December 20189 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. 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 scaleNo 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 COMPANIES THAT WANT TO WIN USING ANALYTICS MUST DEVELOP A TOP-TO-BOTTOM, AS WELL AS A BOTTOM-UP COMPETENCYdata 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 workforceIn 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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