Feature
The Gist
- Stay informed. Data analysts are swept up in AI hype, but their emotions reveal more than awe.
- AI reshapes. Natural language queries replace syntax-heavy techniques in AI's data analysis revolution.
- Crucial insights. Effective prompts and context are vital in analytics for crafting chain-of-thought prompts.
Data analysts are just as swept up with the AI in analytics hype as any other professional, but their emotions reflect more than just being in awe of the technology.
The world of AI has reshaped the rules of data analysis, enabling natural language queries to replace syntax-heavy data visualization techniques.
But insights are crucial for crafting effective prompts and providing context for chain-of-thought prompts, commonly used in analytics.
While ChatGPT Plus and Gemini Advanced provide extensive features for programmatic analysis, analysts are encountering more AI assistants in other tools, altering their workflow.
So how does an analyst keep up and organize the right workflow using AI alongside their analytics tools?
The answer lies in how analysts can organize their workflow tasks.
Let's explore the dos and don'ts to stay ahead in analytics for your campaigns and marketing strategy.
Related Article: How AI and Data Analytics Drive Personalization Strategies
AI in Analytics Tips
AI in Analytics Tip No. 1: Recognize How Features Impact Your Work
AI is revolutionizing how people work with information, particularly content. However, its introduction is akin to other technology introductions in the workplace.
Technology always shifts how people accomplish tasks. Software enhances this shift with iterative changes in features and capabilities, introducing new elements to a previously unavailable workflow.
The rise of AI is accelerating these changes. AI acts as a data layer, using sophisticated statistics to synthesize information and sources together.
AI significantly impacts analytics solutions by rapidly bringing together data and its sources. With data fragmented across storage sources, the ability to explore data without extensive programming saves time and allows analysts to consider the broader application of their exploration.
Marketing teams must understand the changes affecting a wide range of workflow tasks. This awareness helps maintain an overview of challenges and opportunities, enabling the team to meet deliverables.
Analytics software changes should not radically alter the user interface, creating workflow issues and forcing users to relearn the tool. Practitioners should focus on insights, not the idiosyncrasies of a tool.
Related Article: 3 Ways AI-Powered Predictive Analytics Are Transforming Ecommerce
AI in Analytics Tip No. 2: Journal the Updates
With the rapid introduction of many technologies, maintaining a note repository becomes a crucial way to track their impact over time.
Journaling provides insights into past work activity and ongoing issues with analytic solutions, aiding in data analysis.
Advanced analytics tools simplify tasks for insights, but users must stay vigilant about their usage and personal experience with the data, solution and project.
When maintaining a journal, record observations and consider the skills or activities that drive progress each day toward a goal, whether it's developing a new data model or monitoring metrics for key performance indicators (KPIs).
Keep in mind that journaling extends beyond personal observations. Reading about industry trends can provide valuable insights, and subscribing to newsletters and podcasts is essential for learning new information and gaining fresh perspectives.
All of this aligns with AI usage. Crafting prompts demands creativity to convey the desired output to the model. Prompt techniques are largely agnostic to a specific generative AI tool. A journal can help identify the qualities of a prompt, indicating a framework that can be consistently applied, such as the self-consistency technique mentioned in this post on prompt engineering.
Related Article: Customer Data Analytics and AI: The Smart Path
AI in Analytics Tip No. 3: Know the KPI Data That Is Hard to Stitch Together
Senior leadership pays attention to the latest cutting-edge tech. They believe having the latest and greatest will translate into first-class ROI, especially when it comes to analysis.
But the attention given to tech must align with meaningful metrics — KPIs crucial to company performance. KPIs reflect business concerns stakeholders aim to address, guiding prioritization of relevant metrics.
The key task is identifying the data stakeholders consistently request related to KPIs. Analysts should be aware of where stakeholder conversations are happening to assess tech against those needs. Whether AI-based or not, new tech must demonstrate the ability to efficiently consolidate KPI-related data, enabling analysts to scale insights and actions effectively.
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Gathering Resources
Data professionals are navigating past the hype around AI's role in simplifying their workflow. They're not alone; many professionals express mixed feelings about AI integration. A Gartner study found that 50% of marketers believe martech is complex and challenging to use, with two-thirds saying learning it detracts from daily responsibilities.
The workflow tips mentioned above provide an excellent framework for managers and analysts to start considering how new technology can help address complexity issues.
Below are some CMSWire posts that can help you get started regardless of the dashboard task.
To start integrating AI into dashboards, consider whether the dashboard is outdated, as discussed in this post. You can also explore a real-time framework for a new dashboard. Various measurement options are being developed for working with data and AI.
To effectively manage a team around data, analyze your communication strategies regarding a dashboard solution. Consider who is accountable, as explained in this post, or examine how communication functions within your remote team, as detailed in this post.
Finally, refer to this post for guidance on leading a remote team effectively for a new workflow application.
The insights from this post, along with the earlier workflow posts, can assist you in planning measurement and reporting needs that support using AI in analytics.
