Automated Data Governance: Why Data Contracts Are Key

Implementing data governance, and keeping it relevant as your data needs change, is an exercise in vigilance. Data is ingested from so many places — one-fifth of enterprises draw data from 1,000 or more sources — and implementing governance is an important investment that impacts your entire enterprise.

Despite attempts to streamline your data governance workflows, reactive processes late in an analytical dataflow still involve manual effort, are highly complex and costly, and often deliver less than desirable results. It becomes even more difficult to scale as your data volume grows and your data needs evolve. The growth need is usually non-linear as complexity increases. These are common challenges for many companies, as data sprawls and becomes difficult to manage.

To keep up with the pace of data ingestion and growth, seeking out automated data governance approaches and tools makes sense. Automation is a proven tactic in multiple areas of a modern enterprise and is very applicable here.

When applied correctly, automated elements of data governance will help you reduce the time spent on the manual tasks of managing your data as well as the complexity of your initiatives. However, it’s still important to identify and address the root causes of many data governance challenges. Here’s what to consider as you explore governance automation, and why data contracts are key to streamlining your processes.

The Approach to Automation

Data governance isn’t just about creating policies and implementing them through technology. Automation can and should be an important part of a data governance plan. A truly comprehensive data governance strategy considers a wide range of factors inclusive of people, process, and technology:

Based on your business needs, how do you define data quality?
Does everyone who engages with data (whether they work in IT or other parts of the business) understand the role they play in data governance?
Do you have policies in place that determine how data is acquired, transformed, and connected?
What are your procedures for data cleansing, updating systems, and deploying operational processes to adhere to policies?

Being able to answer these questions allows you to begin to address the root causes of common data governance issues. When everyone is aligned about the definition of data quality and the role they play in maintaining it, you can eliminate many of the quality issues that occur after data is created or ingested. Achieving this requires upfront engagement between IT and the business to establish the data governance strategy and implement it as early as possible within the data lifecycle.

Moving your data governance activities upstream within the data lifecycle is referred to as “shifting left on data governance”. It’s similar to making a recipe. When you’re cooking, you don’t combine ingredients, cook them, and then determine what you’ve made after you eat it. But this is how data is often approached. Organizations ingest large amounts of data and then define how they plan to use it afterwards.

The correct approach to making a recipe is to decide what you want to make, assemble the proper ingredients, and then follow the instructions for preparation. The same approach can be applied to your data. Determine what your data products are and which elements they’re made up of. This is your recipe. With that information, you can ensure that you capture and define your data properly from the beginning. This process requires collaboration between IT and the business. Once people are in alignment, you can implement processes and technology to support your decisions.

Implementing an effective shift-left approach to data governance doesn’t just reduce the strain on your resources. It also helps companies realize the benefits of better analytics capabilities, data quality, and collaboration while reducing the risk of non-compliance. Technology can help you move governance upstream and achieve these outcomes, but it’s important to consider people and processes as well before pursuing automation.

Data Contracts and Data Governance

There’s no tool or combination of platforms that will advocate for business rules that facilitate processes, understand the long-term hazard of compromising data decisions, or create alignment within your business. That’s why it’s important to take a key step before diving into tools.

That step is the creation of data contracts. A data contract is an agreement between a data producer and data consumer that defines the data at a high level. They’re like cheat sheets that outline what your data should look like, which allows you to proactively manage the inflow of data into various platforms.

An example data contract may include:

The type of data being exchanged
The structure of that data
How the data will be used
Service level agreements
Ownership details

Creating data contracts allows you to shift left on data governance. They require data stakeholders to work together to define the structure (metadata) and types of data that are ingested and how its quality is determined. This allows you to implement a metadata-driven ingestion framework. When the ingestion mechanics of your platforms rely on metadata captured in data contracts, data never lands in the data environment without being documented or understood. The contract establishes a baseline of structure and quality from the time data is ingested.

