To truly understand data-as-an-asset requires Enterprise Data Literacy, an organizational capability to take, analyze, and use data to remain secure and competitive. But achieving a high Enterprise Data Literacy can remain daunting when business and IT interact together.
All too often in the middle of a project sprint, IT gets stuck on a minor problem, such as new customers only being able to see their monthly invoice in landscape view. IT implements a fix, and the bill is sent. However, new customers get billed twice. Communication between IT and business missed the need for an extra check before sending an invoice. Throughout the ordeal, both IT and business tear out their hair, trying to work with each other, as the company’s Data Literacy remains low.
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Danny Sandwell, Product Marketing Manager at erwin, has seen this type of misunderstanding around data play out repeatedly in the last decades. As an interface between business and IT, Sandwell attended many company meetings, including at Hallmark Cards, where IT facilitated Data Modeling sessions with business. Sandwell said: “Business attended these technical data meetings, sometimes kicking and screaming.”
In a recent interview with DATAVERSITY®, Sandwell discussed where gaps in Enterprise Data Literacy exist, solutions that can be implemented to address these holes, and how to obtain a Data Literacy with a shared IT and business understanding, in addition to higher Data Literacy for everyone.
A Lack of Coherent Enterprise Data Literacy
Sandwell believes that the Data Literacy problem stems from specialized information needs and a lack of shared context. He remarked:
“Data Literacy affects all organizational levels. Everyone uses data for different reasons, including senior managers and the Chief Data Officer (CDO). The CDO tends to come from the business side and takes that perspective. However, he or she may have a steep learning curve about making technical infrastructure ready to serve and deliver.”
On the technical side, workers have a good data inventory; however, they have less of an
understanding of what the data contents mean to the business. Meanwhile the more data literate data scientists and business analysts put business and technical information together faster, with more direct data querying and manipulation.
So, across the enterprise, everyone has a different Data Literacy perspective and talks at cross purposes to one other.
Add to the situation various data maturity levels across departments and enterprises. Some ask about “the data on-hand, where to access it, and how it gets used and by whom.” Others have figured out these basics and have different questions on how to do Metadata Management and create a data catalog of all the data sets. Since everyone has different data requirements at different times, getting to a uniform Enterprise Data Literacy remains elusive.
Self-Service Data and Data Models
To get to a higher level of Data Literacy, many firms try self-service data and data models. Both promise to help a company get its arms around data.
Self-service provides business independence, doing data analysis without a lot of handholding and interaction with IT. To achieve this, companies purchase or create a self-serve data tool with a data interface that does not depend on a high level of technical knowledge. Sandwell argues that even with a less technical interface, business benefits from self-service depending on Data Literacy levels.
“Companies want people to be smarter around data in order to use it effectively and appropriately. For example, workers need to understand what a ‘customer’ means, the marketplace context around a customer, and how to do business in this environment. That means getting businesspeople to be more effective partners in obtaining the resources they need.”
Getting business to be data literate, however, poses a challenge “when companies value a deep technical knowledge of Data Management.” For example, understanding Metadata Management can get very detailed in coding. Businesses create and use business glossaries (shared internal vocabularies across all enterprise functions) to bridge this Data Literacy gap with IT.
Business glossaries drive self-service, especially when connected to technical assets handled by IT. Then the enterprise has a “top-to-bottom data view, the first step,” said Sandwell. But the glossary terms don’t necessarily connect to the physical models that provide “an end-to-end knowledge about enterprise data.” Business needs “a one-pane glass” with IT to talk intelligently about data with the same level of literacy.
IT tries to provide this “one-pane glass” through Data Modeling, documenting both software and business design. It automates and streamlines business processes wherever possible. But in talking with business and teaching enterprise Data Literacy, IT takes a technical bent. As a result, the Data Literacy rift deepens.
Automation takes away the heavy lifting for business to be data literate; however, it ends up “out of date when reporting or operating on the data.” The business requirement has shifted over the period taken to develop, test, and deploy the code. It gives incorrect data, and then business mistrusts the data it displays.
The Mind Map: A Timely Snapshot with Many Levels of Granularity
Business and IT need that one conceptual snapshot that shows everything about a data asset and is up-to-date during daily business operations. At the same time, each department needs to drill down or filter data according to what specific tasks require.
Sandwell believes a mind map combining self-service and data modeling meets both needs. He said:
“So, a nontechnical person can enter a term like ‘customer.’ The mind map starts with the business glossary and then displays related assets to customer: business policies and rules, data sharing agreements, technical details, governance, and other business assets. All things customer appear in one pane for business self-service, depending on the company’s ability to catalog and relate the information. At the same time, IT can dig down from that view to get to the physical assets it needs to build business conception. Both business and IT cross-pollinate, in real-time, increasing Data Literacy.”
Since business and IT get a handle on their data and improve their Data Literacy from the start, misunderstandings get cleared up earlier. According to Sandwell:
“Business, IT, and others uncover requirements, come up with a structure, and test that structure through some use cases at the conceptual stage. They find the problems at this point, before building a data system, saving costs. Intelligent business and technical people have an easy view of each other’s perspectives.”
Mind Map Automation Under the Hood
Sandwell explained how mind map automation, under the covers, keeps enterprise data assets up-to-date and everyone data literate. Automated algorithmic connects and queries the database, harvesting data at that moment. He stated:
“Say I want to find data lineage — data’s origins, movements, characteristics, and quality. When you say, ‘Show me the lineage today at this moment,’ then the system goes out and harvests that information, as often as you want. This metadata retrieval occurs as business activities happen. The metadata retrieved fits back into the mind map structures you configured.”
The mind map engine considers that business data assets can pose more challenges in staying current than the technical ones. Sandwell explained that the system goes into where the technical data resides, reads it, gets it, and refreshes the mind map. However, everyone has a different set of business rules, policies, and operations. So erwin made it simple and easy to configure and create custom business assets that automatically update every time it refreshes. It is easy to light up the read-only view. “Data Management, Data Governance, and business go into the mind map do work in this business user portal.”
Mind Map to Leverage Social Media
He spoke about a social media app built off of the mind map concept. Data value changes over time, and business and IT need to be on the same page about it. For example, knowing when a state lifts its COVID-19 restrictions has a lot of value before business returns to normal, and less importance a year afterward.
So, in addition to giving “business the ability to share textual information about a data asset and tag it to a data catalog,” in late 2020, users will be able to vote its value up or down in the mind map. This rating system “drives metrics and quantifies data’s worth.”
From these ratings, the enterprise understands what characterizes useful data vs. incorrect data. “Then that data valuation can spill into the on-boarding process when new employees learn right from bad data,” suggested Sandwell.
Enterprise Data Literacy happens day-by-day when business, IT, and the community come together, elevating data as an asset and trust. “That business trust comes from building out the data catalog, from the data glossary, an automation foundation, and then Data Literacy on top of that,” he concluded.
Real data understanding emerges as does Data Literacy, making the company more productive.
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