Today, data has become a form of currency across industries, with businesses mining data more intensely than ever before. The objective has always been to transform data into powerful insights, unlocking pathways to hyper-personalisation, cost optimisation, and new innovations in artificial intelligence (AI). Indeed, 73% of CEOs surveyed by Frost & Sullivan concur that extracting value from data has become a top business priority. However, the reality has often been quite different from the aspirations many had.
The relentless accumulation of data has left companies overwhelmed by vast quantities of information they struggle to interpret. As a result, the once golden promises are frequently left scattered and forgotten across disjointed workflows.
Merely viewing information as power is no longer sufficient for businesses. To unlock the true potential of this data deluge, it needs to be shared, understood, and embraced across all levels of the organisation, from the boardroom to the shop floor. In other words, a cultural shift towards agile, yet cohesive, data strategies is crucial for companies to become the data-driven enterprises they aspire to be.
Data interrupted: the limits of a disjointed data culture
Without a clear culture defining how data should be handled, standardised processes and ownership of data interpretation can never truly emerge. This can lead to data inconsistencies and inaccuracies that become obstacles to daily operations and business outcomes.
A seamless data strategy begins at the top. While most C-level executives agree that data use is a top priority, stakeholders bring their own priorities and perspectives to data transformation initiatives. For example, a chief data officer’s (CDO’s) “data strategy” is likely to focus on integrating data sets of varying formats, while a chief marketing officer (CMO) might focus more on the innovations resulting from data use. These differing viewpoints rarely align with wider business goals, leading to fragmented strategies that struggle to support basic functions, let alone drive profits at the right time and place.
This disjointed strategy often leads to superficial and short-term investments in educating and upskilling employees on data skills, without creating lasting competence. Companies today cannot afford to believe that a single team or a one-time expenditure on new software can bring about significant change within its data network.
As data continues to evolve rapidly, it is logical that both technology and people’s skillsets should be upgraded in tandem. The lack of ongoing investment in digital literacy, for both technical staff and other employees, has made the task of streamlining data even more challenging, adding to the overwhelming amount of unused data.
Ultimately, teams are often pressured to deliver results under patchwork data strategies and are beginning to lose focus, goodwill, and trust in data initiatives. These issues highlight the significant influence that company culture has on paving the way to data-driven business outcomes.
Paving roadmaps to a strong data culture
Shifting culture is never easy. It requires stakeholders and employees to reach a collective understanding of priorities and implement changes to everyday workflows and communication channels. However, the end results make the investment worthwhile. The top three recommendations for businesses aiming to foster a strong data culture are:
- Starting anew: setting a new rule book for data strategies
Cross-functional teams and stakeholders need to align on a new perspective and use of data. This can begin with CDOs participating in discussions with their C-level peers where business goals are discussed. This allows them to ensure the organisation’s data strategy aligns with these goals, including KPIs to measure progress, and to communicate more ambitious steps forward. In addition to aligning your data strategy with your business goals, crucial changes include reformulating and sharing data governance policies that include accountability and ownership guidelines. Standardised regulations are also essential to ensure the production of “good data” and the effectiveness of its products, such as new AI models that depend on the quality of their data inputs. This move will help to establish the necessary foundation to scale the new rule book for data management, while simultaneously removing silos across all functions.
- Keep the data conversations going
A comprehensive and cohesive data culture cannot rely on a one-off change initiated from the top. To ensure its continued relevance, businesses need to constantly reflect, review, and update data strategies and processes according to evolving situations, goals, and KPIs. For example, even after data governance policies have been established, regular processes for re-evaluation must be put in place to ensure that legacy systems or ways of working can be updated for better outcomes, when applicable. Consistent communication with and feedback from teams, especially those who need to access and analyse data the most, would be particularly beneficial in refining these processes.
- Making data accessible and understandable to all
A true data culture cannot rest with a single team. Everyone needs to have a stake in the game, especially given that quality data mining, storage, and processes require everyone to trust the standardised procedures set in place. This means committing to ongoing investments in people and upskilling, whether that involves expanding teams or scaling data literacy programmes across all levels. Ultimately, we need the understanding of data, and the importance of its treatment, to be embedded within the wider ecosystem. This responsibility could also be shared with trusted third-party partners, who can offer the expertise to make these programmes more accessible, in addition to providing recommendations on the right technology and methodology needed to bolster innovation in and new knowledge about data.
In many ways, data is the true enabler of new technologies, including AI and all the possibilities it offers. The insights we hope to uncover to drive profits and efficiencies are only as strong as the quality of inputs being fed to the machines. This can only be achieved when a company culture empowers its employees to become citizen data scientists. Through comprehensive and consistent strategies, training, and access to user-friendly tools, data-driven decisions can become the norm for any business.