We live in the age of big data now. And if industry buzz is to be believed, the companies of the future must be capable of harnessing massive data analytics and its famous “V” attributes.
Volume, velocity, variety, and veracity of data are all characteristics of this new world, both demanding and justifying the use of AI to process large, complex datasets. Even small companies may need to cool down their machines using server room environments and maintenance services required by data centers.
But while big data can be beneficial in many use cases, there are also times when it under-delivers on investment. Companies might end up overpaying for analytics that could be replicated based on their competitors’ market research. Or the insights derived might require substantial changes to the business itself that isn’t in line with current strategic thinking.
Before committing entirely to the big data revolution, there’s a fundamental aspect you must get spot-on: how you maximize the small data your organization has at its disposal.
Small data, actionable insights
Our recent experience with the disruption brought about by Covid-19 gave strength to calls for digital transformation across all industries. Businesses that embraced digital channels during the pandemic were able to survive by understanding customers’ evolving needs and being agile enough to respond. Moving forward, such openness to innovation and flexibility towards change will continue to help organizations thrive in the face of shocks and uncertainty.
Although big data is a separate trend from digital transformation, there’s considerable overlap between the two. The abundance of data points you can collect serves as a better guide in your efforts to go digital, finding new opportunities and unlocking efficiencies.
Yet big data is only one of the means towards achieving the goal of digital transformation. Often, companies make the mistake of investing in big data without first demonstrating that they are making the most of the limited information they currently have.
Small data sets don’t require you to commit more resources to crunch the vast amounts of big data, whether this is done in-house or by outsourcing it to partners. They are often derived from simple, cost-effective data mining strategies. And the data obtained tends to be much more accessible, informative, and actionable for your people.
Maximizing what’s there
Even in the world of big data and algorithms, humans still have a vital role to play. In the paradigm known as Industry 4.0, data-driven insights must still lead to better decisions made by people, not machines.
Consider the evidence offered by recent high-profile corporate disasters, such as the explosion of BP’s Deepwater Horizon rig or the auto industry’s Takata airbag recall. These failures took place amid a vast amount of data that was readily available to the companies involved. Yet, the warning signs were ignored or mistaken for random noise.
The insights that can prevent massive failure or uncover critical opportunities only result from linking multiple relevant data sets. Machines can’t be expected to put together the pieces of the puzzle in this way. They are trained to crunch information in the petabytes, exabytes, and zettabytes in the big data world, but not to make intuitive leaps.
For that, you need to double down on a resource you already have: people. And they work best with small but meaningful batches of data.
Adapting to new needs
You don’t need to hire a team of data scientists or develop your proprietary algorithms to keep pace with digital transformation objectives. You’ll gain more bang for your buck by investing in people and encouraging diversity and breadth of experience. This encourages more useful insights, as individuals are better able to perceive hidden threads and commonalities.
Simple data mining methods, such as surveys, focus groups, and interviews, still hold their own in this new world if your team knows how to use them well. Basic reports from your sales and CRM data can suffice to link top-earning products and services to client demographics and regional markets. In turn, this can inform your marketing strategy, shaping your website design and social media presence.
Before investing further in big data, consider what other information can be gleaned through secondary sources. Reliable research journals and trade publications can offer useful insights for a fee that’s a fraction of what you might pay for in-house analytics. Relevant government and nonprofit data may be available for free.
And if you think AI assistance is truly warranted, consider developing better algorithms for smart filtering. This is the best way to prune down big data to only what’s needed to answer the questions that are really relevant to your business.
