Lara Fox, MD of Objective, a data analytics specialist, is seeing an uplift in the number of businesses enquiring about how to effectively use AI, data analytics and software to help them grow their business. In this article she debunks the myths around AI and analytics software and shares tangible and workable tips for businesses on how to embrace the benefits.

Buzzwords such as AI, generative AI, natural language processing (NLP), data analytics solutions have been around for a while now. But what do these words mean, what do they do and how can we use them in business to help drive efficiency?

Businesses can leverage AI solutions in several ways to help drive efficiency, innovation and help gain a competitive edge. It gives us customer insights and personalisation for marketing purposes, it can improve operational efficiency, optimise sales and revenue, automate processes. The ability to use the data and insights for enhanced decision making is hugely valuable, helping decision makers spot trends, patterns and insights. Predictive analytics can be used to look into the future, forecasting trends, customer behaviour and market conditions to enable more proactive planning and risk management.

The great advancement in technology, particularly when it comes to data analytics and software solutions, means business owners can harness their data and insights to tackle challenges and move forward. But with so much noise and endless amounts of options, it can be difficult to know what we need and what we need it for.

What can business owners use AI solutions and analytics for?

Automating financial processes

One of the most common uses of analytics we see is automating financial processes. This might include tasks such as bookkeeping, payroll, invoices and expense management. Here we can use Application Programming Interface (APIs) to link together different systems in use such as Salesforce or Xero. This enables the systems to share data and perform analytics to produce insights. Taking this a further step forward, Robotic Process Automation (RPA) can help automate some of the more mundane tasks, freeing up employees to focus on more strategic activities.

Inventory management and forecasting

Taking this a step further, some businesses are using machine learning and AI for predictive analytics in inventory management. Databases and web apps accumulate data from multiple sources, adding in machine learning (ML) algorithms to forecast information. Machine learning can also be used to forecast demand and optimise inventories. Microsoft’s ‘Azure Auto ML’ can run 50 machine learning models on your data without needing a single line of code. It can be linked with Power BI to produce dashboards which allow business owners to visualise the data and use it for reporting. Being able to put data sets, machine learning and intelligence together helps to facilitate data-driven decisions.

Supply chain optimisation

Real-time data analytics alongside forecasting machine learning algorithms can help supply chain optimisation by predicting disruptions and identifying areas of inefficiency, minimising any impact and waste of unnecessary stock and resources. Again, something like Power BI can help visualise the flow of goods, information and finances across the supply chain which can help businesses identify ways to significantly enhance operational performance. Trend data from machinery and equipment can also help predict the lifespan of equipment, identifying failures before they occur and reducing downtime and maintenance costs.

Job allocation and logistic planning

We have also seen first-hand how automated timesheet retrieval and job allocation can be made more efficient with APIs, bringing data together for a single version of the truth. This can also be used for logistics planning, for example, tracking hire vehicles or other goods. AI technology can use data from databases and IOT devices such as sensor data to automate the allocation of resources, staff and route planning by using a variety of factors including location. Saving the manual resource allocation to be used where issues arise.

Sales and marketing

The capabilities of analytics for sales intelligence can also be a powerful tool. We can use data analytics to gain insights into our clients and use this to personalise marketing efforts. You’re probably using this already, perhaps unknowingly, as many marketing tools utilise this technology and insight – using your open and click through rates from marketing emails to advise you on the best times to send communications to your audience. It also allows us to automate creating segments in our marketing data, using machine learning to classify our customers into segments based on their actions and profile. NLP can also analyse customer feedback from social media, reviews, surveys to understand and identify areas for improvement. To do this manually would take days.

Getting started

You don’t need to be an expert to get started when it comes to AI data analytics. There are several tools available that allow you to experiment with low or no code involved, such as Microsoft Azure Auto ML suite. Microsoft tools also come with Co-Pilot to navigate you through each step.

The first step to take is putting your data into Power BI or a similar reporting tool to visualise your data. Once you know your data you can start to ask questions and with the right information you will be able to answer them. We can look at the what ifs and the can dos.

Make use of free data available from Government websites and input the data to allow machine learning to see if there are any correlations and trends emerging between that data and your own sales records. Doing this allows you to access lots of insightful data with minimal effort and cost. If you think you don’t have the data required, every business with an accounts system should be able to give it a go. You can connect your numbers and costs to a visualisation tool see any shifts and trends. Then when you have questions feed these into a low-code machine learning solution to get forecasts or classifications.

The lack of knowledge around AI and data analytics is holding businesses back, but with such a wealth of information out there, it is difficult to know where to start. But starting is the hardest part. Being able to use the data you do have combined with other tools allows business owners to truly see where their business is at and how it can be streamlined to become more efficient. Along with insights into the numbers with AI and machine learning, generative AI can also be used to help provide text output to help you generate ideas.

The window of opportunity is open for businesses to explore and with many low-cost options available businesses can play around with the data without too much initial outlay. The possibilities with the technology at our disposable today really are endless.