The sheer volume of data available is staggering, presenting both opportunities and challenges. Our ability to harness and interpret this data can have a direct impact on the direction and success of businesses.
There is increasing pressure on businesses to not only adapt to this new world of AI and data but to thrive. Right now, there is huge potential for organizations to gain a competitive edge through effective data analytics.
The goal of this blog post is to demonstrate the link between data analytics and the decision-making process. We’ll focus on a framework for going from analysis to decision, as well as some practical applications used by tech giants so that you can find ways of applying them to your own business.
The Four Stages of Analytics
In the book Competing on Analytics by Thomas H. Davenport and Jeanne G. Harris, four stages of analytics are given that serve as a guide for approaching data-related problems and make it much easier to select the most appropriate solution.
These stages, in order, are:
- Descriptive analytics — made up of standard and ad hoc reporting of historical data, providing a snapshot of what happened in the past. At this stage, you look into the past to determine where the problem is and what actions are needed.
- Predictive analytics — these are statistical analyses, forecasting, and predictive modeling. Here, you’re analyzing historical data and trends and asking why and what’s next if the data follows the same pattern.
- Prescriptive analytics — consisting of experimentation and optimization. At this stage, you run experiments and find the best possible scenario.
- Autonomous analytics — machine learning and beyond. From this point onwards, you are trying to reduce the amount of human involvement, building models that learn from the data on their own.
Going From Analysis to Decisions
Regardless of which stage of analytics your company is in, the goal of analytics is to support the decision-making process so that companies can achieve a specific strategic objective or solve a problem. There are two main aspects to going from analysis to decision:
- A clear data analytics strategy
- A solid decision support system
Data Analytics Strategy
The first step in ensuring your data projects successfully go from analysis to decision is with a clear data analytics strategy.
A data strategy starts with understanding the goal, identifying the problem, and then choosing the best solution. What are the elements of a good data strategy?
- An outline of the company’s current strategic objectives along with the problems analytics could solve or the decisions that analytics can support.
- An indication of the best analytical solutions for these problems or support areas.
- An assessment of the current and future data sources in the context of how the data will be used, analyzed, and reported on.
- An evaluation of current infrastructure and whether further development is required.
- An assessment of the analytical talent within the organization and whether there is a need to hire more talent.
If you want to learn more about creating a good data strategy, I wrote a step-by-step blog post on how to do just that.
Finally, analytics projects must be interpreted, explained, and clearly communicated to stakeholders. This goes beyond technical concepts, algorithms, and code. You or your analytics team need to be able to tell a compelling data story that provides actionable intelligence.
Decision Support Systems
It is common to find a large gap between the decision-makers in an organization and the data scientists and analysts. This is where decision support systems are crucial. We can draw on the field of decision intelligence to figure out how to bridge this gap.
Decision intelligence requires a new way of thinking for both decision-makers and analysts. By focusing on decisions first and allowing them to be the driver of everything else, we can do a much better job of working together. Ultimately, models, reports, and analyses are not of any value (no matter how complex or cutting edge they may be) unless they lead to some action.
Leveraging decision intelligence to build a decision support system usually involves two broad steps:
- Develop a Causal Decision Diagram (CDD) to evaluate the decision levers that lead to a certain outcome.
- Create an interactive decision model that visually represents how data and analytical solutions can objectively determine the effect of a decision.
You can read my introduction to decision intelligence for more information.
Practical Applications for Businesses
If you use Netflix, you will know that your watchlist is curated with precision. Netflix utilizes analytics to understand viewer preferences, delivering a personalized streaming experience. This customization isn’t limited to content suggestions; it extends to optimizing user interfaces and enhancing overall user satisfaction.
They collect data such as the time of day that users are streaming shows, whether or not a show was binge-watched or watched over a longer period of time, and other viewing behaviors such as pausing, leaving, and never coming back to a show, and so on.
Amazon, the e-commerce giant, relies heavily on analytics to optimize its operations. There are two stand-out ways that Amazon showcases its analytical prowess.
First is their dynamic pricing algorithm. Amazon changes its prices many times daily based on customer shopping patterns and behaviors on its site, competitors’ prices, and historical sales data for a product.
Second, their product recommendations. Amazon has developed algorithms that deliver product recommendations based on purchases, clicks, views, and cart contents. This allows them to learn, with amazing accuracy, what their customers want or like.
Across industries: the adaptability of analytics
The beauty of analytics solutions lies in their versatility. Whether it’s healthcare, finance, or retail, the principles of analytics can be tailored to suit the unique needs of each industry, dataset, or business problem.
In business, the impact of data analytics on decision-making is nothing short of transformative. However, it is not always clear or obvious how we can go from analysis to decision.
In this blog post, we discussed the four stages of analytics, how you can develop a data strategy and decision support system to go from analysis to decision, and some of the practical applications, using the example of Netflix and Amazon to demonstrate how analytics can be used for customization and optimization.
I hope you found this helpful! If you have any questions or comments, drop them below. I’d love to hear from you!
You can also connect with me on LinkedIn where I post more data science content.