Approximately 2.5 quintillion bytes of data are created each day, according to LinkedIn. When they’re organized, optimized and visualized, data can create countless opportunities. A murmuration of birds is an excellent illustration of the power and opportunity of organized data.
The sight of millions of birds swooping, diving, and whirling in motion to create beautiful, swirling patterns is nothing short of awe-inspiring. Similarly, data experts help to wrangle the “super swarm” of data. We look beyond the bits and bytes, see the larger picture of datasets, and identify patterns to assist with business decisions.
Another example is Hershey’s harnessing predictive analytics to foresee consumers migrating to backyard activities during the 2020 pandemic. The confectioner ramped up production of their iconic chocolate bars and promoted the fun and comfort of making s’mores around a campfire. The smart use of data analytics allowed Hershey’s to post a $70 million boost in sales.
But there’s no denying, more data leads to more problems in data quality, governance, integrity and more.
How did we get here? The demand for more data and advancements in artificial intelligence and machine learning to speed business processes have underscored the shortage in trained and experienced data experts.
Enter low-code or no-code technologies, which are allowing people with little or no experience working with AI to use artificial intelligence and machine learning applications.
The shift to more data democratization and codeless data structures and algorithms creates even greater reliance on data engineers, data scientists and data managers. These professionals will need to make sense of the chaos often created by non-data experts who are working on complex datasets.
To make matters more urgent, a recently released report from The Harris Poll and Appen says 88% of organizations are using external AI data providers to implement solutions. Alarmingly, “The State of AI and Machine Learning 2022” reveals 78% of companies are getting less than 80% of accuracy in their AI data models.
Those are astounding numbers that should make us pause and double down on data integrity.
Think about how much of your business relies on artificial intelligence and machine learning capabilities to make confident decisions.
Data Quality and Governance
If your organization is employing a data solutions provider, make certain they maintain the highest standards. When you provide your first-party data, these professionals should be able to update records, remove duplicates and ensure your campaigns begin with quality information.
Your data consultant should collaborate with you on creating data policies for gathering, storing, processing and disposing of the information. As part of data governance, they will ensure the data is secure, private, accurate and usable. They will support compliance with industry standards, as well as rules and regulations set by associations, government agencies and other stakeholders.
Data Integrity and Ethics
Increased reliance on data, AI and machine learning invariably requires access to personal data. Some of the most meaningful and useful ways to activate AI will require sensitive data such as financial information or health records.
In addition to enhancing the quality and integrity of data, there’s growing focus on overcoming the so-called “black box” problem of AI. For most AI-based tools, we see only the input and output with no visibility of the processes, algorithms and innerworkings of the programs. Moving forward, those responsible for AI systems will explain what information was used to arrive at conclusions and describe how decisions are made.
This includes the role of ethics in eliminating unfairness and bias from automated decision-making. Using biased data leads to unfair treatment and discrimination.
Make certain you’re working with a data specialist who has access to consumer data sources and first-party data collection that is compliant with HIPAA and other rules and regulations.
Leading data consultants will deliver interactive dashboards that combine visuals with real-time data. They will have experience in data visualization and presenting data in graph or pictorial format.
Your audience analytics partner will help you build scalable machine learning pipelines using optimization methods to improve dataset performance. They will use advanced technologies and bring expertise in predictive analytics and prescriptive modeling, providing intelligent insight into what motivates your target audiences.
Working together, you’ll save marketing costs, boost sales and uncover opportunities.
About the author: William Skelly is CEO of Causeway Solutions, a provider of acquisition analytics and innovative data services. Causeway Solutions empowers clients to make smart, timely, data-driven decisions through real-time consumer insights to better reach target audiences.
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