The Hidden Value in Data – Mining data for business reward
In an age of technology-driven change, there’s never been a greater need for businesses of all sizes to harness the power of new technologies to maintain a competitive advantage. Just as mechanisation transformed workplaces of the past, new and emerging technologies are set to transform organisations in the near future, with artificial intelligence and machine learning already making waves across many sectors.
The fourth industrial revolution as its often dubbed, is seeing technology employed in a “service optimisation” role. Using the technologies we’ve mentioned, organisations are now able to extract value from dormant data streams in ways which were previously unthinkable. To understand what’s possible, it’s helpful to understand some of the technologies at play…
Artificial Intelligence (AI)
AI refers to the ability of computers to perform tasks and solve problems in ways that mimic human cognitive abilities. Computing once involved machines following pre-programmed “scripts,” but now thanks to the advent of AI, systems are now able to execute decisions with varying degrees of autonomy. AI has many applications, but one of the most accessible examples in in grammar checking software, where the technology is now implemented to suggest changes to text to improve readability.
A subgroup of AI, machine learning denotes the ability of algorithms to make accurate predictions by “learning” from historical data sets. Machine learning algorithms are “exposed” to data sets through time, and each time they intelligently assess the data looking for commonalities and anomalies. Comparable to the way humans learn languages through exposure, the algorithm uses each successive data set to make more accurate future predictions the same way a language-learner uses mistakes to learn and improve. Machine Learning is being applied in a wide variety of settings, but one you might be familiar with is its use in email filtering, where algorithms are used to spot trends in malicious inbound mail.
Data science is the field of study devoted to the analysis and rationalisation of data in ways that solve problems and facilitate performance improvements. Data science often applies artificial intelligence and machine learning algorithms to large data sets in order to extract valuable insights and draw meaning from abstract/hidden trends and correlations.
Data Science can seem like a daunting term, particularly for small/medium sized enterprises. It may have connotations of science fiction or feel like an expensive undertaking, reserved for massive corporations with deep pockets. Fortunately however, solutions which enable deep and meaningful data analysis are more accessible than ever, available to even the smallest organisations on a cost-effective basis.
How can data analytics, AI and Machine Learning benefit my business?
It’s hard to effectively summarise the transformative potential of these technologies, as each can be deployed in a variety of settings to achieve a wide range of aims, but the benefits are numerous!
AI-capable workflows can be used to connect disparate data sets, and look for trends, anomalies, consistencies across different environments. With applications across marketing, HR, accounting and operations, data analytics techniques can be used to draw attention to operational deficiencies or spot opportunities for revenue gains, with the ultimate aim or creating a leaner, more efficient and productive enterprise.
You could employ an extra team member to manually trawl through multiple, standalone data sets in order to develop a marketing strategy or optimise resource allocation. While this method may attain some results, the process is likely to take time and the staffing costs might not necessarily be recouped in operational savings. By applying AI and machine learning to such tasks, you could derive results in minutes as opposed to hours and draw actionable insights from your data resulting in improved performance, boosted productivity and increased revenue.
Stay tuned for our next article where we dive deeper into some of the practical applications of data analytics and some of the surmountable challenges that can be encountered when implementing it.
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