Five ways Analytics and Data Science can add business value

The conversation around big data has grown, well, big in recent times. So much so that it is now part of the day to day vernacular for businesses around the world. Nowhere is this more prevalent than in the thriving technology ecosystem happening right now in the UK. Any organisation can leverage the exponential data growth but size is on the side of smaller businesses who are perfectly suited to act on data-derived insights with speed and efficiency, unlike large organisations that are often less nimble and hindered by clunky, legacy IT infrastructure. All that’s required is somebody in the business that understands the key fundamentals: how to extract business value through data analytics and data science.

However, while a business can be built on a combination of inspiration and perspiration, being able to manage, analyse and interpret data requires a very specific skill set that will actually enable growth through innovation. From predicting and reducing churn to winning business from new and existing customers, the opportunities are endless. Whether you are looking for funding, thinking about the best way to deploy your latest round of investment or a scale-up looking to fuel growth to stay ahead of the competition, here’s five quick ways analytics and data science can help you:

1. Evidence-based decision making: One of the rarest commodities when a business is in the growth stages, is time. Decisions are taken in days, sometimes hours, that in more established organisations would take months. Young businesses especially spend most of their early stage time probing the market and looking for the right product offering to execute upon. Unlike an established company, one mistake can cost its future so having a data scientist on board is key to being able to gather and analyse data from multiple channels and use proven approaches to mitigate risk and improve decision making.
2. Test your decisions: Making decisions and implementing change is only half of the battle; it’s vital to know how those changes affect the company. A data scientist can measure key metrics related to important changes and quantify their success (or lack thereof) so that learnings are made and substantiated when it comes to playing back results to investors and moving the business forward.
3. Perfecting the target audience: Everything from social media profiles to website visitor reports – the IoT ecosystem – contains data which can help a startup pinpoint its target audience – and therefore target them more effectively. Even if it has gone as far as roughly identifying its demographics, a data scientist can identify key groups and consumer patterns with laser precision through using the latest technologies enabling careful analysis of disparate data sources. This in-depth knowledge can help tailor products and services to meet the consumer patterns of the key customer groups.
4. Making use of the information: Data has to be at the fingertips of every decision-maker and key stakeholder across the organisation, which is usually most people in the business at its early-stage. This is reflected in the data science and analytics space right now with predictive modelling and machine learning both attracting huge amounts of interest – a sentiment underlined by the recent acquisitions of DeepMind and Swiftkey. It is not hard to see why when this particular type of data management enables real-time responsiveness when it comes to translating the raw data into insights, which can be transformed into actionable applications to propel business growth.
5. Attract the best talent: With a wealth of information on the talent available to businesses today, a data science or an analytics specialist can hunt out the candidates who fit best with a company’s needs. Through data mining the vast amount of data talent already available, in-house processing of CVs and applications, and even sophisticated data-driven aptitude tests and games, data science can help recruitment teams make speedier and more accurate selections saving money in both the short and long term.

You don’t have to be a large company to develop a big data strategy, and as a startup, you can gain a significant competitive advantage when you engage an experienced data scientist to start leveraging your data. Implementing a data strategy in an intelligent, structured way is what differentiates a big data-driven enterprise from one that is simply using data on an ad-hoc basis. And the basics are no different for a small, agile and growing company than they are for the tech industry giants who have been using big data for years. After all, most small companies don’t want to stay small. Data analysis can lead to big things for small business – but it’s much more likely to happen if you go about it in a smart way.