The process of developing and implementing data contracts has numerous benefits:

Ensures all parties understand their role in data governance processes and have shared expectations for data quality and reliability. Data contracts also include some standardization of data consumers’ activities, so data governance is applied throughout the data lifecycle.
Allows you to gain insights into dependencies and coupling between domains and applications.
Eliminates many reactive tasks, since metadata is critical to ingestion, not an afterthought.
Serves as living, active documentation.
Creates repeatable processes. Once you have data contracts in place, you can use them as a scalable framework as you begin to ingest new data types.

Data Contracts Set the Foundation for Automated Data Governance

Data contracts can also be used to program automation platforms that monitor data as it’s ingested and perform automated audits on an ongoing basis to ensure data meets the terms of the contract. This approach can also help you operationalize security and compliance requirements. You can flag regulated data so that it’s handled properly as it flows through your systems.

This allows for more efficient and seamless workflows as data moves through its lifecycle. It also frees up the data governance team for more strategic activities and initiatives rather than working on a neverending data cleanup project.

Data Contracts Are for Every Company

Who can benefit from data contracts? Everyone. Whether you have streaming data, batch data, or data that’s manually entered, governance will be a constant, uphill battle. Every business needs a clearly defined idea of what makes for good data, and what is required to execute within the company’s rules. Breaking down silos between technology and the business is also essential for every business. Many stakeholders play a role in governance, so they all need to be engaged and working towards the same outcomes.

Data contracts don’t require you to hire new resources or learn a new programming language. If you already have in-house development capabilities, then you can create data contracts, which can live inside your existing tech stack.

Implement Automated Data Governance Effectively with Kenway

While there’s no quick and easy solution to improving data quality, there are ways to automate data governance tasks. Data contracts help you set the foundation for using automation platforms and require that you break down silos between the business and IT department. If you don’t have the in-house resources to create your own data contracts, or if you need guidance on creating your framework, Kenway can help.

We can coordinate between the various players in the data lifecycle and guide you through the processes and technology solutions needed to help you shift left on data governance. Our data experts work across industries, including highly regulated sectors like healthcare and finance. We also can take on platform-specific projects such as Salesforce data governance.

For example, we helped a financial services firm move their data governance processes upstream. Using YAML, which can be read by both humans and machines, we streamlined their data architecture. Instead of 100 different data integrations between 100 different files, we created one integration point with 100 different data contracts.

Now, business users can bring in new files with minimal developer support. The developer is engaged for a matter of hours, instead of a couple of weeks. Because of the simplicity of data contracts, the client already had the right infrastructure and skills in place, and we were there to walk them through the process.

For guidance on implementing data contracts and automating data governance, reach out to our experts: [email protected].

Automated Data Governance FAQs

What is automated data governance?

Automated data governance is the process of automating some of your data policies, business rules, and procedures. It’s not a replacement for a comprehensive data governance strategy, but it can help operationalize some aspects of your plan.

What is a data contract?

A data contract is an agreement between a data producer and data consumer that defines the data at a high level. It includes key metadata, such as how a piece of data is structured, when it’s available, and who owns it.

Defining the Modern Data Stack and Its Key Benefits

Data holds the key to solving many of today’s business challenges. However, there’s a big gap between what businesses can do with data, and what they actually are doing. Up to 73% of data goes unused, with data silos, complexity, and quality issues being major hindrances to adopting data-driven practices. To address these challenges, businesses are turning to modern data stacks.

Modern data stacks are made up of an ecosystem of best-in-breed data tools that help businesses deal with the growing volume, velocity and variety of data, as well as the increasing demand for insights, scalability, and flexibility. These data stacks, working in alignment with people and processes, enable companies to collect and store data, analyze it, automate workflows, and scale to support the growing number of analytics use cases over time.

Here’s what businesses need to know about the components and benefits of the modern data stack.

What Is The Difference Between Traditional and Modern Data Stacks?

Traditional data stacks were built for an outmoded approach to data. They were highly customized and required extensive in-house resources to maintain. Because of these limitations, traditional data stacks could not handle the complexity or volume of data needed to support modern data and analytics.

