Making decisions with data – finding a practical role for Machine Learning

Machine Learning has gone from a niche area of technology to being the saviour of companies across multiple industries. Others see it as a technology that is overhyped and doesn’t deliver. Indeed, the Gartner Hype Cycle for Emerging Technologies has Machine Learning, or ML, at the Peak of Inflated Expectations. This is where technologies end up when there are huge amounts of attention and marketing exuberance on display but not necessarily the results to show adoption. 

While this position can be unfair – after all, ML technologies have been around for years and delivered in their own niches successfully in the past – the application of ML across a broader range of use cases can be more difficult. Automating the analysis and decision process can lead to a huge potential improvement in value creation for businesses, helping individuals make better decisions and boosting performance overall. The challenge is how to make this potential work in practice. 

The ML value creation process … or what can ML do for you? 

Machine Learning exists to speed up the process of spotting patterns and applying those patterns to new sets of data. By finding correlations in data sets, employees can make better decisions based on this guidance. However, ML initiatives have to start from initial sets of data that can be used to train the algorithms appropriately. 

As an example, many companies are looking at how they can improve their sales performance. For smaller organisations, it can be possible to look through customer lists and pick up on trends such as success with particular buyers, with companies in specific markets, or based on common business needs. With small sets of data, this process can be done by people, based on their insight and experience, not to mention a spreadsheet or two. 

However, for enterprise companies, the amount of data available on customers’ behaviour is spread too widely and comes from too many sources at once. For people to spot patterns in multiple sets of data that can change continuously is difficult; for machines, it can be much easier to go through the data at the required speed. 

The other challenge here is that algorithms don’t build themselves. Traditionally, getting accurate and useful algorithms in place relied on having a suitably large set of data, insight into how this data was created and a data scientist that could train the ML system to make use of the data. In the past, this would put ML beyond all but the largest enterprises that both the data and the skills to use it.   

Today, more companies than ever are creating data sets that can be used for analytics. However, making this data consumable and useful to more people requires a different approach than relying on data scientists. ML automation aims to make data consumable by a broader and non-specialised audience. By making it easier to analyse data – and most importantly, to see the relationships between sets of data - ML automation has the potential to put highly advanced analytical capabilities in the hands of employees in Line of Business teams. 

Taking ML further … what can ML do for everyone? 

Looking at our sales example, knowing more about patterns in customer behaviour can help employees in the department target their activities more efficiently. Rather than spending time on campaigns that have statistically lower chances of success – or targeting customers with lower probability of buying – efforts can be guided towards customers that should be more receptive. 

Now, taking a recommendation on specific customer types to target or deals to pursue can improve selling strategies, but these recommendations won’t be successful if other departments are not aware of them. However, just sharing data on its own is not enough. Instead, teams have to work on how they collaborate around data in the first place. Using this approach it’s easier to bring more people into the network of data and other users, and then enable them to augment that network with their own data and their insights.  This makes it easier to empower users to make decisions as part of a group, with a complete and trusted view of the business to provide the right context for each decision, as opposed to everyone having their own individual (or siloed) view of the business. 

By allowing people to bring their own data in for analytics, as well as central data sets, teams can avoid some of the problems that exist in spreading analytics throughout the business. Helping individuals prepare their data for analysis automatically makes it easier to create further insights, which go across departmental or other boundaries. ML can help through automatically recognising data within individuals’ reports or data sets and then creating links between them. An example would be Marketing adding the lead qualification and tracking data. Showing where a set of customers came from over time, and comparing this against sales results, can demonstrate where the best rate of return can be achieved. 

Machine Learning can be applied by linking each customer record, in the different sets of data, automatically to build a more complete picture of activity. Automating the process for describing each customer record and its properties then makes it possible to apply more thought to the results and their implications. By looking at a company-wide objective like sales, each team involved can use analytics to create insights based on the information that is coming in. This can help teams find out more of the unknown relationships that exist within data sets that go across departments or other business divisions. – in this example, the data might show a pattern of wins with companies of a particular size and location.   

Following this data discovery exercise, there is the opportunity to consider how this data is used in practice. A preponderance of customers in a specific location could point to a localised demand for the product that can be exploited, or it could be used to show that a particular sales rep is performing well. By carrying out smart profiling for join recommendations, across a network of analytics, and cross–data source sampling for rapid exploration, a lot of the “grunt work” of preparing data and getting insights from it can be removed. In turn, this can help analytics and data teams work on how to understand and share the insights that are created. 

This process is not as simple as it sounds. To start with, bringing the right data together in the first place can be a challenge. For enterprises with dozens of applications – all of which might contain relevant data – getting a central data store in place can be difficult. Alongside this, the data will be updated all the time. The result of this is that analytics run at one point can be different to another point in time, depending on whether the updated sets of data have been loaded up. 

Instead, it’s worth looking at how to couple up sets of data into a network that can then be kept updated over time automatically. As any data is changed, that new version of the data can be made available for analysis, either by people or by algorithms. Put simply, everyone is looking at the same information over time and using it for their decisions. 

Once companies start looking at ML, the teams involved can see how they can speed up their processes around analytics. This insight can then be used to focus on improvements to the processes that are in place within teams. By working on how to get more value out of data for everyone within the network, analytics teams can help improve results and get to their desired end goals faster. 

Pedro Arellano, Vice President, Product Strategy, Birst

Image Credit: Zapp2Photo / Shutterstock

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