Data Scientists: Overcoming challenges and making them stars

Data scientists are the rockstars of the data world, but they face significant challenges in demonstrating the true value of their work. Let's delve into these challenges and explore how organizations can systematically manage data to support their efforts.

Lack of clear questions

Often, communication gaps between business stakeholders and data scientists lead to a technical approach to business problems. Bridging this divide requires enhancing data literacy on the business side and business literacy on the data side. A data-driven organization must have clearly mapped business concepts that align with data, enabling data scientists to work with a deep understanding of business requirements.

Inaccessible data

Data silos plague most organizations, making it difficult to access and consolidate data from various platforms and formats. Manual data entry and time-consuming searches lead to errors, repetitions, and redundancy. Overcoming this challenge requires a robust data management strategy to ensure seamless access to the right data.

Dirty data

Data preparation becomes a time-consuming task as data scientists spend 80% of their time on data cleansing and classification. Ensuring data quality should be a collective responsibility within the organization, not solely on data scientists. Establishing clear quality rules and standards across the organization is crucial to tackling this issue.

Insights not used

Despite investing heavily in data-driven insights, many organizations see these insights ignored during management decisions. Trust in data is paramount, and it must be established throughout the data life cycle to ensure insights are valued and acted upon.

Making data scientists stars

Data scientists are given the task of producing valuable insights, but they often lack the fundamental base for their work – meaningful, reliable, and quality data. To address this, organizations must manage their most critical asset – data – strategically.

Aligning data strategy with business objectives

Data strategy must be oriented towards the organization's strategic priorities and key business objectives. Business stakeholders and units should drive data initiatives, focusing on solid business cases. By involving business stakeholders in identifying necessary data, rules, concepts, and quality standards, the burden on data scientists is reduced, and trust in data insights grows.

An iterative and agile approach

Taking an iterative, agile approach allows data management initiatives to gain momentum and continuously evolve. Starting with tactical deployments and small successes paves the way for the data strategy to grow organically and build upon those achievements.

Data governance and quality

Addressing governance and quality should not impede data initiatives; instead, organizations should emphasize delivering results and ensuring ongoing data quality management and governance.

The path to making data scientists stars lies in embracing a persuasive data management approach. Addressing challenges such as lack of clarity, inaccessible data, dirty data, and ignored insights is crucial for unlocking the true potential of data scientists.

While aligning data strategy with business objectives and engaging business stakeholders, organizations empower data scientists to deliver valuable, actionable insights. Embracing an agile methodology and starting with small, tactical deployments will build trust and momentum, propelling data initiatives to success.