As an executive leader, you know that analytics can help you make better decisions, improve performance, and drive innovation in your organization. But how do you leverage analytics to identify and solve problems effectively? Here are some tips to help you use data to your advantage.
Before you dive into the data, you need to have a clear understanding of the problem you want to solve. What is the goal, the scope, the impact, and the urgency of the issue? How does it align with your strategic priorities and objectives? How will you measure success and track progress? By defining the problem clearly, you can narrow down the relevant data sources, metrics, and methods to analyze it.
Goal-Setting Glasses: Begin by defining your goal. What's the endgame? ?? Scope-Scoping Telescope: Blow it up to look at the issue from as far away as possible and define where it begins and ends. How big or how small is this jigsaw puzzle? ?? Impact-ometer: Rate the impact that the problem is having. Is it a ripple or tsunami in your organization? ?? Urgency Alarm: How quickly is a solution required? Is it a slow burn or a five-alarm fire? ? Strategy Alignment Compass: Keep the path charted from problem to solution staying aligned to your organization's North Star. ?? Success Barometer: Define how you will measure victory. After all, what is success? ??
At NKBA our research team is continuously iterating the questions and topics in our quarterly, semi-annual, and yearly research reports based on feedback from our research partners, members, and third parties to provide our members with content that will best inform their business decisions. The success of these iterations is later validated through multiple KPIs including word of mouth feedback and email open rates.
Data must be integrated ‘in-situ’, while defining the problem. Using data during the inception stage must be a part of the organisational cultural. Most organisations have access to metadata but fail to utilise it during the ‘define’ stage. Defining the problem must be backed by data instead of using it in a reactive way. Here, descriptive statistics play a pivotal role in presenting the ‘situation’ to the leadership.
To have Data-driven decision making for Improved and leading to better overall organizational performance,Increased transparency and accountability and adaptability. IUse analytics for Trend Analysis,Segmentation Analysis,Anomaly Detection and Predictive Modeling to facilitate Root Cause Analysis,Effectiveness Testing, Resource Optimization and Performance Monitoring.
Leveraging analytics in an organization is akin to a detective meticulously examining a crime scene. Just as a detective gathers evidence, analyses patterns, and connects dots to solve a mystery, an organization uses analytics to collect data, identify trends, and draw insights. This process illuminates hidden problems and opportunities, much like a flashlight revealing clues in a dark room. By scrutinizing data with advanced tools and expertise, the organization can develop strategies, akin to a detective's hypothesis, which are then tested and refined. This methodical approach ensures decisions are not based on gut feelings but on empirical evidence, steering the organization towards effective solutions and strategic growth.
Once you have defined the problem, you need to collect the data that will help you understand it. Depending on the nature and complexity of the issue, you may need to use different types of data, such as quantitative, qualitative, internal, external, historical, or predictive. You also need to ensure that the data is accurate, reliable, and consistent. You can do this by checking the data quality, validity, and reliability, as well as the sources, methods, and assumptions behind the data.
I look at data collection as the foundation on which a building is constructed. There are 2 methods of data collection, primary & secondary. Both come with their own advantages & challenges. In primary data collection one needs to be unbiased & neutral while collecting data. Necessary techniques like simple random sampling, stratified sampling etc must be planned. On the other hand, for secondary data collection the source must be reliable. It would be better to compare the data from other sources like UN, Bloomberg, Govt Publications etc. The process of data collection is akin to watching grass grow, it begins mundane at one point of time. Patience & perseverance is important during this stage.
Data Detective : Unveil the variance from different types of data - quantitative, qualitative, internal, external, historic, or predictive. ???♂? Accuracy Magnifying Glass: Assure that your data are not just numbers but reliable numbers. ?? Consistency Scale: Don't let your data flip-flop. Consistency is the key! ?? Quality Control Shield: Protect against poor quality data. It's quality, not just quantity! ??? Validity Checkpoint: Make sure your data is relevant and appropriate. Does it pass the test? ? Reliability Radar: Ensure your data reliability over time. ?? Source and Method Spyglass: Drill into where your data comes from, and how that data is being collected. ??
