With proper planning and implementation, enterprises can effectively use data to make business decisions. However, a strong data culture is still a theory for many business organizations. Here, we’ll discuss the steps to create a data-driven culture in an enterprise.
Businesses need to work with quality data to make effective business decisions. While we cannot ignore the importance of human expertise, combining both is the best way to boost a business in a competitive market. This requires using data and data analytics to make decisions.
Statista reports that the global big data analytics market will reach $655 billion by 2029, while the predictive analytics market is estimated to touch $41.52 billion by 2028. A business organization can enjoy the benefits of data analytics and business intelligence by adopting a data-driven culture. Another report shows that 57% of leading brands are already using data to drive innovation.
So how do you create an effective data-driven culture in your enterprise? Let’s find out in this blog. But first, let’s dive into the basics.
Is data the key to a data-driven culture? Absolutely! A data-driven culture is where the workforce uses statistics, analytics, facts, insights, predictions, etc., to make everyday business decisions and optimize their tasks. Team leaders, managers, and C-level executives use insights to understand various elements of work and how these affect business performance.
Many components contribute to creating a data-driven culture in an organization. However, the major aspects are as follows:
Data maturity refers to the process of storing and retrieving data over time. It depends on your data governance policies and how well you manage and maintain the datasets with accurate tags, metadata, etc.
Data leadership defines the role of leaders and decision-makers in managing business data. These people understand the importance of quality data and help maintain a work culture where decisions are made based on data analytical reports.
Data literacy is the act of ensuring business data is structured, accessible, reliable, and accurate. It also involves training employees to understand and use datasets effectively for day-to-day work.
Investing in a data-driven culture requires expert guidance and support. SMBs and large organizations partner with a reputed data analytics company to revamp their internal processes and work culture the right way.
The data management framework has to be structured and aligned with the business process. Here’s how to implement successful data management in your enterprise and create a strong foundation for data-driven culture.
Understand that creating a data-driven culture is not limited to technological investments. The focus is equally on changing the existing work culture to help employees use new technology and tools at work.
The following steps will help you build an effective data-driven culture in your enterprise.
The top management and C-level executives play a vital role in influencing other employees. They need to understand the importance of data-driven solutions and establish it in the organization. When the management makes it a norm to use data and evidence for decision-making, employees will follow it over time.
For example, the team leader or manager can allocate a few minutes at the beginning of a meeting to go through the analytical reports and observe whether the proposals are backed by data. Then, they can discuss the proposals and the reports to help other employees realize why they should work with data. When the top management sets an example, it becomes easier for employees to adapt to the changes.
How do you analyze the business performance? It can get complicated and confusing without metrics. Every enterprise has its own metrics for analytics. However, there’s no guarantee that the existing metrics are correct or suitable for accurately measuring the business.
For example, many businesses use competitor analysis because they need to keep track of what other brands in their industry are up to. An FMCG company will have to decide its pricing and marketing strategy based on customer behavior, market trends, and competitor’s offerings. Not factoring in either factor can result in skewed analytics, which invariably leads to wrong decisions.
Define metrics carefully and make sure they are always aligned with the business vision, goals, and industry standards.
One major mistake many organizations make is to keep the data scientists and business leaders in separate units. While the analytical reports are shared between the teams, the people responsible are not always collaborating and usually work in isolated teams. This can affect the quality of analytics and will soon widen the gap between reality and analysis.
Leading brands have managed to avoid this issue by eliminating the boundaries between data scientists and business leaders. The staff (team members) is rotated between different departments to keep communication flowing. Another method is to ensure that the top management has the necessary technical know-how to directly interact with data scientists and work with them. While it is not necessary to replace existing executives with AI and ML engineers, it is important to train them in the basics.
A common complaint from employees is that there don’t have access to data or analytics at work. It takes too much time and energy to obtain even the basic data, and this isn’t enough to make data-driven decisions. Despite democratizing the storage systems, analysts aren’t given access to information from other departments due to different constraints.
