Data analytics is changing at the hands of advanced AI technology. It’s undergoing a seismic shift that’s upending how businesses are used to handling valuable information. While artificial intelligence in some form or another has been handy in data analytics for some time, newer, more sophisticated models can do much more, which means modern data analytics as a whole requires a different skill set than called for by more traditional AI data tools and techniques.
Read on to learn more about how AI has impacted data analytics and how to begin aligning your marketing practices.
The What and Why of Data Analytics for Marketers
Most marketers are familiar with the concept of data analytics, but there’s nothing wrong with a brief refresher. In practice, data analytics examines raw data to uncover patterns, trends, insights and useful information that can inform decision-making. It involves techniques such as data collection, processing, visualization and interpretation — and today, often with the help of software tools and AI algorithms.
For marketing, methods like prescriptive analytics help us better understand customer behavior, preferences and pain points. We can then take that data and more effectively create personalized marketing campaigns and marketing strategies that effectively speak to defined audience segments. You can even use data analytics to define audience segments if you haven’t done so already or if it’s been a while since your segments were last evaluated.
More than that, a data analyst working for a marketing department can help the business forecast future trends using techniques like predictive analytics. Armed with those projections, it becomes easier to prepare for likely scenarios with the right content and messaging to convert more customers.
Artificial intelligence has essentially supercharged these capabilities, and the skill sets data-driven marketers need to succeed with their data are transforming.
Subscribe to
The Content Marketer
Get weekly insights, advice and opinions about all things digital marketing.
Thank you for subscribing to The Content Marketer!
Shifting Skill Sets for Marketers Doing Data Analytics
Advanced AI models can automate routine data analysis tasks like data cleaning, integration and preprocessing faster than you or I could even blink. Quicker and more accurate data analysis is the new norm, and it can happen with minimal human intervention. As such, there’s a growing emphasis on data analysts building their higher-order critical thinking skills to work alongside AI to achieve tremendous outcomes.
What Are Higher-Order Critical Thinking Skills?
High-order critical thinking goes beyond basic observation of facts and rote memorization. These skills enable individuals to analyze, evaluate, synthesize and create knowledge rather than merely recall or understand it. With AI handling a majority of the tedious and routine backend, marketers’ and data analysts’ jobs have changed to focus on complex problem-solving and strategic decision-making, which are characterized by:
- Open-mindedness: Willingness to consider different perspectives and challenge assumptions.
- Metacognition: Reflecting on one’s own thought processes to improve reasoning and understanding.
- Inquiry: Asking deep, meaningful questions that provoke further exploration.
What’s Most Important Now for AI Data Analytics?
With higher-order critical thinking all the more important in data science, the data game is more about choosing the right tools for your specific purpose, knowing how to use them effectively, feeding them the right information and asking the right questions to begin with — you get the idea.
Model Selection
AI models and their applications serve tons of different purposes, so it’s important to familiarize yourself with each to choose the right option:
- Econometrics: Utilizes statistical methods to analyze economic data, suitable for understanding economic relationships and forecasting trends.
- Generative AI: Capable of creating new content or data, Gen AI is useful for small- and medium-sized companies with limited resources for content generation and creative tasks.
- Deep Learning: Employs neural networks to model complex patterns, ideal for image and speech recognition tasks.
- Rule-Based Automation: Applies predefined rules to automate processes, effective for tasks with clear, logical steps.
Asking the Right Questions
There are a few specific types of questions common in data analytics, such as:
- Descriptive Questions: These focus on understanding what has happened by summarizing past data.
- Diagnostic Questions: These aim to uncover why something happened by identifying causes and relationships within the data.
- Predictive Questions: These seek to forecast future events based on historical data.
- Prescriptive Questions: These focus on determining the best course of action to achieve desired outcomes.
- Exploratory Questions: These involve analyzing data to discover patterns or relationships without prior hypotheses.
Descriptive and diagnostic questions were the first two to go when AI came onto the scene; it can handle those with ease. AI can even take care of some aspects of predictive, prescriptive and exploratory questions, but not everything.