From my experience, successful problem-solving hinges on collecting and validating diverse data types. I believe in rigorously ensuring data's accuracy and reliability to deliver actionable insights. From my perspective, understanding the sources, methods, and assumptions behind the data is just as vital as the data itself.
Collect high-quality, complete data that connects directly to the key metrics you wish to improve. Clean the data by addressing missing values, duplicates, and biases. Validate that the data accurately reflects ground truth through audits, reconciliation, etc.
After defining the problem, collect relevant data to analyze it, which may include quantitative, qualitative, internal, external, historical, or predictive types. Ensuring data accuracy, reliability, and consistency is crucial; validate the data by assessing its quality, validity, and the credibility of sources and collection methods. This step is essential for building a strong foundation for your analysis, enabling you to draw meaningful insights and make informed decisions to address the issue effectively.
After you have collected and validated the data, you need to analyze and interpret it to find patterns, trends, insights, and opportunities. You can use various analytical tools and techniques, such as descriptive, diagnostic, predictive, or prescriptive analytics, to answer different questions and scenarios. You also need to apply critical thinking, logic, and creativity to interpret the data and draw meaningful conclusions. You can use visualizations, dashboards, and reports to communicate your findings and recommendations.
Data has a life cycle. Analysis must keep in mind the various stages in which data is being captured & the error factor must be incorporated in order to account for any decay. This would be more relevant when analysing data collected from secondary sources. Once clarity is achieved on the ‘what’, we would need to look at the ‘why’ & ‘what next’ in order to analyse as per the business problem. Statistical tools like regression analysis, hypothesis testing, EDA, ANOVA etc may be applied as per the situation. Here I also echo the thoughts of Armand (fellow contributor) where he says that analysis must be reviewed with the stakeholders for continuous improvement.
?? Data Analysis and Interpretation in Analytics: Pattern: What do the patterns and trends in data speak to? ?? Analytical Toolbox: How does descriptive, diagnostic, predictive, or prescriptive analytics slice across different situations? ??? Thinking Cap: Apply logic and creativity on interpreting the facts. What does it really mean that fact? ?? Insight Magnifier: Zoom in with insights and opportunities. What's beneath that surface? ?? Visualization Canvas: Create a picture of your data with visualizations, dashboards, and reports. ?? Recommendation Megaphone: Make your findings and recommendations heard loud and clear. ??
Making sense from data is critical. Most business problems are ultimately solved through understanding a high R-Squared regression equation. There are typically 4-6 variables that matter - it's key that you figure out what those are and eliminate the rest of the noise around you. Then you track and measure relentlessly to ensure the entire organization is focused on joint goals.
This is the most important step. The same data can be given different directions basis our curiosity, judgements, biases, and sometimes our compulsions. Once i was given a market research project by my manager, and after explaining everything, he said - do know, this is for a product that i designed. I was thus to take the hint and present data & interpretations accordingly. Fortunately for me, the survey i did threw some positive findings and i could present the true and favourable analysis. I still wonder, what would i have done had the survey data showed an altogether different picture.
Additionally, I encourage a culture of continuous improvement within our team. We leverage analytics not only for retrospective analysis but also to predict potential issues. To be effective one need to Implement predictive analytics models to forecast project risks, resource needs, and potential roadblocks. This foresight enables you to take preemptive measures, ensuring smoother development cycles and improved overall organizational performance. Regularly review analytics findings with your team, fostering a collaborative environment where data-driven insights drive informed decision-making and innovation.
Finally, you need to implement and evaluate the solution based on the data analysis and interpretation. You need to align your solution with your problem definition, goals, and metrics, as well as your stakeholders, resources, and constraints. You also need to monitor and measure the results and impact of your solution, and adjust it as needed. You can use feedback loops, experiments, and tests to validate and improve your solution.
