The world of Business Intelligence Trends to Watch in 2017 is dynamic and constantly evolving, making it crucial for organizations to stay ahead of the curve. From advancements in artificial intelligence to the increasing importance of data visualization, understanding these trends can be the difference between success and stagnation. Businesses that embrace these changes and adapt their strategies accordingly will be best positioned to leverage the power of data and gain a competitive edge. The upcoming year promises exciting developments in how we collect, analyze, and utilize data to drive informed decision-making. Therefore, staying informed about key Business Intelligence Trends to Watch in 2017 is paramount.

Augmented analytics represents a significant shift in how data analysis is performed. It leverages machine learning and artificial intelligence to automate many aspects of data preparation, analysis, and insight generation. This democratization of data allows more users, even those without deep analytical expertise, to access and understand complex information.

• Automated Data Preparation: Cleansing, transforming, and integrating data becomes faster and more efficient.
• AI-Powered Insights: Machine learning algorithms identify patterns and anomalies that humans might miss.
• Natural Language Processing (NLP): Users can interact with data using natural language queries, making analytics more accessible.

Data visualization continues to be a critical component of business intelligence, but it’s evolving beyond simple charts and graphs. Interactive dashboards, dynamic visualizations, and storytelling with data are becoming increasingly important. The goal is to present information in a way that is not only visually appealing but also easily understandable and actionable.

• Interactive Dashboards: Allow users to explore data and drill down into specific areas of interest.
• Storytelling with Data: Present data in a narrative format to engage audiences and drive understanding.
• Mobile-First Design: Ensure visualizations are accessible and effective on mobile devices.

Self-service BI empowers business users to access and analyze data independently, without relying on IT departments or data scientists. This agility allows for faster decision-making and greater responsiveness to changing business needs. Self-service tools are becoming more user-friendly and feature-rich, making them accessible to a wider range of users.

To fully capitalize on self-service BI, consider these advisory points:

• Invest in User Training: Provide comprehensive training on self-service tools and data literacy.
• Establish Data Governance Policies: Ensure data quality and consistency across the organization.
• Promote Collaboration: Encourage users to share insights and best practices.

The demand for real-time insights is increasing as businesses seek to react quickly to changing market conditions and customer needs. Real-time analytics involves processing data as it is generated, providing immediate visibility into key performance indicators (KPIs) and emerging trends. This allows for proactive decision-making and faster responses to opportunities and threats.

• Choose the Right Technology: Select a platform that can handle high-volume, high-velocity data streams.
• Focus on Critical KPIs: Identify the metrics that are most important for real-time monitoring.
• Integrate with Actionable Systems: Connect real-time insights to systems that can trigger automated responses.

Predictive analytics uses statistical models and machine learning algorithms to forecast future outcomes and trends. While it has been around for some time, predictive analytics is becoming more accessible to a wider range of businesses thanks to advancements in cloud computing and automated machine learning platforms. This allows organizations to anticipate future events, optimize operations, and improve decision-making.

To maximize the value of predictive analytics, consider the following:

• Define Clear Objectives: Identify the specific business problems you want to solve with predictive models.
• Ensure Data Quality: Accurate and complete data is essential for building reliable predictive models.
• Iterate and Refine: Continuously evaluate and improve your models based on real-world results.

Ultimately, navigating the landscape of business intelligence requires a strategic approach. By carefully considering these trends and adapting your strategies accordingly, your organization can harness the power of data to drive innovation, improve performance, and achieve its goals.

The world of Business Intelligence Trends to Watch in 2017 is dynamic and constantly evolving, making it crucial for organizations to stay ahead of the curve. From advancements in artificial intelligence to the increasing importance of data visualization, understanding these trends can be the difference between success and stagnation. Businesses that embrace these changes and adapt their strategies accordingly will be best positioned to leverage the power of data and gain a competitive edge. The upcoming year promises exciting developments in how we collect, analyze, and utilize data to drive informed decision-making. Therefore, staying informed about key Business Intelligence Trends to Watch in 2017 is paramount.

The Rise of Augmented Analytics

Augmented analytics represents a significant shift in how data analysis is performed. It leverages machine learning and artificial intelligence to automate many aspects of data preparation, analysis, and insight generation. This democratization of data allows more users, even those without deep analytical expertise, to access and understand complex information.

• Automated Data Preparation: Cleansing, transforming, and integrating data becomes faster and more efficient.
• AI-Powered Insights: Machine learning algorithms identify patterns and anomalies that humans might miss.
• Natural Language Processing (NLP): Users can interact with data using natural language queries, making analytics more accessible.

Data Visualization Gets Smarter

Data visualization continues to be a critical component of business intelligence, but it’s evolving beyond simple charts and graphs. Interactive dashboards, dynamic visualizations, and storytelling with data are becoming increasingly important. The goal is to present information in a way that is not only visually appealing but also easily understandable and actionable.

Key Considerations for Data Visualization:

• Interactive Dashboards: Allow users to explore data and drill down into specific areas of interest.
• Storytelling with Data: Present data in a narrative format to engage audiences and drive understanding.
• Mobile-First Design: Ensure visualizations are accessible and effective on mobile devices.

