There was a lot of talk this year about how Big Data was going to change the face of business and the changes we’re going to see in the future thanks to Big Data. However I had no idea about what businesses were currently doing with Big Data, so I got in contact with Luc Burgelman, CEO of NGDATA to discuss how SMEs and larger organisations are using Big Data already.
I believe that we are seeing a rapid evolution in how organizations deal with Big Data – one that is leading to a new generation of data science.
Math is a crucial element to data science, but by no means the only one. Over reliance on this traditional skill set is a recipe for tired, derivative analysis that will do little to benefit an organization’s bottom line – because it is hard to find something if you don’t really know what you are looking for. Specifically, employees interested in interpreting Big Data must bring a broad skill set to the table, unlike, say, a statistician, who can spend all day lost in numbers.
The skills fall into two camps statistical and technical expertise coupled with an analytical mindset with an acute business sense:
Statistical and technical expertise are needed as data is the fuel that feeds the machine, so employees must have an intimate knowledge of data analysis, data security, data visualization and data quality.
They must be comfortable with massive volumes of unstructured data, and be able to organize it in a consumable way. Employees must be open to using advanced technology and tools in machine learning.
We’ve reached a point where manual analysis of data the scale most organizations are dealing with is inefficient at best and impossible at worst. To make big data useful, embracing these technologies is a must. This is where knowledge of Hadoop and a wide range of programming languages can make a big difference.
Similarly an analytical mindset with an acute business sense is needed as harnessing Big Data is about finding ways to solve problems and create opportunities for the business. It’s how you attack a problem (or opportunity). It’s as much about asking the right questions as it is about creating the right algorithm.
Understanding how to solve the true business problems vs. the mathematical formula is important, but the ability to evaluate relationships between multiple variables and use data to shape predictive insights is essential. This all must be done in the context of your organization’s business needs.
Employees must be able to effectively tie Big Data solutions to the issues that are driving their business. Employees must also possess the ability to “sell” their ideas to the C-level – something that cannot be done without making a compelling business case. At the same time, employers themselves must be open to strategies and ideas that will help them transform into more data-driven businesses.
Big Data is a philosophy indeed, especially since data volumes grow and evolve over time. Data is hardly finite or stagnant, and there is no point at which you can define Big Data or claim, “Here starts Big Data.”
Rather, the philosophy around Big Data should focus on the way we approach the analytics and the usage of Big Data. This is philosophy is based on just two basic principles:
With traditional Big Data analytics, the philosophy has always been about data quality. The priority has been defining a sample and a representative data set in order to analyse and compute a model that returns values that indicate the likeliness that something might occur. Now, however, we know that we can do more with Big Data.
When we want to predict what is going to happen in a certain situation, we can leverage all of our available data, because chances are this situation has occurred before and that a pattern already exists in our data. Looking at a current reality can serve as a much more valuable analytic data model, and detecting trends offer far greater insights than viewing exact values.
This is an interesting question as we’re seeing SMEs realise more so than ever before that leveraging Big Data is no longer just for larger organisations.
From financial service companies such as community banks and credit unions to communications companies, SME’s recognise the importance of using data to foster productive and authentic customer relationships that improve the customer experience and drive customer lifetime value.
For instance, one financial institution that we work with is using big data to optimise its merchant-funded offer program and better anticipate customers struggling with credit card payments. By accumulating customer DNA and leveraging predictive analytics, this company is able to offer relevant loan packages to the right customers at the right time. They’re also able to predict future credit difficulties and warn those customers to help them improve their credit profile and avoid negative consequences. This is exactly what businesses—of all sizes—need to do.
Developing a customer-centric strategy that requires the anticipation of future needs—looking at behavioural patterns, market trends, and user experiences for proactive measures to secure a personalised, unique and memorable experience across multiple channels. This, in turn, enables the customer to feel understood and valued, and likely to develop a loyalty that will be a good basis for customer retention, up-selling and cross-selling.
We have customers across the globe leveraging Big Data to do amazing things. They are leveraging massive volumes of data to create sophisticated, nuanced understanding of their customers – and seeing great returns.
Our users are reducing churn, increasing sales, and developing loyalty among their customer base, all thanks to a newfound ability to absorb, analyse and act on Big Data. Specifically, one international energy company that we work with is using Big Data in some very innovative ways.
First, they’ve used predictive modeling and analytics to determine which customers are most likely to call the customer service line with questions about their bill, and have been able to send a video explaining billing details – and as a result have seen a huge decrease in call volume.
This same energy company has used a wide range of data points – income level, location and position of users’ homes, etc. – and created a predictive model that will help them determine which customers might be more likely to install solar panels. Use cases like this – where Big Data is not only helping a company, but contributing to the greening of the planet – are great to see.
As we head into 2015, I believe that the Big Data space will evolve significantly. To date, we’ve seen analytics and data visualisation as two of the cornerstones of Big Data – but I believe we will start to see more data-driven examples on a much larger scale in 2015 across a broad swath of industries.
First and foremost, I believe that demographic-based segmentation will become a thing of the past – at least for organisations ahead of the curve. Historically, common characteristics have such as age, income level or post code have united customers in the eyes of organisations that assume those shared traits would lead to similar buying tendencies. But with advances in machine learning, analytics and computing power, we’re going to see a huge improvement in the ability of organisations to target customers at the individual level, creating personalised offers for each customer.
Deeper, more sophisticated understanding of each customer will lead to better, more relevant offers. At the same time, the current level of data that organisations are charged with analysing and acting on will look like a trickle compared to what we see in the near future. We’re going to see huge increases in the quantity and types of data, including unstructured data, that organisations will want to understand. At the same time, we’ll see a quantum leap in the number of use cases that businesses are implementing as some really smart people learn how to put Big Data to work for them.