How to Make Your Company's Data Easy to Understand and Use

Whether you are a small startup or a massive enterprise, every business faces the same problem: resource scarcity.

 min. read
May 27, 2022
How to Make Your Company's Data Easy to Understand and Use

There is a time and place for a hack

Whether you are a small startup or a massive enterprise, every business faces the same problem: resource scarcity. Most companies are bound by time and money constraints. Taking the time and manpower to document your data is no exception. By understanding your data, you unlock knowledge about your customers and business. Unlocking the power of your data can accelerate your business by sharpening your competitive advantage, improving your understanding of your customer, and driving your outcomes. To do that, you need a good guide for the people spinning raw data into sweet insights. Creating a seamless experience for data users to handle seemingly never-ending sets of data requires those scarce resources of time and money.

Before today’s metadata management platforms, data documentation was done either through a very expensive data intelligence platform or through creative hacks. Data intelligence platforms can be upwards of hundreds of thousands of dollars, unnecessarily complex to use, and may not even meet all your needs. While you might need an easy way for everyone in your organization to explore your available data, the use of traditional, expensive platforms requires extensive certifications or training courses. Thus, your data “enablement” platform may require significant enablement itself, occupying your employee’s time.  

Your data “enablement” platform may require significant enablement itself...

Hacks documented in google docs or sheets can be a good starting point, but fail to scale. We all have that “Salesforce Business Glossary” sheet created by the college intern who did her best, but never really got the hang of the Campaign versus Campaign Member objects- and left some pretty unclear (perhaps, useless) definitions of revenue metrics. Or that time that you had the whole company abbreviate asset names with the first letter of each word so “generic-marketing-campaign-2022” became “gmc22”. Suddenly you found as you had more than 3 people creating data assets, the naming standard was gibberish. Or maybe you had to completely change fiscal quarters, flip the whole reporting structure, and your previous naming conventions were instantly out of date and inconsistent. This leaves you investing precious time and money into a solution that misses the mark - plus you still end up with a lot of potentially useless or harmful data.

Naming conventions rarely remain current as organizations evolve

Bad data leads to bad business.

Data must be trusted and understood to make your business repeatable

Making decisions on bad data has some serious consequences. What would happen if you missed the mark on your revenue predictions and let down shareholders? Missing data leaves important information out. When you’re missing data, your business is also missing out on big opportunities or failing to notice the root of problems. Duplicate data could slow down your storage, cost you money, and create workflow issues. Incorrect data could lead to inconsistent messaging or unhappy customers. Outdated data could leave your business in the dust as the world changes around them. The pursuit of accurate, complete data could also leave your business with significant tech debt. If you aren’t careful, you could be paying your precious cash for useless, orphaned systems.

When data is good, data is your best friend. Your data should have all the qualities of a great friend: trustworthy, supportive, and dependable. And when you’re a good friend to data, she has some pretty sweet gifts. To deliver relevant products loved by your customers and users, you have to understand what your customer is doing, not just what they are telling you. Thankfully, data makes personalization in a multi-channel world possible.

Your data should have all the qualities of a great friend: trustworthy, supportive, and dependable. And when you’re a good friend to data, she has some pretty sweet gifts.

If delivering a loved customer and user experience are the heart and soul of your business, getting high-quality insights from data is necessary to serve your customers well. And to understand data well, technical metadata alone isn't enough - the business context- the business context must be there to get reliable insights. Business metadata gives context to what the data is within your business. Behavioral metadata gives insight into who, where, and how your data is being used. Social metadata gives context to how the company is talking about the data, shedding light on a common language around data and the lifecycle of data assets. All of this together paints the most current, accurate picture of what data and insights exist and should be at the fingertips of all your data users so that you can run your business proactively.

Data Assets don’t age well unattended

How well do new employees do at their job if they have no onboarding? Lack of onboarding is setting them up to fail and the same goes for data users and new data assets. Data onboarding and enablement could be in the form of centralized wikis. But keeping wikis up-to-date is particularly onerous and it often falls to the wayside. So, how relevant would past onboarding be to your business today? Chances are you don’t use last year’s new employee onboarding guides when you update policies, processes, or strategy and the same goes for the data user experience. In order to have a seamless experience for data users, the onboarding experience should include:

  • A curated version of all the data sources useful to the user and available at their level of permissioning
  • Summaries of assets should capture the dashboards, databases, schemas, and views at an easy glance
  • Indicators of trust about the data not only include technical and quality signals but also allow seeing if their trusted colleagues also trust the data. In effect, a trust by proxy effect.
  • The documents, points of contact, and lineage accompanying each data asset relevant to them

PII and other sensitive data are of utmost importance to protect and monitor. The interconnectedness of data means when one data asset decays, the whole lineage is affected. A lack of current and accurate documentation can lead to some serious data issues. Corruption, incompatibility, redundancy, and data loss can render your models useless.

The Data Stewardship model fails

Who maintains that expensive data catalog or expansive data catalog hack? Some companies have dedicated data stewards who spend hours documenting. Modern data transformation has introduced roles like analytics engineers to the scene. The data mesh paradigm shift has ushered in the rise of the Data Product Manager. Whatever you call the ringleader of your data quality and enablement, this person is coordinating a cross-functional effort and lasso-ing together constantly prolific datasets. Implementations of traditional data management platforms can take months to years depending on the size of your business, not to mention cost you a whole lot of cash along the way.

Most startups can’t afford a role dedicated to data stewardship and the daunting task drops to the bottom of a jam-packed to-do list of the data owner or data user. Suddenly, the creative hacks fall apart. People once responsible for datasets leave the company, critical tasks pop up, or data is onboarded without stewards. Without any accountability, one day you will look around and no one at your company understands what data is “good” or “bad”, or what the standards for “good” or “bad” were in the first place.

No matter your organizational structure, an effective approach to metadata management should support all your data users.

Your business can’t use data at scale without a metadata management platform that makes understanding and using your data assets available easy for everyone.

About Metaphor

The Metaphor Metadata Platform represents the next evolution of the Data Catalog - it combines best in class Technical Metadata (learnt from building DataHub at LinkedIn) with Behavioral and Social Metadata. It supercharges an organization’s ability to democratize data with state of the art capabilities for Data Governance, Data Literacy and Data Enablement, and provides an extremely intuitive user interface that turns even the most non-technical user into a fan of the catalog. See Metaphor in action today!