Five Ways to Become a Data-Driven Organisation
These last several years, many organizations have promoted the importance of being data-driven as a critical strategic goal in their digital banking solutions. While there has been an increase in investment in the goal, progress has been gradual. According to a 2021 survey, 92 percent of companies are increasing the rate at which they spend on Big Data and artificial intelligence. Yet, just 31 percent consider themselves to be data-driven. In 2013, it was 37%, indicating that data-driven behavior is on the wane, despite the increase in investment in data analytics. The evidence implies that technology investments are not generating the intended returns; thus, what should be done?
Being data-driven implies making judgments based on factual facts rather than on intuition. According to the Economist’s Intelligence Unit, which conducted a survey of 174 company leaders (51 percent from the C-suite), just 10 percent of those surveyed would follow the course of action recommended by the data if the evidence opposed their gut sense. This implies pretty clearly that the problem seems to be cultural and behavioral in nature rather than technical, as previously stated.
What cultural and behavioral adjustments can organizations undertake in order to become more data-driven in light of these troubling statistics?
1. Serve the data where it is needed
Someone makes a choice to conduct a data request in order to inform a decision much too seldom. Big decisions are often made in the absence of data for various reasons, including a worry that the data would not back their gut instinct, difficulty in obtaining the data, or just a lack of knowledge that relevant data exists.
Data-driven decision-making is more likely to occur in organizations that provide data to decision-makers on a proactive basis. This begins with an evaluation of which data would be most valuable to a person and when it would be most appropriate to provide it. Devote time to identifying the most essential data for someone’s function and collaborating with them on how and when to offer it.
This goes well beyond the usual cyclical Management Information (MI) that is delivered to stakeholders on a regular basis to provide them with actionable information. It should be about ensuring that the appropriate data, in the proper format, is accessible on-demand for decision-makers.
2. Embrace and address bias
While not always intentional, evidence is often exploited to promote a pre-existing idea rather than being regarded in its whole as an objective fact. This confirmation bias must be recognized, thoroughly understood, and intentionally handled in order to make better, more informed judgments based on the results of the analysis.
Through the use of fact-based dialogues to uncover bias that has gone unrecognized or the evaluation of data to verify it is accurate, the company may have a better understanding of what the data is genuinely telling them, allowing them to make adjustments that might have gone undiscovered otherwise.
3. Collect data to answer better questions
Most often, organizations feel that increasing their capacity to analyze data will have the most significant impact on their ability to make better business decisions. A lack of appropriate skills and equipment may result in a situation where statistical procedures are not implemented with the requisite rigor to yield reliable analytical results.
The recruiting policy of a company may be an excellent place to start for certain businesses. Are they seeking individuals who have the analytical skillsets necessary for Oracle digital banking experience, or are they looking for people who have been successful in their previous positions? There are a variety of approaches that may be used to recruit excellent analytical talents into a team. Still, the first step is to make it a mandatory prerequisite. Companies that increase the amount of data scientists, data analysts, statisticians, and all-around mathematical whizzes in their workforce will reap significant benefits.
4. Get the best analytics you can afford
Most often, organizations feel that increasing their capacity to analyze data will have the greatest impact on their ability to make better business decisions. It is possible that a lack of appropriate skills and equipment will result in a situation in which statistical procedures are not implemented with the requisite rigor to yield reliable analytical results.
The recruiting policy of a company may be a good place to start for certain businesses. Are they seeking individuals who have the analytical skillsets necessary for tomorrow’s decision-making, or are they looking for people who have been successful in their previous positions? There are a variety of approaches that may be used to recruit excellent analytical talents into a team. Still, the first step is to make it a mandatory prerequisite. Companies that increase the amount of data scientists, data analysts, statisticians, and all-around mathematical whizzes in their workforce will reap significant benefits.
5. Reward those that trust the data
When one’s ideas are firmly held, it is natural for one to resort to argumentation in order to invalidate facts that contradict those beliefs. It is critical to ensure that judgments are made on the basis of actual evidence rather than emotional impressions. Because 90 percent of us will not take data at face value if it is in conflict with our gut instincts, we must put systems in place to prevent us from dismissing the data when it has the potential to assist us in making better judgments.
When decision-makers can demonstrate that they employed hard evidence and comprehensive research to achieve a conclusion, they should be recognized for their efforts. Taking the time to revisit judgments and consider how the data supported or contradicted the decision should result in increased confidence in the data. We will be less reliant on subjective views as we place more faith in the statistics.
Conclusion:
That 62% of companies have received demonstrable outcomes from their investments in OBDX, and AI is fantastic, despite their difficulties in becoming data-driven. Because most of the problems stem from people and processes rather than technology, this is a positive sign. Suppose the emphasis of Digital Transformation programs is on removing cultural impediments rather than adding new technology. In that case, progress may be made more quickly.
