Empowering Mid-Market Companies with Data Management

OVERVIEW OF DATA LANDSCAPE

We begin with an exploration of the sources of data available to mid-market businesses. From internal operations to customer interactions and third-party sources, you will gain insights into the types of data that can be leveraged to drive business decisions and strategies.

DATA GOVERNANCE PRINCIPLES

Data governance is essential for maintaining data integrity, privacy, and security. In this section, we provide a detailed explanation of data governance principles tailored to mid-market businesses. You will learn about data ownership, privacy considerations, security measures, and data quality assurance. Understanding these principles will help you establish effective data governance practices within your organization.

DATA ARCHITECTURE

Building a solid data architecture is crucial for managing and utilizing data effectively. We provide guidance on designing a scalable and cost-efficient data architecture for mid-market businesses. From data warehouses to data lakes, databases to data pipelines, you will learn how to set up the infrastructure to support data storage, processing, and delivery.

Want to decrease ad hoc data requests by 90%? Schedule a demo

DATA DICTIONARY AND CATALOG

To ensure clarity and consistency in data usage, establishing a data dictionary and catalog is essential. We guide you through the process of creating a comprehensive data dictionary that defines data elements, their sources, formats, and relationships. This resource will serve as a reference point for all stakeholders, facilitating effective communication and understanding of your organization’s data.

DATA QUALITY METRICS

Maintaining high data quality is critical for making accurate business decisions. We delve into the measurement and management of data quality, providing practical strategies and key metrics for mid-market businesses. You will learn how to assess data quality, identify potential issues, and implement data quality improvement initiatives.

USE OF DATA IN DECISION-MAKING

Data-driven decision-making is a competitive advantage for mid-market businesses. Through real-world examples and case studies, we illustrate how data can inform and enhance decision-making processes. You will gain insights into using business intelligence reports, data analytics, and data visualization to drive informed decisions and achieve strategic goals.

DATA TOOLS AND TECHNOLOGIES

Navigating the vast landscape of data tools and technologies can be overwhelming. We provide an overview of essential tools and technologies suitable for mid-market businesses. From database management systems to data analysis platforms, you will discover the right tools to effectively collect, process, analyze, and visualize data within your organization.

DATA LITERACY TRAINING

Enhancing data literacy among your employees is vital for maximizing the value of data. We offer resources and training materials tailored to mid-market businesses, covering key data concepts, tools, and best practices. Empower your team to become data-literate, enabling them to leverage data for improved

DATA POLICIES AND COMPLIANCE

Compliance with data regulations is critical for mid-market businesses. We provide an overview of relevant data policies and legal requirements, such as GDPR or CCPA, and offer guidance on establishing data handling and storage practices that align with compliance standards. Ensure your organization adheres to regulations and protects customer data appropriately.

Want to decrease ad hoc data requests by 90%? Schedule a demo

DATA STRATEGY AND FUTURE PLANS

Developing a data strategy is crucial for long-term success and growth. We guide you through the process of formulating a data strategy specifically tailored to mid-market businesses. You will learn how to set goals, prioritize initiatives, and leverage data for innovation, market expansion, and competitive advantage.4.

As you embark on this comprehensive journey through the data landscape, tailored to the needs of mid-market businesses, we trust that this guide will equip you with the knowledge and tools necessary to unlock the potential of data

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OVERVIEW OF DATA LANDSCAPE

Explanation of the sources of data, types of data, and how it’s used in the company. This may include data generated from internal operations, customer interactions, third-party sources, etc.

1 CAN YOU IDENTIFY THE PRIMARYSOURCES OF OUR DATA AND THE TYPES  OF DATA THEY GENERATE?

ZENLYTIC APPROACH:

◉ Focus on the business process and think in terms of verbs, nouns, and adjectives.

◉ Identify the valuable business processes and data-generating processes specific to your business.

◉ Consider actions people take in relation to your business, such as placing orders or interacting with a website.

◉ Start by focusing on the verbs (actions) that are important to your business and ensure you have data sources for them.

◉ Begin with the end in mind by considering how you will use the data and then identifying the sources that enable those use cases.

◉ Don’t forget about the nouns (segments) that matter, such as different types of customers, users, or suppliers.

◉ Adjectives (dimensions) provide additional context to the nouns and help create a comprehensive understanding of the data.

2 HOW IS THE DATA FROM DIFFERENTSOURCES INTEGRATED?

ZENLYTIC APPROACH:

◉ Analyze the fundamental relationship between different business processes and how they are linked.

◉ Centralizing data is important and beneficial for successful companies.

◉ Benefits of centralization include control over first-party data, the ability to join and analyze data sets, and easier data consumption.

◉ Choosing a data warehouse, such as Snowflake, is a crucial step in the modern data stack.

◉ Bias towards adding more data sources rather than fewer, as it is likely to be useful in the future.

3 WHAT ARE THE MOST CRITICALDATASETS FOR OUR OPERATIONS?

ZENLYTIC APPROACH:

◉ Identify the most important data sets related to your core business loop.

◉ Focus on the data that influences revenue generation and sustains the business.

◉ Understand the inputs that can impact your revenue-generating processes and gather data on those inputs.

