Harnessing the Value of Data – How AI, Automation and Data Science Can Support Business Success

In our interconnected online age, businesses are required to embrace new digital solutions in order to operate efficiently and maintain a competitive advantage. Just as the digital revolution transformed workplace communication beyond all recognition in the 20th century, today, a new wave of technologies looks set to reinvent the workplace as we know it. Among these are artificial intelligence, machine learning, automation, and data science.

This new period of technological development has been coined the ‘fourth industrial revolution.’ This new age will be characterised by the convergence of multiple technologies, including those we’ve mentioned, alongside internet of things, robotics, blockchain and others, which is expected to have a profound impact on the nature of work, communication and commerce. This is an exciting time for businesses of all sizes, with great rewards and opportunities available to businesses that adopt these innovative new solutions. To help you understand the possibilities, let’s take a closer look at these emerging technologies, and some of the things they can help businesses achieve.


Artificial Intelligence (AI)

Artificial intelligence refers to computer programmes that have the ability to think and learn like humans. Traditionally, computers would follow linear scripts, with expected inputs triggering pre-programmed outputs. AI on the other hand gives computers the ability to interpret unclear or indistinct information, and create its own output or make a judgement with varying degrees of autonomy. AI’s current uses extend to a range of tasks and activities that have traditionally required intensive human input, including visual perception, speech recognition, decision-making, language translation and problem solving.


Machine Learning (ML)

AI and machine learning are terms that are often used interchangeably, and while they are closely related concepts, the two terms shouldn’t be conflated. Machine learning is a subcategory of AI that focuses on the development of algorithms capable of improving their performance through the ongoing analysis of data. These algorithms are continuously fed data, which they subsequently analyse in an attempt to spot patterns, trends, and detect deviations from the norm. In many instances, machine learning models are calibrated to make future predictions, with each successive dataset providing the algorithm with more knowledge upon which to base its forecasts, loosely mimicking the way humans learn from experience.

The practical applications of machine learning are extensive and varied. The field of cyber security demonstrates the power and capabilities of machine learning, with ML-powered email filtering technologies able to scan the contents of emails to detect the characteristics of malicious intent.


Data Science

Data science is an academic field that centres around extracting insights from structured and unstructured data using a range of technologies and scientific methodologies, including machine learning and AI. In the context of business, the insights derived from data science can be used to empower better decision-making, craft effective business strategies and optimise processes. By applying AI/machine learning algorithms to large data sets, organisations can hunt for subtle correlations that a human would struggle to perceive, and extract value from dependencies and relationships that exist between isolated data metrics.

At first glance, data science may seem like an involved subject, and an undertaking that’s only relevant to resource-rich large companies. However, it offers benefits to organisations big and small, and the solutions required to implement it are more accessible than ever, thanks largely to the rise of cloud computing.  

For instance, Microsoft 365 customers can take advantage of the suite’s powerful data analytics tool, Power BI, which enables users to draw together data from numerous sources for the purpose of extracting valuable business insights. Modelling and visualisation capabilities allow users to rationalise and derive meaning from abstract data, and convert it into easily understandable charts, graphs, maps and tables that can update in real-time to support informed live decision-making. 



Automation can be fundamental in helping businesses unlock value from data. By bridging the gaps between previously isolated data sources, automated solutions enable the seamless integration and coordination of workflows, helping to streamline operations and enhance workplace efficiency, and consequently, profitability.

By automating workflows, business can eliminate, or vastly reduce, the time spent on low-value manual data handling tasks. This gives employees more time for strategically valuable activities, and reduces the likelihood of errors, resulting in data that is more accurate and therefore more valuable. Workflow integration provides managers with reliable, up-to-date information, creating the climate for optimised decision-making that translates into measurable business benefits.


The Business Benefits of AI, Machine Learning, Data Science and Automation

While these exciting technologies can give rise to extensive benefits for the businesses that adopt them, it’s difficult to summarise the advantages afforded due to the varied purposes, settings, and use cases to which they can be applied.

By incorporating the solutions we’ve discussed into your workflows, you can seamlessly unite dispersed data sources, hone in on elusive patterns, trends and anomalies concealed within your data, and leverage analytics and visualisation technologies to translate raw data into meaningful and valuable business insights. Data analytics solutions can be applied across almost every business setting, including marketing, HR, finance, operations and more. Once established, these technologies can help businesses identify and rectify operational deficiencies, exploit opportunities for revenue growth, and develop more efficient and economical processes which support greater profitability.

Today, extracting value from data no longer requires the use of staff resources, or the employment of additional staff members. AI- and ML-powered data analytics workflows allow you to derive worthwhile insights from information in seconds, rather than the hours such a task might take to perform manually, delivering all the benefits of data analysis without the drawbacks that such activities used to entail. Thus, you can derive actionable insights that yield improved productivity, increased revenue and enhanced efficiency for a minimal outlay.

In our next article, we’ll provide further guidance on introducing data analytics to your business, including some of the business settings and purposes it can be applied to.