Why a Data-Driven Culture Matters and How To Build One
- By Paul Mah
- May 29, 2022
The digital revolution has given us a new currency called data and opened new vistas of opportunity. For savvy businesses, data is a way to understand the market more intimately, take better care of customers, further tighten efficiency in operations or production, and adapt faster to changing market conditions than competitors.
This explains why organizations are investing more than before to leverage the power of their data. According to an Ernst & Young study, 93% of companies indicated that they plan to continue to increase investments in data and analytics.
But is spending more money on new data-centric tools and capabilities enough to get ahead?
Why data culture matters
Businesses traditionally operated with top leaders ruminating on and making key decisions, which are then communicated across the organization and acted on by the rank and file. While this has worked in the past, the growing pace of work today means that decisions often arrive too late, or without the necessary context and details to let frontline workers make independent decisions.
Moreover, the exponential increase in the quantity and recency of data has also opened the door to new possibilities with data sets that are now more comprehensive and granular than before, or which arrives quickly enough to support real-time decisions. A top-down approach simply doesn’t offer the responsiveness required to take full advantage of data.
To fully harness the power of data and deliver the quality insights needed for data-driven decisions, businesses must establish a strong data culture where employees are encouraged and empowered to act on data at every level.
From promptly escalating an irate customer with a long history of cordial communication to agreeing to a refund for a customer who hardly ever asks for one, the judicious use of data can shape the customer journey in a positive direction and increase customer retention.
Yet for this to work, leaders must be seen as adherents of data-driven decisions, avoiding “gut decisions” when deciding on new services to launch or products to make. The onus is also on them to set up a centralized “source of truth” from which performance is measured.
Establishing a data culture
Of course, a data-centric culture is not something that can be imposed forcibly or cultivated only within certain departments in the organization. Certainly, culture is not something that can be established by hiring a handful of experts or even by implementing a new analytics solution. For a data-driven culture to materialize, employees must genuinely believe in it.
On this front, an observation by a McKinsey blog still rings true today, “You develop a data culture by moving beyond specialists and skunkworks, with the goal of achieving deep business engagement, creating employee pull, and cultivating a sense of purpose, so that data can support your operations instead of the other way around.”
And maximizing the effect of data requires putting the right data into the hands of workers. Achieving this typically necessitates breaking down existing data silos and facilitating the smooth flow of data to those who need it through data democratization – alongside the appropriate governance and privacy controls.
In my many conversations with business leaders and data executives, one recurring suggestion that keeps coming up would undoubtedly be the appointing of data champions as advocates for a data-driven approach. But should these champions be a data expert or an experienced employee who is well-versed in business operations?
This chicken and egg problem keeps cropping at various CDOTrends events, mentioned by speakers and participants alike. Some think training hired data experts in the business works best, while others called for employees familiar with the intricacies of the organization to be trained in data skills.
If the nature of the business is complex, however, then training existing employees with aptitude in data or analytics with the requisite technical know-how would probably work better.
The fuss over the cloud
Finally, a perennial question with data initiatives would be whether to go cloud or not. To this, I’ll say that while it is possible to establish a data-driven organization entirely on-premises, the cloud can greatly amplify the speed of rolling out data-centric deployments, as well as the ease of manipulating data.
There is why cloud-based data platforms such as Snowflake have thrived, while analytics and visualization platforms such as Informatica and Tableau have in recent years focused heavily on their cloud capabilities.
For one, the cloud breaks down data barriers to data and analytics by removing barriers to access and trust. Moreover, the inherent scalability of cloud computing makes sharing data easier and is particularly useful for data scientists digging into large datasets.
“The popularization of cloud computing and SaaS software offerings has made it easy to improve data integration capabilities without deploying new hardware… Having your data available on-demand is crucial for digital transformation,” noted Veronica Zhai of Fivetran.
So, go for cloud unless legacy or regulatory considerations render that option out. But if cloud-based data tools are the foundation, then the data culture is surely the amplifier. As observed by the McKinsey report, “[When] excitement about data analytics infuses the entire organization, it becomes a source of energy and momentum. The technology, after all, is amazing. Imagine how far it can go with a culture to match.”
Paul Mah is the editor of DSAITrends. A former system administrator, programmer, and IT lecturer, he enjoys writing both code and prose. You can reach him at [email protected].
Image credit: iStockphoto/Tirachard
Paul Mah
Paul Mah is the editor of DSAITrends, where he report on the latest developments in data science and AI. A former system administrator, programmer, and IT lecturer, he enjoys writing both code and prose.