Companies should be aware of the machine learning capabilities required to analyse data sets

Companies should be aware of the machine learning capabilities required to analyse data sets

Today, the algorithm is the point of entry for the majority of tech start-ups. For example, London-based Butternut Box, recently reached £1 million in investment thanks to an innovative solution born out of an algorithmic process.

However, an algorithm is only one element for a business to consider when looking to make a name for itself in an increasingly competitive industry.

Having a unique algorithm is a good start for a new business, but competing against enterprise behemoths such as Facebook and Amazon, who have the resources available to crush smaller competition and monopolise the market, it’s simply not enough. Henceforth, it’s vital to facilitate and nurture the development of large data sets to feed into and compliment algorithms, which data must then be analysed and crunched by machine learning technologies to provide valuable outputs. Failing to do this will soon stifle a start-up’s ability to grow and innovate.

It can be nearly impossible for a modern-day start-up to succeed without the machine learning capabilities required to analyse data sets, and there are several factors as to why that might be.

Data, data, and more data

A business or solution that applies machine learning capabilities based upon their existing data sets can provide valuable outputs, especially in terms of making customer experiences unique and personalised, or streamlining the supply chain through automation or alerts which anticipate potential issues. Machine learning, accompanied by a large and robust data set, is crucial for today’s new businesses.

For example, at Pindrop, we’ve clocked up over 400 million analysed calls in the past year alone. This guarantees access customers to an immensely rich wealth of valuable data. Our technology detects which calls are fraudulent versus legitimate, feed this back to a database that tracks repeat offenders, categorises fraudulent cases and creates fraudster profiles. Thus enabling the naming and shaming of real-life fraudsters targeting consumers across the world.

Having access to such a large dataset means suspicious behaviours can be quickly flagged and even identify the most sophisticated patterns to build a bigger picture, and inform clients in real-time when and how fraud is detected. The data also has multiple uses beyond that of fraud detection, it can help customers avoid fraudulent breaches, and be used as a proof-of-concept when talking to prospects.

Data is everywhere

In our 24/7 connected world, it’s increasingly the case that a business is only as good as the data it has access to or collects. For a modern-day small business to succeed, access to vast amounts of valuable data can be the difference between success and failure. It’s all about using that data to tailor an approach to customers and business goals and remain competitive in a saturated tech startup market.

It’s all very well having a unique solution or algorithm in the market, but without access to data how will a business tailor their product to specific customer needs and adapt to changing market demands? Startups looking to continue their growth have several issues they need to address, such as:

  • How to keep consumers up-to-date with new products and innovations
  • How best to follow up with customers who have made an initial purchase or interaction
  • Maintaining customer brand loyalty

All of these questions have something in common, they can all be answered in the same way, by making use of the data customers willingly share when registering on a website or buying products to tailor their experiences. A startup that’s able to build a data profile log of existing customers or prospects and implement those insights into their algorithm to create a tailored service is certainly on to a winning startup formula.

The data can then be run through and bolstered by machine learning programs which reveal valuable outputs. This is what elevates a start-up ahead of its competition. It’s not the base algorithm, nor the amount of data collected and stored, but the right type of data and the ability to process and utilise it effectively.

The case for data protection

As a start-up begins to accumulate masses of data from their customers, it is of course crucial that they keep in mind the nearing General Data Protection Regulation (GDPR) deadline. When GDPR comes into force this May, data protection will have to be integrated into the core of all business procedures, products and services, across all channels. On top of these GDPR measures all employees will have to be aware of their obligation to protect consumer data across all channels including both online and via the phone.

It’s for this reason that GDPR needs to be viewed as a way for businesses, especially small businesses and startups to introduce a robust data protection strategy that protects call and voice data as well as all other data that runs through digital channels.

As more and more things become connected resulting in larger and more sophisticated sets of data, a startup must look beyond the functionality of an algorithm to the existing data and insights they have available to them, in order to put their business idea at the forefront for investors and compete with ever increasing tech behemoths.

Nick Gaubitch, is research director EMEA at Pindrop. 

Further reading on data sets

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