◉ Start with your core loop and a few levers that can be used to make changes.

◉ Begin with simplistic data related to the core loop, which can still provide value, and then expand from there.

◉ Consider the concept of business loops instead of funnels for a comprehensive understanding of growth.

◉ The core business loop is a critical element to prioritize in data analysis.

4 HOW FREQUENTLY IS OUR DATA UPDATED, ANDWHAT’S THE PROCESS FOR THESE UPDATES?

ZENLYTIC APPROACH:

◉ Data refresh frequency should strike a balance between timelines and cost.

◉ The process for data updates should be 100% automated to avoid errors and costly manual intervention.

◉ Focusing on achieving faster refresh times can be a trap and may not be necessary.

◉ Data sources and consumption often don’t require high-frequency updates.

◉ Starting with an overnight refresh is a common and practical approach.

◉ Instead of prioritizing faster refresh rates, focus on improving data quality.

◉ Consider the actual operational use case before pursuing faster refresh time sand explore alternative approaches if necessary.

5 ARE THERE ANY LIMITATIONS OR CHALLENGESWITH OUR CURRENT DATA SOURCES THAT WE  SHOULD BE AWARE OF?

ZENLYTIC APPROACH:

◉ The integrity of data is crucial for the success of data projects.

◉ The top reason why data projects fail is due to people losing trust in the data.

◉ Data quality management is essential and should be prioritized.

◉ Display and usefulness of data are irrelevant if the data itself is not accurate.

◉ Repeated instances of incorrect data erode trust and discourage dashboard usage.

◉ Regaining trust once it is lost is difficult.

◉ Emphasize integrity, accuracy, and quality of data from the outset to ensure project success.

Want to decrease ad hoc data requests by 90%? Schedule a demo

DATA GOVERNANCE PRINCIPLESA

detailed explanation of the company’s data governance policy, including data ownership, data privacy, data security, and data quality principles. This would also detail the roles and responsibilities of various team members in ensuring data governance.

1 WHO ARE THE DATA OWNERS/STEWARDSWITHIN THE ORGANIZATION, AND WHAT ARE THEIR RESPONSIBILITIES?

ZENLYTIC APPROACH:

◉ Recommended structure for data teams in midmarket (MM)companies is centralized.

◉ Hub and Spoke model is recommended for larger enterprises.

◉ Centralized structure involves 1-3 data people managing all data infrastructure.

◉ Hub and Spoke model consists of a central team for data governance and embedded data analysts in domain-specific teams.

◉ Start with a small, centralized team and expand as teams become more data-driven.

◉ Aim for a ratio of 1 data person per 50 people, which may decrease over time due to tool advancements.

2 WHAT MEASURES DO WE HAVE IN PLACE TOENSURE DATA PRIVACY AND SECURITY?

ZENLYTIC APPROACH:

◉ Minimum requirement is implementing controls over Personally Identifiable Information (PII).

◉ Special considerations may apply to certain industries (e.g., financial services, healthcare).

◉ Controls can be implemented at the metrics layer, warehouse level, or BI tool level.

◉ Warehouse level provides the most secure control for sensitive privacy-relate dissues.

◉ Start with a data policy of “need to know” and grant access permissions accordingly.

◉ Focus on controlling access at the metric definition level to ensure specificity and mitigate misconfigurations.

◉ For highly sensitive data (e.g., salary information), consider limiting access at the warehouse level or avoiding its inclusion altogether.

◉ Need-to-know approach is preferred over granting wide access and then restricting it later.

3 HOW DO WE HANDLE DATA ERRORS ORINCONSISTENCIES?

ZENLYTIC APPROACH:

◉ Implement basic testing in either DBT or SQL Mesh.

◉ Emphasize the importance of having tests in place to identify and address errors.

◉ Tests help ensure that fixed errors do not reoccur in the future.

◉ Avoid relying solely on fixing errors without proper testing.

◉ Testing is crucial for confirming the effectiveness and sustainability of fixes

4 WHAT IS OUR POLICY FOR DATA RETENTIONAND ARCHIVAL?

ZENLYTIC APPROACH:

◉ Storage costs are trivially cheap, so there’s no significant operational reason to get rid of data at a midmarket company level.

◉ Consider compliance and legal reasons when deciding to keep or delete data.

◉ Previously, companies would use cold storage or transfer data to old data lake storage, but it has become less necessary now.

◉ Keeping data in the warehouse simplifies the architecture and maintains accessibility.

◉ Historical data should be readily available for potential analysis or reference, rather than stored in cold storage and requiring retrieval.

Want to decrease ad hoc data requests by 90%? Schedule a demo

5 CAN YOU OUTLINE THE PROCESS FORACCESSING AND SHARING DATA WITHIN THE  ORGANIZATION?

ZENLYTIC APPROACH:

◉ Self-serve is crucial for efficient data access and analytics.

◉ The 1 per 50 rule for data person capacity is insufficient without self-serve capabilities.

◉ Self-serve systems empower domain experts to effortlessly access and analyze data.

◉ Quality of understanding and analytics improves when domain experts have direct access to their own data.

◉ Self-serve platforms enable domain experts to define access permissions and foster better decision-making.

