Abstract
- Stay informed. Data analysts are caught up in the AI hype, but their emotions reveal more than awe.
- AI will be reshaped. Natural language queries are replacing syntax-heavy techniques in AI's data analysis revolution.
- Important insight. Effective prompts and context are essential in the analysis for creating thought chain prompts.
Data analysts, like other professionals, are being swayed by AI in the analytics hype, but their feelings go beyond awe of the technology.
The world of AI is reshaping the rules of data analysis, making it possible to replace syntax-heavy data visualization techniques with natural language queries.
However, insight is essential to creating effective prompts and providing context for thought chain prompts commonly used in analysis.
While ChatGPT Plus and Gemini Advanced offer extensive functionality for program analysis, analysts are encountering more AI assistants in other tools that are changing their workflows.
So how can analysts use analytics tools and AI together to maintain and organize proper workflows?
The answer lies in how analysts can organize their workflow tasks.
Let's take a look at the do's and don'ts to give you an edge when analyzing your campaigns and marketing strategies.
Related article: How AI and data analytics can drive your personalization strategy
AI tips for analytics
AI Tip 1 in Analytics: Recognize how features impact your work
AI is revolutionizing the way people interact with information, especially content. However, its implementation is similar to the implementation of any other technology in the workplace.
Technology always changes the way people accomplish tasks. The software reinforces this change through iterative changes in functionality and introduces new elements to the workflow that were previously unavailable.
The rise of AI is accelerating these changes. AI acts as a data layer, integrating information and sources using advanced statistics.
AI has a huge impact on analytical solutions by rapidly integrating data and its sources. Because data is fragmented across storage sources, data can be explored without extensive programming, saving time and allowing analysts to consider broader applications of exploration.
Marketing teams need to understand changes that impact a wide range of workflow tasks. This awareness helps maintain an overview of challenges and opportunities, allowing the team to achieve deliverables.
Changes to analysis software should not fundamentally change the user interface, create workflow issues, or force users to relearn the tool. Practitioners should focus on the insights, not the specificity of the tool.
Related article: 3 ways AI-powered predictive analytics is transforming e-commerce
Analytics AI Tip No. 2: Record updates
As many technologies are rapidly introduced, maintaining a notes repository has become an important way to track their impact over time.
Journaling helps with data analysis by providing insight into past work activities and ongoing issues with analytical solutions.
Advanced analytical tools simplify the task of gaining insights, but users must remain vigilant about their usage and personal experiences with data, solutions, and projects.
When managing a diary, record observations and consider skills and activities that drive daily progress toward your goals, such as developing new data models or monitoring key performance indicator (KPI) metrics.
Keep in mind that journaling is more than personal observation. Gain valuable insight by reading about industry trends. Subscribing to newsletters and podcasts is essential to learning new information and gaining fresh perspectives.
These are all consistent with the use of AI. Creating prompts requires creativity to convey the desired output to the model. Prompting techniques are largely independent of specific generative AI tools. Journaling helps you identify the quality of your prompts and gives you a framework that you can apply consistently, such as the self-consistency technique mentioned in this post on Engineering Prompts.
Related article: Customer data analytics and AI: The smart path
AI Tip 3 in Analytics: Know KPI data that is difficult to piece together
Senior executives pay attention to the latest cutting-edge technology. They believe that having the latest and greatest will lead to top-notch ROI, especially when it comes to analytics.
However, the attention given to technology must be aligned with meaningful metrics, the KPIs that are important to a company's performance. KPIs reflect the business concerns that stakeholders are trying to address and guide the prioritization of related metrics.
A key task is to identify the data that stakeholders consistently request in relation to the KPIs. Analysts need to be aware of where stakeholder conversations are taking place in order to evaluate technology against those needs. New technologies, whether AI-based or not, must demonstrate their ability to efficiently integrate KPI-related data and enable analysts to effectively scale insights and actions.
Related article: 5 AI analytics trends for CX personalization
Gathering resources
Data professionals are trying to rise above the hype around AI's role in simplifying workflows. they are not alone. Many experts have expressed mixed feelings about the integration of AI. According to a Gartner survey, 50% of marketers think martech is complex and difficult to master, and two-thirds say learning martech will reduce their day-to-day responsibilities.
The workflow tips discussed above provide a good framework for managers and analysts to start thinking about how new technology can help address complex problems.
Below are some of CMSWire's posts to help you get started, no matter your dashboard task.
To start integrating AI into your dashboards, consider whether your dashboards are outdated, as discussed in this post. You can also explore our new dashboard real-time framework. A variety of measurement options are being developed to manipulate data and AI.
To effectively manage your team around data, analyze your communication strategy around your dashboard solution. Consider accountability, as discussed in this post, and how communication works within remote teams, as detailed in this post.
Finally, check out this post for guidance on effectively leading remote teams with new workflow applications.
The insights in this post, along with previous workflow posts, can help you plan your measurement and reporting needs to support the use of AI in analytics.
