“Knowledge is power” and today’s businesses have access to more knowledge, in the form of digital data, to power their growth than ever before. Establishing a data-driven corporate culture is key to increasing efficiency and productivity, making informed decisions, improving the customer experience and managing risk. However, implementing a data-driven culture can come with challenges, such as resistance to change, data silos, and a lack of data literacy among employees.
Anticipating and understanding these challenges is key to meeting them and ensuring that the entire team shares and benefits from a data-driven culture. Below, 20 of its members Forbes Technology Council Discuss some of the challenges that can arise with creating a data-driven culture and share strategies for overcoming these obstacles.
1. Hide sensitive information
Controlling access to sensitive data is difficult, especially when that control is applied to professionals who must use the data. An organization must build limited access into its data culture in such a way that professionals expect to access only samples of data, while receiving large-scale responses from systems that access larger volumes of data. In this way, sensitive information is hidden. – Dorit Dor, Checkpoint
2. Provision of Paradata
One challenge is providing deliverables—information about how the data was collected—in addition to accurate metadata. In the age of artificial intelligence, it is important not only to provide metadata along with enterprise data, but also deliverables. The use of blockchain technology can help in this matter. – Jamil El-Imad, Imperial College, Institute of Biomedical Engineering
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3. Ensuring that everyone uses data in decision making
A challenge in building a data-driven corporate culture is ensuring that everyone in the organization, regardless of position or seniority, consistently uses data to support their decisions. To address this, all employees should know how to identify and present the data that best shows their results and outcomes. – Doron Sitbon, Compliance with dots
4. Overcoming confirmation bias
Most people are driven by their intuition, which leads to confirmation bias. It is easy to agree with data you prefer and difficult to accept contrary data. Techniques to combat confirmation bias include ensuring that teams have a diverse range of perspectives, respect each other’s opinions, and ultimately work to build consensus among the team interpreting the data. – Kevin Marcus, Version
5. Building around the customer experience
A good data-first culture prioritizes and builds on the customer experience. Companies that rely on spreadsheets or shared drives to manage data will struggle to deliver a differentiated experience and reap the benefits of AI, which requires a robust database. If the goal is to increase customer familiarity and trust, consolidate your data into a single system designed around them—not you. – DJ Paoni, Certinia
6. Addressing Ethical Concerns
One challenge is the ethical and philosophical debate about gathering so much data about customers. Intentional use and placement—understanding where specific data fits into an organization’s goals—will help alleviate employee and customer concerns. – Arjun Bhatnagar, With a cloak
7. Track Duplicate Data
Due to cloud computing, organizations now use a huge amount of data that moves seamlessly across the data set. While data can be moved, it can also be hidden. Even if an organization thinks it knows where to find valuable data, there may be copies of it elsewhere. To solve this, an organization must adopt a holistic, cloud-native security strategy that recognizes its own unique security requirements and risks. – Asaf Kochan, Sentra
8. Overcoming employee resistance to change
One challenge to building a data-driven corporate culture is employee resistance to change. To overcome this, provide comprehensive data literacy training, encourage open communication about the benefits of data-driven decision-making, and motivate employees to adopt data-driven approaches through recognition and rewards. – Mohit Gupta, Damco Solutions
9. Making timely decisions
One challenge is the inability to make timely decisions. Data may not always be perfect and occasionally data points may conflict with each other. Setting up a distinct set of KPIs and metrics will help clarify decision making. Additionally, learn to make decisions without complete or perfect data. In some cases, speed is just as important as accuracy. – Ken Ringdahl, Cantata
10. Fixing Previously Unknown Functional Issues
In manufacturing, data comes from many sources and devices. When manufacturers start using data-driven manufacturing, the initial numbers may highlight previously unknown operational issues. Leadership must ensure that addressing these truths is part of the improvement journey. Emphasizing that transparency and accuracy in data are essential for long-term improvement can help build trust in the process. – Ravi Soni, Amazon Web Services
11. Setting SMART Goals
Start your team by setting a goal that can be broken down into small, achievable goals. These goals should be SMART (Specific, Measurable, Achievable, Relevant and Time-Bound) and act as milestones to track progress. You should also regularly ensure that the data is accurate and relevant. Finally, you should invest in training in data analytics best practices. – Alfredo Ramirez, Viewpoint
12. Overcoming a “Siloed Data” Mindset.
A challenge to building a data-driven culture is the “data silo” mentality. Employees may be reluctant to share information they consider “theirs.” To combat this, create data governance guidelines and educate employees about the benefits of data sharing and how it can improve overall performance. – Jas Bagga, A company LLC
13. Data integration in all workflows
When building a data-driven culture, teams may fear that their input will be rejected unless it is supported by data. However, a data-driven culture equips organizations with knowledge to make customer-centric decisions, promoting trust in data over intuition or past experience. Organizations need to educate skeptics on how they can integrate data into their workflows so they don’t feel left behind. – Alan O’Herlihy, Everesen
14. Maintaining high quality data
Maintaining high-quality data can be a significant challenge, as low-quality data can yield inaccurate information and lead to poor decision-making. Organizations can overcome this by establishing strong data governance frameworks that include data quality standards, regular audits and clear responsibility for data management. – Andrey Kalyuzhnyy, I have 8
15. Changing the attitude of reluctant workers
Employee resistance to change is a common challenge when building a data-driven culture. Resistance may be due to concerns about mastering new technology, an “if it ain’t broke, don’t fix it” mentality, and/or fear of replacement. One solution is to appoint employee “change champions” to demonstrate how the new technology will be easy to learn, improve each employee’s performance, and make them even more indispensable. – Carl D’Halloween, Datadobi
16. Data storage just for the sake of it
Many organizations have built a data-driven corporate culture, and this leads them to store vast amounts of information in their businesses. The challenge is when an organization stores data just for its own sake and it gets out of control, contradictory and inaccurate. A data-first company should invest in a sound data governance strategy to ensure quality and relevance. – Eric Helmer, Rimini Street
17. Optimization of data collection and management
One challenge an organization may face in building a data-driven culture relates to the data itself. For the extracted knowledge to be truly valuable and useful, the data analyzed should be representative and of high quality. So take an audit of your data and review your current approach to data collection and management. Think of ways to improve it before moving in a different direction. – Yuri Gnatyuk, Kinjek
18. Briefing of leaders
A key barrier is a lack of knowledge and skills among leaders, who often lack an understanding of new AI technologies or how and for what purposes the data could be used. This is especially true for traditional, established companies such as banks and insurance brokers. To overcome this challenge, start by educating leaders and addressing the question of how the business will change its operating model to use data. – Lumboslava Uram, UniCredit
19. Ability to access and investigate employee data
Very often, organizations hire data and/or analytics teams to interface between employees and data. This creates both a crutch and a bottleneck. To create a true data-driven culture, all employees need access to data and the tools that allow them to ask questions about the data. Large language models are quickly replacing visual query tools for this, allowing employees to ask questions in natural language. – Sam Glasenberg, Level Ex
20. Enhancing understanding of the value of data
The biggest challenge—fostering a team understanding of the value of data—can only be solved at the top by creating a value and reward system for leveraging data. Without it, it will be difficult for teams or individuals to prioritize data literacy, data debt reduction, metrics ownership, and other aspects of data culture over functions and activities that align with key organizational roles and metrics. performance. – Elliott Cordeau, Data Center