◉ Having a closed loop of data within a domain expert’s control leads to more effective use of data compared to relying on data person requests.

DATA ARCHITECTUREA

clear depiction of the company’s data architecture, including data warehouses, data lakes, databases, data pipelines, and data processing systems. This shouldalso explain how data flows through the system, from ingestion to processing to delivery.

1 CAN YOU PROVIDE A HIGH-LEVEL OVERVIEW OFOUR DATA ARCHITECTURE, INCLUDING DATA IN GESTION, STORAGE, PROCESSING, AND DELIVERY?

ZENLYTIC APPROACH:

◉ Emphasize buying off-the-shelf tools instead of building in-house solutions.

◉ For data collection and movement, consider tools like Fivetran, Airbyte, and Matillion.

◉ Use tools like Snowplow or Segment for user event tracking.

◉ Choose industry-leading data warehouse platforms like BigQuery or Snowflake.

◉ Use DBT and SQL Mesh for data testing and transformation.

◉ For self-serve analytics, consider Looker or Zenlytic for defined metrics and accessible data exploration.

◉ Avoid getting overwhelmed with additional tool categories like data observability or data catalogs initially.

◉ Start with the necessary tools outlined above and ignore other categories while getting started.

2 WHAT ARE THE KEY DATA TECHNOLOGIES WEUSE AND WHY WERE THEY CHOSEN?

ZENLYTIC APPROACH:

◉ Ingest data using the mentioned tools (previously discussed).

◉ Choose a data warehouse platform: Snowflake or Big Query.

◉ Perform data transformation using dbt or SQLMesh.

◉ Utilize Looker or Zenlytic for business intelligence (BI) needs.

◉ Consider Zenlytic as a reliable large language model (LLM) for data-related queries.

◉ Trust cloud vendors like Snowflake or BigQuery for built-in features, such as data retention and recovery options.

◉ No immediate need for additional tool categories like observability.

◉ Select a cloud vendor that provides the necessary tools for data management and protection.

3 HOW DO WE HANDLE DATA REDUNDANCYAND BACKUP?

ZENLYTIC APPROACH:

◉ Select a warehouse vendor that offers built-in tools for data management.

◉ Recommended options are BigQuery and Snowflake.

◉ These vendors can handle data scaling requirements up to a significant level.

◉ Emphasize the importance of buying tools instead of building them in-house.

◉ Using a warehouse vendor like Snowflake allows for scalable solutions without encountering significant scaling problems.

◉ Focus on leveraging the capabilities of these vendors for efficient data scaling.

4 WHAT MEASURES ARE IN PLACE TO ENSURETHE SCALABILITY OF OUR DATA SYSTEMS?

ZENLYTIC APPROACH:

◉ Opt for buying tools instead of building them in-house.

◉ Snowflake allows for scalability up to Facebook-level data.

◉ Maintain a separate analytics data warehouse from the production database.

◉ Running analytics on the production database can slow it down or cause crashes.

◉ The production database is designed for fast data addition and changes, not efficient analytics.

◉ Move data from the production database to the separate analytics data warehouse (e.g., Snowflake) on a nightly schedule.

◉ Running analytics on the separate data warehouse reduces the risk of failures and downtime.

5 CAN YOU EXPLAIN THE ROLE OF REAL-TIMEDATA PROCESSING IN OUR ARCHITECTURE?

ZENLYTIC APPROACH:

◉ Real-time data processing should only be considered if it is crucial to the core business, such as for real-time machine learning models.

◉ Examples include scenarios like online loan approval in the financial services industry.

◉ For most mid-market companies, real-time data processing is not recommended due to the significant costs involved.

◉ The expense includes purchasing a duplicate database dedicated to real-time processing.

◉ “Real-time data processing” can refer to both real-time data warehousing and real-time access to data.

◉ It is important to ensure clarity when discussing real-time processing.

◉ The recommended approach is to prepare and process data overnight, while providing anytime access for users to perform live analytics on the data within the warehouse.

◉ The mentioned stack of tools supports regular ingestion and always-available data consumption for analytics

Want to decrease ad hoc data requests by 90%? Schedule a demo

DATA DICTIONARY AND CATALOGA

comprehensive data dictionary that provides a common language for all stakeholders. This would include definitions of all data elements, their sources, their format, and their relationships with other data elements.

1 CAN YOU EXPLAIN THE PROCESS OFMAINTAINING OUR DATA DICTIONARY AND CATALOG?

ZENLYTIC APPROACH:

◉ Avoid maintaining a separate data dictionary and catalog.

◉ Keep metric definitions within the BI tool where they live.

◉ Separate tools for maintaining definitions can lead to trust issues and are time-consuming.

◉ Only consider separate tools if you have multiple BI tools that require synchronized definitions.

◉ Airbnb is an example of a company with a robust data catalog, but such cases are rare.

◉ The costs of maintaining a separate tool are high, and the benefits are low.

◉ Inline maintenance of metric definitions in the BI tool or other relevant platforms is recommended

2 HOW DO WE ENSURE CONSISTENCY IN DATADEFINITIONS ACROSS THE ORGANIZATION?

ZENLYTIC APPROACH:

◉ Use a semantic layer to define metrics and their descriptions.

