For firms eager to embrace AI, it presents a significant opportunity. There’s also a sense of impending doom: 84 percent of CEOs feel they will not meet their growth targets, and three-quarters believe they will go out of business in the next five years unless AI is scaled.
The stakes are high. However, quickly scaling new ideas necessitates a well-designed data strategy, which we define as a design and intent that guides what data is acquired, how it is captured, and what purpose. An AI solution constructed on shaky data and without a sound plan, like a home built on shaky foundations, may provide some immediate value but has no hope of scaling or producing long-term outcomes. Data strategy is just as important as AI in terms of generating value.
Senior leaders should ask themselves a few critical questions when developing a data strategy to ensure AI is successfully deployed and scaled. These fundamental issues will assist executives in developing a data strategy focused on data-driven culture, data quality, and privacy to maximize the value of AI.
What Steps Should We Take To Create A Data-Driven Culture?
The first step in creating a data-driven culture is to get buy-in from the top. Senior leaders must demonstrate what can be accomplished with data and invest in the tools and resources to enable their people to achieve those goals. They must communicate the advantages of working with data and encourage behavioral changes by making data-driven insights more accessible and motivating their use in corporate decisions.
We may start to construct a data-driven culture that welcomes data and analytics organically by designing tools that mirror how people naturally behave and use data daily. However, having the right tools and incentives is only half the battle. Building data literacy requires extensive, continuing training, as well as the hiring of the proper personnel.
What Is The Best Way To Ensure Our Data Is Accurate?
To begin, businesses should set up efficient data-quality processes and frameworks for storage, administration, and transfer. In each domain, designated data owners must function as stewards of data quality.
A corporation can undertake ad hoc, manual spot checks on each data attribute (for example, recency), but this is inefficient and insufficient. There are also solutions, such as Accenture’s Data Veracity Offering, that help evaluate the origin, context, and integrity of data on a regular basis. To identify the quality, risk, and relevance of data, pre-built tools and frameworks are utilized, as well as a data veracity score to measure the data’s quality and track improvements over time.
How Can Data Quality Be Measured And Maintained?
To begin, businesses should set up efficient data-quality processes and frameworks for storage, administration, and transfer. In each domain, designated data owners must function as stewards of data quality.
Corporations can perform manual spot checks on every data attribute on an ad-hoc basis, which is inefficient and insufficient. There are also solutions, such as Accenture’s Data Veracity Offering, that help regularly evaluate data’s origin, context, and integrity. To identify the quality, risk, and relevance of data, pre-built tools and frameworks are utilized, as well as a data veracity score to measure the data’s quality and track improvements over time.
This combination of direct ownership and accountability and tools to verify data integrity can assist in providing data quality and gaining business users’ trust.
How Can We Make Our Data Platforms More Innovative?
While data-driven culture and data quality are crucial factors in developing a successful data strategy, platform innovation ensures that plan’s long-term viability. You can give significantly more OK, near real-time insights across the organization by bringing in new data sources, diversifying underlying technologies, and employing new technical methodologies.
Where might businesses go for new data sources to fuel innovation? We advocate utilizing unstructured data sources that may have previously gone untapped. Manufacturers can, for example, use camera photos and video to check the quality and usefulness of what’s being created on the manufacturing line, as long as they stay within legal bounds.
Alternatively, suppose valuable data sources within a company’s walls have been depleted. In that case, firms can attempt to bring in reputable, third-party data to supplement or fine-tune the insights they already have. They can also try to incorporate data from the edge, whose real-time analysis of sensor data can help enhance operations by, for example, predicting maintenance for a variety of industrial and energy production equipment.
Best Ways To Make Use Of Cloud Services For Our Data Platforms
Many companies are developing or implementing an enterprise-wide “journey to cloud” strategy, which focuses on transferring applications to the cloud to gain flexibility and lower hosting costs. We propose broadening this perspective to include the incremental value derived via a “journey to intelligence.” This necessitates looking beyond the organization’s present AI and analytics applications to examine how to leverage new datasets in novel ways.
Who Is In Charge Of Ethical Data Use?
It’s critical to have clear duties for ethical data use and a dedicated team to establish the appropriate policy, governance, and accountability frameworks throughout the data supply chain.
When considering data ingestion, the process of transporting data from a source to a data platform’s landing and staging area—, you must examine what data is required and the permissions required. You’ll need to look at whether information needs to be anonymized or encrypted for data processing—or modeling data to prepare it for insight development.
Conclusion
Having a data strategy to support your AI strategy is crucial for competitive advantage and will help you get to market faster. Seventy-two percent of Strategic Scalers.
As you embark on a new or revised data strategy, answer these questions to guarantee you’re ready to scale with confidence and speed.
So, if you wish to include data-driven culture in your business, contact the ONPASSIVE team.
Comments
Post a Comment