Testing: The solution must be checked by an A/B test for feasibility before full-scale implementation. Implementation Plan : Must be clear & concise with steps, responsibility matrix & timelines. Familiarisation/Training : The team must get to know the “nuts & bolts”, hence training must be given accordingly. Change Mgt : There will be initial hiccups & resistance to embrace the solution. An empathetic yet firm change mgt plan is reqd. Data Security & Compliance : The solution must not transgress security/compliance stds, so crucial for protection of sensitive business information as well as maintaining legal compliances. Continuous Feedback : Data analytics solutions are iterative hence data driven feedback is key for implementation.
Continuous evaluation is key. It's not a one-time event. Mastering the insights and value of the top 3 - 5 metrics is most important as those should be what drives customer/patient value as well as the top line. Create a feedback loop with all relevant functions in the company, including sales, operations, customer service, etc., and refine and improve to drive more value as opposed to defend. Be quick to improve as there is no time to waste. Get great at certain metrics before rolling out more metrics, as the quality and value of your key metrics are more important than the quantities of metrics. The race is not for the most metrics - it's for the right metrics leveraging the best possible data from multiple data sources.
Implement data-driven solutions tailored towards addressing the root causes identified from analytics. Continuously evaluate impact through A/B testing and monitoring key metrics. Refine approaches based on evaluation.
Implementing and evaluating the solution is a critical phase where insights derived from analytics are put into action to address the identified problem - ensuring that the implemented solutions are effective and aligned with the organization's broader goals. By thoughtfully implementing and rigorously evaluating the solution, you ensure that your organization not only addresses the immediate problem effectively but also strengthens its overall strategic approach to problem-solving and decision-making.
The solution has to be always simple, cost oriented, easily adaptive by the tech team and stake holders, auditable reusable and with minimal maintenance.
To leverage analytics effectively, you need to foster a culture of analytics in your organization. This means that you need to encourage and empower your employees to use data to support their decisions, actions, and innovations. You also need to provide them with the necessary skills, tools, and resources to access, analyze, and apply data. You can do this by offering training, coaching, mentoring, and recognition for analytical skills and achievements.
To cultivate a culture of data analytics, vision, infrastructure & training are needed: Vision: Leaders must espouse the cause with commitment, endorsement & participation by using data analytics in decision making. Infrastructure : Creating a collaborative environment, establishing data governance, integrating analytics into daily operations rather than separate initiative & providing necessary tools like R, JMP, Minitab, Power BI etc will create a viable infrastructure. Training : The members must be trained with the correct concepts so that they start looking at data through the lenses of ‘central tendency’ & ‘dispersion’. Questions must be encouraged, tools must be tinkered with, successes must be highlighted.
Fostering a culture of analytics is essential for effective problem-solving within organizations. While implementing technology is crucial, addressing behavioral change and cultural shifts proves more challenging. To leverage analytics effectively, it's imperative to encourage transparency, foster collaboration across teams, and provide adequate training and support. Leaders play a pivotal role in leading by example, showcasing the value of data-driven decision-making. Following a structured approach ensures a systematic process. Continuous learning and adaptation to evolving business landscapes are key for sustained success in leveraging analytics to identify and solve problems.
To foster a culture of analytics, invest in a platform and leader; involve ALL key constituents, including sales, operational, and functional heads. Define the top 2-3 revenue (growth) metrics to monitor and do so across all dimensions possible - geography, demographics, referral sources, etc. make the metrics visible to all, via laptop and mobile. Eliminate data validity questions and concerns early by ensuring internal data is good. Garbage in is garbage out. Data must be clean. Starting with everyone in the organization looking at the same key 2-3 metrics to start will bring to drive a culture of analytics. Once everyone is aligned on the top 2-3 metrics, you can begin to add other metrics. Quality over quantity is key.
I facilitated a data-driven storytelling culture by providing access to analytics capabilities and personalized training sessions. I also created feedback loops through review sessions to promote continuous learning and innovative thinking. As a result, data analysis became a shared language, enabling more informed decision-making and continuous improvement.
