We all know that data-driven decision-making processes can bring huge 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 on how to deliver data-driven decision-making projects successfully.
In a previous article, we talked about how important it is to choose the right project when implementing a data-driven decision-making process. We said that businesses should think about the goals they want to achieve, consider the available data, and the actionable solutions that might be implemented thanks to their data-driven knowledge.
Let’s now assume we have identified the project 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 include 3 phases:
- Project Scope (<1 week)
- Pilot (between 4 and 6 iterations, each iteration lasts for one week)
- Production deployment (between 2 and 4 weeks)
The phases can be tailored according to your needs but, in principle, we recommend you follow them as closely as possible.
Let’s imagine your data-driven decision-making project is highly complex and requires many months of work to get to the final results.
We suggest you reduce the initial scope to give you a first deliverable in 2 or 3 months.
This has two major advantages:
- You focus your efforts on something immediately usable
- You build trust in all stakeholders, who see results from the outset.
One of the advantages of Rulex is that it is extremely flexible and interactive thanks to its drag and drop functionality and eXplainable AI. As a result, it delivers value from the very first iterations.
The graph below sums up the life cycle of every Rulex project: at an early stage, little effort is involved 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 to 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 might be seriously compromised.
Typical activities during the assessment phase are:
- Goal definition: set out the goal in detail, identifying performance measures and expected KPIs
- Data definition and audit: identify the available data, evaluating data quality and suitability for analysis
- Deployment in production: plan how the new decision-flow will be deployed in production, e.g., how it will take place within the daily work routine and how it will interact with all company processes
- Assessment: evaluate whether objectives can be met or if corrective actions should be taken
If the assessment is positive, project sponsors should sign-off so you can move to the pilot phase.
DEVELOP YOUR PILOT, FOCUSING ON VALUE CREATION
In the pilot phase, the team should provide proof of concept, and clients should see the first results. With conventional methods, customers expect these steps to take several months, requiring a large number of billable hours from data scientists, and delivering 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 into the customer’s IT infrastructure.
The scenario for deploying the proof of concept strongly influences implementation activities. Among other factors, two main aspects are critical to determining the complexity of this phase:
- How different the input / output format is compared to the format used in the pilot phase
- The solution’s level of autonomy with respect to 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 numerous project monitoring features.