In today’s business landscape, data reigns supreme. It is the cornerstone of effective decision-making, fuels innovation, and drives organizational success. However, despite its immense potential, many organizations struggle to harness the full power of their data due to a fundamental disconnect between IT and business teams. This division not only impedes progress but also undermines the very foundation upon which impactful insights are built.
Out of the 97% of organizations investing in data initiatives, only 26.5% report successfully creating a data-driven organization, according to NewVantage Partners. This is partly because data teams today must navigate complex data ecosystems and a constantly changing technological landscape. They also have to deal with the intricacies of data governance, quality, and integration, as well as internal barriers – such as siloed data and resistance to change – that can prevent them from collaborating effectively and delivering value to their organizations.
However, there are steps that organizations can take to establish a unified, cross-organizational approach to data initiatives, such as fostering a sense of innovation collaboration, unifying IT efforts, and consolidating data ecosystems. By prioritizing these three strategies, organizations can be on their way to leveraging data to its fullest potential.
Today’s Disconnect Between IT and Business Teams
The heart of the disconnect lies within the fragmentation of data ecosystems within organizations. Historically, IT and business teams have operated in separate silos, each with its own set of priorities, objectives, and methodologies. This not only hampers collaboration but also leads to inefficiencies, redundancies, and missed opportunities. For example, business teams may struggle to access the data they need in a timely manner, while IT teams may lack the necessary insights to prioritize their efforts effectively.
This disconnect between IT and business teams often extends beyond operational issues to cultural differences and communication barriers (i.e., IT professionals may speak in technical jargon to their business counterparts). In contrast, business teams may struggle to articulate their data needs in a way that IT can practically apply. This lack of communication and understanding breeds mistrust and frustration, further exacerbating the divide between the two groups, with far-reaching consequences. Siloed data and communication drain valuable time and resources and give rise to many issues, including poor data quality, heightened risk exposure, and sluggish responsiveness.
When data is scattered across disparate systems and departments, it becomes difficult to maintain consistency, accuracy, and integrity. This, in turn, undermines the credibility of insights derived from that data and erodes trust in the decision-making process. Siloed communication channels also inhibit knowledge sharing and collaboration, stifling innovation and inhibiting organizational growth. Instead of working together towards common goals, IT and business teams often find themselves at odds, each pursuing their own agenda without regard for the organization’s broader objectives. This fragmented approach undermines the potential for impactful insights and creates a disjointed organizational culture resistant to change and adaptation.
Steps Toward a Cross-Organizational Approach
To overcome these challenges, organizations must take proactive steps to bridge the gap between IT and business teams and establish a unified, cross-functional approach to data initiatives. This requires a multifaceted strategy that addresses both technological integration and cultural transformation.
1. Consolidate Data Ecosystems
By adopting a centralized architecture that can support hybrid data and cloud systems, organizations can simplify their data landscape, eliminate redundancies, and streamline processes. This reduces the burden on IT teams and provides business teams with easier access to the data they need to make informed decisions.
Consolidating large volumes of data allows businesses to make it more accessible for business teams to further optimize customer experiences, facilitate financial control, and detect fraudulent activity more quickly. For example, Bouygues Telecom, a universal communications provider offering technology services to over 27 million customers daily, was able to consolidate its data ecosystem on one modernized platform. This transition enabled them to process more data faster than before, bringing huge operational efficiencies to the company.
2. Establish Data and AI Regulation Frameworks and Education Organization-Wide
Defining AI regulation frameworks and implementing education initiatives from the outset is critical to the success of any AI-driven data initiative. AI governance is essential because it ensures that AI technologies are used responsibly, ethically, and in compliance with regulations. As AI becomes the core of value creation across industries and a substantial part of various data initiatives, robust governance frameworks are necessary to mitigate risks related to fairness, equity, privacy, and security.
For instance, the EU’s proposed AI Act mandates strict compliance across various industries for all AI systems developed, deployed, or used within the EU. Without clear guidelines and policies, organizations risk AI misuse, leading to significant legal, financial, and reputational damage. Defining what effective AI governance must look like in your company helps employees understand the boundaries and best practices for AI usage, ensuring they can leverage AI tools to their full potential while maintaining trust and compliance.
To ensure organization-wide adherence to AI governance, it is essential to foster a culture of collaboration and continuous education. Organizations should form cross-functional teams that include members from both IT and business departments, breaking down silos and encouraging open communication. For example, a cross-functional team might develop a new product feature powered by AI, ensuring it meets both technical and ethical standards. Training programs should be implemented to equip employees with the necessary skills and knowledge to understand and follow AI governance principles. Additionally, product teams should attain certifications in relevant areas to stay updated on AI regulations. These not only educate product teams, but also show your customers and partners that your organization has a deep understanding of responsible AI usage.
3. AI Regulation Initiatives Must Be Driven from the Top
AI and data governance frameworks and education expectations must flow from the top – the CEO must play a hands-on role in establishing these guidelines and expectations. An engaged CEO ensures that the organization is not only focused and invested but also prepared to navigate these complexities. A Boston Consulting Group study reveals that 79% of companies with actively involved CEOs report being prepared for AI challenges, compared to just 22% among those with less engaged leadership. Moreover, organizations where CEOs take a hands-on approach to AI programs see 58% more business benefits from their AI activities.
Engagement from the entire C-suite and all leaders in AI initiatives is also critical. While leadership may share common goals, the responsibility for AI mandates must originate from the CEO. This top-down directive ensures clear lines of authority and decision-making, which are vital for the success of AI initiatives. Less than one-third of CEOs currently take an active role in AI programs, yet their engagement can significantly amplify the effectiveness of these initiatives. CEOs who are actively involved help sustain the necessary investment and focus, ensuring that responsible AI is integrated into the core operations and culture of the organization. As companies face intense commercial and public pressure to leverage AI responsibly, having a CEO who champions these values can make a significant difference in achieving sustainable success and maintaining public trust.
Establishing a cross-organizational approach to data initiatives is paramount for organizations seeking to thrive in today’s data-driven landscape. By consolidating data ecosystems, fostering collaboration, and unifying efforts towards common goals, organizations can unlock the full potential of their data, driving innovation and securing a competitive edge in an increasingly interconnected world.
The path ahead may be challenging for today’s organizations, but the rewards of a truly integrated approach to data are boundless. As organizations navigate the complexities of the digital age, bridging the divide between IT and business teams will be essential to unlocking the transformative power of data and driving sustainable growth and success.