◉ The semantic layer can be stored in various places such as dbt or the BI tool.

◉ It is crucial to have a single, consistent location for definitions.

◉ Consider using DBT, Symlyc, or LookML for storing metric definitions.

◉ Agreement on choosing the appropriate location is necessary.

3 ARE THERE ANY AUTOMATION TOOLS USED TOMANAGE THE DATA CATALOG?

ZENLYTIC APPROACH:

◉ Use the same tools used for managing the semantic layer, such as Git.

◉ Git provides version control, history, ownership, and branching capabilities.

◉ Using a ‘monorepo’ is recommended, keeping all metrics, transformations, and the data catalog in one place.

4 HOW DO NEW DATA ELEMENTS GET ADDED TOTHE DICTIONARY/CATALOG?

ZENLYTIC APPROACH:

◉ Central data person/lead on the data team should manage adding new data elements.

◉ New metrics and data sources should be added to the semantic layer.

◉ The semantic layer automatically includes new metrics accessible to end users.

5 WHO SHOULD EMPLOYEES CONTACT IF THEY HAVEQUESTIONS ABOUT A PARTICULAR DATA ELEMENT?

ZENLYTIC APPROACH:

◉ Employees should contact the data team for questions about specific data elements.

◉ The goal is for the data team to provide comprehensive documentation to minimize contact.

◉ Data team members should update descriptions based on email inquiries to avoid repetitive questions.

Want to decrease ad hoc data requests by 90%? Schedule a demo

DATA QUALITY METRICS

Explanation of how data quality is measured and maintained, including any key metrics used to track data quality.

1 WHAT KEY METRICS DO WE USE TOMEASURE DATA QUALITY?

ZENLYTIC APPROACH:

◉ Look for consistency in data over time.

◉ Measure the number of data quality issues.

◉ Implement pipeline tests in transformation tools like dbt.

◉ Focus on defining relevant tests rather than aiming for an overall data quality percentage.

◉ Consider saving historical data as a static file to validate calculations and detect underlying data changes.

2 HOW ARE THESE METRICS TRACKEDOVER TIME?

ZENLYTIC APPROACH:

◉ Use qualitative measures and eyeball tracking.

◉ Track the number and severity of data quality issues.

◉ Avoid diving too deep into metrics for mid-market companies and prioritize stability in the data stack before focusing on robust tracking systems.

3 WHAT PROCESSES DO WE HAVE IN PLACE TORECTIFY ANY DATA QUALITY ISSUES?

ZENLYTIC APPROACH:

◉ Testing is the primary process to address data quality issues.

◉ Fix issues at the source and document the cleanup process.

◉ Implement tests to prevent recurring issues.

◉ For example, if a tag is dropped off, create a test to ensure it doesn’t happen again.

4 HOW DO WE HANDLE MISSING ORINCOMPLETE DATA?

ZENLYTIC APPROACH:

◉ Document how missing or incomplete data will be handled.

◉ Clearly define the chosen approach in the data pipeline.

◉ Document cases where data appears as null or unknown to avoid ambiguity and facilitate future modifications.

5 CAN YOU PROVIDE EXAMPLES OF HOW THESEMETRICS HAVE INFLUENCED OUR DATAMANAGEMENT STRATEGIES?

ZENLYTIC APPROACH:

◉ Prioritize basics and don’t aim for excessive testing.

◉ Focus on tests that cover past issues and ensure general data integrity.

◉ Continuously add tests to cover newly identified broken scenarios.

◉ Emphasize doing the basics well rather than investing in expensive automation tools, especially for smaller companies.

Want to decrease ad hoc data requests by 90%? Schedule a demo

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USE OF DATA IN DECISION-MAKING

Examples and case studies of how data is used in the company to drive decision making. This could include business intelligence reports, data science projects, or other examples of data-driven decisions.

1 CAN YOU PROVIDE SOME EXAMPLES OFHOW DATA HAS INFLUENCED DECISION-  MAKING IN OUR ORGANIZATION?

ZENLYTIC APPROACH:

◉ Data influences decision-making by providing insights and enabling faster product development iterations. It helps organizations make informed decisions based on evidence rather than relying solely on intuition or assumptions.

◉ For example, the shoe company KOIO used data to assess the performance of different product lines in the women’s category. They were able to track sales, cohort retention, and SKU performance more efficiently.

◉ This allowed them to identify successful styles and collaborations, which informed their decisions on future product launches. Through rapid iterations based on data analysis, KOIO improved their product development process and delivered better products to their target market.

2 HOW DO WE ENSURE THAT DECISION-MAKERSHAVE ACCESS TO THE MOST RELEVANT AND  ACCURATE DATA?

ZENLYTIC APPROACH:

◉ It is essential to define core metrics that align with the organization’s goals and objectives. These metrics should be identified and agreed upon to ensure consistency and relevance.

◉ Establishing a self-serve tool or platform can provide decision-makers with direct access to relevant and accurate data. This eliminates the need for time consuming back-and-forth communication and enables decision-makers to retrieve the data they need independently.