Enter the modern data stack. Modern data stacks set themselves apart by being built for today’s data challenges and future data demands. There are a number of factors that separate traditional and modern data stacks, starting with their architecture. Traditional data stacks generally used on-premise technologies or leveraged cloud platforms inefficiently rather than utilizing modern, cloud-based platforms which offers scalability and cost-efficiency.

Within the traditional approach to data, centralized teams often controlled all aspects of the data lifecycle from sourcing, transformation, and distribution. This approach often led to bottlenecks, preventing users from obtaining their core data assets efficiently.

Modern data teams, however, are focused on curating high-quality, standardized data outputs which are readily available to business users. Business users will work in conjunction with the data team to produce high-value data products, which allows for an increased focus on the highest ROI generating activities.

Modern data stacks are also built to accommodate the reliability, flexibility, and scalability that companies need to improve their data capabilities.

Streamlined Integration: Modern data stacks work well with sophisticated ETL or ELT tools. These tools come with pre-built connectors that streamline data ingestion without the need for costly, time-consuming development.
Storage: Modern data stacks leverage data lakes and/or lake houses, which provide greater flexibility in data storage and analysis. Data lakes allow you to store raw data, and are built to provide flexible, scalable storage as your data volumes grow.
Data Governance: Modern data stacks aim to shift left on governance. Instead of retroactively cataloging and inventorying metadata, modern data stacks incorporate governance and the definition of data from the start.
Advanced Analytics: To help businesses unlock deeper insights and predictions, modern data stacks are built to accommodate the specialized needs of advanced tech like machine learning and artificial intelligence.
Security and Compliance: Modern stacks often include advanced, integrated tools for data access, security, and compliance.

What Constitutes a Modern Data Stack?

A modern data stack is an ecosystem of tools that supports the speed and accuracy companies need to make data a competitive advantage. The components of a modern data stack can vary based on the needs of the organization, the types of data being used, and the specific use cases that the data stack is meant to support. Here are some elements that typically make up a modern data stack:

Data Sources

Data sources are the various databases, files, APIs, or applications where the original data is stored and generated. This can include a wide swath of technologies, such as relational databases, NoSQL databases, IoT sensors, customer relationship management(CRM) platforms, and data lakes.

Data Pipeline and Ingestion Tools

Pipelines manage the flow of data from its original source to its final destination. They handle processes such as extraction, transformation, and loading (ETL) or extraction, loading, and transformation (ELT). These typically include plug-and play connectors for common data formats and sources, which ease the ingestion process. Modern approaches favor reusable code and components when it comes to pipelining - much like an object oriented programming approach.

Data Storage

Data storage within a modern data platform usually favors a file based approach and aims to keep as few copies of the data as possible. Data is stored in flexible semi structured formats to allow easy modeling on demand as needed.

Data Governance and Security Tools

Data governance and security features help organizations ensure the availability, integrity, and confidentiality of their data. Modern data stacks integrate upfront definitions, policy enforcement, data lineage, and access management so that governance is incorporated in data processing mechanisms from the start.

Downstream Enablement Tools

Modern data stacks support a wide range of use cases, such as business intelligence and machine learning, that enable businesses to derive value from their data. The architecture of these platforms supports tasks such as feature engineering, model training, model validation, and model deployment.

Benefits of a Modern Data Stack

The modern data stack enables businesses to keep up with the velocity and scale of modern data analytics while also reducing costs and the burden on internal resources.

Improve Data Capabilities

Modern data stacks allow you to consolidate data from disparate sources into a single, accessible location. This allows users across the enterprise to explore, analyze, and report on data more easily.

Because data volumes are growing rapidly every year, modern data stacks are designed to handle massive amounts of data and can easily scale up or down depending on the needs of the organization. They also allow companies to easily adapt to changes in data variety or analytics needs. Typically, resources can be provisioned or de-provisioned as needed, which makes scaling more cost effective than it would be with a traditional approach.

