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Digital transformation is well underway at most companies these days. As more processes become digitized, more companies recognize the opportunities for Artificial Intelligence-driven efficiency gains. However, greater AI adoption still faces stumbling blocks, often present in the nature of an organization’s workflow.
Despite automation and digitization taking hold across industries, most companies lack a data-driven culture. A data-driven culture is much more than looking at trends on a BI platform and running scenarios — it’s a culture that helps companies reorient themselves toward their customers and uses data to justify every decision.
Companies cannot install data-driven cultures overnight, but now is the best time to begin. As AI usage increases, so too does the number of data volumes that exist, which has led big data analytics to become more essential than ever. As such, organizations must move away from a “gut feel” approach toward decision-making in favor of a data-oriented decision structure.
Related: Building Data Driven Organizations And Taking Hiring To the Next Level
1. Prioritize important business functions
AI adoption is shining a spotlight on data quality. Companies have collected customer data for a long time, with few paying attention to accuracy and integrity. AI algorithms trained on poor-quality datasets result in less-than-optimal business outcomes.
An investigative piece by The Markup in 2021 detailed instances of mortgage underwriting algorithms rejecting minority loan applicants far more often due to historical biases in training data. Shoddily gathered and unverified data creates such outcomes and perpetuates negative brand perception, something financial companies need less of.
Examining data collection sources is the first step towards uncovering potential landmines, such as the example above. Companies must review the data they’re gathering and also the data they’re discarding. Often, teams discard data irrelevant to their processes, but those datasets might come in handy in other workflows.
More importantly, data classified as “noise” often contain valuable clues that offer AI algorithms context. Not all noise is useful, however. Data-driven companies have a broad view of which variables play an important role throughout their organization and classify data accordingly.
Data gathering and analysis is thus a centralized function. While individual units might have data scientists embedded in them, a central data team must define schemas and governance practices. Without this centralized view, organizations will lack vision, leading to flawed outcomes that damage their businesses.
If an organization is taking its first steps to disentangle its data, the best place to begin is in the most important business functions. Often, infrastructure will need an overhaul as well. Linking technology investments to high-level business goals will secure buy-in and push companies along the data-driven path faster.
Ultimately, technology such as AI is a tool, not a solution. It is only as good as the input it receives.
Related: 9 Cool Ways You Can Use Data-Driven Marketing to Gain Customers
2. Execute pilot projects with demonstrable outcomes
Despite the massive attention AI and ML algorithms have received in recent years, only a surprisingly small number of companies trust them. A 2021 survey by New Vantage Partners highlighted that only 12.1% of firms surveyed implemented AI in widespread production. The rest were either disillusioned by AI (thanks to faulty outcomes) or were wary of expanding its use.
Transformational change takes a long time in business. However, technology has warped our understanding of what “long” is. As innovation has grown rapidly over the previous decade, companies cannot afford to sit on the sidelines and ignore the potential that AI and a data-driven posture have for their businesses.
Securing buy-in from executives is a crucial hurdle to overcome. While most executives cannot claim ignorance of AI’s potential, securing their approval hinges on convincing them of demonstrable business results. The key in these situations is to demonstrate quantifiable numbers that justify investments.
Most AI pilot projects focus on avoiding disaster first and achieving goals second. For instance, an image recognition engine must avoid misclassifying people and products in situations that could lead to negative brand publicity. The business goal, in this instance, is neglected.
As a result, AI initiatives are viewed by top executives as exercises in damage avoidance. To successfully transition to a data-driven environment, AI pilots must be tied to ROI metrics. Furthermore, these initiatives must demonstrate stable returns over time. Only then can companies steadily scale their efforts and justify investments.
3. Democratize data
One of the easiest ways to achieve a data-driven mindset is to democratize data throughout the organization. Centralized data science teams have their place. However, this centralization doesn’t mean organizations should silo data analysis to a few teams.
Embedded analytics is the way forward. Embedding analytics into every enterprise app allows companies to draw insights from every employee, boosting ROI. While some of these insights might lead teams down the wrong routes due to poor data analysis skills on the employee’s part, the long-term benefits are immense.
Companies can guard against faulty data analysis conclusions by embedding data scientists in every team. This personnel can validate analysis conclusions and prevent flawed outcomes. One can never predict where great insights come from, and data democratization is the way forward.
This approach also re-orients every team in the organization towards the customer. Teams can view customer-related data, analyze trends, measure their contributions, and model real-time decision impact. The result is better products and customer alignment.
Data-driven for long-term results
“Data-driven risks” is becoming a buzzword in most organizations due to a lack of planning. As organizations adopt AI and other sophisticated technologies, a lack of data-driven processes will let them down and cause high failure rates. To succeed, companies must reorient their approach toward data right now.
