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Data mining: why three is the magic number

(Image credit: Image Credit: Pitney Bowes Software)

More than half of the organisations responding to a recent survey stated that they weren’t yet treating data as a business asset, with a similar number admitting that they weren’t competing on data and analytics.

This suggests these businesses are missing out on a huge opportunity – after all, when used correctly, the value of data to business is immeasurable. For businesses today, effective data collection and analytics can be key to maintaining a competitive edge – informing business decisions, predicting trends, optimising operations and improving efficiencies.

Take finance teams, for example. From business travel to supplier relationships, invoices to expenses, they have access to a wealth of data, analysis of which could provide them with invaluable insight into the state of their business, and – perhaps more importantly – into trends upon which they could capitalise for future growth.

Access to these insights depends on their capacity to fully exploit this data, however; without this, it will be largely meaningless. There can also be such as a thing as too much data.

Often businesses are unable to filter, manage and analyse the sheer volume of data they collect on a daily basis. They can find themselves at risk of drowning under the weight of data available to them. And while there is a growing abundance of analytics engines available on the market – many of them offering predictive analytics, powered by AI and machine learning – they may not always be enough to enable finance teams to exploit the full potential of the information they hold. Sometimes, a helping hand may be required.

Unrealistic expectations:

With so much data available to them, the ideal situation would be for businesses to capture as much from as many data points as possible. But if there’s nothing or no-one within an organisation able to read that data, and to draw meaningful conclusions from it, it serves no real purpose. The data must be analysed if it’s to be of any use. The problem is, even when that analysis is performed by the latest set of business tools, it can still prove time-consuming for human operatives.

In fact, taking the wrong approach or using the wrong solution can often prove a hindrance when it comes to collecting and analysing data. Having purchased and installed an AI  –  or machine learning  – based analytics engine, an organisation might expect it to work its way quickly through the data it holds, and deliver meaningful insights that can be used to inform business decisions. Instead, the opposite might occur - the organisation can be left none the wiser as to its future direction, and with mountains of data still to work through.

AI is widely seen by many businesses as a silver bullet for the challenges they face. The truth is, however, that although the technology may be evolving and being increasingly widely adopted, it’s not yet capable of carrying out much of the heavy lifting required when it comes to data analytics. The speed at which AI and machine learning are able to transform and translate data is undeniably impressive. To deliver actionable insight without the need for human insight, however, an AI-powered analytics engine would have to collect and analyse an organisation’s data for several years at its current pace.

There’s no argument that the analysis carried out by an organisation’s experts, such as its finance team, will be augmented by the implementation of AI and machine learning. But for businesses to get the most from the AI analytics, they should entertain the idea of consultative intelligence; an approach which supplements the use of technology with the dedicated expertise, knowledge and guidance of experienced third-parties.

Three-pronged approach:

Organisations will often buy analytics tools without giving much thought to how they will be used. It’s likely that the lines of business, such as finance teams that actually need the tools, aren’t especially technical-minded. As a result, they might expect their newly purchased solution to work out of the box. But it’s not that simple, of course. A great deal of preparation and human input is required for the tool to deliver the best possible results.

It’s in situations such as that that consultative intelligence can be especially beneficial. The approach is based around the collective expertise of an organisation’s own data analysts – in this case, its finance team  – the speed and efficiency of an AI-powered analytics and reporting tool, and the expertise of one or more of the tool’s developers.

Indeed, this third-party insight can help an organisation achieve the actionable insights it needs much quicker than it could by itself. After all, as the brains behind the analytics tool in question, these experts will have seen similar data many times before, and will therefore know how it can be analysed most effectively.

Ready to take a call, or to proactively reach out, these experts can offer guidance on how to read a particular dataset, based on how the solution and they – its developers – have translated it.  In providing this support, vendors will help their customers get the most from their analytics solutions, and derive the most meaningful insights from their data.

A huge volume of data is generated each day, and with ongoing technological developments such as 5G and the Internet of Things, this will only continue to grow. Properly harnessed, it can be used to inform every area of a business, improving effectiveness, efficiency, decision-making, and providing insight into future trends. AI and machine learning are incredibly useful when it comes to improving speed and quality, but the rigorous data mining required for this insight still needs something more.

Augmenting the work of an organisation’s in-house data analysts with powerful analytics engines and the people who created them, consultative intelligence provides a potent mix of expertise. Vendors become trusted advisors, and organisations enjoy the insight they need for future growth.

Thomas Schneider, Head of UK Commerical Operations, SAP Concur