The Low-hanging Fruits of Data Analytics Are Ripe for Harvest

The world’s data doubles every two years. Massive data resources have led to considerable hype around data analytics, and demand for data science skills is growing in most every organization. Competence in data analytics remains scarce in Finland, although learning the basics does not require special aptitude for math. Motivation and hard work will get you far.

Joanna Sinclair, 03.12.2019

Data has been coined the new oil, air or even water. In 2012 Harvard Business Review went so far as to name data science the sexiest profession of the 21st century. Although the field of data analytics has boomed in recent years, it is by no means a new phenomenon.

Computerized predictive analytics has been utilized since the 1940s and large corporations in particular have used analytics since the 1970s to aid decision making. The first mechanic, programmable aid for data analytics – the tabulating machine – was developed to help process data for the 1890 U.S. Census.

The first steps yield the highest rewards

The low-bearing fruits of data analytics are ripe for harvest. Companies that are just starting out with analytics are set to gain the most.

“The greatest business benefits from data analytics typically come from the first steps,” emphasizes Senior University Lecturer at Aalto University, Jaakko Hollmén, whose work focuses on machine learning, data mining and artificial intelligence.

The greatest business benefits from data analytics typically come from the first steps."

“I recently heard of a financial organization that quadrupled its sales with focused advertising. It was a new company, founded a few years ago. It gathered data on clients for two to three years and then carried out one, well-planned data analysis to better understand its clients. Sales skyrocketed,” Hollmén remarks.

Once a company is more proficient in data analytics, it can start using predictive models.

“Take for example an industrial process in which material samples are sent to a lab. Results may take days to come. A predictive model could give the same results in a fraction of a second, based on things that can be measured directly. Companies are now ardently seeking these kinds of predictive models, which save time and money,” he adds.

Analytics will become commonplace


Hollmén has over 20 years of experience in data analytics, both from industry and university research. He was not surprised to see his own field turn into a business buzzword.

“During the past 5-10 years data analytics has become a new favorite for businesses, alongside machine learning and AI. The enthusiasm towards data analytics feeds itself: when forerunners gain competitive advantage with analytics, others join the bandwagon,” Hollmén points out.

“I don’t think that the significance of data analytics will diminish after the hype calms down. In a digital world analytics will become a basic skill required in every organization – either inhouse or as an outsourced service,” he ponders.

Work backwards: start with the desired results


Hollmén emphasizes that in data analytics, it’s a good idea to work backwards: start with the business benefits you wish to attain. In order to do this, you need talent who understand both business and analytics.

“You need to know what data resources you already have – or systems that you can get business-relevant data from. In addition, you need to know how results can be taken into practice,” he says.

“If a company aspires to reap benefits from data analytics, employees with proficiency in the field are worth their weight in gold. Competence in data analytics is a significant, career-boosting talent for any knowledge worker,” he continues.

Results only come through collaboration


Google-owned online community of data scientists and machine learners, Kaggle, conducted a survey in 2017 examining barriers that prevent companies from making the most of data analytics. Dirty data was the most common hindrance. In addition, professionals in the field named several barriers linked to a lack of collaboration. Either companies don’t know how to present clear questions to solve with data analytics, results are poorly communicated, management support for data science projects is insufficient, or the results are simply not put into use.

All employees should know at least one thing about data analytics: data quality is crucial." 

“Many companies have a centralized team for data analytics. This can be a good solution, if the analysts have good working relationships with the rest of the organization, and the analysts understand business. If the organization’s central functions also employ professionals with aptitude for analytics, interaction becomes much smoother,” Hollmén highlights.

“All employees should know at least one thing about data analytics: data quality is crucial. Every member of an organization produces data – and if the data is shoddy, you can’t expect analytics to reveal growth drivers or ideas for cutting bottlenecks,” Hollmén reminds.

The Data Lab training has been created for people who have at least basic skills in Python, R, Java, or similar tools. After the program your skills, attitude, and way of thinking will have taken a leap forward towards those of a world class data scientist. Read more about the program.

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