This challenge can be handled by identifying the data related to the KPIs for the project/ quarter and providing enterprise-wide access to this information. For example, if the sales analyst has to create a demand forecasting report, they should have access to information about past sales, customer feedback, inventory, etc. While data security is a concern, it can be handled through effective data governance and by setting up authorized access employees based on their project requirements.
To build an effective data-driven strategy, you should assess the extent of uncertainty experienced by the employees. It gives three major benefits to the organization. Firstly, the top management and decision-makers have to answer the most important question- is data reliable? Then ask if there are enough examples of dependable data-driven models. Next, find out how to deal with cases with little or no data to perform analytics and make decisions. The answers to these questions are a must.
Secondly, asking data scientists to consider uncertainty when building data models will help them create a better system to manage risk. The model can be strengthened to consider factors it would otherwise ignore.
Thirdly, understanding uncertainty allows teams to push the boundaries of experimentation and take calculated risks. The testing environment will be more rigorous and controlled, leading to greater chances of success.
Many ideas are not implemented mainly due to the lack of feasibility. They could be risk and cost-intensive or require too many changes to the system without guaranteeing results. You may not understand the complexity of the idea until it’s time to develop the proof of concept. Scrapping the project at this stage can be demoralizing for employees.
The focus of a data-driven culture should be to build a simple yet powerful proof of concept instead of something intricate or challenging. Use existing systems and resources to develop the models and ensure viability. Sometimes, a small change to the current model can lead to great results. Don’t go for elaborate models unless you absolutely need them.
Knowing when to train your employees to accept and adopt data culture is crucial for the business to succeed. Training employees, even before the models are ready will be ineffective in many ways. Firstly, the actual model could be different from the one the employees train on. Secondly, employees may forget what they’ve learned if there’s too much gap between training and usage.
Planning the training programs just before the model goes into a trial run will help. Employees can right away use their newly gained skills and knowledge to work with the analytical model. They can fine-tune their skills and also identify potential issues with the model. The intention is to ensure the training is effective and employees retain the knowledge.
The end goal of using analytics is to enhance customer experience. However, businesses should also consider employee satisfaction when creating a data-driven culture. How you explain the developments to employees will make a lot of difference. Instead of telling employees they need to learn or do something for customers, show how the data models benefit them.
Tell employees how the new analytical model will save time and reduce the workload. When they know that the new developments will reduce work pressure, employees will be more excited and open to accepting the changes. Furthermore, they will be involved in the process and find solutions effectively from the initial stages.
Flexibility and scalability are two important factors to consider when developing data models. However, businesses tend to forget the importance of consistency in the short term. Furthermore, if more than one data team is involved, it could lead to more inconsistencies as they may choose different programming languages, data sources, metrics, frameworks, etc. Things can turn into a jumbled mess very soon and result in unwanted losses.
To know how to make your business a purely data-driven entity, start by focusing on consistency. Everyone involved in the process should be on the same page and work with the same parameters. Partnering with a data analytics consulting company can solve the issue and bring a balance between flexibility and consistency.
There is no single solution to most analytical problems. Data scientists make choices depending on the situation, internal and external factors, tradeoffs, etc. So, instead of simply assuming how a team will solve a problem, ask them to explain what they intend to do. Discuss alternative options and the reasons to choose one approach over the other.
This allows data analysts and teams to gain a deeper understanding of the problem and come up with the best possible solution. Furthermore, the alternatives can be a better choice in some instances, and not discussing the issue can limit your business from achieving the desired results. Make it a habit in the enterprise to adopt new approaches and create effective data-driven solutions.
Building data-driven processes in your enterprise offer several benefits, such as:
Creating a data-driven culture doesn’t happen overnight. It’s a lengthy and long-term process where everyone, from the top leadership to the entry-level employees, has to be involved. The leaders play a vital role in setting the tone of adoption.
However, working with a reliable data analytics consulting company will eliminate the risk of error and wrong decisions in developing data models and analytical solutions. With the right support from experts, you can shape the data-driven culture in your business to increase ROI and business value.