What is a Data Scientist and Why You Should Invest In One For Your Business
The role of a data scientist is to transform data into valuable insights that enable a business to develop a well-informed strategy. Traditionally, this has been carried out using Python, R, and SAS, where data scientists develop models using bespoke code that can take months to finalise and do not transfer to other areas of the business.
This classical approach to data science is lacking in three main ways:
Agility - now more than ever, business requirements are everchanging, and thus the information required from a data scientist can change dramatically in the months that it takes to develop and implement the results from a model using traditional tools.
Adaptability - the difficulty in building models that are flexible enough to accommodate continuously evolving data using outdated platforms often causes projects to become obsolete by the time they are complete.
Transparency - the predictions made by models often lack clarity when presented to management, falling short in providing the business with enough confidence to implement them into production.
These shortcomings of classical data science are reflected strongly in the data.
A McKinsey report showed that 88% of data science projects never even make it into production, and Gartner predicts that through 2022, only 15% of use cases leveraging AI techniques will be successful.
A business can avoid this fate by taking a coherent and agile approach to data science by utilising faster, accessible modern tools such as Alteryx, DataRobot and TangentWorks’ TIM, with whom we have partnered. These platforms are a code-free gateway into empowering users to deploy data science successfully.
They ensure that the insights provided are always aligned with the actual needs of the business. Powerful data-driven decisions can be made almost instantly, as and when they are required.
Citizen Data Scientists
But that is not all. The all-new accessibility of data science has lead to the birth of the so-called “citizen data scientist” - a non-data scientist but analytical business user, who is now able to harness the power of predictive analytics, machine learning, and complex model building, all of which would have previously required significant expertise.
These users are already at the heart of the business and hence often have a better understanding of how to drive the data towards the desired target. By no means do they make data scientists obsolete.
Data scientists are scarce and expensive; citizen data scientists can provide a flexible and cost-effective solution to more rudimentary tasks. They can also act as a useful addition to work in parallel with any formal data scientists the business may have, enabling the experts to focus on valuable high-level analysis.
That does not paint the full picture, however. We have found more and more that organisations are ready to take advantage of these tools to drive their decision-making, but their data is not. Poor data quality is so frequently the root of all evil when implementing data science initiatives.
In fact, around 80% of the time spent by data scientists goes into collecting and cleansing data until it is of a suitable standard to use.
Alteryx
This is where Alteryx comes into the data science grand plan. While also having stellar ML and analytics capabilities of its own, Alteryx truly shines in the reliable automation of transforming poor quality data into a sufficient standard for tools like TangentWorks’ TIM or DataRobot, which integrate with Alteryx seamlessly.
This automation significantly reduces the inefficiencies of gathering quality data, resulting in a business that can adapt to the environment on-demand by drawing data insights swiftly.
To better understand how Alteryx can enable modern data science tools like TangentWorks and DataRobot to benefit your organisation by harnessing your data to drive business solutions, contact us for a demo today.
