Data Science is reshaping every business function. But while some businesses are making the most of data science, many are not.
To make data science more effective, businesses must make some changes for getting the desired impact.
But first, why Data Science?
- Organizations leverage trend analysis techniques to make vital decisions and improve corporate performance, consumer engagement, and ROI
- Data Science formats use current data to simulate various operational aspects
- It also assists businesses to identify and refine target audiences by assimilating existing data with further data points to deliver meaningful insights
In this data-driven world, businesses want to mine meaning from data. As a result, the business and
IT teams are under intense pressure to deliver data science impact despite the challenges.
5 Major Data Science Adoption Challenges
One key step of any Data Science initiative is to find and gather valuable data assets. However, suitable data may not be available. Data scientists and organizations face a major challenge that directly affects the ability to develop robust models.
Why is it so difficult to source data? It’s because companies fail to isolate useful data from bad data.
Firms must understand that sourcing a massive amount of data is not enough; rather, it is important to determine that the sourced data is useful. As most organizations fear missing major insights and due to widespread data availability, organizations are overwhelmed with useless data that harms more than helps.
The sheer profusion of data resources often makes it difficult to find the most suitable data.
|Data Security & Privacy|
Data Science aims to identify better business opportunities, improve performance, and drive better decision-making. However, with security concerns, come many practical hindrances caused by the many systems and protective measures that need to be put into place to stay safe, secure, and compliant.
Information theft has been a common concern with data security for organizations having access to sensitive financial information and the personal information of clients. With increasing online transactions, this threat has amplified exponentially. Companies now need to strictly adhere to the fundamentals:
Accessing the right sets of data is an even bigger challenge in this scenario. With growing compliance requirements, regulatory concerns, and privacy mandates, it often becomes harder to access the relevant datasets. Data encryption, data penetration, pseudonymization, and privacy policies protect information. To address all security concerns, while still being able to access the data required, it is crucial to have a data science roadmap in place that factors in the security concerns. It takes an expert in this field to design such a roadmap.
|Infrastructure and Process Strategies|
Enterprises can only realize the true potential of data science resources when they become universally accessible and the strategies become scalable. This is only possible when enterprises can merge cloud, daily business enablers, and data science. In essence, to drive up the applicability of data science, business leaders, contributors, and beneficiaries should aim to:
- Move to cloud storage
- Integrate data science into standard project plans
- Redefine data scientist roles to align them with business priorities
The cloud offers economy, scalability, universal access, and inbuilt tool sets for data science.
The move to the cloud is no longer up for debate. The challenge here is many organizations have adopted the cloud in a piecemeal or fragmented fashion. It’s not easy for them to look at the cloud in the right context for their data science initiative. It takes an expert in data science as well as the technologies and techniques inherent in the cloud to design a cloud migration, adoption, and maintenance plan designed for data science impact.
|Undefined Metrics & KPIs|
Data scientists are experts in analytics, software, and mathematical model design. There’s little doubt that they can produce the models they are tasked with creating. The challenge usually lies in the business definition of the problem these models are intended to solve. A wrong question will always elicit a wrong answer.
While algorithm development is a major part of data science, it is not enough unless conditions are in place to set the right requirements. The right choice of KPIs and metrics can boost the business impact.
Also, it is important to:
- Set realistic goals
- Reuse artifacts
- Focus on actionable insights
- Define the ROI
|Finding the right talent|
To cut right to the chase, this may well be the reason for the maximum number of data science projects to run aground or fall apart. Businesses struggle to source the right data scientists having in-depth knowledge as well as domain expertise.
Data Science projects are only successful when businesses can convey their story through data. Therefore, it is crucial to find the right scientists and analysts, who have a perfect combination of storytelling and problem-solving abilities. Without the right people, data science initiatives are doomed to failure.
Role of Ellicium
Ellicium is an expert in developing data science road maps and use cases for businesses. Our talented team specializes in sourcing both internal as well as external data to build data sets. From building data science cloud infrastructure to algorithms, we specialize in helping our customers embrace the aspects of data science that contribute to business growth.
In this fast-paced digital era, it is necessary to adopt innovative solutions and develop data-driven strategies. To pursue achieve success, it is important to adopt a nuanced and well-planned data science initiative. As we have seen, there are several significant challenges to overcome along the way. And partnering with a data science expert like Ellicium can help you achieve success.