Start by embedding analytics into the DNA of the company, making data accessibility and analysis a part of every team member's daily routine, and encouraging a mindset where decisions are made not on intuition alone but backed by data-driven insights. Leaders plays a pivotal role in modeling this behavior, demonstrating the value of analytics through their actions, investing in the right tools and training, and celebrating when data-driven initiatives lead to meaningful outcomes. Once this culture is established, it will minimize subjective decisions made by gut instincts, trial and error and faulty assumptions with logic.
Finally, you need to learn and improve continuously from your analytics experience. You need to reflect on what worked well and what did not, and identify the lessons learned and best practices. You also need to seek feedback, input, and ideas from your customers, employees, partners, and peers. You can use surveys, interviews, focus groups, and forums to gather and share insights. By learning and improving continuously, you can enhance your analytical capabilities and solve problems more effectively.
Develop feedback loops to learn from experience and new data. Retrospect on issues and successes. Seek to improve analytic approaches through reviews. Automate reports and recommender systems where possible.
The idea of a continuous feedback loop is not just there to correct wrong assumptions or incorrect numbers; it also fosters the belief to not take anything for granted. This can be enforced by having periodic check-ins; on how the actuals compare to the forecast, for example. A culture of 'how did we do, and can we do better?' This is not intended to blame or punish; it is intended to make the organisation self-learning and self-correcting. For the new generation that thinks that A.I. will solve all of this: it won't. You need people to use their brains every day and don't take things for granted; learn to interpret all the signals and take corrective action.
Create an environment that champions data-driven experimentation. Promote a culture centered on testing hypotheses, learning from failures, and driving continuous improvement. Teams must approach challenges with curiosity and see setbacks as valuable opportunities for growth. Strong data governance policies must be established to guarantee data quality and consistency, which are vital for reliable analytics. Additionally, develop a simple and robust process for developing proofs of concept. This will demonstrate the value of analytics and give teams the information they need to make informed decisions, leading to enhanced organizational performance and innovation.
Ensuring that your organization remains agile and informed in its decision-making processes is an essential component of leveraging analytics effectively in your organization. By emphasizing learning and continuous improvement in the context of analytics, you create an organization that is not only data-driven but also dynamically responsive to new information and insights. This ongoing cycle of learning and adapting is crucial for maintaining a competitive edge and ensuring that your organization is well-equipped to navigate the complexities of the modern business environment.
In executive coaching, we often emphasize the importance of a data-driven approach to problem-solving. By fostering a culture of analytics, leaders can make strategic decisions based on evidence rather than intuition. This not only leads to more effective solutions but also empowers teams by involving them in the analytical process, enhancing their problem-solving skills for future challenges. Continuous learning and improvement are crucial in the ever-evolving business landscape.
Leveraging analytics starts with a clear definition of the problem. Rigorous data collection and validation follow, ensuring that the information you're analyzing is accurate and relevant. Diving into data analysis, you uncover patterns and insights, interpreting them to inform your decision-making. The final step is implementing a data-driven solution, constantly reviewing its effectiveness and iterating as necessary. This cycle turns data into a powerful tool to tackle challenges and drive organizational success.
While I’m not a huge fan of data analytics, I firmly believe in the power of storytelling with data. Simplifying complex insights into clear, compelling narratives helps my team and clients truly understand the message. By connecting these insights to real-world actions and fostering open discussion, we make smarter decisions together, continuously improving and driving our success forward by avoiding assumptions.
Data dictionaries are vital, particularly in large organisations. They ensure clarity in the purpose and intent of data elements, laying a solid foundation for accurate analysis. In the realm of complex and voluminous data, understanding what and why we collect is crucial. The real value of analytics emerges only when it's supported by well-defined, purposefully collected data. Quality analysis depends not just on data quantity, but on its integrity and clarity.
A way to make data top of mind for the team and incorporate it into the organizational habits is to display it all over the office environment. Install screens showcasing certain dashboards that are significant to the company or department. Do you have a certain metric that the team should be paying attention to? Craft a dashboard and have it running and displayed for the team to see in real time. This can spark conversations and creative ideas around how things can be improved.
Consider factors like data quality, algorithm biases, updating models over time, and ethics around data collection and use. Collaborate across teams and functions. Focus analytics on creating business value.