Jay Allardyce, Managing Director of Data and Analytics at Insight Software, explores the challenges and opportunities presented by the abundance of data, highlighting the importance of leveraging AI and machine learning to extract insights valuable information.
The amount of data we produce every day is exceptional. This pace will only accelerate in the coming years with the growth of innovations such as artificial intelligence (AI) and machine learning (ML).
In fact, as Rachel Galvez of Precisely reported, 77% of data and analytics professionals cite data-driven decision-making as the primary goal of their data programs – but unfortunately, only 46% have “high” or “very high” confidence in the data used for decision-making.
Organizations that can effectively leverage data as a strategic asset will inevitably gain a competitive advantage and outperform their peers over the long term. However, it is a double-edged sword. Poor data quality can cost organizations money in the long run, if not a priority at all. 70% of professionals who struggle to trust their data say data quality is the biggest problem.
For organizations to effectively harness the power of data and succeed in their environment, they must understand why better data storytelling is needed, the difference between predictive analytics and augmented analytics, and how to best apply analytics predictive. According to a recent reportup to 94% of business leaders believe their organization should get more value from its data.
Fortunately, Integrating these analytics into an organization’s current web applications brings a new level of experience and engagement. The results are threefold: (i) different “actionable signals” for users, (ii) leveraging an existing experience they are accustomed to, and (iii) focusing on the use case for the work needed to perform (JTBD). Users don’t need another tool; they need a more enhanced experience to get their work done – faster.
Decision making with data storytelling
Embedded analytics is an industry that is still experiencing a wave of innovation, improving the traditional operating reports and dashboards we know to become more adaptive experiences and more responsive to business conditions.
Although this concept of data storytelling is still new and evolving, understanding the benefits is essential, and continuing investments to advance an organization’s data culture to understand the best way to work with data is essential.
By integrating an analytics layer into an application, organizations will pave the way to delivering better data experiences that lead to stronger user engagement and promote a strong data culture, fundamental to becoming more data-driven and the decisions. Ultimately, this essentially delivers better data-driven product experiences.
For example, operations and product teams can stay ahead of the curve by simplifying data preparation and visualization for decision-making while optimizing applications to gain insights from near real-time data, visualizations, interactive reports and other features. Not to mention, it saves time, increases productivity, and allows business users to innovate faster rather than becoming data experts.
Combined, storytelling and data analytics platforms are purpose-built to build a narrative around data that can provide users with greater context and help extract insights from a distributed or centralized data governance framework , leveraging what businesses might have in place today.
This helps guide decisions and ideas and evoke feelings that users won’t forget as easily as a static dashboard. Additionally, organizations with a data governance program see improvements in the quality of data analysis and insights (57%) as well as the data itself (60%).
It’s time for business leaders and users to recognize that data storytelling is becoming increasingly critical to businesses today. To maximize these insights, organizations are leveraging predictive and generative AI to help amplify data storytelling with actionable, knowledge-based insights.
See more : Why your data analytics aren’t working
Understand the potential of predictive analytics
As mentioned, every day more companies are harnessing the potential of integrating predictive analytics – some using AI/ML and others using more rules-based analytics. Thanks to the integrated analysis, the results are impressive. Embedded analytics can drive user adoption, build apps people love, and be a game-changer for software and software-as-a-service (SaaS) providers.
The ability to include predictive analytics in these applications creates a more complex level of engagement, as predictions can change based on context, context, and business dynamics. In its simplest form, predictive analytics can provide insights into creating “next best action” scenarios. Users are alerted based on historical conditions to guide future actions, helping them best understand the domain and defining how to get work done.
For example, financial institutions, teams and CFOs can use these predictive analytics for credit risk assessment, fraud detection and investment planning. This will prove crucial in better preparing organizations for continued market volatility. Additionally, product and operations teams can benefit from AI/ML with their integrated strategy, immediately creating new data-driven experiences for their application users, instead of building separate AI applications and disjointed.
With this in mind, clients can use ML and AI-powered predictive analytics to predict whether a change will help them reduce risk, improve operations, and/or increase revenue. Basically, predictive analytics answers two common questions:
- What is most likely to happen based on my current data?
- What can I do to change this result?
Approximately 60% of 116 companies surveyed indicated that they are driving innovation through data in 2023. As organizations prepare for 2024, it is important to consider getting up to speed on implementing data and analytics in their environment, and it all starts with asking questions about what is most likely. will happen based on current data, what can be done to change that outcome, and how they can use historical data, ML, and AI to create more adaptive and predictable experiences for their customers.
Predictive or augmented: harnessing the power of analytical strategies
Above all, predictive analytics and augmented analytics are two different approaches that will undoubtedly shape the analytics landscape in the years to come. In its current state, predictive analytics uses a combination of data sets from multiple sources to find relationships and correlations, primarily by presenting people with numbers without telling them what it means. In short, it is a prediction about future scenarios or the next best actions that can be taken.
On the other hand, augmented analytics uses ML and AI to make data easier to understand and analyze to improve workers’ ability to analyze data – starting with a key point before diving into numbers. We often hear about how these technologies can be applied to predictive analytics while helping organizations predict the future. trends in several sectors. However, very few companies are experimenting with this technology, and even fewer have it in production.
Businesses should prioritize streamlining data analysis through augmented analytics. This makes the information more accessible to a wider audience, allowing more users to take advantage of the data. Predictive and augmented analytics have different processes and benefits. However, they have one thing in common. These are two powerful technologies that can be used together to improve decision-making and problem-solving.
In today’s digital world, making data-driven decisions and creating strategies informed by analysis is essential to successful leadership in any industry. Fortunately, 92% of all IT and analytics decision makers understand that reliable data is needed more than ever, and another 95% of global leaders agree that new data architectures and strategies are needed to manage significant changes in their organizations’ data environments.
Integrated data and analytics are powerful tools that help businesses create resilient, agile operations that can withstand even the most unpredictable environments. If we look to the future, the success of modern organizations will be based on high-quality data powered by predictive and augmented analytics, integrating AI and ML with predictive analytics and, therefore, driving storytelling insightful.
How ready is your organization to revolutionize decision-making with the help of powerful integrated AI, machine learning and analytics tools? Let us know on Facebook, XAnd LinkedIn. We would love to hear from you!
Image source: Shutterstock
