We all know that data-driven decision-making processes can bring great benefits to companies, such as greater confidence in taking decisions, more transparency, and a higher chance of improving in the long term.
Gartner estimates that more than 33% of large organizations will have analysts practicing decision intelligence by 2023, and 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures by the end of 2024.
But at the same time, businesses often have doubts about how to deliver data-driven decision-making projects succesfully.
In a previous article, we already talked about how important it is to choose the right project to implement data-driven decision-making process. We said that businesses should think about the goals they want to achieve, consider the data available to them, and the actionable solutions that could be done thanks to the data-driven knowledge adopted.
Let’s now assume we have identified the project that we want to put in production.
How to structure data-driven decision-making projects
Once the project team has been created – it should be balanced, well defined, and with all the stakeholders involved – we advise you to plan 3 phases:
- Project Scope (<1 week)
- Pilot (between 4 and 6 iterations, each iteration lasts for one week)
- Deploy in production (between 2 and 4 weeks)
The phases can be tailored according to your needs but, in principle, we recommend that you respect them as much as possible.
Let’s imagine that your data-driven decision-making project is very complex and requires many months of work to get to the final results.
We suggest you reduce the initial scope to have a first deliverable in 2 or 3 months.
This has two major advantages:
- Focus your efforts on something usable immediately
- Build trust in all stakeholders, who see results coming from the very first days.
One of the advantages of Rulex is that it is extremely flexible and interactive thanks to its drag and drop functionality and eXplainable AI, so it can deliver value from the very first iterations.
The following graph sums up the life cycle of every Rulex’s project: at an early stage the effort is small and the return in value is already greater than the investment.
Over time, the management effort grows slowly, while the value of the solution grows exponentially.
How to define your project scope
The scope phase is crucial for the success of any data-driven decision-making project.
If you make a mistake in this phase, for instance, setting an incorrect goal or misunderstanding your data, the project can be seriously compromised.
Typical activities during the assessment phase are:
- Goal definition: you should define the goal in detail, identifying performance measures and expected KPIs
- Data definition and audit: you should identify the available data, evaluating data quality and suitability for analysis
- Deployment in production: you should plan how the new decision-flow will be deployed in production, e.g., how it will take part in the daily work routine and how it will interact with all the company processes
- Assessment: you should evaluate if objectives can be met or if some corrective actions should be taken
If the assessment is positive, project sponsors should sign-off so that you can go to the pilot phase.
Develop your pilot, focusing on value creation
In the pilot phase, the team should provide a proof of concept and clients should see the first results. With conventional methods, customers expect these steps to take several months, require a high number of data scientists’ billable hours, and deliver little or no directly actionable insights.
With Rulex, customers will quickly receive tangible business value in the form of understandable, actionable insights.
In this phase, typical activities are data preparation, model creation and refining, and dashboard creation.
Deploy in production fast and successfully
In the final stage of the project, the proof of concept will be put into production, integrating the solution in the customer IT infrastructure.
The scenario in which the proof of concept will be deployed strongly influences implementation activities. Among other factors, there are two main aspects which are critical to determining the complexity of this phase:
- The diversity of the input / output format with respect to the format used in the pilot phase
- The level of autonomy of the solution with respect to the client IT systems
The good news is that, with Rulex, production deployment is much easier than traditional methods, thanks to its no-code approach and the numerous features for monitoring projects.
Data-driven decision-making processes are revolutionizing the world and can make a huge contribution to your business.
Once the best data-driven decision-making project for your business has been chosen (see our dedicated article on the subject), execution is essential.
A few reminders for you:
- Start small to achieve more ambitious goals over time
- Try to deliver value to all your stakeholders as soon as possible
- Stay focused on your final goal
These best practices may seem obvious, but these are crucial concepts to keep in mind to avoid making the most common mistakes.