Power to the People! Altering Enterprise Analytics Competency for the Future

Celine Siow, Regional Vice President, APAC & Japan, Alteryx
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Celine Siow, Regional Vice President, APAC & Japan, Alteryx

As Singapore gears up towards SmartNation, and a digital-first reality, the digital capabilities of the local workforce have become a priority.

Beyond educating the next generation in such competencies, employers currently contend with the challenges of the skills gap, especially in the area of data and analytics. These are becoming increasingly key in growth sectors, and even the public sector, with programs like the recent Digital Government Blueprint (DGB) set to fast-track adoption. More than skillsets, businesses are increasingly recognising the importance of deploying the right technologies and platforms to keep them ahead of the curve.

Yet, beyond the abstraction of analytics and its promise of business value, how can Singaporean companies solve business challenges by getting to insights faster and addressing issues relating to skills gaps within their organisations? Forward-thinking business leaders across industries are asking these questions right now.

Despite these common challenges, the promise of analytics has not been oversold. Companies that get it right build sustained advantages and outpace their competition. So how do they do this? What must happen within an organisation to harness the power of its data?

The answer lies in ensuring that the right culture, skillsets and tools are set within the organisation to empower individuals to have access to data and derive meaningful outcomes with it—without this foundation, initiatives flounder and fail to produce results.

Freedom for Exploration

Companies that want to win using analytics must develop a top-to-bottom, as well as a bottom-up competency—an enterprise ability to perform meaningful data analysis. This involves giving individual business analysts the freedom to utilise all available resources, collaborate with colleagues across different departments, and make better-informed decisions with the faster insights.

Developing an analytics Centre of Excellence across the enterprise however is no small undertaking. It requires a great deal of self-awareness—the organisation must step outside itself and evaluate how it goes about achieving its goal on a daily basis, across its various executives and business units.

This will be challenging. Some would ask, ‘why not leave analytics to the data scientists?’ It’s because the people who understand the problem should be the ones solving it.

Companies that want to win using analytics must develop a top-to-bottom, as well as a bottom-up competency

The empowerment of business analysts is thus crucial, as they are the ones with the deepest functional knowledge of the business, the way it works, and they will be the ones to create real value out of the data, that in turn translates into machine learning models for end users. They will be able to figure out the right questions to ask—often the hardest part in the analytics process.

In the case of Hong Kong Polytechnic University (PolyU), the use of self-service analytics helped its cross-functional team perform analytics in a flexible and timely manner, with deeper insights gleaned in a matter of hours. This has allowed them to even uncover factors related to student progress, and use data to predict outcomes for the improvement of the student experience.

With access to the right data and an environment of exploration, the exploration of insights continues to spur excellence in business outcomes for organisations like Hong Kong PolyU.

Specialised analytics teams don’t scale

No matter how talented data scientists are, there simply aren’t enough of them to support the number of requests that come from across the business. In fact, Data Science and Analytics sits at the top of skills shortage in Asia Pacific, according to the Asia Pacific Economic Cooperation.

There is a legacy belief that analytics are best left to IT personnel or someone with a technical background. This belief persists because early analytics tools were not user-friendly and they required advanced coding knowledge to be of any use. The thought was that people on the business side can’t be trusted with the data because they’re untrained in these tools and analytic methodologies. They don’t know what they’re doing, they don’t understand the output, and they’re going to interpret things the wrong way.

Yet, companies like 3D printer and production systems manufacturer, Stratasys, are proof that this is not the case. Today’s analytics tools no longer require deep coding knowledge or special skillsets. With a small amount of training, a code-free environment and a collaborative data foundation, business analysts can derive answers from the data, with platforms that provide a seamless and reliable data exploration process for users across the business to glean from analytics insights together.

In Stratasys’ case, the use of analytics is involved in all aspects of business intelligence and management of its customers and channel partners. Pulling together all required data from its channel partners into a synchronised single dashboard, the company is able to glean comprehensive insights from sales, for use across the business.

A digitally-enabled workplace begins with a confident workforce

In conclusion, when individuals are given the tools they need to be more efficient, offer value-added service and collaborate across lines of business with self-service tools, a culture of engagement and innovation is created. The more people get their jobs done better and more productively, the more engaged they become.

Businesses hire smart people who want to make a difference. They invest in top-tier talent, but if it stops there and they are sequestered inside an isolated data science vault, the business is stuck without the data it needs and employees become disengaged, eventually leaving to find a more fulfilling job elsewhere.

Give power to the people—putting analytics in the hands of the people allows them to break barriers and experience the thrill of solving that they never thought possible.

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Machine Learning