Modern data analytics requires real-time insights. With modern data stacks, users can make timely, on-the-spot decisions with real-time data. In situations where speed and accuracy are critical, such as customer-facing interactions, these capabilities can be a key competitive differentiator.

As companies incorporate advanced analytics tools like machine learning and artificial intelligence, modern data analytics enables them to implement and use those tools effectively. This supports more accurate predictions, better understanding of customer behaviors, and enhanced operational efficiency.

Refine Data Processes

The most successful data-driven organizations are built on a foundation of practices that support data quality. Modern data stacks incorporate data governance and support compliance measures to ensure that data is accessible, accurate, consistent, and reliable. These practices also reduce risk by supporting processes to ensure compliance with regulations like HIPAA and General Data Protection Regulations (GDPR).

Cybersecurity is another source of risk, and hackers are as enterprising as ever. Modern data stacks prioritize data security, including features like encryption and access control mechanisms. This allows companies to protect sensitive data and control who can access networks and systems.

Empower Data-Driven Processes

To become a data-driven organization, businesses need to make accurate, up-to-date data available to users across the company. A modern data analytics stack can support self-service tools so users can self-serve without needing to rely heavily on IT teams or waste valuable time collecting and normalizing data.

Having a centralized and accessible data analytics stack also facilitates improved collaboration between departments. Each department can make use of the same, consistent data, leading to better cross-functional insights and decision-making. Promoting digital collaboration is especially vital now, since roughly two-thirds of employees who can work remotely are doing so at least some of the time.

Data-driven collaboration is also increasingly important for compliance. With the Securities Exchange Commission (SEC) and the European Union making progress to enact standards for Environmental, Sustainability, and Governance (ESG) compliance, companies need to ensure that they have the right data to plan, execute, and report on a wide variety of activities. Modern data analytics tools incorporate governance practices that allow companies to make data-driven decisions and share their results with stakeholders and regulators with confidence.

Modern Data Stack FAQs

What is the difference between a legacy data stack and modern data stack?

A legacy data stack is usually made up of a highly customized set of tools, which are typically housed on-premise. A modern data stack is made up of an ecosystem of cloud-based tools. They require less customization and allow companies to easily scale and adapt as their data needs change.

What are the key fundamentals of the modern data stack?

The key fundamentals of the modern data stack are:

Leveraging cloud-based technologies that require minimal hands-on provisioning and maintenance
Promoting scalability and agility, so companies can easily adapt as data needs change
Incorporating data governance up front, as well as security and compliance practices to ensure data integrity, safety, and availability

What is the value of a modern data stack?

A modern data stack offers numerous benefits:

Supports improved data capabilities and advanced analytics tools like machine learning and artificial intelligence
Enables companies to scale and adapt their data infrastructure with ease
Optimize costs by allowing companies to purchase only the resources they need, and making it easy to remove any tools they don’t need

What are some of the challenges in building a modern data stack?

Building a modern data stack can be challenging due to the number of resources available. Understanding what you need and implementing it correctly are essential to reaping the benefits of a modern data stack.

Also, it’s necessary to have the right people and processes in place to ensure that you’re properly leveraging every aspect of the data analytics stack.

How can machine learning and AI be integrated into a modern data stack?

AI and machine learning can generate automated analytics and insights and route them to users throughout the company. This enables you to make faster, more accurate decisions and collaborate more effectively.

How does a modern data stack contribute to data security?

A modern data stack can incorporate security protocols that protect sensitive data. This includes features like encryption and managing data access so users only see what they need to.

Explore the Capabilities of a Modern Data Stack

The demand for timely, accurate analytics has never been higher. As the amount of data businesses manage increases, the pressure to modernize will continue to grow. Adopting a modern data stack allows you to be more competitive and innovative. With the right ecosystem of data sources, advanced processing tools, robust storage options, and cutting-edge analytics capabilities, you can unlock the full power of your data.