◉ Timely access to data is crucial for decision-making, as it allows decisionmakers to make informed choices in a timely manner, keeping up with the fast pace of business operations

3 WHAT ARE SOME OF THE KEY REPORTS ORDATA VISUALIZATIONS THAT OUR TEAMS USE  REGULARLY?

ZENLYTIC APPROACH:

◉ The specific reports and data visualizations used regularly depend on the nature of the business and the core business loop. It is important to identify the key metrics that summarize the core business loop.

◉ For example, a subscription-based business may focus on acquisition and retention metrics, while a SaaS business might emphasize user engagement and satisfaction metrics.

◉ These reports and visualizations should provide a comprehensive view of the organization’s performance, enabling teams to monitor progress, identify trends, and make data-driven decisions.

4 HOW DO WE ENSURE DATA TRANSPARENCYWHILE MAKING DECISIONS?

ZENLYTIC APPROACH:

◉ Data transparency is achieved by clearly describing and defining metrics. It involves providing detailed explanations of how metrics are calculated, ensuring that the generating process is transparent.

◉ Decision-makers should have a clear understanding of the data and metrics they are using, including the underlying methodologies and any assumptions made.

◉ Transparent data empowers decision-makers to have confidence in the metrics and make informed decisions based on a solid understanding of the data.

5 ARE THERE ANY CHALLENGES IN USING DATAFOR DECISION-MAKING THAT WE SHOULD BE AWARE OF?

ZENLYTIC APPROACH:

◉ Data is not infallible and can be misleading in certain cases. While data provides valuable insights, it should not be the sole factor driving decision making.

◉ Decision-makers should also consider anecdotal evidence and qualitative insights alongside the data to gain a comprehensive understanding of the situation.

◉ It is crucial to ensure data accuracy and validity by conducting thorough validation processes, including data cleansing, verification, and cross referencing with other reliable sources.

◉ Decision-makers should be aware of potential biases or errors in data collection and analysis, as these can impact the accuracy and reliability of insights derived from the data. Critical thinking and careful interpretation of data are necessary to make informed decisions.

Want to decrease ad hoc data requests by 90%? Schedule a demo

DATA TOOLS AND TECHNOLOGIES

Overview of the tools and technologies used in the data landscape. This would include databases, data processing frameworks, data visualization tools, data science platforms, etc.

1 CAN YOU OUTLINE THE KEY DATA TOOLSAND TECHNOLOGIES WE USE IN OUR DATA PIPELINE?

ZENLYTIC APPROACH:

◉ Keeping the data tools simple is important, focusing on the essential ones:

◉ Data movement tools: Examples include Segment, Snowplow, FiveTran, or Airbyte. These tools facilitate the extraction, transformation, and loading(ETL) process, allowing data to be transferred from various sources to the data warehouse.

◉ Data storage: Utilize data warehousing solutions like BigQuery, Snowflake, or Redshift, which provide scalable storage and querying capabilities for large volumes of data.

◉ Data transformation and cleaning: Tools like DBT (Data Build Tool) or SQLMesh can be used for data transformation, cleaning, and maintaining consistent data definitions. They help streamline data pipelines, ensure data accuracy, and enable version control for data transformations.

◉ Self-serve BI tools: Looker, Zenlytic, or other business intelligence platforms empower users to explore data, create dashboards, and gain insights without relying heavily on the data team.

2 HOW DO WE DECIDE ON ADOPTING NEW DATATECHNOLOGIES?

ZENLYTIC APPROACH:

◉ Start with the core tools and functionalities needed for data processing and analysis, including:

◉ Data movement and storage: Evaluate tools based on their compatibility with existing systems, ease of use, scalability, and cost-effectiveness.

◉ Data transformation and cleaning: Consider tools that offer advanced transformation capabilities, support testing frameworks, and align with your organization’s data governance practices.

◉ Observability and data quality: For larger companies, consider solutions like Monte Carlo or BigEye to monitor data quality, detect anomalies, and ensure data reliability.

◉ Assess new technologies based on specific needs:

◉ Consider the scalability and flexibility of the new technology to accommodate future growth and changing data requirements.

◉ Evaluate the level of support, documentation, and community around the technology to ensure accessibility and future-proofing.

◉ Pilot projects or proof-of-concepts can help evaluate the feasibility and impact of adopting new technologies before implementing them organization-wide.

3 ARE THERE ANY GAPS IN OUR CURRENT DATATOOLSET THAT WE NEED TO ADDRESS?

ZENLYTIC APPROACH:

Common gaps in data toolsets and potential solutions include:

Transformation and testing tools:

• Implement DBT or SQL Mesh to establish data transformation pipelines with standardized code and version control, ensuring consistent and reliable data transformations.

• Introduce testing frameworks to validate data transformations, ensuring accurate and reliable data outputs.

Metric definition and management:

• Create a centralized repository or data catalog where metric definitions are documented and accessible to all stakeholders, ensuring consistency and alignment in metric calculations and reporting.

• Implement metadata management tools or data governance frameworks to maintain data definitions, lineage, and documentation.

Data quality monitoring:

• Adopt data quality monitoring tools like Great Expectations or opensource frameworks that provide automated validation and continuous monitoring of data quality.

