Data-driven decision-making (DDDM) is revolutionizing almost all industries and departments. Experts agree that those who now embed DDDM into their organization will have significant benefits and competitive advantages in the future.
This brief article will look into the topic while providing you with some useful tips on how to choose the DDDM project that is best for your business.
What is data-driven decision-making?
However, we don’t always fall in this class of decisions. When the decision regard the granting of a loan based on the risk profile of the applicant or the launch of a new product based on a market study, we cannot rely exclusively on an algorithm.
Surely data analysis can help decision-makers take a more informed decision, but they are the ones who have the final say.
Why data-driven decision-making?
Data-driven decisions bring numerous practical benefits.
Frist of all, when driven by data, the decision-making process relies on concrete evidence and not on personal opinions (which sometime can lead to long discussions, if they are not shared). Furthermore, data-driven decisions are transparent and reliable, and they can be continuously improved by new data.
Data-driven decisions enabled by emerging technologies are having a major impact on almost all industries (finance, healthcare, manufacturing) and all departments (marketing, supply chain), bringing important benefits in terms of cost saving, revenue growth, and margin optimization. Many repetitive decisions can be partially or fully automated, leaving the more complex and strategic tasks to humans.
Recent studies confirm these trends. According to Gartner, 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 by the end of 2024.
So far so good! Fast automated decisions, less cost, more revenue… isn’t that wonderful?
Yes, but that’s not so easy.
Many data-driven projects fail. Why?
There are different possible explanations. Sometime projects fail because of a poor choice of technology – for example, a complex tool creating barriers between business and IT experts – and at other times, because of a bad choice of use-case.
Before starting a new project, it is crucial to ask ourselves some questions to avoid making the most common mistakes.
1. IDENTIFYING CLEAR BUSINESS GOAL
The first ingredient for successful projects is to identify a clear business goal. This should be specific, realistic, with measurable objectives and a limited time frame. You can start “small” and then extend your goal after you have gained more experience.
Let’s think of two examples of well-defined business goals:
- Improving revenues by 3% in the first quarter of the year thanks to dynamic pricing
- Reducing the daily cost of your supply chain for transporting goods by 10%
Conversely, increasing sales is not a good business goal since it is hard to measure and poorly defined.
2. Understanding if your data fit the problem
Is your data representative of the problem you want to solve?
Imagine this scenario: you sell products in France, and you want to use this experience (and its data) for launching your products in Italy, assuming that European markets are similar. Italian and French market are probably alike (it’s up to you to decide), while applying French market data to a very different market is not a good idea.
But data representativeness is not the only issue.
One of the most present and often underestimated problems is the quality of the data.
It became popular the expression: “Garbage in, garbage out”. If you have poor-quality data (tables full of typos, missing values, etc.), it will be hard to get something good out of them. Fortunately, there are many data quality tools, such as Rulex RDC, which allow to reach and maintain data accuracy at 100%.
3. Definying Actionable solutions
Can predictions based on data lead to actionable solutions and then to achieving your business goals?
If the answer is no, you should think it over. In the past, many AI projects were prompted only by curiosity, without identifying an actionable solution, and consequently they often ended up with disappointing results.
Imagine you have created an AI solution that tells you the exact number of goods that need to be in the warehouse day to day, but you have no resources to move good in and out. So, your piece of information turns out to be completely useless from an operational point of view. The new solution, maybe wanted by a curious manager, risks putting artificial intelligence in a bad light as something superfluous and time-consuming.
Choosing carefully your new project builds trust in new technologies.
4. Designing a user-friendly and integrated data environment
It is highly advised to design an integrated technical environment, where different professionals (technical experts, business analysts, and IT people) can work seamlessly, without having to go through a fragmented set of solutions.
Easy-to-use, no-code tools such as Rulex Factory allow you to manage the entire data value-chain, from integration to machine learning, from optimization to visualization, from deployment in production to monitoring.
5. INVESTING IN TRAINING
Lack of competences can cause misunderstandings and delays in delivering a project. It is therefore necessary to invest in training to ensure the project success and avoid unnecessary waste of time. Full training improves communication between corporate teams, as it allows professionals from different fields to understand the project and work seamlessly on that.
Rulex Academy can be a good starting point to reinforce both technical know-how (from data preparation to machine learning, from optimization to dashboarding and maintenance in production) and business know-how (especially, in financial services and supply chain).
Reducing churn, a real example
In the previous section we focused on theory, now it is time for some real examples. Let’s consider a real use-case where we are trying to reduce customer churn – a recurrent problem in many industries such as energy, retail, finance, etc.
- The business issue is very clear: the churn rate is high (12%), causing significant revenue loss compared to the previous year. It is a major problem since, as explained in the video, attracting new customers is 6-7 times more expensive than keeping existing ones.
- The company has a good dataset which is representative of the problem. It contains customer personal information (customer ID, education, number of family members etc.), customer behavior (total energy consumption, % of energy used in different time slots, number of service calls made), and pricing information.
- The number of rows is high (around 34.000), data quality is good, and no privacy issues have been detected. The company identifies two actionable solutions: proposing discounts or modifying queue management. The decision depends on the output of the algorithm.
Data-driven decision processes can dramatically improve businesses, in all industrial sectors.
There are no more excuses for not adopting them, as they are widely accessible at different levels. However, it is necessary to embed DDDM into your organization consciously to avoid unnecessary waste of time and resources, always asking the right questions (which are my business goals?; how is the quality of my data ?; and how can I change my business?).
Now it’s your turn to answer: where can I get the most benefits from data-driven decisions? In what time? How do I begin?