As the business landscape becomes increasingly competitive, more and more companies are turning to data-driven decision-making and data-driven customer experiences to stay ahead of the curve. However, becoming data-driven is not without its challenges and should not be a goal on its own. In an interview with Benelux CEO, Jeremy Bonnevalle, we discuss some of the biggest risks and rewards of data-driven decision-making and how companies can best prepare themselves for success.
What are some of the key benefits that companies can expect to achieve by becoming data-driven?
Data-driven decision-making provides insights that can be leveraged in multiple point-of-views:
- Firstly, it gives insights in the financial performance of a company. It helps any director understand which departments are performing well and which are not. It gives a clear picture of where you should take action first.
- Secondly, by utilising data to enhance operations, companies can increase their operational excellence, particularly in industries like retail and manufacturing, where stock and workforce planning can be improved.
- Additionally, data can be used to enhance the customer experience across multiple channels, which ultimately leads to increased revenue.
What are some of the biggest risks or challenges that companies might encounter as they try to become more data-driven, and how can these be overcome?
One of the biggest risks is getting too caught up in building the foundations: constructing the infrastructure for gathering data and transforming data (ETL). This results in spending too much time on the data rather than focusing on business results.
The solution to this is to create an incremental roadmap. “Firstly, my recommendation for every company is to start by defining use cases that are closely linked to the business strategy, and prioritise data ingestion and transformations for those specific use cases.”
“Secondly, our Cloud & Data experts have built G Cloud Accelerators, like Cloud foundations or Data Foundations. Both can help customers who are not yet on Google Cloud to accelerate their onboarding process, being about 6 times faster than when having to set up everything from scratch.”
How can companies ensure that their data is accurate, reliable, and of high quality?
Ensuring that data is accurate, reliable, and of high quality is a critical task for companies. One way to achieve this is by creating a data governance board to define and refine how their data and metadata should be governed, protected, quality checked, and catalogued. This ensures that everyone has a global view of the data assets, and it is managed consistently across different analytics teams and departments.
Within this data governance board, we’ll always appoint data stewards. These are the responsibles within the business for managing the data lifecycle. Data stewards, who are business users themselves, understand their colleagues and their own data needs best and provide guidance on best practices for data management. They should be responsible for ensuring that the data is stored, processed, and used appropriately, rather than relying solely on IT departments.
In addition to that, data owners and data stewards should define and implement a set of data quality checks on that data. One approach is to use dbt tests within the data model framework to develop quality checks alongside the metadata. This allows for a consistent and integrated approach to data quality management and to visualise the quality checks. The results of these checks can be pushed to BigQuery, allowing users to configure alerts or schedule reports for a more proactive approach to data quality management.
How can companies use data to improve the customer experience, and what are some examples of companies that have successfully done this?
To use data effectively, companies must first define the customer journey and how data can be used to improve it. Here are some of the companies we’ve worked with:
- Royal Belgian football association
Helped the Royal Belgian Football Association in its journey to become a data-driven organisation and have a true 360 view of its data to deliver hyper-personalised services across all digital channels.
- Toyota Motor Europe, an international manufacturer
Supporting Toyota Motor Europe to design and build an EMEA-wide marketing data lake that ingests and unifies their marketing data in BigQuery. Built several ML models such as churn prediction as well as persona clustering based on behaviour.
- Carrefour, an international supermarket chain
Migration of the Data Warehouse for the Belgian entity to gain better customer insights. This was done through Flycs, our proprietary solution. Next to that, we helped Carrefour Belgium sunset their local data centre by migrating all of their on-premise workloads to Google Cloud.
What are some best practices that companies should follow when implementing a data-driven strategy, and what are some common mistakes to avoid?
At Devoteam G Cloud we have completed many data projects. Over the years, we’ve seen these common pitfalls among our customers:
- over-focusing on gathering and structuring data
- spending too much time on data ingestion
- Neglecting the (potential) business results and how the projects contribute to business objectives and key results.
When implementing a data-driven strategy, companies must also consider change management to ensure the successful adoption of the new approach. Change management involves a structured process and set of tools to lead the people’s side of change and achieve desired outcomes. At the start of the process, it is crucial to involve business decision-makers to align the data strategy with the overall business strategy. Moreover, data should be easily accessible to employees to encourage effective use.
For change management to be successful, companies should use a methodology that considers different user groups, teams, departments, and layers of the company. In our case, we always recommend a tailored change management methodology which has been enhanced with the Prosci methodology.
What role do you see AI and machine learning playing in the future of data-driven decision-making, and how can companies prepare for this?
AI and machine learning will be critical to identifying trends and making recommendations based on past data or third-party data. Today, companies should focus on creating a data platform to enable the use of AI and machine learning in a later stage.
How do you see the business landscape evolving in the next 10 years, and how can companies use data to stay ahead of the curve?
Very recently, all businesses and consumers were shaken up by the arrival of prescriptive AI like Chat GPT and the announcement of Google’s Bard.
10 years from now, AI and automation will play an increasingly important role in business. To stay ahead, companies must start today by collecting and structuring the data effectively to unlock the potential to deploy new use cases through prescriptive AI or automation. Ultimately, those who effectively utilise data will have a competitive advantage, adding business value, in the years to come.
Are you struggling to make sense of your data? Do you feel like you’re missing out on generating business value?
Let us help you build a winning Data & Analytics Strategy that generates revenue, operational excellence or optimises financial performance. With years of experience in data and analytics, we know how to turn raw data into actionable insights that drive growth and success.