The digital transformation is now in full swing at most companies. As more processes become digitized, more companies see the opportunity for AI-driven efficiency gains. However, greater adoption of AI still encounters stumbling blocks, often present in the nature of an organization’s workflow.

Despite the fact that automation and digitization are commonplace in all sectors, 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 refocus on their customers and uses data to justify every decision.

Companies can’t install data-driven cultures overnight, but now is the best time to start. As the use of AI increases, so does the number of existing data volumes, making big data analytics more important than ever. As such, organizations need to move away from a “gut feeling” approach towards decision making in favor of a data-oriented decision tree.

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1. Prioritize important business functions

The adoption of AI puts the spotlight on data quality. Companies have been collecting customer data for a long time, paying little attention to accuracy and integrity. AI algorithms trained on poor quality datasets result in suboptimal business outcomes.

A research piece from The Markup in 2021 detailed cases of mortgage insurance algorithms much more likely to reject minority loan applicants due to historical biases in training data. Sloppy and unverified data creates such results and perpetuates a negative brand perception, something that financial companies have less need for.

Investigating data collection sources is the first step to uncovering potential land mines, as in the example above. Companies need to review the data they collect and also the data they discard. Often teams discard data that is irrelevant to their processes, but those datasets can come in handy in other workflows.

More importantly, data classified as “noise” often contains valuable clues that provide context to AI algorithms. However, not all noise is useful. Data-driven companies have a broad view of which variables play an important role throughout their organization and classify data accordingly.

Thus, data collection and analysis is a centralized function. While data scientists may be embedded in individual units, a central data team should define schedules and management practices. Without this centralized vision, organizations will lack vision, leading to flawed results that are detrimental to their business.

If an organization takes the first steps to untangle its data, it is best to start with the most important business functions. The infrastructure is also often in need of an overhaul. Linking technology investments to high-level business objectives secures buy-in and pushes companies onto the data-driven path faster.

Ultimately, technology like AI is a tool, not a solution. It is only as good as the input it receives.

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2. Carry out pilot projects with demonstrable results

Despite the massive attention AI and ML algorithms have received in recent years, only a surprisingly small number of companies trust them. A 2021 study by New Vantage Partners stressed that only 12.1% of companies surveyed implemented AI in widespread production. The rest were either disillusioned with AI (thanks to flawed results) or were hesitant to expand its use.

Transformational change takes a long time in business. However, technology has distorted our understanding of what constitutes “long”. As innovation has grown rapidly over the past decade, companies cannot afford to sit on the sidelines and ignore the potential that AI and a data-driven attitude have for their business.

Getting buy-in from executives is a critical hurdle to overcome. While most executives can’t claim to be ignorant of AI’s potential, securing their approval depends on convincing demonstrable business results. The key in these situations is to demonstrate quantifiable numbers that justify investments.

Most AI pilot projects focus on disaster prevention first and goal achievement second. For example, an image recognition engine should prevent people and products from being misclassified in situations that could lead to negative brand advertising. The business purpose is neglected in this case.

As a result, AI initiatives are viewed by top executives as harm avoidance exercises. To successfully transition to a data-driven environment, AI pilots must be linked to ROI metrics. In addition, these initiatives must provide 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 across the organization. Centralized data science teams have their place. However, this centralization does not mean that organizations have to outsource data analysis to a few teams.

Integrated analytics are the way forward. By embedding analytics into any business app, companies can extract insights from every employee, increasing ROI. While some of these insights can put teams on the wrong track due to poor data analysis skills on the part of the employee, the long-term benefits are huge.

Companies can protect themselves against incorrect conclusions from data analysis by embedding data scientists in every team. This personnel can validate analysis conclusions and prevent flawed results. You can never predict where great insights will come from, and data democratization is the way forward.

This approach also reorients 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 alignment with the customer.

Data-driven for long-term results

“Data-driven risk” is becoming a buzzword in most organizations due to a lack of planning. As organizations adopt AI and other advanced technologies, a lack of data-driven processes will fail them and cause high failure rates. To succeed, companies must now refocus their approach to data.