytics by adopting a cloud data warehouse. This allowed their aspirations to scale effortlessly without incurring additional costs. The serverless architecture ensured growth and empowered teams with self-service access to data. Cloud connectivity enabled real-time data integration from various sources, enriching insights. It also laid the groundwork for machine learning, which was previously unattainable. With a suitable cloud model, BI can transition from being rigid to flexible.

Real-time BI revolutionizes decision-making by providing immediate insights into dynamic market conditions. Picture a logistics company using real-time BI to track shipments, detect delays instantly, and reroute vehicles efficiently. This agility optimizes operations, ensuring timely deliveries and enhancing customer satisfaction. Real-time BI empowers organizations to stay ahead of the curve, adapting strategies swiftly based on emerging trends and customer preferences. It's a game-changer in today's fast-paced business landscape, driving efficiency and innovation across industries.

Real time BI is a crucial approach in organizations that lean towards operational day-to-day work. That enables fast decision making and ability to react on spot. Manufacturing businesses and operations benefit the most out of real time reporting e.g. on inventory or even target / goals (i.e. progress tracking). This type of BI has a very distinctive role and it is not a "classic BI" that many are used to, where the emphasis lies more on strategic reporting.

A couple of trends are emerging based in the near horizon:- 1. AI + BI - Customers are looking for a single platform for their descriptive and predictive insights. They want to see the historical data trends and predictive recommendations in the single dashboard itself. This is one area which has blurred the boundaries for BI and AI. 2. GenAI with BI - GenAI based interactions and recommendations integrated with BI reports and more personalised insights.

BI has evolved over a period of time and there are multiple new terms and mechanisms have been introduced. Few emerging trends which should definitely be part of BI Strategy are- - Self Service - Natural language Query - Narratives based on Natural language generation - Real time Analytics - AI/ML usage within BI setup - Augmented Analytics - Modern Data stack of data layer - Data lake or Cloud DWH

Real-time BI revolutionizes decision-making by instantly processing data as it's generated. For instance, a global e-commerce platform employs real-time BI to monitor sales, inventory, and market trends continuously. This enables swift responses to demand changes, ensuring optimal pricing and inventory management. Moreover, by analyzing customer behavior in real-time, the platform delivers personalized experiences, boosting satisfaction. Ultimately, real-time BI enhances agility, efficiency, and customer-centricity, crucial for success in today's fast-paced market.