• Implement anomaly detection systems to identify and resolve data anomalies, ensuring data integrity and reliability.

Considerations for larger organizations:

• Evaluate observability tools such as Monte Carlo or BigEye to gain visibility into data pipelines, monitor data quality, and proactively detect issues.

• Assess data governance and compliance tools to ensure adherence to regulations, maintain data privacy, and enforce data security policies.

4 CAN YOU EXPLAIN HOW THE INTEGRATIONBETWEEN DIFFERENT TOOLS IS MANAGED?

ZENLYTIC APPROACH:

Effective management of tool integrations involves:

Implementing robust testing practices:

• Set up comprehensive test suites to validate data integrity and consistency at each integration point.

• Perform regular testing to identify and resolve issues arising from changes in data sources, transformations, or tool configurations.

Monitoring data flows and errors:

• Utilize monitoring tools to track data movements, detect errors or anomalies, and trigger alerts for timely intervention.

• Implement logging and error handling mechanisms to capture and resolve integration failures.

Establishing data governance and data lineage:

• Maintain documentation of data pipelines, including information on data sources, transformations, and destinations.

• Establish data lineage to trace data flow, understand data dependencies, and facilitate troubleshooting in case of issues.

Collaboration between teams:

• Foster effective communication and collaboration between data engineering, analytics, and business teams to ensure alignment and understanding of data pipelines and integrations.

• Implement clear ownership and responsibilities for managing and maintaining different components of the data pipeline.

5 WHAT TRAINING OR RESOURCES ARE AVAILABLEFOR EMPLOYEES TO LEARN THESE TOOLS?

ZENLYTIC APPROACH:

Empowering employees to effectively use data tools involves:

Prioritizing training in the BI tool:

• Provide comprehensive training resources, including video courses, documentation, or vendor-led training sessions, to familiarize employees with the BI tool’s features, functionalities, and best practices.

• Encourage employees to explore and experiment with the tool to gain hands-on experience and enhance their data analysis skills.

Establishing a knowledge-sharing culture:

• Encourage internal knowledge sharing sessions, where employee scan share their expertise, tips, and tricks related to data tools and technologies.

• Create a central repository or wiki for employees to access resources, tutorials, and user guides related to the data tools.

Continuous learning and upskilling:

• Encourage employees to stay updated with the latest advancements in data tools and technologies through webinars, conferences, or industry specific events.

• Provide opportunities for employees to attend workshops or participate in online courses to enhance their proficiency in using data tools effectively.

Collaboration with the data team:

• Foster a collaborative relationship between employees and the data team, enabling employees to seek guidance and support when utilizing data tools or encountering data-related challenges.

• Establish channels for communication and feedback to address any tool related issues or improvement suggestions raised by employees.

Want to decrease ad hoc data requests by 90%? Schedule a demo

DATA LITERACY TRAINING

Resources and training materials to help employees improve their data literacy, including explanations of key concepts, tutorials on using data tools, and best practices for data analysis.

1 WHAT DATA LITERACY RESOURCES ARE CURRENTLY AVAILABLE TO EMPLOYEES?

ZENLYTIC APPROACH:

◉ Training resources for using the tool effectively: Employees have access to training materials, such as video courses or vendor-led trainings, that provide guidance on utilizing the data tool efficiently.

◉ These resources help employees understand the tool’s functionalities, navigation, and best practices for data analysis and visualization.

◉ Core dashboards documented in a company wiki for easy reference and onboarding: The organization maintains a wiki or knowledge base where key dashboards are summarized and documented.

◉ New employees or those seeking guidance can refer to this centralized resource to quickly understand the core dashboards and metrics relevant to their roles. This wiki acts as a starting point for employees to gain insights and navigate the available data visualizations.

2 HOW DO WE ASSESS DATA LITERACY AMONGOUR EMPLOYEES?

ZENLYTIC APPROACH:

◉ Conduct surveys to gauge employees’ self-assessment of data literacy: The organization periodically conducts surveys to assess employees’ level of data literacy. These surveys allow employees to reflect on their understanding of data analysis, interpretation, and utilization.

◉ By self-assessing their data literacy skills, employees can provide insights into areas where they feel confident or require additional support.

◉ Identify areas where additional training resources may be needed based on survey results: Analyzing the survey results helps identify specific areas whereemployees may require further training or resources.

◉ This assessment enables the organization to tailor training programs or provide targeted resources to bridge any gaps in data literacy. It helps align the organization’s data literacy initiatives with employees’ needs and ensures a continuous improvement process.

3 WHAT IS THE PROCESS FOR UPDATING ORADDING TO OUR DATA LITERACY TRAINING MATERIALS?

ZENLYTIC APPROACH:

◉ Maintain a central wiki that summarizes all training materials and keep it up to date: The organization maintains a central wiki or documentation repository dedicated to data literacy training materials.

◉ This wiki serves as a single source of truth for all training-related information, including tutorials, guidelines, and best practices. Regular updates are made to ensure the training materials reflect the latest tools, technologies, and data analysis techniques.

◉ Document data-related information and metrics within the BI tool for self documentation and synchronization: To enhance the accessibility and accuracy of training materials, data-related information, and metric definitions are documented within the BI tool itself.

