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.
Synthetic and open data sources
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.
Customized data sorting
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.
While low-code enables a streamlined process for sorting data, the process need not be manual. The latest developments in AI/ML can analyze data automatically and transform it into visualizations that can be easily processed and shared across the organization to influence business decisions. Less time producing accurate, actionable results means more time for determining organizational changes to correct issues identified by the data.
Zoho's customer PREMO Group, a Spanish company that produces electronic parts for IoT and VR devices (among others), discovered that automatically combining data from multiple sources pays dividends even beyond savings in required manpower. Before contacting Zoho, the company had adopted a scattershot approach to data collection. Salesforce, SAP, Zoho Projects, and Zoho Creator each contained a portion of the relevant data, and at a certain point, manual consolidation and data preparation were simply not possible.
The solution entailed intertwining these disparate systems into Zoho Analytics, where the data could be standardized and automatically processed into shareable bites, including visualizations. This empowered high-level employees to make informed decisions about sales, operations, and customer experience (CX) without worrying they were each looking at, or misinterpreting, different pieces of data.
Building expertise in-house
It's a great time to be a data scientist. Data collection can happen at any phase of a company's CX journey, and those who possess the skills to turn data into immediately actionable insights are in high demand.
With that high demand, coupled with the low supply of this limited skillset, comes the ability for data scientists to charge hefty sums for their expertise. Larger companies can afford to employ an entire department of these folks, while others might not have the funds for even a single one. At the same time, industries of all stripes are advancing to the point where data science is a mandate, not a nice-to-have.
Rather than attempt to compete with major industry players for talent, many companies are opting to upskill current employees with the know-how to make sense of their data.
It starts with continuing education courses in basic data literacy. By committing to training folks in data science, companies open themselves to all types of new hires, not just the in-demand people with highly specialized backgrounds. For example, Zoho customer CIMCO Refrigeration, a 109-year-old, Toronto-based division of Toromont, was facing a labor shortage a few years ago and wanted to take a two-pronged, retention-based approach to fixing it: relieve current employees of menial tasks and provide them with skills training to improve in their career.
The former was achieved via low-code, allowing specific employees to build precisely the sorts of apps that would utilize automation to accomplish tedious tasks like updating client records. The initiative resulted in considerable bottom-line growth and productivity gains, but had the added benefit of turning its employees on to the power of data and customization. One employee, in fact, became so motivated by what she learned that she redirected her career to become CIMCO's in-house data scientist.
Adopting a productive data strategy requires the marriage of usable datasets, powerful business intelligence tools, and knowledgeable people who can turn information into action. It is for this reason that successfully leveraging data to drive growth had, until recently, been the purview of large, resource-heavy corporations. Now things are changing, allowing the Davids of the business world to compete with the Goliaths, making smarter decisions from more accessible data using tools that abstract much of the complexity associated with the process.
As technology improves and certain businesses focus on building knowledge and expertise around data, expect increased adoption of BI solutions from smaller organizations and the marked improvement in the accuracy and efficacy of data-driven business strategies across the board.