Kenway can help you fuel data-driven decision-making and foster agility and adaptability with a modern data stack. We work with enterprises, including those in highly regulated industries, to ensure people, processes, and technology work in alignment to reap the full benefits of data.

Our data experts helped one asset management firm address the data silos, inefficient processes, and communication gaps that prevented it from gaining a 360-degree view of its customers. We assessed their current state and developed a modern data platform architecture that supports the firm’s current requirements and is flexible enough to adapt to future demands. Today, the firm’s employees can easily access all the information they need on prospective and current clients from a single source.

We can help you realize benefits like these for your business. To learn how Kenway can help you extract meaningful insights from your data, predict future trends, and create a competitive advantage, reach out to us for a consultation.

Databricks Data + AI World Tour 2023: Conference Highlights

At Kenway Consulting, we specialize in helping our clients with modern data enablement and unified data for analytics. One of the technology solutions we endorse for data product teams is Databricks.

For those not yet familiar with Databricks, it is a full stack and cloud-based platform that supports data engineering, data analytics, data science, and machine learning. Ultimately, it enables data teams to easily collaborate by auto-scaling compute resources leveraging interactive notebooks to run SQL/code and immediately visualize data in a single document.

Insights from Databricks Data + AI World Tour 2023

To keep up with recent trends and evolving capabilities, we recently attended a Databricks Conference called Databricks Data + AI World Tour in Chicago on October 4, 2023. Databricks left us awestruck with their relentless pace of innovation in data and AI. The advancements in metadata driven development (data ingestion) using Unity Catalog show their commitment to making data easily accessible and governed across the organization.

By integrating natural language processing models into workflows through Koalas, they are bridging the gap between business users and complex AI. The continuous improvements in simplifying machine learning development and deployment through MLflow highlight Databricks' leadership in MLOps. Their upcoming Salesforce integrations. will unleash new possibilities for customers.

Streamlining Data Governance for Innovation

Ultimately, by making machine learning, MLOps, and data governance frictionless through products like MLflow, Koalas, and Unity Catalog, Databricks is freeing companies from the drudgery of data wrangling. Instead, we can now focus our energy on running experiments with predictive analytics to create asymmetric value. The brilliance of Databricks lies in empowering us to swiftly turn raw data into extraordinary insights.

We left buzzing with excitement about incorporating these cutting-edge capabilities into our own data projects. Databricks has ignited our appetite for innovation, and we eagerly anticipate their next groundbreaking developments through their Data AI Summit. They have proven themselves trailblazers in data and AI, and we are thrilled to be on this journey with them.

Data Silos: Why They’re Problematic and How to Break Them

Diane is the head of sales for a financial services firm that wants to target more high-value clients and build deeper relationships with its most successful accounts. To create the strategy to support this goal, Diane needs to build a comprehensive client profile. To gather the information she needs, she finds herself pulling data from multiple sources. Her CRM and sales enablement tools contain some customer intelligence, but they’re incomplete. To paint a more complete picture of the company’s clients, she schedules multiple meetings with her colleagues from marketing, account management, and finance. She then compiles all of the data she’s gathered into complicated spreadsheets and slide decks. It’s time-consuming for Diane and her colleagues, and she’s still not fully confident in all of the information she pulls together.

Does this scenario sound familiar?

The problem is siloed data. All of the information Diane needs to inform her strategy is available, but it’s held in disparate systems, owned by different teams. Even when those teams communicate regularly, it can be difficult to extract and share data that’s actually meaningful. Data silos create a ripple effect across organizations, and breaking them down can be a critical first step to improving productivity, revenue, and customer loyalty.

What Are Data Silos, and Why Are They Problematic?

Data silos are isolated information sources that are owned by one department and are inaccessible to others in the same organization. Siloed data has wide-ranging negative implications. Employees like Diane spend a significant portion of their time looking for information instead of performing more productive tasks. In fact, more than 50% of office professionals say they spend more of their time looking for files instead of doing their actual work.