◉ This approach promotes self-documentation, enabling users to easily access information about the data sources, transformations, and metrics used in their analyses. Keeping this documentation synchronized with the BI tool ensures that employees have up-to-date information readily available.

4 ARE THERE ANY SPECIFIC AREAS OF DATALITERACY WHERE OUR ORGANIZATION COULD IMPROVE?

ZENLYTIC APPROACH:

◉ Focus on consistency in data literacy across the organization: It is essential to establish a consistent level of data literacy across the organization. This includes ensuring that employees possess a basic understanding of metrics, data analysis, and interpretation.

◉ By promoting a culture of data literacy, organizations can empower employees to make data-informed decisions and collaborate effectively.

◉ Encourage employees to regularly review core dashboards to develop intuition and ask informed questions: To improve data literacy, organizations should encourage employees to regularly review core dashboards relevant to their roles.

◉ By engaging with these dashboards, employees can develop a deeper understanding of the data, identify patterns or anomalies, and ask meaningful questions. This practice cultivates data intuition, enabling employees to leverage data effectively in their decision-making processes.

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DATA POLICIES AND COMPLIANCE

Information about legal and regulatory requirements related to data, such as GDPR or CCPA. This would also include company policies for data handling and storage.

1 WHAT LEGAL AND REGULATORYREQUIREMENTS RELATED TO DATA DO WE NEED TO COMPLY WITH?

ZENLYTIC APPROACH:

◉ Compliance depends on the applicable jurisdiction. For example, if the business operates in Europe, the General Data Protection Regulation (GDPR)needs to be followed. If the business is based in California, the California Consumer Privacy Act (CCPA) applies. It’s important to identify the specific regulations that are relevant to the jurisdiction in which the business operates.

◉ Apart from GDPR and CCPA, there may be other data protection and privacy legislations in progress or already in place in various locations. Staying updated on emerging regulations is crucial to ensure compliance.

◉ Additionally, there are universal regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the United States, specifically for handling health information. The specific regulatory requirements will depend on the nature of the business and the data it handles.

2 CAN YOU EXPLAIN OUR POLICIES FOR DATAHANDLING AND STORAGE?

ZENLYTIC APPROACH:

◉ It is recommended to centralize data in a data warehouse. This serves as the official record of all data within the organization. Having a central repository simplifies data management and reduces the chances of data being scattered across multiple locations.

◉ Access controls play a significant role in data handling. It’s crucial to differentiate sensitive data from non-sensitive data and restrict access to sensitive information. Not everyone in the company should have access to all data. Determining which roles and departments need access to specific data and implementing appropriate access controls is important for data security and privacy.

◉ By implementing access controls, the organization can ensure that data is only accessible to authorized personnel who require it for their job responsibilities. This helps prevent unauthorized access and minimizes the risk of data breaches or mishandling.

3 HOW DO WE ENSURE COMPLIANCE WITH THESEPOLICIES AND REQUIREMENTS?

ZENLYTIC APPROACH:

◉ To ensure compliance, it is important to establish a process for handling compliance-related complaints. Having a designated portal where users can submit complaints or requests related to their data helps in centralizing and addressing these concerns.

◉ A documented process should be in place to handle compliance issues. This could involve assigning a specific person or team responsible for executing compliance-related actions. For example, on specific days or a regular cadence, the designated person/team can review the list of entities that need to be removed for compliance and proceed with deleting the relevant data from different tables or databases.

◉ By having a scheduled cadence for compliance-related activities, such as every other week, the organization ensures that compliance actions are consistently carried out. This minimizes the chances of overlooking compliance requirements and helps meet the timeline for executing necessary changes. Regulations often have a specific timeframe, such as a 30-day window, within which changes need to be implemented.

4 WHAT IS THE PROCESS FOR UPDATING OURDATA POLICIES AS LAWS AND REGULATIONS CHANGE?

ZENLYTIC APPROACH:

◉ Keeping track of changes in laws and regulations can be challenging, especially for mid-market companies with limited resources. However, it’s important to stay informed about regulatory updates that may impact data policies.

◉ Changes in regulations often have a long gap between the announcement and the effective date. This provides organizations with time to prepare and make necessary adjustments to their data policies.

◉ Vendors and third-party service providers can also assist with compliance efforts. For example, platforms like Shopify may have built-in features that help handle compliance requirements. These vendors may help in receiving and forwarding requests, such as deletion requests, ensuring that the organization can fulfill its obligations.

◉ Regularly reviewing data policies, ideally on a quarterly basis, is important to ensure alignment with the latest laws and regulations. This allows the organization to identify any gaps or areas that need updating to maintain compliance. Additionally, vendors and service providers are often required to notify their clients of any changes that impact data handling

5 WHO SHOULD EMPLOYEES CONTACT IF THEYHAVE QUESTIONS ABOUT DATA POLICIES OR COMPLIANCE?

ZENLYTIC APPROACH:

◉ The ideal point of contact for employees with questions about data policies or compliance is the legal department, if available within the organization. The legal department is well-versed in regulations and can provide guidance and clarification on data policies and compliance matters.

