Are Your Business Analytics Aligned with Your Key Objectives?

Introduction

I often see businesses of all sizes struggle to tell a compelling story with data. A common reason for underutilising data is the choice of inappropriate data analysis methods and a lack of expertise to adeptly employ various categories of data analysis.

Using the right type of analytics to extract actionable insights ensures that you are using the most appropriate tools and techniques to make informed decisions. The five main types of analytics available to business users looking to engage in analysis are: descriptive, diagnostic, predictive, prescriptive, and cognitive.

Descriptive Analytics: Using the Past to Inform the Present

Descriptive analytics focuses on summarising and visualising data to help organizations understand what has happened in the past. Imagine you want to easily track sales data for a shoe store from the previous year. You can find out that you sold 10,000 pairs of white trainers in the summer and 5,000 pairs of black boots in the winter. This information can help you understand which products are popular during specific seasons.

It's great for understanding trends, identifying patterns, and making straightforward comparisons. It allows you to quickly see which products sell well during different seasons and make inventory adjustments accordingly.

However, descriptive analytics is limited. It tells you what happened, but it doesn't explain why or predict what will happen in the future. So, whilst you know which shoes sold well in the past, you still need other types of analytics to understand why they sold well and what you should do next to keep your sales soaring. Descriptive analytics is like the foundation of a house, essential, but you need more to build the entire structure of data-driven decision-making.

Diagnostic Analytics: Finding the Why Behind Data Trends

Diagnostic analytics steps in when you want to uncover the 'why' behind these data trends. It essentially represents a detective for the data world, helping you investigate and understand the root causes of your business challenges.

Imagine your manufacturing line's efficiency has declined, and you're concerned. Diagnostic analytics allows you to dig deeper into the data and find out that the slowdown is due to a specific machine's frequent breakdowns. By identifying this issue, you can take corrective actions like maintenance or replacement to improve efficiency.

Diagnostic analytics goes beyond just showing you what happened; it explains why it happened. It's excellent for pinpointing bottlenecks, identifying problem areas, and guiding data-driven decisions. It not only tells you about the drop in efficiency but also uncovers that machine breakdowns are the root cause, enabling you to take targeted actions.

While diagnostic analytics is valuable for root cause analysis, it still focuses on historical data. It doesn't predict future trends or prescribe solutions for improvement. So, while it helps you understand why, it doesn't tell you ‘What’s next. Diagnostic analytics is like solving a mystery; it provides valuable insights but doesn't plan your next move.

Predictive Analytics: Looking into the Future to Make Informed Decisions

Predictive analytics steps in as the fortune teller of the data world, allowing you to peer into the future and make proactive decisions based on forecasts and probabilities.

Imagine you run a wholesale business, which deals with seasonal products, like swimsuits. You would like to optimise your inventory management. Predictive analytics can analyse historical sales data, market trends, and weather patterns to predict the demand for swimsuits in the upcoming summer season. Armed with this insight, you can order the right quantity of inventory, avoid overstocking or understocking, and maximise your profits.

Predictive analytics empowers businesses to make data-driven forecasts and anticipate future trends. It aids in inventory optimisation, demand forecasting, risk assessment, and customer behaviour prediction. It ensures you're well-prepared for seasonal shifts in demand, reducing wastage and capital tied up in excess inventory.

While predictive analytics is a powerful tool, it's not guaranteed. It relies heavily on accurate historical data and assumptions, and unforeseen events or sudden market changes can disrupt predictions. Additionally, it doesn't prescribe specific actions to take in response to predictions; that's where prescriptive analytics comes in. Predictive analytics is like having a crystal ball, helpful for glimpsing into the future but not immune to surprises.

Prescriptive Analytics: Guiding Informed Actions for Optimal Outcomes

Prescriptive analytics serves as your invaluable consultant, offering precise recommendations on what actions to take to achieve the best possible results.

Imagine your distribution business encounters a sudden spike in demand for a specific product due to a viral online trend. Prescriptive analytics swiftly assesses real-time data on available inventory, transportation logistics, and market dynamics. It then advises you to prioritise the distribution of the trending product, adjust pricing strategies, and optimise delivery routes to meet customer demands.

Prescriptive analytics doesn't just forecast the future; it provides actionable steps to tackle it effectively. It plays a crucial role in optimising decision-making across various sectors, from retail to finance. In our distribution scenario, it ensures that you respond proactively and efficiently to ever changing market conditions, resulting in enhanced customer satisfaction and revenue.

Despite its advantages, prescriptive analytics relies on accurate, up-to-date data, which may not always be available. Furthermore, it doesn't eliminate all uncertainties and risks. It offers recommendations based on existing data and assumptions, so there's always some degree of unpredictability involved. However, when combined with other analytics types, it can significantly improve decision-making capabilities.

Cognitive Analytics: Elevating Data Insights with Artificial Intelligence

Cognitive analytics harnesses the power of artificial intelligence (AI) to interpret complex patterns and make predictions that go beyond human capabilities.

Imagine your marketing agency aims to enhance customer engagement for a client's online retail platform. Cognitive analytics delves into vast datasets, incorporating natural language processing and sentiment analysis to uncover hidden customer preferences and emotions. It then recommends personalised marketing strategies, content, and product offerings, resulting in increased customer satisfaction and conversion rates.

Cognitive analytics leverages AI to process vast amounts of unstructured data, enabling businesses to gain deeper insights into customer behaviour, market trends, and operational efficiencies. It automates complex tasks, identifies emerging opportunities, and enhances decision-making. In our marketing agency example, it empowers you to create hyper-targeted campaigns, boosting client success.

While cognitive analytics offers advanced capabilities, it requires access to substantial computational resources and high-quality data. Additionally, its algorithms may not always provide interpretable insights, making it challenging to understand the reasoning behind recommendations. Ensuring ethical data use and addressing privacy concerns are also paramount when implementing cognitive analytics. It can sometimes be difficult to explain and requires careful oversight.

Choosing the Right Analytics for Your Business

Selecting the right type of analytics can make all the difference in data driven decision making. Sometimes, a straightforward descriptive analytics approach, like summarising historical data, is sufficient to gain valuable insights and guide decisions effectively.

On the contrary, many businesses rely exclusively on simple descriptive analytics, missing out on the potential for greater value and competitive advantage. Embracing more advanced analytics, like prescriptive analytics or cognitive analytics for deeper insights, can unlock untapped potential. These advanced analytics categories can uncover hidden trends, automate complex decision-making, and provide a deeper understanding of customer behaviour.

It’s essential to recognise that a one-size-fits-all approach doesn't always apply. In many scenarios, combining different categories of analytics, such as descriptive, diagnostic, predictive, prescriptive, and cognitive, can yield optimal results. Picking the correct type of analytics isn't just an advantage; it's often a necessity for staying competitive and making the most informed choices.

Conclusion

Each type of data analytics has its unique strengths and picking the right one is instrumental in driving success for your business and objectives. Sometimes simplicity suffices. While in other instances, a combination of analytics categories can be the key to unlocking your data's full potential.

However, I understand that navigating this analytical landscape can be a daunting task, especially if you're unfamiliar with these methodologies or lack the necessary skillset. This is where a data strategist can help you assess your specific business needs, data availability and quality, user requirements, and technology infrastructure. Ultimately advising you on the analytics approach that suits you best.

As I conclude, I encourage you to take a moment to evaluate your current analytics practices. Are you harnessing the power of data effectively? If you find yourself unsure or in need of guidance, feel free to reach out for an insightful discussion to explore how the right analytics can propel your business towards greater success.