For organizations with siloed data, addressing this issue can often lead to a never-ending data cleanup process, where resources are devoted to fixing the symptom instead of the cause. Processes are slow since many tasks require manually compiling data from multiple sources, later leaving employees with the looming task of figuring out how to aggregate all the data all into one place to create a single source of truth. Company culture can also be a casualty in the battle against bad data, causing friction and frustration between departments when trying to gather all the different data sources together.

These issues can lead to serious consequences for companies in heavily-regulated industries like healthcare and financial services. And they make it hard to innovate, which is vital to remaining competitive in a fast-changing business environment.

Tools Help Break Down Data Silos

Data warehouses are used to centralize data and prevent the problems caused by silos. Some of the most popular data warehousing tools are Microsoft Azure Synapse Analytics, Amazon AWS Redshift, Snowflake, and Google Cloud Platform BigQuery. Using these tools as a single source of truth can be an important step toward breaking down data silos. Creating a single source of truth for all data points harnessed by a company is the holy grail when finding remedies to disparate data sources. Providing a full, 360-view into a company and its clients/customers for employees to work with creates an opportunity for a more insightful and digestible comprehension of how and why growth can be achieved.

For example, with a single source of truth, it’s easier to create insightful dashboards and graphs in data visualization tools like Power BI and Tableau, making communication across departments and with leadership more seamless and intuitive. It’s also easier to gain better context around your data, leading to a deeper understanding of the problem and solution at hand.

But Tools Alone Can’t Solve the Problem

Using the right tools is an important aspect of closing data silos, but they aren’t an instant fix. To close data silos, data warehousing tools must be leveraged as part of a broader approach to data governance, architecture, and management. This requires a more methodical approach:

Create a data model
Ensure various data sources know how to speak to one another
Derive insights from a single point of origin

Taking these steps can help you do more with data, from improving the quality of your dashboards to implementing next-gen tools like machine learning and artificial intelligence. But in order to get there, it’s important to take an incremental approach, which we at Kenway like to call “Crawl, Walk, Run.”

Crawl, Walk, Run: Taking a Practical Approach to Breaking Down Silos

The Crawl, Walk, Run approach is all about setting the right foundation. Instead of focusing on achieving the desired end state immediately, we encourage organizations to focus on increasing data maturity in increments. Oftentimes, consultants will agree to help a client reach their desired goal (such as implementing advanced analytics or AI), but neglect to assess whether the client is ready to achieve it, and if the goal is what they need right now. At Kenway, our approach is to be practical and honest with our clients, which allows them to actually realize their short-term goals and be successful in the long term.

Crawl

At this stage, organizations are using Excel for analytics, reporting is done on an ad hoc basis, and data is limited. We help companies crawl by setting up more robust data warehousing and visualization tools. Companies at this stage can begin the process of upskilling their employees and identifying the capabilities they need to take data management to the next level.

Walk

Organizations that are ready to walk have gained better institutional knowledge around data management. At this point, we introduce a full analytics suite so they can access insight across multiple data sources. Dashboards can be created in minutes instead of days or weeks, making them more valuable. Organizations at this stage are also ready to introduce self-service analytics.

Run

When an organization is running, they consistently practice good data governance, approach master data management effectively, and have high-quality data as well as a solid data infrastructure. Their change management practices are effective, ensuring that data and processes remain intact even as the organization evolves. It isn’t until this stage that companies are ready for leading-edge applications like machine learning and artificial intelligence.

Best Practices for Removing Data Silos and Improving Business Intelligence

Whether you’re ready to crawl, walk, or run, the following best practices are essential to removing data silos and keeping them at bay.