◉ In cases where a legal department is not present, employees should reach out to the Human Resources (HR) department. HR personnel often handle matters related to state and local compliance requirements, including regulations like CCPA. They can provide initial guidance or direct employees to the appropriate resources for further assistance. HR personnel should have a basic understanding of the organization’s data policies and compliance obligations.

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DATA STRATEGY AND FUTURE PLANS

Explanation of the company’s data strategy, including goals for data management and plans for future data initiatives.

1 CAN YOU PROVIDE AN OVERVIEW OF OUR CURRENT DATA STRATEGY?

ZENLYTIC APPROACH:

◉ Start simple and build a strong foundation: Rather than attempting to collect and analyze every possible data point, it is recommended to start with a focused approach. By starting with a simple, core set of data sources, the organization can establish a strong foundation for data management and analysis.

◉ Identify the core data sources that are most relevant to the business: Determine the key data sources that directly impact the organization’s operations and goals. For example, an ecommerce company may prioritize revenue data, attribution data, and marketing spend data.

◉ Gradually add data sources that can improve business outcomes: Once the core data sources are established, assess additional data sources that can provide valuable insights to further improve business performance. For instance, incorporating data on customer emails, SMS interactions, or other relevant channels.

◉ Invest in areas that have the potential to make a significant impact on the business: Prioritize data sources and analytics that align with strategic goals and have the potential to drive meaningful improvements. Identify areas where data can directly influence decision-making and contribute to revenue growth or operational efficiency

2 HOW DOES OUR DATA STRATEGY ALIGN WITHTHE OVERALL BUSINESS STRATEGY?

ZENLYTIC APPROACH:

◉ Ensure that the data being collected aligns with the core business loops: Data strategy should be closely tied to the overall business strategy. Evaluate whether the data being collected and analyzed reflects the core business processes and activities. As the business strategy evolves, adapt the data strategy accordingly to support new priorities.

◉ Adapt data strategy to changes in the business strategy: If the business introduces new products, services, or changes in billing processes, it is essential to adjust the data strategy to capture and analyze the relevant data related to these changes. Ensure that the data strategy supports the organization’s evolving business model.

◉ Analyze data that reflects new business processes or products: When new business processes or products are introduced, analyze the corresponding data to understand their performance and effectiveness. This analysis helps in identifying areas of improvement and making data-driven decisions.

◉ Maintain historical data for future analysis and evaluation: Historical data is valuable for trend analysis, benchmarking, and measuring progress over time. Ensure that the data strategy includes provisions for storing and accessing historical data to facilitate retrospective analysis and long-term evaluation

3 WHAT ARE OUR GOALS FOR DATAMANAGEMENT IN THE NEAR AND LONG TERM?

ZENLYTIC APPROACH:

◉ Near-term goals involve covering essential data needs and focusing on critical aspects that can drive immediate improvements: Prioritize addressing data needs that have an immediate impact on the business. This may involve filling data gaps, improving data quality and accuracy, and establishing robust data governance practices.

◉ Long-term goals include collecting data for future use, even if its full potential is not realized immediately: Look beyond immediate needs and consider the long-term value of collecting certain types of data. Plan and invest in data collection efforts that align with the organization’s long-term vision and goals.

◉ Consider the costs and potential benefits of planned upgrades or changes to data systems: Evaluate the need for system upgrades or changes in data infrastructure. Consider the costs, potential benefits, and scalability of data systems to support future growth and evolving data requirements.

4 ARE THE

RE ANY PLANNED UPGRADES ORCHANGES TO OUR DATA SYSTEMS?

ZENLYTIC APPROACH:

◉ Select tools and systems that can scale with the company’s growth to minimize the need for frequent upgrades: When choosing data systems and tools, prioritize scalability and flexibility. Selecting tools that can handle increasing data volumes and evolving business needs reduces the frequency of system upgrades.

◉ Upgrades and changes to data systems can be costly, time-consuming, and error-prone: Recognize that system upgrades can be resource-intensive and carry certain risks. Changes to data systems should be carefully planned, tested, and executed to minimize disruptions and potential errors.

◉ Carefully evaluate the tooling early on to avoid excessive system upgrades later: Take a strategic approach to tool selection from the beginning. Choose tools that align with the organization’s long-term vision and can accommodate future data needs. This helps reduce the need for frequent and costly system upgrades in the future.

5 HOW CAN EMPLOYEES CONTRIBUTE TO THEFUTURE OF DATA AT OUR COMPANY?

ZENLYTIC APPROACH:

◉ Employees have valuable insights into the data that would be most helpful for their work: Employees who are directly involved in operational activities often have firsthand knowledge of the data that would be most valuable for their tasks and decision-making.

◉ Encourage employees to communicate their data needs and ideas to the data team and leadership: Foster a culture of collaboration and open communication, allowing employees to share their data-related needs, challenges, and ideas with the data team and organizational leaders.

◉ Understanding frontline needs is essential for aligning data strategy with operational requirements: Actively seek input from employees on the frontlines to ensure that the data strategy addresses their specific needs. This helps in tailoring data collection, analysis, and reporting efforts to provide relevant insights that support day-to-day operations and decision-making.

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