Big Data: An Interdisciplinary Approach Is Crucial

Big data is the word of the moment: every year, the quantity of business data doubles, and nearly 70 percent of firms are investing or planning to invest in analytics to glean business insights from this information. It is thought that better analytics could unlock $100-$200 billion globally.

The question is, where is the big data juggernaut heading? The next phase in big data development is focusing not only on the data and technology, but also on the information, ideas and insights it provides. It’s no surprise that with the exponential increase in data gathering, the search for skills in analytics and data-driven decision making has become a crucial requirement for organisations.

To use big data to its full potential, businesses must consider the entire cycle of creating, translating and consuming analytics. Analytical output has no value on its own accord, it must be converted into key findings, insights and recommendations that the organisation can act upon.  This requires best practice processes and the right people to work with the data, to offer what a machine cannot.

Decisions, not data

Using an interdisciplinary approach (business, maths, technology, behavioural sciences) means businesses are better equipped to understand the data they capture, and can make better decisions as a result. The speed of business transformation means that organisations have to deal with more and more complex problems, which require solutions in real time.  Decision scientists have the advantage of efficiency because they can view the data from different angles, taking each field into consideration.

Creating a test and learn culture

When done right, analytics will nurture business success by driving innovation and better decision making. We know from the experience of thousands of customers that supporting decisions on data-based insights, and creating a ‘test and learn’ culture is much more fruitful than relying on gut instinct.

To get the most out of data, organisations should consider the following:

• Business objectives: make sure these are defined in advance – clarity is key.

• People: you need to have the right people in place, but more importantly, they must have the right mindset and interdisciplinary training.  Creating a culture of analytics requires all stakeholders (creators, translators and consumers) of analytics to be wedded to data-driven decision making. Training and continuous learning across all levels (executives, middle management and practitioners) is a key element in ensuring this.

• Processes: structured frameworks for business problem definition, insight generation, quality checks, and governance models are essential to create a common language across the organisation.

• Platforms: platforms that democratise the use of analytics across multiple user profiles, and not only enable analytics algorithms but also enable a better art of problem solving are required. These include guided analytics workbenches and intelligent systems that embed best practices.

• Engagement and communication: the more you engage your audience, the better your data. Communicate accurately and you will consume accurately. Very often, data-driven insights conflict with traditional business wisdom within the organisation. Encourage this conflict. It will also help to overcome any existing cognitive biases.

There are a number of steps involved in the process of handling important data, and it continues to evolve. Primarily, business problems should be articulated and translated into analytical problems. This will then allow you to solve the analytical problems. Once these analytics solutions have been identified, they need to be translated back into business solutions.

The benefit of these data-driven decisions will only be realised when the business solutions are communicated, implemented and consumed by the whole organisation. This man-machine approach when it comes to decision-sciences is a major shift from the way data analytics has traditionally been handled in the past.

Human capital

Previously, a data team would consist of mathematicians, IT analysts, engineers and business professionals. But with a mixed team working to reach one conclusion for each data set, the practice becomes time-consuming. This is why demand for individuals with combined maths, IT and business expertise is high.

Such individuals are better equipped to analyse and evaluate the data, as well as create business models based on the information. In addition, the layers of business design thinking and behavioural sciences give the same individual the ability to provide data-driven ideas. That is the final piece of the puzzle. These people are known as decision scientists, and they are even more rare – and increasingly more desirable – than pure data scientists who usually posses a combination of maths and technology skills.

It’s clear that analytics is changing the way business operates, and we’re witnessing a shift in culture towards data-driven decision making.  In order to fully reap the benefits of big data, businesses must realise the benefits of a man-machine ecosystem and form models to govern and optimise all aspects of creation, translation and consumption of data. Better decisions will always lead to better business.

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