While enterprise execs have always sought competitive advantage in other areas of operation, here are some tips to consider when leveraging a data project.
The need for competitive advantage sees companies increasingly turning to analytics to operationalize their data. Leveraging analytics from insight to artificial intelligence (AI), business leaders can make sense of their rapidly-growing piles of data to improve their operations. Here are my tips for using analytics to create a measurable business impact.
#1: Put the decision before the data
With a decision-first strategy you define the business objective first, then determine what data and analytics you need to achieve the goal. Extrapolating insights from huge amounts of data can be interesting, but it can also be a tremendous waste of time and resources if it doesn’t solve a specific business challenge. If the modeling and data analytics requirements are defined by the business outcome first, data exploration and analytic development is faster and more productive. This helps enterprises narrow in on meaningful outcomes, shutting out extraneous noise and focus on the insights that address specific objectives.
#2: Get data into decision makers’ hands
Empower the business leaders with the ability to evaluate the complete spectrum of potential opportunities. This requires a combination of insight, advanced analytics and decisioning (prescriptive) to explore, simulate and pressure test scenarios in real-time. To do this, you need user-friendly decision management tools that can be rapidly configured and evolve with the specific needs of the operation. Experience has shown that when business experts have access to the data, insight, and tools to exploit analytics, they can visualize relationships between different variables and actions to quickly identify the preferred outcomes for maximum impact.
See also: Best practices learned from big data projects
#3 AI & machine learning can expand your frontiers
Every decision that is made or action that is taken provides an opportunity to improve. The key is automatically feeding those learnings back into the analytic system to influence the next decision or action. By using decision management tools that incorporate machine learning and artificial intelligence, enterprises can conduct complex analysis that evolves and improves as new scenarios are added. With artificial intelligence and machine learning, you can discover unique insights and meaningful patterns in large volumes of data. Then, add self-learning models that will allow you to adapt quickly to changes in those patterns or take action on those insights. But, to unlock the full business potential, the analytic output must be explainable to a business expert if it is to be understood and accepted.
#4: Keep it open and focus on integration
One of the easiest ways to start a healthy debate about analytics is to pose the question of which tool is the best! The reality is that it depends on what you are trying to accomplish, but even more so who is accomplishing it. One thing is for sure: you most certainly are not starting from scratch and already have technology systems in place. Be certain any further investment is in analytic and decision management tools that are open and can easily integrate with your existing environment. However, the key requirement is to understand how you will eventually use and manage those analytics within your day-to-day operation.
See also: The 5 phases of big data projects
#5 Operationalize the analytics
The real value of analytics comes when they are operationalized. Connecting the data and insights gleaned from advanced analytics to day-to-day operations will tie to positive business outcomes. With prescriptive analytics, you can add business rules or optimization models to the analytics – which will trigger a specific action to be taken in different scenarios based on a deep understanding of the situation, predictions about the future, and other business constraints or regulations.
With these suggestions in mind, business leaders can help move their enterprise to a place where artificial intelligence and human intelligence come together to drive real business outcomes that deliver competitive advantage and better differentiation.
