Digital transformation may be the most used term in enterprise since the pandemic hit and changed the way we do business. While companies took on rapid digitalisation to fill the gaps brought on by social distancing and remote working, the transformation has thrown up new challenges.
Tech talent that deals with IoT, machine learning (ML) and Artificial Intelligence (AI) are in short supply. According to a Deloitte report, in 2020, the number of jobs posted by tech companies for analysis skills — including ML, data science, data engineering, and visualisation — surpassed traditional skills such as engineering, customer support, marketing and PR, and administration. Moreover, the shift to online and connected systems means companies are now generating tons of data. But are they able to use it effectively?
A data-driven culture permeates designations and departments within an organisation. It puts decisions based on data above decisions based on gut feelings, which is crucial since the pandemic changed consumer behaviour and market trends. This is just the tip of the iceberg. Markets are slowly recovering, most companies are still working remotely and the scale of the economic impact is yet to unravel.
In such a situation, it’s less of a risk to assume that everything has changed. Let data inform decisions. Can organisations imbibe a data culture that upskills non-technical roles to leverage data in their daily lives? Can leaders encourage technical talent to build self-service tools and training that highlight how insights can be a precious resource?
Here is how to start:
Start At The Top
Establishing a data-driven culture is a matter of influencing mindset and behaviour. Strict enforcement without training will only lead to disillusionment. CDOs may need to lead the charge with getting C-suite to get onboard with not just investment and infrastructure but also showcasing a top-down example. There are three areas of influence to tackle: business value, cultural change impact and ethical implications. CEOs need to buy-in to the idea that data can drive better decision making and reduce risk. They need to showcase trust in the authenticity of their data. A prerequisite is setting up an infrastructure that prioritises data validation and quality enforcement.
Don’t Drown In The Data Lake
The data lake concept emerged more than a decade ago as a solution to challenges in traditional data management solutions like databases and data warehouses. A data lake is a centralised secure repository that allows enterprises to store, govern, discover and share structured and unstructured data at scale. Organisations need to build their data lakes in a way that breaks down silos and prepares for present and future needs.
Even leading companies like Amazon deal with the complexity of big data and the subsequent challenges to ensure snags aren’t undercutting the value their data lakes are supposed to deliver. Common challenges include security, governance, compliance, accessibility and corruption or duplication of data.
Also Read: Marketing With Data Lakes and Data Warehouses
Let Data Drive HR Decisions
According to a Harvard Business Review report on leadership transitions, 40 per cent of internal job moves made by people identified by their companies as “high potentials” ended in failure. It found that the cohort of star performers often find themselves disengaged as most company assessment systems pool them along with the average employees. This segment of employees, seemingly, is unable to stand out and showcase the impact they made through their work due to interpersonal challenges that get in the way.
Using data as a metric to track employee performance may come across as intrusive and could garner some backlash. Instead, try to encourage employees to proactively use data to validate their success in projects and campaigns. It can encourage a culture of transparency and meritocracy but needs managers to trust the data.
Democratise Data Tools
It’s human nature to resist change. Building a data-driven culture is a continuous and ongoing process. Deploying self-service BI tools may not be the only answer but they offer a step in the right direction. It makes data seem less intimidating and acts as an initiation to break barriers to change.
Next, showcase use cases that add data to processes. For example, DHL Temperature Management Solutions, a division of the global logistics carrier, collects and tracks temperature data throughout its freight fleet to ensure that goods such as pharmaceutical and biological items stay within a safe temperature range. It’s a success story that has saved revenue and delighted customers, not to mention delivered life-saving drugs to patients on time.
The biggest problem with data is limiting it to the hands of the analyst team where it remains a magical resource that employees along the value-chain don’t fully understand. Democratising data will not only encourage usage but also establish best practices early on.
Also Read: Data Enrichment, Transform Your Unstructured Data into an Asset
Discuss Risks And Red Flags
Effective data governance promotes an all-inclusive culture by creating a community approach to data. It tears down the silos between data owners and data consumers and gives a more holistic view on how campaigns or projects are performing. Put regulations in place that are shared organisation-wide to encourage cross-departmental information exchanges without risk.
This approach makes transparent how the company uses personal or protected data which creates a ripple effect with consumers feeling safer. It reduces data inaccuracies and encourages compliance at every level.
Conclusion
Culture can be a compounding problem or a compounding solution. When an organisation’s data mission is detached from business strategy and core operations, it should come as no surprise that the results of analytics initiatives may fail to meet expectations. But when excitement about data analytics infuses the entire organisation, it becomes a source of energy and momentum.
