What businesses still don't know about data, and why it hurts them | 7wData

What businesses still don't know about data, and why it hurts them | 7wData

Modern tools have democratized access to data analysis. This gives a new class of businesses potential insight into their operations, of their markets, and generally more food for thought around company growth. AI and ML technology is more widely available, too, allowing businesses to optimize and automate internal and customer-facing processes, using data to train these tools for better performance.

For many small and mid-sized businesses, however, broad, usable datasets, from which positive decisions are based, remain too costly to purchase or too vast to generate from their existing customer bases.

While the tools exist, data continues to be under-used or difficult to access for most businesses. According to a survey conducted by Microstrategy, while 80% of managers have access to data, only half of frontline workers, the ones who can learn from it most, do not. In addition, 90% of employees rely on others to make data-driven decisions for them. Gartner reports that, through 2022, only 20% of analytics-based initiatives will even prove successful.

Businesses can and want to do more with data, which is supported by the proliferation and adoption of business intelligence tools. Resource-light companies, who aren't able to hire outside consultants, can compete upmarket by leveraging data, even if it's not their own, while large organizations can get more out of the data they have. Here are just a few strategies businesses can try, regardless of company size.

The capabilities of AI/ML are only as good as how they develop to fit the distinct needs of an Organization. The more data fed to the system, the greater its capabilities for performing more complex tasks.

Issues arise when data is sparse or sporadic. Newer companies, for example, likely have not yet collected enough data to influence learning in a meaningful way, and more complicated tasks require specific data that might not be available.

Organizations operating at a nascent level of data collection can consider buying prepackaged, pre-organized datasets, known as "synthetic data," to make initial headway on their AI initiatives. Using the AI technology of Generative Adversarial Networks (GAN), companies selling synthetic data produce realistic data points, such as financial figures or customer profiles, to feed into a system and start constructing models for analysis.

Open data is another way businesses can overcome the expense and size requirements of sourcing large, quality datasets. Vast public data sources are readily available and can be used to fine-tune analytics models and automate business processes for free. The US government, for example, offers a library of standardized, machine-readable data formats online. Zoho uses open datasets to model its AI and ML engines because the practice doesn't jeopardize customer privacy.

Yes, synthetic data will never replace the real thing, nor should it, but it offers a solution for companies to quickstart their internal processes and avoid breaching the privacy of their customers—particularly relevant within the medical field. It also provides companies with a way to test how the system will respond to critical errors and adjust algorithms as needed.

Synthetic, organic, open-source or otherwise, data alone is not what informs smart business decisions; it has to be analyzed and interpreted so the rest of the Organization can improve processes.

What was once only the domain of seasoned data scientists has been democratized across all sorts of employees thanks, in part, to low code application development. This technology comes standard with many CX and EX platforms and allows even the least tech savvy folks to build custom apps regardless of coding experience. Employees with minimal training can utilize an intuitive, drag-and-drop interface to develop functions that remain accessible to everyone at the organization.

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