The majority of companies today are actively pursuing digital transformation. As more processes are automated, more firms are recognising the potential for artificial intelligence-driven productivity increases. One of the challenges to wider AI deployment is typically the structure of an organization’s workflow.
Data-driven cultures are uncommon in the majority of firms, despite the fact that automation and digitization are transforming all industries. A data-driven culture helps firms reorient themselves toward their consumers and supports every decision with data, going well beyond just monitoring trends on a BI platform and running scenarios.
Although data-driven cultures cannot be implemented quickly, now is the ideal time to start. The amount of data volumes is growing in tandem with the use of AI, making big data analytics more crucial than ever. As a result, companies must switch from a “gut feel” to a data-oriented decision structure when making decisions.
1. Set critical business functions as a priority.
Adoption of AI is drawing attention to the quality of data. Customers’ data has been gathered by businesses for a while, but few have paid attention to accuracy and integrity. AI systems that were trained on low-quality datasets produce subpar business results.
A 2021 The Markup investigation documented instances when mortgage underwriting algorithms routinely rejected minority loan applicants because of previous biases in training data. Such results are produced by hastily acquired and unconfirmed data, which financial companies need to avoid at all costs.
The first step in identifying possible landmines, like the one mentioned above, is to look at the data sources used for data collection. Companies must examine both the data they collect and the data they delete. Teams frequently toss out data that is not relevant to their operations, yet those datasets may be useful in other workflows.
More significantly, data that are labelled as “noise” frequently include insightful hints that give AI algorithms context. However, not all noise has a purpose. Data-driven businesses classify their data in accordance with their wide understanding of the variables that are significant throughout their entire enterprise.
Thus, acquiring and analysing data is a centralised task. While data scientists may be integrated into specific units, schemas and governance procedures must be defined by a central data team. Organizations without this concentrated perspective will lack vision, resulting in faulty results that hurt their bottom line.
The most crucial business functions are the ideal place to start when a firm is starting to untangle its data. Infrastructure frequently needs to be updated as well. Investments in technology can be tied to high-level business objectives to gain support and hasten the transition of businesses to a data-driven culture.
In the end, technology like AI is a tool rather than a fix. Its quality depends entirely on the input it gets.
2. Carry out trial projects with tangible results
Despite the considerable attention that AI and ML algorithms have received lately, shockingly few businesses actually trust them. According to a 2021 poll by New Vantage Partners, just 12.1% of the businesses surveyed used AI extensively in production. The remainder were either sceptical about using AI more widely or had lost faith in it as a result of flawed results.
Business transformational change is time-consuming. However, technology has distorted how we perceive what is “long.” Companies cannot afford to remain on the sidelines and overlook the promise that AI and a data-driven approach have for their operations given how quickly innovation has increased over the past 10 years.
A significant obstacle to get past is getting support from executives. Although the majority of executives cannot claim ignorance of AI’s potential, winning their support requires persuading them of tangible commercial benefits. The objective in these circumstances is to present measurable figures that support investments.
The majority of AI pilot initiatives prioritise averting disasters above attaining goals. An image recognition engine, for instance, must prevent misclassifying individuals and objects in circumstances that can result in unfavourable brand exposure. In this case, the corporate goal is disregarded.
Top CEOs therefore see AI programmes as exercises in damage control. AI pilots need to be connected to ROI metrics in order to successfully shift to a data-driven environment. These programmes also need to provide consistent results over time. Companies can then gradually increase their efforts and defend investments.
3. Make data democratic
Democratizing data across the firm is one of the simplest methods to develop a data-driven mindset. There is a place for centralised data science teams. This centralization does not, however, imply that businesses should confine data analysis to a small number of teams.
Analytics that is embedded is the way to go. Companies may gain insights from every employee by integrating analytics into every workplace software, increasing ROI. Due to employees’ lack of data analysis abilities, some of these insights may send teams in the incorrect direction, but the long-term advantages are enormous.
By integrating data scientists into every team, businesses may protect themselves against erroneous data analysis results. This team can verify analysis results and guard against unintended consequences. Great insights can come from everywhere, and democratising data is the way of the future.
This strategy also reorients every team within the company in the direction of the consumer. Teams can monitor data about their customers, examine trends, assess their own contributions, and simulate the effects of decisions made in real time. Better products and customer alignment are the outcomes.
Data-driven for lasting outcomes
Due to a lack of planning, “data-driven risks” are quickly becoming a buzzword in most enterprises. Lack of data-driven processes will let firms down when they implement AI and other advanced technologies, leading to high failure rates. Companies must immediately realign their approach to data if they want to succeed.