Breaking Down the Barriers to Big Data Opportunity

Big data has shown us the doorway to a world of new business opportunity, but there is a strange reluctance to push that door wide open and embrace the future. CLP’s Big Data experts examine the obstacles and map a way forward.

Breaking Down the Barriers to Big Data Opportunity
Friday, 15 January 2021
4 min read

By Pubs Abayasiri, Associate Director – Digital Products, CLP, and Vidal Fernandez, Director – Big Data, CLP

 

There was a time, not long ago, where many of us operated and made decisions based largely on our knowledge and experience rather than data. That seems almost inconceivable these days when most of us are awake to the benefits of data-driven services.

 

The best examples of utilising data in a meaningful way come from the technology sector.  From the moment we wake up and grab our mobile phones, to the music we listen to on Spotify and the TV shows recommended to us on Netflix, we are bombarded by targeted, fairly accurate content, shaped by countless data points captured through our daily interactions.

 

However, there is still work to be done for many non-technology sector corporates to get to a situation where we can realise the true potential of data science. So why can’t we get there faster and move away more rapidly from analogue services? We believe there are four key factors holding us back: Legacy, Investment, Talent, and Sponsorship.

1. Legacy
Organisations are still burdened with legacy considerations when it comes to utilising data. In the recent past, data collection inputs from systems were included more for reporting purposes rather than capturing behavioural and operational patterns. The one key thing that eluded our collective minds was the value data and artificial intelligence (AI) can offer.

The data that data analysts and data scientists work on today are very rich and diverse, but often have gaps in their scope and quality.  For example, a sensor on a piece of equipment may capture the health of that equipment once a day, which might satisfy the reporting purpose but there are not nearly enough data points for accurate predictive capability.

2. Investment
Although more decision makers now understand and appreciate the value that data brings, there are still challenges when it comes to making a business case for data-related investments. How do you define the return on investment of predictive maintenance when the value will only be realised if – and only if – the predictive models prevent a fault?

One way to address this issue is to run simulations to test ideas and prove concepts. Going through that conscious decision-making process is important from a prudent investment perspective, and also helps ensure buy-in from different stakeholders within an organisation.

 

In Hong Kong, data science is only just moving away from being a cottage industry and into the mainstream, and continuing that movement requires investment in spite of the legacy situations organisations may have.  

3. Talent
A common challenge most hiring managers face in any discipline is finding and hiring the right talent. With data science, this is especially difficult. A data scientist is not just a statistician, a mathematician, or just a programmer. They are able to utilise an orchestra of these skills and beyond, depending on the challenge at hand.

Some of the most effective data scientists also have a good sense of business analysis and project management skills to ensure the work they deliver is not just for research and development, but also for business application. 

4. Sponsorship
In any organisation – whether it is a school, a hospital, or a utility company – having an executive or business sponsor within the organisation who will support a data science initiative is imperative. The biggest value of the sponsor is to ensure data science initiatives have the right focus and legitimacy.

A data science model alone doesn’t provide business value. The value comes from embedding the model into business operations – and that is where a business sponsor comes in. 

 

Strategies that can help in this regard include:

 

• Improving data literacy across an organisation at all levels, including senior executives.

• Training existing business subject matter experts to become data analysts and even data scientists.

• Adopting the right organisational constructs for data science that works for you.

• Cooperating with partners, such as universities or start-ups.

The Data Journey at CLP

CLP has been on a long journey when it comes to data maturity. We still have some way to go, but there have been some notable successes along the way.

 

In the past decade, the focus of adopting data science at CLP has shifted to utilising data to provide improved services, directly or indirectly, for customers. Data and AI algorithms have allowed us to improve our business operations, become more efficient, and better serve our customers. 

 

Predictive maintenance and condition-based monitoring have given us the opportunity to leverage more reliable business by providing better quality service and sustainable energy. AI technology applied in the fields of safety, health, and environment enables CLP to provide greener and safer energy by monitoring operations, preventing incidents, and reducing risks.

 

In recent years, we have shifted towards having a dedicated data science function that focuses on building data models for high-value use cases, ranging from predicting demand to determining health of assets, with an aim of ensuring these models are operationalised within the business.

 

The energy management smart solutions CLP now offers through the Smart Energy Connect platform all have data elements, such as visibility of the real-time health of a building and the ability to determine indoor air quality in office environments.

 

Within Hong Kong we are seeing a shift with increasing numbers of organisations adopting data science as part of their digitisation strategies, more data science-related accreditations available, and more cooperation with universities and start-ups. However, more still needs to be done. Immense opportunities lie within our reach, and it is up to us to seize them.

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