Data is increasingly the fuel that powers decision-making; businesses that embrace data find it easier to adapt to changing markets and stay ahead of the curve. However, fostering a data-driven culture isn’t always easy, especially in non-typical industries where the use of data in decision-making isn’t embedded in the culture.
These environments are more numerous than you might think — real estate, fast-moving consumer goods, consumer services, defence industry, just to name a few. But with a lack of strong historical precedence, fostering a data-driven culture in these environments can feel like an insurmountable challenge.
In this blog post, we’ll discuss some practical approaches to jumpstarting this culture in your organisation.
1. Establish business goals and their drivers
Before you can become data-driven, you need to define what you want to achieve. It’s important to highlight — this isn’t what you want to achieve with data. It’s what your organisation wants to achieve as a business.
It’s crucial to set your business objectives and ensure your team thoroughly understand them, and the drivers behind them. If your team doesn’t understand why we need to achieve X, then even with all the data and insights in the world, they might still make poor decisions as they misunderstand the impacts down the chain.
Only once these business goals are set, understood, and embedded can we start to think about how data can assist in achieving those objectives.
It’s also timely to say that, with more data available, it’s entirely likely you’ll revisit what those objectives are. That’s okay. With more data, you’ll be better placed to understand and express the drivers behind your business objectives, and that can unlock entirely new business objectives.
2. Identify and collect relevant data
Once you have defined your goals, you need to identify the data that will help you achieve them. We’ve already spoken at length about how all organisations generate data in the course of their operations, whether they think so or not.
Now we will acknowledge that, in non-typical environments, finding these data sources can be a challenge. But it is there – we just need to think outside the box.
In such environments, it’s important to divorce the concept of “data” from the concept of your “customer”. Some of the most valuable data you have may be entirely unrelated to your customer. While Facebook and Google may collect data on you as a person, the real valuable data is in what you do and how you use their apps. They use that to fine-tune development, forecast future actions, and ultimately tune their business objectives — and the same applies to you.
Think about your data across a few key pillars:
- Operational data: This includes data related to the day-to-day operations of a business, such as production rates, inventory levels, and supply chain data. This information can help you identify bottlenecks in your operations, optimise your production processes, and reduce costs.
- Financial data: Financial data includes information related to your financial performance, such as revenue, expenses, and profits. This information can help you identify areas where you can cut costs, improve profitability, and make strategic investments.
- Employee data: Employee data includes information about your workforce, such as employee turnover rates, employee satisfaction surveys, training compliance and effectiveness, and performance metrics. This information can help you identify areas where they need to improve their employee engagement and retention efforts.
- Industry data: Industry data includes information about broader economic and industry trends, such as market share data, industry growth rates, and competitive landscape analysis. This information can help you stay competitive and make informed strategic decisions.
- Niche industry data: This includes data specifically generated by your organisation in the course of your business. For example, an organisation delivering property maintenance could collect and use data based on the type of jobs they do, the materials required to complete, the routing/triage of their jobs, and the time taken.
Once you’ve identified your data sources — whether you actively collect them or not — you can start to overlay them with your business processes to identify where they could support decision-making.
3. Overlaying data sources with business process
To start, map out your business processes and highlight where data can be used to optimise your decision-making. This can be game-changing or incremental support.
For example, when ordering stock, you’d certainly be empowered to make a better decision if you had access to forecasted demand levels. When negotiating your electricity and gas contracts, you’d be empowered to make a better decision if you had access to forecast pricing and demand over the contract period.
Ultimately, you’re trying to answer the question: “What could I do better, if only I knew more?”. You should be encouraging your staff to always ask this question of themselves and their work too.
Again, it’s important not to get caught up here in the specific mechanism about how that data would get to the appropriate decision maker in the right format — the data management aspect of the solution.
Once you’ve identified these areas where data can be used to support decision-making, you must determine how to integrate the data into your processes. There’s no one size fits all solution here, as it depends on your existing technology, budget, and approach, but there are a few key guiding principles:
- Choose solutions that cause the least amount of disruption and require the least training
- Remember, your end-users aren’t analysts, nor should they be. The solution will need to provide insights, not just raw data.
- Timeliness and trust in data are critical – things that are very hard to correct after the fact.
4. Develop a data-driven culture
Developing a data-driven culture is a critical step towards leveraging data to support decision-making in non-typical environments. However, this can be a significant challenge as decision-making in these environments may be based on intuition or experience, rather than data.
To develop a data-driven culture, it’s important to first educate your employees on the importance of data and how it can help them make better decisions. This can involve training sessions, workshops, and ongoing communication about the value of data-driven decision-making. It’s also important to provide employees with the tools and training they need to access and analyse the data effectively.
It’s crucial to emphasise that data is not there to highlight employee shortcomings or replace them. Rather, it’s about empowering them to make the best decisions possible and to improve overall organisational performance. By providing employees with the necessary tools and training, you can create a culture that values data and uses it to inform decision-making.
Creating a data-driven culture won’t happen overnight. It may be more effective to take a team-by-team approach rather than trying to implement it organisation-wide all at once. Additionally, leadership commitment and buy-in are essential to ensure the success of a data-driven culture. When leadership is committed to the value of data and supports the necessary changes to create a data-driven culture, employees are more likely to adopt these changes and drive positive outcomes.
In terms of specific programs to foster your data-driven culture, several types of support can be made available to employees. These include:
- Training: One of the key aspects of developing a data-driven culture is to provide training and education to employees. This can include training on data literacy, how to access and analyse data, and how to use data to make better decisions.
- Data governance: Data governance ensures that data is managed effectively, including data quality and security. Establishing a data governance program can help to ensure that data is accurate, timely, and trustworthy. It’s also important for your staff to understand where the data comes from, where it’s made available, and the limitations of that data set for a specific application. We’ve spoken about how to start with data governance as a SME, if of interest.
- Access to tools: Access to data analysis tools such as data visualisation software, statistical software, and data analysis platforms is critical to enable employees to access and analyse data effectively — but remember, they’re not there to be analysts, just users.
- Analytics support: Organisations can provide analytics support to employees by having dedicated analytics teams or experts who can help employees analyse and interpret data. This can include data scientists, business analysts, or data analysts. They need to have an intimate appreciation and understanding of the business operations to be effective.
- Communication and collaboration: Ultimately, like all initiatives in an organisation, effective communication and collaboration with staff are key. This can involve sharing data insights across teams, encouraging cross-functional collaboration, and establishing regular data review meetings.
In conclusion, while data increasingly becomes a crucial component of decision-making across all industries, fostering a data-driven culture is not always easy, particularly in unconventional industries where data may not be embedded in the culture. To develop a data-driven culture, businesses must define their business objectives, identify and collect relevant data, overlay data sources with business processes, and integrate the data into decision-making processes. While developing a data-driven culture is a challenge, it is also an opportunity for businesses to gain a competitive edge, stay ahead of the curve, and achieve new business objectives.