There was a time when computer access was limited and most organizations had only a few computers in each office. Decades later, digital literacy is now a prerequisite for nearly every job.
As we move slowly but inexorably toward a future where data literacy requires know-how, how can organizations build a strong data culture that will stand the test of time? Here is:
If you think the data is wrong,
Data-driven decision-making is a commendable goal that can provide the insight and clarity organizations need to innovate faster than their competitors. But should all decisions be based on data? What happens when data isn’t good and enthusiastic employees are trying to extract insights from it when they can’t find anything?
Speaker at a recent event hosted by CDOTrends We talked about this recently by warning against “forcing” data to fit a particular narrative. After all, data can do little to say anything to protect against misinterpretation. But how can an organization know when this has happened?
According to this speaker, data analysts and CDOs should act on their “gut feeling” when the data looks wrong. In his view, if it seems wrong for employees with extensive experience and deep expertise in the business, it’s probably time to pay more attention.
The computer engineering concept of “garbage in, garbage out” or GIGO is probably a good analogy here. GIGO is the idea that the quality of the output is determined by the quality of the input, and that if you feed wrong data into a software program you will simply get the wrong output. In a nutshell, give yourself the flexibility to ignore the data.
Ditch the top-down or bottom-up approach
Should cultural change be driven from the top down or grassroots? According to Keith Ferrazzi, founder of a global consulting firm, successful cultural change requires both a push and a pull. Is required.
After all, centralized top-down models typically slow business units to adopt new data initiatives, and can weaken executive sponsorship before things get off the ground. Bottom-up or decentralized models, on the other hand, can result in success stories that are dismissed as irrelevant or irrelevant to other parts of the business.
Instead, Ferrazzi advocates both a push-and-pull strategy, with a core team of data scientists and experts acting as internal consultants. However, rather than putting the responsibility of demonstrating financial results on CDOs, he proposes to give the responsibility of demonstrating financial results to business units using data analytics.
So how can an organization develop a culture that supports this push-and-pull strategy? I’m proposing to put it in the analysis group. Organizations should also work to improve data literacy and facilitate push-pull collaboration by rotating data support team assignments.
Finally, to demonstrate the value delivered and ensure accountability, an advisory board should be established to update the CEO and board on various data-centric initiatives.
Focus on making money with your data
Finally, data analysis should show a clear return on investment. This means companies must find new ways to connect and use their data with the right marketing platforms and decision-making tools. Only then can the data be leveraged for new marketing campaigns to drive new sales or other tangible business outcomes.
Also, as mentioned earlier, organizations need to start small to do great things with their data. But don’t just stay small, says Jeff Beck, Leaf Home’s chief growth officer.in the work posted smart businesshe said, rolling out the changes to a wider audience will help companies learn faster and “not spend time in an endless cycle of misleading results.”
Scaling requires a clear and structured system for collecting feedback so that the company can learn and adapt at a faster pace. This means identifying KPIs and long-term goals and setting them early to avoid being left behind when the data starts flooding in.
Only when companies make a concerted effort to apply data to technology can they drive better marketing results and positively impact bottom line.
Paul Mah is the editor of DSAITrends. A former sysadmin, programmer and his IT lecturer, he enjoys writing both code and prose.you can contact him [email protected].
Image credit: iStockphoto/ChakisAtelier
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