For example, when fed the right data, AI could easily answer “What are the expected sales for the next quarter?” But it’s up to us — the human, marketer, data scientist — to come up with optimal courses of action based on that predictive insight. So instead, we might ask “How can we capitalize on customer behavior throughout each season to achieve or exceed our sales goals?”
Deep Industry Knowledge
AI only knows what its training data has told it, which means those with deep industry knowledge still bring tremendous value to the table. Someone with such expertise in a particular niche can help contextualize AI-driven insights more effectively, which helps when it is time to tell the data’s story (hint hint).
Storytelling Skills
Now more than ever, professionals need strong storytelling skills to present actionable insights clearly to stakeholders. To a board of directors unfamiliar with data analytics, its principles and concepts, raw data means nothing. So, we have to create a story that educates, informs and persuades decision-makers to take the action we want them to take in the best interest of the business.
Do’s and Don’ts: AI for Data Analytics
AI for data analytics and business intelligence is a no-brainer. But that doesn’t mean that it’s a plug-and-play strategy for streamlining processes. Having clear goals and a steadfast commitment to responsible AI use can help set you up for success. So, with that in mind:
- Do define clear goals that align with business objectives.
- Do commit to high-quality data. Dedicate time to cleaning and preprocessing data to ensure it’s AI-ready.
- Do choose the right AI tools and platforms that can grow with your data collection and business needs.
- Do optimize models for performance on large datasets or real-time analytics.
- Do continuously validate the performance of AI models with fresh datasets to prevent degradation.
On the inverse:
- Don’t implement AI for the sake of it. A lack of a clear roadmap can lead to wasted resources.
- Don’t fail to address bias in data or algorithms, which can result in unfair or discriminatory outcomes.
- Don’t rely solely on automation. AI is a tool, not a replacement for human judgment. Always validate insights using those higher-order critical thinking skills covered earlier.
- Don’t assume AI guarantees success. While it can help you on your path to a successful campaign, AI still requires thoughtful implementation, validation and alignment with business goals to truly succeed.
All-in-One Tools for AI Analytics
Jheez … are we ever going to talk about the actual tools?! Yes! There are tons of AI tools built specifically for data analytics and business intelligence; it would be impossible to cover them all. But below are a few popular options that have seen success out there in the world.
Microsoft Power BI
You may already be familiar with Power BI seeing as it comes from the giant that is Microsoft, but if you aren’t, allow me to make your acquaintance.
Power BI integrates advanced AI features, such as AI Insights in Power Query Editor, allowing users to apply machine learning models directly within the platform. It also has a neat Q&A feature that allows users to ask questions in natural language and receive answers in the form of visualizations, making data interaction intuitive. It shines in its user-friendliness, which helps make even advanced analytics a little less daunting.
Tableau
What’s cool about Tableau is that it seamlessly integrates with Salesforce to provide enhanced analytics for CRM data. For business intelligence applications, that’s invaluable as it provides deeper insights into customer behaviors and sales performance.
Tableau is also quite renowned for its set of data visualization tools that help users create interactive and shareable dashboards, facilitating better data comprehension. Remember when I said storytelling is increasingly important in data analytics? Well, the better you can understand and comprehend it, the stronger a story you can build.
Polymer
Polymer’s power lies in its ability to simplify reporting. You can create interactive dashboards and reports for your data without a ton (or any) technical expertise. Polymer’s AI recommends visualizations and builds insightful dashboards automatically.
Talk about democratizing data analytics. By leveraging AI, the platform doesn’t just automate complex processes but helps users create professional-looking reports without the need for extensive technical knowledge. Such accessibility can accelerate decision-making and enhance data-driven strategies across organizations.
Start Changing How You Think About AI and Data Analytics
AI has always been applied to data analytics, but now it’s better than ever. Gen AI tools like ChatGPT and other models and algorithms can help you achieve quicker wins when it comes to data analysis, but it requires a change in the way you approach it.
When using these tools, emphasize higher-order critical thinking and ask the right questions, and you’ll be well on your way to quicker, more insightful data.
Note: This article was originally published on contentmarketing.ai.