Identify your most critical data. When you understand which data is most critical to your business, you can eliminate clutter and redundancies while providing stakeholders with the information they need most.
Establish and maintain data governance. A strong data governance strategy provides the standards, routines, and accountability needed to maintain data quality.
Implement a centralized data model. No matter how many new systems are implemented, maintaining a single source of truth is essential to ensuring high-quality data.
Create a data-driven culture. Everyone across the organization, from leadership to frontline employees, should understand the value of data and have the capacity to use it in their day-to-day work.
Know the audience. Who uses your data, and how? Regulators? Executives? Partners? When you understand how your data is used, you can manage information and share insights more effectively.

Break Down Data Silos to Access Better Business Intelligence

When you effectively break down data silos, you can use your time and resources more efficiently to identify business opportunities and make internal improvements to your culture. Employees are more engaged when they can work on forward-thinking, exciting projects that deliver high ROI. Not to mention, you can prevent the knowledge loss that occurs when “the analytics guy” leaves the company. Getting to that point doesn’t happen overnight. At Kenway, we help clients make incremental but significant progress towards removing data silos.

One of our clients, a financial institution, was challenged with the inefficiencies of disjointed data. The sales team struggled to understand the true breadth of their client relationships, and often performed manual tasks to piece together the client journey. Kenway performed an assessment to understand their current state, created a white paper to communicate the steps needed to realize its vision, and established a scalable data model. The financial institution now has enriched insights into its current and prospective clients and is better equipped to prevent data silos from cropping up as it grows.

Are you ready to tackle data silos at your company once and for all? We’re ready to help. Get in touch with a Kenway consultant today.

Data Literacy Framework: The Key to Accomplishing Your Data Goals

You have a data strategy. You have a roadmap of expensive projects and impressive technology to help you implement them. There are ambitious goals tied to your success. You’ve even created new policies, processes, and procedures. Then, you launch. Low to moderate success is achieved.

Expectations were so high! The project or tool had so much potential! So, what went wrong? Front-line staff bypassed procedures and entered the bare minimum data to fly through tasks more quickly. Executives ignored carefully developed KPIs in favor of "gut instinct."

Despite all your preparation, something was missing. Just as a garden’s success is dependent on how well you prepare and enrich the soil, execution of your data strategy relies on how well you prepare and enrich the people implementing it. Everyone, from front-line staff to executives, needs to have the skills to participate.

That’s why it’s not just enough to invest in tools and planning. Investing in data literacy, so your employees can leverage data and technology to achieve the ambitious goals you set, is also critical. The way you approach this process—your data literacy framework—makes a difference. The right framework should incorporate considerations for your data goals, your workforce’s current capabilities, and the tools you will use to implement your data strategy.

Here’s why it’s important to cultivate a data-literate workforce and what you should consider as you build your data literacy framework.

Understanding Data Literacy

According to MIT, data literacy is the ability to read, work with, analyze, and argue with data. To build a data-literate company, employees need to have different levels of competency with each aspect of data literacy. For example, front-line workers need to be more adept at reading and working with data, whereas managers need to be more skilled in analyzing and arguing with data.

Improving employees’ data literacy skills enables them to:

Know what data to use for specific purposes
Be able to interpret data visualizations
Understand analytics tools and think critically about the information they yield
Communicate information about data to people who lack data literacy

With these capabilities, they can incorporate data in their day-to-day tasks and bring your business closer to realizing its data strategy goals.

Why Is Data Literacy Important?

Businesses that invest in data literacy programs see wide-ranging benefits, from higher levels of productivity to increased data utilization.

Improve Employee Productivity, Satisfaction, and Propensity to Innovate

According to a Tableau report, data literate employees are more productive and make faster, better decisions, which translates to a better customer experience. With better access to data, and the skills to use that data effectively, employees are more capable of innovating. Offering a data literacy program also increases loyalty—nearly 80% of employees say they’re more likely to stay at a company that offers data upskilling.

Make Better Use of Data

By closing the data literacy skills gap, you can empower employees to leverage data to solve business challenges. For example, there’s no shortage of people analytics tools available to help HR teams track turnover, engagement, diversity, and other key metrics. When they know how to choose the right data sources, interpret data sets, delineate between causation and correlation, and communicate their findings, they can take full advantage of these tools.

