Six Hurdles of Modern Data Teams
A data team’s value is only as clear as the data they operationalize for the business.
Data Collection
Yes, gathering data is essential to your business. But In order for data collection to be effective, it must be relevant, timely, and high quality.
Only 44% of data & analytics leaders reported that their teams are effective in providing value to their organization.
The Symptoms
Inaccurate, excessive, and irrelevant data collection
Too much complexity that doesn’t align to strategic goals
Unnecessary risk with data privacy and compliance
The Remedies
Complete a comprehensive data audit to identify areas of improvement
Identify and document a data strategy that maps to overarching business goals
Document and communicate your data governance standards to ensure adherence to privacy and compliance standards
Data Silos
Silos are a dreaded word, especially when it comes to datasets.
The number of people who report that silos are their biggest problem when it comes to gaining insights from data.
Source: Treasure Data Research Report 2023
The Symptoms
A lack of collective insights across the organization
Inaccessible data, outside of what’s available in one’s own department
Inconsistency data formats and structures
Higher costs to manage, consolidate, and store data
The Remedies
Document specific data sharing principles and practices
Conduct a technology stack audit, specifically focused on tools that serve data collection needs and goals
Regularly review data tool and technology costs
Data Quality
High data quality is essential for generating valid insights, making informed business decisions, and achieving strategic objectives.
Number of companies that are confident in their internal data quality.
Source: LinkedIn B2B Institute data
The Symptoms
Inaccurate, inconsistent, and outdated data
AI-generated insights that don’t reflect observed customer behaviors or aren’t in line with current market trends
Diminished engagement, conversions, or revenue
The Remedies
Conduct a comprehensive customer data audit
Create a streamlined process for regularly updating and cleansing datasets
Train and monitor AI-driven outputs to ensure AI models learn from high-quality and diverse data
Data Literacy
Being able to read, analyze, and communicate data effectively is what sets successful companies apart.
Amount of US leaders who consider data literacy essential for their teams’ daily operations
Source: DataCamp’s 2023 State of Data Literacy Report
The Symptoms
Low (or no) engagement with data tools
A tendency to make decisions based on gut feeling rather than data-driven evidence
Possession of data but no clear insights or conclusions made based on it
The Remedies
Document a unified vision and language for data usage
Provide customized data management and analysis training for employees at all levels, in all roles
Adopt tools that are intuitive, are easy to use, and help foster a data-driven culture
Org Design
Having a thoughtfully-structured arrangement and coordination of teams, processes, and tech within your company is the key to maximizing your data.
Number of organizations that do not have centralized customer data management teams
Source: 2023 Gartner Market Survey Report
The Symptoms
Lack of effective collaboration between data scientists and analysts
Ambiguity in responsibilities, leading to overlaps or gaps in data management tasks
Variability in data handling and analysis methods across different teams
Data strategies that do not align with overall business objectives
The Remedies
Identify whether your data team should be centralized or decentralized, based on your business needs
Have clear and consistent alignment and communication with clearly defined roles and responsibilities
Document a unified data strategy that guides all data-related activities
Culture & Leadership
Without proper leadership, data can only go so far. Emphasizing the importance of data in strategic decision-making across all levels will lead to better outcomes.
Culture challenges & accepting change are considered to be one of the top three roadblocks to successful data and analytics initiatives.
Source: 2023 Gartner CDAO Study
The Symptoms
Overemphasis on surface-level metrics, as opposed to deeper behavioral analyses
Absence of personalized experiences due to a lack of in-depth data analysis
Lack of priority on data-driven innovation, like strategies, technology, and training
The Remedies
Invest in data technology and ecosystems that support a comprehensive data strategy
Conduct regular trainings and workshops focused on data utilization and analysis
Embed data scientists across various teams to improve data fluency across the organization