Introduction to Big Data and AI
A few years ago, the business technology world’s favourite buzzword was ‘Big Data’ – a reference to organisations’ mass collection of information that could be used to suggest previously unexplored ways of operating, and float ideas about what strategies they may best pursue. What’s becoming increasingly apparent is that the problems companies faced in using Big Data to their advantage still remain, and it’s a new technology – AI – that’s making those problems rise once again to the surface. Without tackling the problems that beset Big Data, AI implementations will continue to fail.
Problems with Big Data and AI
The vast majority of problems stem from the data resources themselves. To understand the issue, consider the following sources of information used in a very average working day. In a small-to-medium sized business, these sources include spreadsheets, stored on users’ laptops, in Google Sheets, Office 365 cloud, the customer relationship manager (CRM) platform, email exchanges between colleagues, customers, suppliers, word documents, PDFs, web forms, and messaging apps.
Data Sources in Enterprise Businesses
In an enterprise business, the sources of information are even more extensive, including all of the above, plus enterprise resource planning (ERP) systems, real-time data feeds, data lakes, and disparate databases behind multiple point-products. It’s worth noting that this list isn’t comprehensive, and nor is it intended to be. What it demonstrates is that in just five lines, there are around a dozen places where information can be found.
The Challenge of AI-Ready Data
What Big Data needed (perhaps still needs) and what AI projects also rest on, is somehow bringing all those elements together in such a way that a computer algorithm can make sense of it. Marketing behemoth Gartner’s hype cycle for artificial intelligence, 2024, placed AI-Ready Data on the upward curve of the hype cycle, estimating it would be 2-5 years before it reached the ‘plateau of productivity’. Given that AI systems mine and extract data, most organisations – save those of the very largest size – don’t have the foundations on which to build, and may not have AI assistance in the endeavour for another 1-4 years.
Underlying Problems with AI Implementation
The underlying problem for AI implementation is the same as dogged Big Data innovations as they, in the past, made their way through the hype cycle – from innovation trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, to plateau of productivity – data comes in many forms; it can be inconsistent; perhaps it adheres to different standards; it may be inaccurate or biased; it could be highly sensitive information, or old and therefore irrelevant.
Transforming Data for AI
Transforming data so it’s AI-ready remains a process that’s as relevant today (perhaps more so) than it’s ever been. Those companies wanting to get a jump start could experiment with the many data treatment platforms currently available, and as is becoming the common advice, might begin with discrete projects as test-beds to assess the effectiveness of emerging technologies. The advantage of the latest data preparation and assembly systems is that they are designed to prepare an organisation’s information resources in ways that are designed for the data to be used by AI value-creation platforms.
Benefits of Data Preparation Systems
They can offer, for example, carefully-coded guardrails that will help ensure data compliance, and protect users from accessing biased or commercially-sensitive information. But the challenge of producing coherent, safe, and well-formulated data resources remains an ongoing issue. As organisations gain more data in their everyday operations, compiling up-to-date data resources on which to draw is a constant process. Where big data could be considered a static asset, data for AI ingestion has to be prepared and treated in as close to real-time as possible.
Conclusion
The situation therefore remains a three-way balance between opportunity, risk, and cost. Never before has the choice of vendor or platform been so crucial to the modern business. With the correct approach to data transformation and AI implementation, businesses can unlock the full potential of their data and stay ahead of the competition.
FAQs
Q: What is Big Data?
A: Big Data refers to the mass collection of information by organisations that can be used to suggest new ways of operating and strategies to pursue.
Q: What are the problems with AI implementation?
A: The problems with AI implementation stem from the data resources themselves, including inconsistencies, biases, and sensitivities.
Q: How can businesses prepare their data for AI?
A: Businesses can prepare their data for AI by using data treatment platforms and beginning with discrete projects as test-beds to assess the effectiveness of emerging technologies.
Q: What is the benefit of using data preparation systems?
A: The benefit of using data preparation systems is that they can help ensure data compliance and protect users from accessing biased or commercially-sensitive information.
Q: Why is it important to prepare data in real-time?
A: It is important to prepare data in real-time because data for AI ingestion has to be prepared and treated in as close to real-time as possible, unlike big data which can be considered a static asset.
