A surge in sources and data-collection processes means that we’re constantly bombarded with heaps of facts and figures. In the age of information, we’re definitely not at a loss for data.

However, how much of this data is useful for driving effective business decision-making? Or, are these immense amounts of information largely considered dumb data? Rather than focusing on big data, businesses now strive to capitalise on a different concept — smart data analytics. 

Defining smart data

What is smart data?

Simply put, it is big data made actionable in real time. Such data can be applied to a wide array of business functions, ranging from industrial applications and marketing decisions, to process optimisation and designing HR strategies. Rather than focusing on the volume of data, a greater level of importance is placed on how organisations can take actions based on the information they possess.

Let’s use the example of a monthly sales report generated by a firm. Generating an extensive list of figures will not provide executives with much insight into the sales trends of a company. On the other hand, a report that indicates the peaks and falls in sales volume will allow key management staff to identify patterns, and gain deeper understanding in the purchasing behaviour of consumers. This places them in a better position to make well-informed decisions, conceptualise business development plans and design customer-centric sales strategies.

Big data VS. Smart data

How does big data differ from smart data?

Big data refers to all types of data. Gartner defines big data as information that is high in volume, velocity and variety. Cost-effective methods of information processing are used, so that the data can provide insights for improved decision-making. Four Vs are often used to describe the elements of big data: volume, velocity (the speed at which large amounts of information can be processed), variety (types of data) and veracity (degree to which the data is accurate or reliable).

On the other hand, smart data is more about veracity and value. Questions that users of smart data will raise are: how will data impact upon the decision making process, and how can it be applied to a business context?

Ali Fouladkar, a researcher at the Center for Studies and Research in Management (CERAG), indicates that smart data should possess the following attributes: accuracy (data quality), actionability (the data should drive action that is prompt and scalable) and agility (data should be available in real-time, and possess flexibility so as to adapt to constant business challenges and changes).

Capitalising on smart data analytics for effective business decisions

Research findings from IBM revealed that one in three business leaders make important decisions without the information they need, and 53 percent do not have access to information within their company to perform their job well.

But, this does not have to be the case. Business leaders can learn from organisations that have capitalised on smart data analytics to drive effective decision-making:

1. Improving HR strategies: Project Oxygen at Google

A People Analytics Department has been created within Google’s global HR function. As its name suggests, this department gathers data to support the firm’s decision-making processes related to its HR operations.

The department was responsible for carrying out Project Oxygen, a research project created to determine if having managers actually mattered in the organisation. A variety of data was collected from performance reviews, employee surveys and interviews with managers, and insights were gained after the data was analysed through regression analysis and text analysis.

The research findings revealed that good managers had a positive impact on team performance, productivity levels and employee engagement. Acting on these insights, Google revised its management training program, and continued with its Great Manager Award program, and institutionalised behaviours that high-scoring managers share within the company.

2. Making multimillion dollar decisions: Netflix

Selecting movies, creating content and making multimillion dollar decisions boils down to one factor at Netflix — analytics. This data-driven company has a total of 69.17 million customers worldwide, which serves as a huge user base for the firm to gather data from.

Netflix collects data extensively, tracking metrics such as the number of customers that have completed an entire season of a show, the moments in which they pause, rewind or fast forward during a show, the timing at which they consume content, their location and the devices used. All of these data are analysed to see trends in usage, and to gain a deeper understanding into consumer engagement.

Such insights have enabled the company to make multimillion dollar decisions, such as investing US$100 million to create a U.S. version of House of Cards, with greater assurance. These well-informed decisions have yielded positive results; Netflix saw an increase of 2 million new U.S. subscribers in the first quarter of 2013, and a significant majority (86 percent) of its current subscribers indicated that they were less likely to cancel due to House of Cards.

3. Giving frontline operations a boost: ORION system at UPS

As the world’s largest package delivery company and supply chain management service provider, UPS isn’t new to big data. The company started tracking package movements and transactions since the 1980s, collects information on approximately 16.3 million packages daily, and receives a staggering 39.5 million tracking requests each day.

In carrying out the firm’s ORION initiative, telematics sensors were installed in over 46,000 trucks to track a variety of factors such as speed, direction, braking and drivetrain performance. Online map data and optimisation algorithms are used to reconfigure a driver’s deliveries and pickups in real-time, providing drivers with suggestions on efficient routes to undertake. The usage of these data has produced significant savings; in 2011, the firm saw a reduction of 8.4 million gallons of fuel, and a total of 85 million miles were cut out of the drivers’ routes.

How can you introduce smart analytics data into your organisation?

● Keep up with faster technologies and methods of analysis

To tap on speedier insights produced from faster methods of analysis and technologies, companies need to implement changes in functions relating to its operations, product development and decision-making processes.

● Create cross-functional data teams

Instead of letting data scientist work independently, organisations should create a collaborative work environment, whereby these individuals work with data hackers and IT experts to analyse and derive insights from data collected.

● Integrate data into decision-making process and organisational culture

It is important that management recognises the importance of smart data in the decision-making process, and actively seeks ways to incorporate it into the organisational culture.

Simple ways to introduce smart data to employees include starting a marketing program on collecting and analysing data for a product decision, or questioning team members during meetings so that they learn to make decisions based on a combination of hard facts derived from data analytics, as well as using their gut feeling and personal experiences.

    Globetrekker Challenge is a corporate health technology company for employee health engagement and HR data analytics. By integrating multiple features across wellness, technology and social, Globetrekker Challenge creates robust outcomes to improve employee retention, performance and culture.

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