Data Science Adoption Challenges for Enterprises and a Simple Solution

Data Science is reshaping every business function. But while some businesses are making the most of data science, many still need to.

Businesses must make changes to make data science more effective to get the desired impact.

But first, why Data Science?

• Data is the fuel, and data science is the combustion engine. It helps identify linkages hidden within the various data points collected across the enterprise. Understanding these correlations helps business leaders pinpoint the root causes behind specific outcomes and how inputs in one area impact results in another. Data Science, therefore, allows businesses to monitor, collect, and manage performance measures that improve decision-making 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

Data Source/Collection

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; instead, it is important to determine its usefulness. Most organizations need to gain 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 finding the most suitable data difficult.

Data Security and Privacy

Data Science aims to identify 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 must be implemented 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 clients’ personal information. With increasing online transactions, this threat has amplified exponentially. Companies now need to adhere to the fundamentals strictly:

• Integrity
• Confidentiality
• Accessibility

Accessing the right data sets is an even more significant challenge in this scenario. With growing compliance requirements, regulatory concerns, and privacy mandates, accessing the relevant datasets often becomes more challenging. Data encryption, data penetration, pseudonymization, and privacy policies protect information. To address all security concerns while still being able to access the required data, it is crucial to have a data science roadmap 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 that many organizations have adopted the cloud piecemeal or fragmentedly. They need help to look at the cloud in the right context for their data science initiative. It takes an expert in data science and the technologies and techniques inherent in the cloud to design a cloud migration, adoption, and maintenance plan for data science impact.

Undefined Metrics and 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 right question will always elicit a right answer.

While algorithm development is a major part of data science, more is needed if 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 need help to source the right data scientists with in-depth knowledge and 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 with 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 internal and 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, adopting innovative solutions and developing data-driven strategies is necessary. To achieve success, adopting a nuanced and well-planned data science initiative is essential. As we have seen, several significant challenges are to overcome along the way. And partnering with a data science expert like Ellicium can help you achieve success.