Big data is no longer the sole domain of large enterprises – small-to-medium businesses (SMBs) that leverage data effectively can gain significant competitive advantages.
Almost all businesses now collect data to some extent. However, many businesses are still overwhelmed by the sheer volume of data available to them and they wonder how to make sense of it.
Even smaller organisations can evolve to the point of becoming data-centric. Those that do will reap benefits including being faster to market with new offerings, swifter decision-making, smarter operations, and streamlined processes. They will be empowered to put the customer at the heart of everything they do, because they will have the data insights available to tell them exactly what their customers want, when they want it, and how they want it to be delivered.
Used in the right way, data can be an exceptionally powerful tool. In fact, some companies have realised the value of data so completely that they have started to value data as an asset on the company balance sheet.
To make the most of data, businesses need to employ a mix of the right talent and the right technology. For example, the 2016 Teradata ANZ Index showed that the number of organisations considering hiring a data scientist has risen by 7% in the past year to 21%. A further 19% will either access data science skills externally, or will develop those skills in-house. That means 40% of organisations plan to access data science skills, in some way, to help them make sense of their data.
This is important because becoming truly data-centric is difficult, if not impossible, for organisations without ready access to a data scientist. While many organisations are capable of analysing data to some extent, simply collecting and analysing data is unlikely to deliver a competitive advantage in the long-term. Instead, businesses will need to place a higher priority on taking data analysis projects to the next level.
Data scientists can manipulate and analyse data, and, more importantly, they do so with the organisation’s overarching business goals in mind. This is important because the vast quantities of data available, coupled with myriad possibilities for analysis, can be distracting to inexperienced analysts. They can end up following data down a rabbit hole, wasting time and money pursuing insights that aren’t relevant to the organisation.
A data scientist is skilled at identifying the right questions to ask of the data, based on business objectives. They can also help organisations broaden their data sources to more fully understand the landscapes in which the organisation and its customers operate. This means collecting data beyond direct transactions with the organisation, considering other channels such as social media or the Internet of Things (IoT).
Once a data scientist has fleshed out the organisation’s data collection and analysis approach, it becomes possible for the organisation to become sentient. Sentience, or autonomous decision-making, is the ideal state for organisations of any size because it removes much of the potential for human error in decisions. Sentient organisations can constantly listen to, analyse, and make automated business decisions based on data, at a massive scale, in real-time.
Businesses must go through five key stages to reach sentience:
- Data agility: a balanced, decentralised framework that enables a mix of workloads and data types.
- Behavioural analytics: asking different questions to gain new insights, considering behaviours instead of just transactions.
- Collaborative ideation: working together to pool data and insights to get a better view of trends and challenges.
- Analytic applications: smaller, self-service apps that let users reproduce insights, letting more people within an organisation leverage data to create analytical outputs and insights.
- Autonomous decision-making: leveraging predictive technologies and algorithms to look at anomalies, reducing the amount of time spent sifting through dashboards and mountains of data to make decisions.
To reach the fifth stage, businesses need to combine the right mix of people and technology. By putting smart, qualified data scientists in place and supporting them with powerful data analysis solutions, businesses can gain a strong competitive advantage, which can contribute directly to the bottom line.
About the author
Alec Gardner is General Manager, Advanced Analytics, Teradata (ANZ) and leads a team of data scientists and business analysts. As a business analyst, he is driven to ensure organisations are able to see value and drive change from the data they capture.