Increase Data Maturity

Improving data literacy is a key aspect of progressing through the stages of data maturity. At the highest level of data maturity, data management isn’t solely the responsibility of IT. Instead, IT works in unison with the larger business to develop and maintain data management strategies and employees at all levels are capable of using data to drive decision-making.

Barriers to Establishing Data Literacy

Considering these benefits, why is it so difficult to increase data literacy and realize its potential? Cultural and technical hurdles often get in the way. As you build your data literacy framework, it’s important to think about how these barriers impact your business.

Data ownership and management are often owned by IT or data science functions and there is no common awareness across the organization of how data is used. It’s “Someone else’s job.” This is often caused by a lack of data governance and can lead to a divisive culture. Teams work with data in silos and have no way to build on one another’s work.
Technical hurdles, such as information silos and tools that require specialized expertise, keep employees from accessing the data they need to grow their skills. It’s hard to develop data literacy skills without putting them into practice.
Employees can be overwhelmed when new tools and practices are introduced too quickly. If data literacy programs aren’t tailored to meet them where they are, it’s harder for them to engage with or retain the information provided.

Building a Data Literacy Framework

Effective data literacy programs are geared towards empowering employees at all levels of the organization to use data effectively. According to the above Tableau report, organizations that offer training for a wide variety of skills to all employees see better results than those that only offer narrowly focused training programs. So, how do you offer the right education without overwhelming your workforce?

By following a data literacy framework, you can take a methodical approach to develop a program that will have lasting, tangible benefits for your workforce and your business.

1. Generate the Need

Because improving data literacy requires a cultural shift, getting leadership buy-in is essential. To get leaders and other key stakeholders on board with the data literacy program, show them its value to the business.

2. Assess the Data Literacy of Your Workforce

Based on their previous experience and the data functions they’re currently expected to perform, individual employees’ current data literacy skill levels will vary. By assessing their current capabilities, you can create tailored programs that will drive comprehension and better data utilization.

3. Teach Basic Data Concepts

At this stage, it’s important to get individual contributors engaged with data. They need to understand the “why” behind the project. Teach the value of data, and how it can improve day-to-day workflows, so that individual employees understand its role in their work.

4. Develop a Common Language

Even though you’re asking employees to level up their skills, they shouldn’t be expected to become data gurus. Engage employees at all levels by simplifying difficult concepts and using ordinary language instead of technical jargon.

5. Develop Employee Data Management Capabilities

As employees gain more awareness and access to data, they play a larger role in maintaining its accuracy, accessibility, and safety. Educate them on the data management best practices and company policies they need to know to promote data integrity.

6. Apply Data Knowledge

As employees become more data literate, encourage them to use data more often. Promote data as a tool that empowers them to solve problems, innovate, and collaborate with confidence.

Advance Data Literacy in Your Workforce

A successful data literacy program backed by a solid framework can help your company transform into a data-driven organization. It ensures that the time, energy, and money you invest in your data strategy, roadmap, and tools pay off. Instead of experiencing the disappointment of poor-performing projects, you can execute data initiatives with confidence.

At Kenway, we understand the important role education plays in the success of any data initiative. Whether you need help developing your data literacy program, or if you want to ensure that literacy is a key component of your next data project, we can help. To learn how we help other companies like yours, read our case studies.

FAQs

What is a data literacy framework?

A data literacy framework guides your data literacy program. It includes considerations for your workforce’s current capabilities, your data goals, and an educational plan geared towards helping them gradually develop their skills.

What are the main characteristics of data literacy?

According to MIT, the main characteristics of data literacy are reading, working with, analyzing, and arguing with data. The more competent employees are in these areas, the higher their level of data literacy.

How do you develop data literacy?

To develop data literacy, follow a well-developed plan to promote a data-literate culture. Employees should be encouraged to become more aware of and engaged with data in their day-to-day work. To promote success, an effective data literacy program should be tailored to meet employees at their current level of competency.