The Growing Importance of Self-Service BI

Self-service BI empowers business users to access and analyze data independently, without relying on IT departments or data scientists. This agility allows for faster decision-making and greater responsiveness to changing business needs. Self-service tools are becoming more user-friendly and feature-rich, making them accessible to a wider range of users.

To fully capitalize on self-service BI, consider these advisory points:

• Invest in User Training: Provide comprehensive training on self-service tools and data literacy.
• Establish Data Governance Policies: Ensure data quality and consistency across the organization.
• Promote Collaboration: Encourage users to share insights and best practices.

Real-Time Analytics Gains Traction

The demand for real-time insights is increasing as businesses seek to react quickly to changing market conditions and customer needs. Real-time analytics involves processing data as it is generated, providing immediate visibility into key performance indicators (KPIs) and emerging trends. This allows for proactive decision-making and faster responses to opportunities and threats.

Advisory for Implementing Real-Time Analytics:

• Choose the Right Technology: Select a platform that can handle high-volume, high-velocity data streams.
• Focus on Critical KPIs: Identify the metrics that are most important for real-time monitoring.
• Integrate with Actionable Systems: Connect real-time insights to systems that can trigger automated responses.

Predictive Analytics Becomes More Accessible

Predictive analytics uses statistical models and machine learning algorithms to forecast future outcomes and trends. While it has been around for some time, predictive analytics is becoming more accessible to a wider range of businesses thanks to advancements in cloud computing and automated machine learning platforms. This allows organizations to anticipate future events, optimize operations, and improve decision-making.

To maximize the value of predictive analytics, consider the following:

• Define Clear Objectives: Identify the specific business problems you want to solve with predictive models.
• Ensure Data Quality: Accurate and complete data is essential for building reliable predictive models.
• Iterate and Refine: Continuously evaluate and improve your models based on real-world results.

Ultimately, navigating the landscape of business intelligence requires a strategic approach. By carefully considering these trends and adapting your strategies accordingly, your organization can harness the power of data to drive innovation, improve performance, and achieve its goals.

The Convergence of BI and Collaboration Platforms

The future of business intelligence is increasingly intertwined with collaboration platforms. Imagine seamlessly integrating data insights directly into your team’s workflow. Instead of static reports buried in email, data visualizations and key findings can be embedded within platforms like Slack or Microsoft Teams. This integration promotes data-driven discussions, faster decision-making, and a more collaborative approach to problem-solving. The advisory here is to carefully evaluate which collaboration platforms best suit your organization’s needs and explore their integration capabilities with your existing BI tools.

Benefits of Integrated BI and Collaboration:

• Enhanced Communication: Share insights and visualizations directly within team channels.
• Streamlined Decision-Making: Facilitate data-driven discussions and accelerate the decision-making process.
• Increased Adoption: Make data more accessible and relevant to a wider audience.

The Data Lake Evolves into a Data Hub

While Data Lakes provided a centralized repository for vast amounts of data, they often suffered from issues related to data governance, quality, and accessibility. In 2017, we’re seeing a shift towards a more structured and managed approach: the Data Hub. A Data Hub acts as a central access point for data, providing not only storage but also data cataloging, data lineage tracking, and data quality monitoring. Think of it as a curated library of data, making it easier for users to find, understand, and trust the information they need. When considering a Data Hub solution, prioritize platforms that offer robust data governance features and seamless integration with your existing data sources. The successful implementation of a Data Hub enables your Business Intelligence Trends to Watch in 2017, and beyond.

Key Features of a Data Hub:

• Data Cataloging: Provides a comprehensive inventory of available data assets.
• Data Lineage Tracking: Tracks the origin and transformation of data throughout its lifecycle.
• Data Quality Monitoring: Identifies and addresses data quality issues.

The Continued Growth of Cloud BI

Cloud-based BI solutions continue to gain popularity due to their scalability, flexibility, and cost-effectiveness. Moving your BI infrastructure to the cloud allows you to access the latest technologies without the upfront investment and ongoing maintenance associated with on-premise solutions. Furthermore, cloud BI platforms often offer advanced features such as automated data integration, machine learning capabilities, and self-service analytics. Before migrating to the cloud, carefully assess your security requirements and choose a provider that meets your compliance needs. Consider a hybrid approach, leveraging the cloud for certain workloads while keeping sensitive data on-premise. Cloud BI will revolutionize the way Business Intelligence Trends to Watch in 2017 will be implemented.

A cloud-first strategy may be beneficial. Here are some suggestions:

• Evaluate Cloud Providers: Compare the features, pricing, and security capabilities of different cloud BI platforms.
• Develop a Migration Plan: Create a detailed plan for migrating your data and applications to the cloud.
• Optimize for Cloud Performance: Take advantage of cloud-specific features to optimize performance and scalability.

By keeping a close eye on these evolving trends and strategically adapting your business intelligence initiatives, you can unlock the full potential of your data and gain a significant competitive advantage. Remember to prioritize data quality, invest in user training, and foster a data-driven culture throughout your organization.