Zeenea’s participation in the AI & Big Data Global Expo in London on the 25th and 26th of April has officially opened the window in becoming the leading data catalog solution for data-driven enterprises. Zeenea is confident that the core of every company’s success is the ability to leverage its data assets, which can be achieved by being a truly data-driven enterprise.
During this expo, we attended some Big Data Business Solutions conferences that aimed to inform and educate on how data assets are the make-or-break of successful business decisions. A common theme across the board was how Data Science and Business Analytics are an integral component of adding value within enterprises. But how exactly can this be built into an existing company?
Dr. Andreas Gertsch Grover, the director of Data Science at Charlotte Tilbury shed light on this hot topic in his conference, How small steps get you to the promised land of a data-driven company, by showing us examples of what actually doesn’t work.
A make-up brand’s own sensational makeover
A UK beauty and makeup brand, Charlotte Tilbury is growing at a rapid rate, with a pre-money valuation at $561.22m. With revenues doubling every year, Charlotte Tilbury is headed towards becoming a unicorn company by the end of 2019 [1]. Aiming to be the best selling celebrity make-up brand, the company invested in building a Data Science team in an effort to use prediction models to vamp up their marketing and customer personalization.
With Dr. Andreas Gertsch Grover leading the way, he explains how Charlotte Tilbury has managed to build a data-driven culture to deliver successful data science projects.
The company’s discrepancy between a company’s expectations and a data scientist
“Know the roles you need in the company and not just hire a data scientist,” says Grover. Data Science projects are very complicated and need to involve all employees in the enterprise. To list a few issues data scientists can face when they join a company:
- There is no Data Science infrastructure.
- There are loads of data with only some identified areas in need of improvement.
- Access to data is difficult with no documentation on these data.
Thus, data scientists are forced to make their own environments and laboriously work on large Data Science projects virtually on their own. But when prediction models are created, they ultimately aren’t used as the company doesn’t even know how to apply it to their particular systems!
So what are the steps that need to be taken to close the gap between a company’s expectations and a data scientist’s role?
The must dos
Grover explains that due to the complex nature of Data Science projects, they must start small and be treated iteratively. By doing this, everyone in the company will be able to be involved in the learning process together. Within this collaborative framework, both employees and business stakeholders will be able to understand the business and ask the right questions, which will lead to the next small, successful project.
The must-haves
Grover supports the necessity of using tools when researching and developing their projects. As data acquisition and exploration can take up an enormous amount of time, by investing in tools that will expedite the process, it will save precious time and improve efficiency. Every person should be able to be independent and find the data they need. This particular need is Zeenea’s main goal by providing a data catalog.
The promised land of a data-driven company
Understanding and managing a company’s expectations is never easy but if everybody in an enterprise works together, the Promised Land of becoming a data-driven company is attainable. By working in small steps, iteratively, employees can learn, collaborate, and deliver major business turnovers that are both tried and true.
Sources
[1] Armstrong, P. (2018, August 13). Here are the U.K. Companies That Will Be Unicorns In 2019 Retrieved from https://www.forbes.com/
