Artificial Intelligence has enabled an enormous leap forward in the insights generation process. In the last five years, AI adoption has skyrocketed from less than 10% to around 80% across organizations incorporating machine learning and AI into their core operations. Moreover, most companies are building AI systems like data management tools and predictive analytics that capture today’s insights, anticipating a future competitive advantage.
Although there are a lot of such debates on the efficiency gains from AI automation, businesses can unlock long-term value by forging a collaborative alliance where machines augment human capabilities. This means that subject expertise and judgments remain unique to humans, which AI cannot easily replicate.
Rather than focusing solely on short-term efficiency gains from automation that could displace human jobs, organizations should work to achieve a balanced and mutually beneficial partnership between people and AI to scale at speed.
What is the insight generation process?
The insight generation process refers to the steps involved in analyzing data to uncover meaningful insights that can inform strategic decisions. Typically, the critical stages in the insight generation process include:
1. Defining business objectives
It is essential to determine the key questions that need to be addressed or opportunities that need to be uncovered through data analysis based on organizational goals. Articulating what the business wants to address is crucial for automating insights generation.
Companies should frame their challenges and automation requirements to guide the analysis. For example, the goal may be to improve customer retention by 5% in 6 months. The critical question can be: What factors drive churn for high-value customers? Clearly defined objectives determine the required data and appropriate analysis methodologies for machines and humans.
2. Data collection
Companies gather relevant data from all available sources, including structured and unstructured data. Data significantly collected depends on the business objectives. This may include data collected through customer surveys, website behavior, sales, operations, and a lot more.
More data does not necessarily lead to better insights, so data quality is crucial to achieving relevant insights. Humans carefully select data sources and determine the data quality based on the business’s needs. For instance, collecting essential customer data could include customer profiles, purchase history, service tickets, reviews, etc.
3. Data Preparation
Raw data rarely comes in a format ready for analysis. Data preparation involves cleaning (fixing errors and inconsistencies), integrating (combining data from multiple sources), and transforming (restructuring into the proper format) the data.
Every business data will undergo a data preparation process to convert the raw data into a usable form. This process deals with incomplete, duplicate, and inconsistent data, imputing missing values or incorrect data. To enable businesses with expected results as needed for different types of analysis.
For example, data prep may involve consolidating data from CRM and service systems, handling outliers and missing data, grouping customers into segments, etc. Properly prepped data is required to get meaningful results from the analysis.
4. Data visualization
Through this step, businesses can explore the data through various visualizations and summaries to understand patterns, relationships, trends, or outliers. Interactive visuals, like dashboards, charts, and graphs, are intuitive for humans to explore data and uncover insights.
Even with advanced modeling techniques, data visualization is crucial for interpreting and communicating results. Companies can look for unexpected insights from the data, such as customer retention over time for different segments, revealing unexpected churn spikes. Visuals allow viewing data from multiple perspectives to develop hypotheses and identify avenues for further analysis.
5. Modeling and algorithmic analysis
Businesses should apply statistical models, machine learning algorithms, and data mining techniques to detect complex patterns, cluster data into groups, forecast vital metrics, and more, depending on business objectives. Methodologies like regression, clustering, decision trees, etc., are determined by business objectives.
For example, survival analysis and logistic regression could model the probability of churn over time for customer segments. Multiple techniques should be tested, refined, and combined. Although algorithms can analyze massive data sets, human judgment is required to determine whether results make sense and are meaningful.
6. Analyze and interpret results
Determine whether the insights and relationships uncovered through data analysis are meaningful and impactful for decision-making. It is essential to look at results across different studies and verify them against human knowledge and expertise.
Question unexpected findings, check for potential errors or biases, and contextualize the implications. For instance, analyze the factors that most influence customers for high-value purchases and the effectiveness of strategies to determine how insights translate into meaningful outcomes and recommendations.
7. Frame and communicate recommendations
Businesses should frame key takeaways and guidance from the data analysis into a compelling story for stakeholders. It is essential to highlight the insights on explaining why they matter and how they can positively impact the business objectives.
Determine how best to share insights tailored to various levels of the organization, from executives to operational teams, to facilitate change and action. Keep the story simple by focusing on the most significant ideas. Visuals and examples are excellent ways to help others understand the insights and recommendations, especially for complex topics.
The goal is to enable data-driven decision-making across the organization. Communicate in a way tailored to each audience – executives will want higher-level summaries, while teams implementing the insights will require more operational details.
8. Implementation and measurement
Monitor how recommendations from the analysis are implemented and measure the impacts and results to determine ROI over time. Work with stakeholders and teams to put recommendations into action. Determine key metrics to track how well recommendations achieve objectives like increasing revenue, improving efficiency, enhancing the customer experience, etc.
Look for both expected and unexpected results. Use insights from tracking metrics to refine and improve recommendations and objectives for the next analysis cycle, making the whole process iterative.
The Importance of human judgment
Data is an essential part of decision-making for modern businesses. But, raw data with context can be easier to understand and even more complex for human interpretation. That’s why companies are increasingly turning to AI automation to uncover the hidden value of their stored data.
Today, most data processes are automated at an enormous scale, and human guidance is vital in developing these algorithms and models. These machine-learning models require massive amounts of data to detect complex patterns, but they also need people’s oversight, filtering, and adjustment to maximize their potential.
Data scientists set these objectives and determine the questions they want the models to answer based on business priorities. They select, label, and annotate the most relevant data to help algorithms learn relationships and understand the context between them.
Humans must evaluate initial model performance, interpret results, and identify where and how the algorithms must improve to generate valuable insights. People across the organization should steer subsequent iterations of model training by providing feedback for enhancement and tweaking parameters to optimize the models. Through supervised learning from strategic human input and evaluation, AI models become more brilliant, making predictions more accurate and meaningful.
By reviewing AI predictions and recommendations through human judgment, organizations can gain the benefits of AI while mitigating the risks in their strategic initiatives. Thus, a collaboration between AI and humans is the key to bringing the data superpowers to life.
Collaborative Intelligence: A Way to Strike a Balance
AI tools require collaboration with human experts to attain their full potential. Adopting AI technology alone won’t lead businesses to fundamental transformation. Balancing high-volume repetitive data work alongside a human feedback loop makes the change possible.
While machine learning at scale makes robust pattern detection and predictive analytics possible on massive datasets, human judgment about developing, evaluating, and improving models makes them useful in insight generation.
When algorithms are left to learn uncontrolled and unsupervised without strategic input from human experts, they risk detecting spurious patterns that yield nonsensical predictions. Humans possess a contextual, intuitive understanding of business objectives, customer needs, and industry dynamics that AI may never replicate. Machines have an unmatched ability to detect complex patterns in huge datasets through massive computational scale.
Collaborative intelligence allows companies to solve multifaceted problems that hinder humans or AI by approaching challenges from diverse perspectives. AI explores data at scale to surface relationships and patterns for human interpretation. Together they navigate the communication barriers to drive the adoption of data-informed strategies based on a shared understanding of findings.
The bottom line is those who combine human expertise and AI automation will gain a sustainable competitive advantage that will be hard to match. Balancing human and technical elements, such as framing objectives, applying contextual knowledge, and communicating recommendations, lead to the most impactful insights generation process.
