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Trends in Data Science That Will Change Business Strategies

From individual skills to business development, data professionals have many opportunities in the next few years.

Photo by Paweł Czerwiński on Unsplash
Photo by Paweł Czerwiński on Unsplash

In response to an atypical year, companies rely on data and analytics leaders to accelerate innovation and create new routes to generate revenue. However, recent research involving business leaders in the U.S., U.K., and Germany shows a growing concern around employees’ lack of analytical skills [1].

Although there are many challenges ahead, the same research reported that more than half (52%) of companies and organisations are looking to foster a data-driven culture. This is an excellent opportunity for data professionals seeking to climb the corporate ladder and those in a career change to Data Science. That said, here are the top three trends in Data Science and analytics involving business and organisational development for the next years.

1. Data Literacy Incubators

Research shows that companies and organisations have been investing heavily in becoming data-driven businesses [2]. To ensure success, part of that investment is directed towards technology. Still, a surprising 41% is allocated in creating a data literate workforce. There is little use having access to large amounts of data when employees cannot understand the language of data.

Professionals who are comfortable working with data, and able to answer some business questions are the next generation of employees in a data-driven culture. Therefore, companies are solving data illiteracy by drawing insights from academic institutions such as The University of South Florida, which has established a Citizen Data Scientist certificate and launched their data skills programmes and centres of excellence. Also, some non-tech companies seek out training through third-party data programs or subsidise bootcamps. In turn, these initiatives level up employees’ analytical skills.

For example, Lockheed Martin has launched data literacy workshops to teach employees across their U.S. operations. The aerospace and global security company plans to roll out their classes to people working in other non-traditional analyst roles, including manufacturing. As a result, the analytics team has seen shifts in how employees treat data concerning their roles and, equally important, the added value they create as data literate professionals.

2. Artificial Intelligence moves from abstract to actionable

Artificial Intelligence (A.I.) is on everyone’s minds as the next step of digital transformation. Companies, therefore, are diving into A.I. projects to adapt their businesses and beat the competition. Analysts estimate that by 2022 global spending on A.I. will reach US$79.2 billion, which is more than double the investment made on A.I. in 2019 [3]. However, so far, most organisations still fail to recognise the value of their A.I. investments. This is partly because of data illiteracy workforce and the focus on technology over practical use.

Companies have been taking a practical approach more recently, making special adjustments from the start – structuring teams that create, test and implement A.I. projects. In a Harvard Business Review article, the Head of Google Cloud AI, Andrew Moore, said:

"When A.I. becomes an everyday technology, we are the age of ‘deployed A.I.,’… people are more focused on a ‘shared vision’ for A.I. that outlines how A.I. fit into existing processes and team structures.[4]"

Consequently, business leaders will move data scientists and engineers working on A.I. projects in silos to strategic planning discussions. In doing so, they ensure that A.I. and machine learning projects will support the company’s business strategy and answer business questions. Guess what? Data professionals will need to quickly adapt by developing their soft skills, such as communication, and getting comfortable with ‘good enough’ results, despite the imperfections. Otherwise, there is a good chance the clash between data professionals and business executives will continue.

Projects with a collaborative approach can reveal which parts of business decisions are best suited for A.I. requiring human intervention. Machine learning recommendations are valuable as far as the people making the decisions know what they mean. As the principal product manager for A.I. at Tableau, Richard Tibbetts, explains:

"The emergence of A.I. does not mean that an algorithm will tell you how to run a business.[1]"

Consequently, data professionals will gain useful business skills and learn how to apply data to strategic business decisions whilst business executive and marketing professionals will advocate for data proficiency in all areas of the business. Based on their interaction and exchange of knowledge, business experts will play a crucial role in putting the results of A.I. projects into practice across departments and teams. This shift will signal a new era of for A.I. use in business, where A.I. is actionable.

3. Data Storytelling goes mainstream

It is not surprising how consumers became used to companies collecting their data and even anticipating their behaviour in weekly or annual reports. Consequently, brands have increased consumer engagement by making content more interactive, meaningful, intuitive and convenient.

"87% of consumers think it’s important to purchase from brands or retailers that understand the ‘real’ me.[5]"

Photo by Karsten Winegeart on Unsplash
Photo by Karsten Winegeart on Unsplash

Data storytelling is a popular and effective way for companies to illustrate consumer habits and engage us with their brands. As a result, interacting with your data becomes exciting and not intrusive compared with data privacy policies. Personal data interactions have become even more engaging. For example, in Spotify’s Year in Review, their listeners received a quiz to guess which artist they listened the most hours of streaming. Also, Facebook presents a quiz for you and a connection after reaching an online friendship milestone. Questions may involve guessing the number of ‘likes’ or who shared a particular photo. Quizzes make interaction with your data more engaging. They offer an exciting opportunity to fact-check any bias you might have around your own data story.

In the next years, you should expect to see more brands offering similar data experiences and interactive insights. Personalised consumer experiences boost brand loyalty as well as new purchase opportunities. As data storytelling becomes more valuable and informative to consumers, data analysis is essential for most marketing strategies. Therefore, companies and organisations will benefit from data professionals who understand the big picture and apply their data insights to business strategies that offer personalised consumer experience.

Conclusion

Companies and organisations rely on data to drive their innovation agenda. However, business leaders face significant challenges to make the most out of their investment in a data-driven culture. That said, in the next few years, you should expect three main trends in Data Science and Data Analytics:

  1. Companies will become data literacy incubators, creating their qualifications and certificates around data.
  2. A.I. projects will become more actionable and integrated with business strategies, allowing data professionals to participate in business strategies and planning.
  3. As data storytelling goes to mainstream, brands create new opportunities to engage with consumers.

Data professional will play a key role if they adapt to business environments. Finally, despite the significant changes in the last months, most data professionals will thrive in the following years.

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References:

[1] https://www.alation.com/state-data-culture-report-2020-q4/

[2] Data Trends Report by Tableau (2020) **** https://www.tableau.com/en-gb/reports/data-trends#trend2

[3] International Data Corporation (IDC), "Worldwide spending on artificial intelligence systems will grow to nearly $35.8 billion in 2019, according to new IDC spending guide," March 11, 2019.

[4] Harvard Business Review (2019). https://hbr.org/2019/06/when-ai-becomes-an-everyday-technology

[5] Accenture Interactive (2019) https://www.accenture.com/us-en/insights/interactive/see-people-not-patterns


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