Data-driven decision-making (DDDM) is revolutionizing almost all industries and departments. Experts agree that those who now embed DDDM into their organization will reap significant benefits and competitive advantages in the future.

This brief article looks into the topic while providing some useful tips on how to choose the DDDM project that is best for your business.

WHAT IS DATA-DRIVEN DECISION-MAKING?

Data-driven decision-making is the process of making decisions by analyzing the data at your disposal. DDDM processes may be characterized by fully automated data-driven decisions, generally driven by artificial intelligence and optimization algorithms. We are faced with this type of decision every day, for example, when we use a search engine to find something on the internet or when we look for the best way to get from A to B on Google Maps.

However, we aren’t always faced with this type of decision. When a decision involves granting a loan based on the applicant’s risk profile or launching a new product based on a market study, we cannot rely exclusively on an algorithm.

Data analysis can certainly help decision-makers take a more informed decision, but it is they 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 rather than personal opinions (which can sometimes lead to long discussions, if they are not shared by others). 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 (financial services, healthcare, manufacturing) and all departments (marketing, supply chain), bringing important benefits in terms of cost savings, 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, lower costs, more revenue… isn’t that wonderful?

Yes, but it’s not so easy.

Many data-driven projects fail. Why?

There are various  explanations. Sometime projects fail because of a poor choice of technology – for example, a complex tool might create 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 A CLEAR BUSINESS GOAL

The first ingredient for successful projects is identifying a clear business goal. This should be specific, realistic, with measurable objectives and a limited time frame. You can start “small”, 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 through 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

Are 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) to launch your products in Italy, assuming that European markets are similar. The Italian and French markets 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 common and often underestimated problems is data quality.

The expression: “Garbage in, garbage out” has become popular. 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 achieve  and maintain data accuracy at 100%.

3. DEFINING ACTIONABLE SOLUTIONS

Can data-based predictions lead to actionable solutions, and then to achieving your business goals?  

If the answer is no, you should think again.  In the past, many AI projects were launched out of pure curiosity, without an actionable solution, and often ended up with disappointing results.

Imagine you create an AI solution that tells you the exact number of goods needed in the warehouse day to day, but you have no resources to move goods in and out. So, your information turns out to be completely useless from an operational point of view. The new solution, perhaps ordered by a curious manager, risks putting artificial intelligence in a bad light as something superfluous and time-consuming.

Carefully choosing your new project builds trust in new technologies.

4. DESIGNING A USER-FRIENDLY, INTEGRATED DATA ENVIRONMENT

It is highly advisable to design an integrated technical environment, enabling different professionals (technical experts, business analysts, and IT people) to 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 important to invest in training to ensure the project’s success and avoid wasting time. Full training improves communication between corporate teams, as it allows professionals from different fields to understand the project and work seamlessly on it.

Rulex Academy is a good starting point for reinforcing both technical know-how (from data preparation to machine learning, from optimization to dashboarding and maintenance in production) and business know-how (particularly in financial services and the supply chain).

Reducing churn: a real example

In the previous section we focused on theory. Now it’s 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 customers’ 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: offering discounts or modifying queue management. The decision depends on the output of the algorithm.

CONCLUSIONS

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, you do need to embed DDDM into your organization consciously to avoid wasting unnecessary time and resources. Always ask the right questions (what are my business goals?; what is the quality of my data ?; and how can I change my business?).