• Data analytics, artificial intelligence (AI), cloud computing, the Internet of Things and blockchain are evolving and converging, making data insights more achievable
• Future-proofing your business will depend on the sector, its size and financial capability, as well as research, robust specialist advice and staff training
• Find the right talent to monitor regulation, privacy and cybersecurity while operating in a more efficient environment

Digital technologies like blockchain, data analytics and artificial intelligence (AI) are becoming more important for businesses with a keen eye on the customer. In fact, the EY Tech Horizon 2022 report found that over 50% of industry leaders place meeting customer needs ahead of costs, and 51% of consumers said technology has positively impacted the way they engage with companies. 

Gains in areas like sustainability are gathering momentum: companies can now benefit from using high-quality data to report their carbon emissions and consumption. This might be individually or as part of a larger supply chain. 

For digital deployment, you need the right set up. Consider some of these starting points:

• Are staff capable of managing the change? 
• Can a partner be employed to give advice on services and training? 
• Can you access high-speed internet and other services?
• Have you thought about cybersecurity?

Cloud computing

Software as a service – it might conjure up images of fluffy white clouds but, in reality, it provides on-demand online access to areas such as application software, servers and data storage (effectively bypassing the limits of a computer’s hard drive).

Practicalities: 

• You usually pay-as-you-go or agree to a subscription, which can make it more affordable for smaller businesses 
• A service level agreement can lay out what you get for your money; updates and maintenance costs are usually included

The Internet of things or IoT 

This links physical objects with sensors that communicate with each other via a connected network or online. Picture internal tracking of goods and services, the regulation of equipment or energy management. 

The opportunity is here for any size business to collect and store huge volumes of information (data), but it’s what you do with it that extracts value.

Practicalities:

• Employing a consultant can be expensive so training staff might be a more economic option. The Data & Marketing Association is currently running government-funded, cut-price data skills bootcamps for SMEs; plus other providers like Google cover topics such as machine learning

Example: Assets and vehicles connected via a cloud network and IoT allows data-driven logistics. With this improved asset tracking and remote fleet management, data can be collected and assessed in real time. This can prompt informed decisions on optimising delivery routes, for example.

How do you extract the value you need?

‘Predictive analytics’ (such as machine-learning algorithms) can help with customer research and demand, financial planning, and regulatory requirements. In the past, we would have relied on a manual prediction, now analytics can cut through. Here are some examples:

• Cash-flow forecasting and fraud detection (crunching through big chunks of data to achieve unbiased analysis) 
• Market research and marketing campaigns (automating routine tasks, emails and messages to boost customer engagement levels) 
• Equipment malfunction (sensors could detect early wear and tear or unusual correlations that standard monitoring would miss) 

Finance, marketing and telecommunication improvements can create clarity in a competitive market. 

All companies have a consistent flow of time-consuming tasks to tackle, such as manual data entry or slow processing times. Areas that could be worth the initial outlay for improved efficiency might include goods and services inventory, supply chain networks, production systems, manufacturing assembly processes and operational management. 

Definitions:

• Artificial intelligence occurs in machines programmed to perform with human intelligence, for example a chatbot responding to orders, queries and refunds
• Machine learning can bring order and insight to unstructured data, such as a time series (constant intervals of data) of the same variable – it helps with prediction
• Automation can replace time-consuming tasks such as manual data entry or factory work

Along with these capabilities comes the need for data protection, cybersecurity and knowledge of evolving regulation for spaces like AI. Again, this could be a case for training and upskilling.

This is a shared database or digital ledger designed to be highly secure. Often used as a supporting network for cryptocurrency transactions, Blockchain can store data in separate blocks (think cells in a spreadsheet). 

• There can be plenty of users at one time but no one person or business has control so when individuals input new information a ‘block’ is formed and encrypted, and no changes can be made. These blocks are chained together 
• This can be used to store legal documents or other confidential data, not only financial transactions