Business Analysts: Expect the Unexpected with AI
What’s the next phase of data-driven business?
It has become evident that Big Data is the dynamo that drives enterprise growth. According to the Sisense State of BI & Analytics Report, fifty percent of survey respondents use data more often or much more often than before COVID-19 hit. This trend continues to accelerate, powered by the ascendancy of cloud-based data warehousing, analytics platforms, Artificial Intelligence (AI)
This trend adds a new chapter to a constant challenge for all organizations: how to maintain a competitive advantage in their industry. When competitors are jumping on board the data train, what can organizations do to stay ahead of them? The answer is simple. They must continue to adopt the most advanced analytics platforms, processes, and tools to future-proof their business. By harnessing the power of advanced analytics, they can drive the next wave of digital transformation that will enable them not only to handle current conditions but also to anticipate and shape the future.
In this paper, we consider how BI and analytics will deliver this promise and what strategies the next generation of data-driven businesses will implement. Read on to find out about two of the most innovative technological developments that will empower business analysts to unleash the power of data science so that they can improve future outcomes using today’s data.
The rise of Artificial Intelligence. What does it mean for you?
Artificial Intelligence (AI) provides a new level of understanding that extends what existing analytics platforms can achieve. That’s because platforms with AI can learn from the patterns of data they analyze. AI can extrapolate insights and suggest new phenomena that may not have been anticipated. It can be leveraged to power exploration widgets and drill down deeper into data to answer business questions — current and even unasked.
AI algorithms scan entire databases and make suggestions based on both analysis and the usage patterns of dashboard users. As AI algorithms collect more input from users’ activity, their suggestions will become more accurately targeted to users’ needs.
The result is advanced forecasting capabilities and a new way of working for business analysts. Using AI, analysts can become more focused on forward-thinking scenario planning, asking what-if questions of the data, rather than simply analyzing current or previous trends. With this capability, analysts can sharpen their organizations’ competitive edge by identifying future KPIs and improving outcomes. Furthermore, this can be achieved in real time and without hard coding skills to instantaneously deliver new answers to the questions users have in mind when they want to uncover fresher and deeper insights directly within dashboards.
Mindful of these capabilities, data mining, advanced algorithms, and predictive analytics are among the highest-priority projects for enterprises adopting AI and machine learning (ML). Already, 40% of marketing and sales teams say that data science including AI and ML is critical to their success. Sales and marketing share high levels of interest in their efforts to define new revenue growth models using AI and ML. Furthermore, according to Dresner’s 2019 Data Science and Machine Learning Market Study, 70% of R&D departments and teams are most likely to adopt data science, AI, and ML, leading all functions in an enterprise. This level of interest strongly indicates broader enterprise adoption in the future.
Taking the weight of your data
AI can also be used to facilitate and expedite the analysis of data, empowering analysts to reach insights in real time and accelerate decision making. It achieves this by taking the weight of your data.
The volume of data is growing exponentially. To keep pace with this growth, BI and analytics platforms need to categorize, manage, and analyze ever larger and more complex datasets, and do all of it faster than ever. AI engines automate a considerable part of this process because they are capable of tirelessly analyzing huge datasets from any source and in any format. AI doesn’t require data engineers to manually categorize, organize, and identify patterns in data. It learns emerging patterns and builds on what it has learned. Moreover, it can learn and recommend ways of using data from different users — both individuals and groups — that human analysts may not have considered. AI minimizes the need for time-consuming, tedious, and cumbersome tasks so that the skills of data teams can be used more productively.
In the process of identifying these patterns, AI engines enhance the data team’s ability to discover anomalous behaviors and causes based on the analysis of historical data patterns. With that information, data teams can minimize or capitalize on anomalies at the right time to deliver beneficial business outcomes. Data that shows both regularities and outliers can be instructive in forming insights and developing strategies to drive a business or raise new questions to take strategy in new directions. AI and ML make data and insights accessible, however complex its raw form is, and empower users to build dashboards, analytic apps and widgets of their own to harness the data’s full potential. All of these tools combine to deliver a guided experience that will make it easier for even a novice to gain a deeper understanding of their business.
Making AI ubiquitous
With all these benefits, it stands to reason that the best way to maximize the value of data is to integrate AI into every aspect of the BI lifecycle; starting with the preparation and cleansing of data, through to delivering key insights and hidden patterns to end-users. Therefore, the ideal situation is to embed AI capabilities into each facet of a BI platform, so that everyone within an organization can experience the capabilities of AI and ML to streamline or reduce manual processes and get new insights.
Consider the power of the insights and the potential outcomes that could be gleaned from an “AI Throughout” approach — terabytes of rich usage metadata stored on the cloud and spanning use cases, industries, and user profiles. It’s information that could drive the development of user experiences that are more tailored, personalized, and relevant than ever.
With AI-assisted data preparation and modeling, it will be possible to reduce the time it takes for business analysts to build complex data models. Looking forward, AI-assisted data modeling will empower builders with less database administration expertise to make data models and avoid common pitfalls by offering suggestions during the modeling process.
By weaving AI throughout, builders will also be empowered to deliver analytics in a variety of ways, tailored to any situation. Analytics will be integrated into a user’s workflow through embedded analytics and by adding workflow and actionable components to dashboards. By guiding and supporting users, AI will bring value to organizations that goes beyond dashboards and self-service BI to the deployment of embedded, AI-powered analytic apps that create data experiences users will love.
Creating a cultural shift – from reactive to proactive
What arises from the new approach to AI enablement is augmented insights: instant and actionable at just a click. That gives analysts and users without data science skills the ability to immediately discern trends and patterns in their data, predict future outcomes, and make decisions that drive business value. Soon, business analysts will have forecasting capabilities — the ability to deploy advanced predictive data models to any KPI based on current data.
At the heart of this is a move from a reactive, historical data perspective to a proactive, future-focused mindset, as AI enables predictive analytics that delivers a more accurate assessment of what will happen in the future. With AI everywhere, organizations can feel more confident that they’re making the best possible business decisions; not just in response to what’s happening, but in anticipation of what will happen. Changing this perspective creates a powerful point of difference and a strong competitive edge for any business.
The quest to simplify AI
Building this advanced AI environment isn’t easy, but it’s hugely valuable. Maximizing AI’s value comes from increasing engagement throughout your organization, by making the process of integrating and using AI as simple as possible, and by optimizing its impact on decision making.
AI needs to be actionable in a low-code environment, so users can seamlessly implement specific recommendations at the point of insight. Accomplishing this involves evolving dashboards into AI analytic apps, and delivering these advanced analytic apps in a visual, interactive way. After that, historical data and predictive analytics come to life in an easily comprehensible format, making insights, potential outcomes, and decisions as accessible and as understandable as possible for more users.
The type of tools necessary to make this happen are:
- Libraries of out-of-the-box statistical models that deliver new insights from existing KPIs
- Custom ML models deployed by uploading a single Python script
- Suggested actions for each app, or the creation of new ones tailored to specific use cases
- Statistical analysis and advanced forecasting capabilities within a menu-driven, point-and-click-interface
The result is that business analysts can deliver the most relevant, tailored user experience to their colleagues, harnessing the power of data science for all. With these kinds of tools, analysts can integrate predictive analytics and deliver customized, impactful actions that users can take at the point of insight, not just for today, but to shape the future of their business. AI is the future that gives more insights to everyone and further power to the builders.
Find out more about how you can optimize your analytics with AI throughout and deliver predictive insights across the BI lifecycle, in the Sisense BI and analytics platform.Learn more