Is your organization looking to accelerate digital transformation and
become more data-driven? While many tools and practices are required to
transform businesses, experienced leaders seek
digital transformation force multipliers
to improve data quality, grow analytics capabilities, and help instrument
change across the organization.
Almost all organizations pursuing digital transformation strategies have top
priorities to improve customer experiences and enable data-driven
capabilities. Most business leaders recognize that the products and services
they offer today need to progress considerably over the next months, quarters,
and years based on changing customer needs and business opportunities.
The drive to experiment and evolve customer experiences requires people across
the organization to use data, analytics, and machine learning capabilities.
The most progressive C-level executives, including CEO, CIO, CMO, chief
digital officers, and chief data officers,
seek to empower more people with access to data, analytics, and
self-service data technologies.
These leaders recognize the need to challenge the status quo and evolve new
ways to serve customers better. It’s critical for people in all roles – from
sales, marketing, operations, finance, technology, and human resources – to
see patterns, ask questions, and experiment with new models.
So, what technologies should CIO, CDO, IT, and data leaders promote to support
the data-driven organization?
Should CIO and IT leaders look to centralize data in
or other data warehouses and data lakes? Will providing access to data using
self-service data visualization tools like Tableau, Microsoft Power BI, and
others help people across the organization make better, faster, and smarter
decisions with data?
The simple answer is, “Yes, but.”
Centralizing, providing access, and enabling data discovery are just three
legs of the stool. The problem is that people need to identify data sources
useful to their quests, understand the context behind the data, and connect
with data stewards that are the subject matter experts.
Let’s review three use cases on how data catalogs can accelerate digital
1. Simplify Finding the Right Data for the Analysis
Here’s a common question raised by analysts and data scientists. As they
review data sources, they ask, “Which date and financial measures should I use
in my analytics, data visualization, or machine learning model?”
CRMs, ERPs, and other SaaS tools store dozens of dates and currency fields.
For example, a sales pipeline might store dates for the pipeline’s creation
and start, and the analyst wants to know which one to use for calculating the
sales cycle. In a second example, the ERP likely has many fields for
capturing customer revenue, and the analyst wants to determine how to
aggregate them and calculate a customer lifetime value metric.
Even in the best cases where the database or SaaS has metadata or
documentation providing definitions on dimensions, measures, and dates,
chances are, only IT and expert users of each system know where to find this
Now add to this complexity when businesses have multiple CRMs, ERPs, marketing
platforms, and other SaaS tools replicating data in and out of them. How
likely is it that analysts select the correct fields, and how much time do
they waste seeking experts to learn how to leverage different data
And it’s not just financial and sales data that are important to
organizations. Leaders deploy data catalogs and prep to
improve enterprise data quality, analyze
supply chains, and
improve patient care in hospitals.
Data catalogs centralize a listing of loaded data sources, provide entry points for people to request access to data, and offer tools
for maintaining data dictionaries. These dictionaries include metadata loaded
from source systems, supplemental information provided by subject matter
experts, and data catalog usage analytics.
The analyst using this data catalog is more likely to find the right source
and data fields, and
top data catalogs offer natural language processing interfaces
for querying them. If the analyst has questions, then the data catalog helps
them find, collaborate, and ask questions of subject matter experts.
Using the data dictionary establishes a feedback loop as increased usage
improves its accuracy and utility. It creates a force multiplier because
citizen analysts have the tools to find sources, accurate data, and subject
2. Deploy New Apps With Supporting Analytics
A big success factor in digital transformation is to develop minimal viable
products, release frequent app improvements to end-users, capture their
feedback, and then realign priorities and requirements. But, developers
working on apps have the same challenge as analysts, and they need to know
which existing data sources and fields to tap into with their apps.
Most apps also create new data sources with forms, images, audio, IoT data
streams, and other form factors. App usage and alert information may be
standardized or have app-specific observability data.
So, the key question is, how can analysts tap into these data sources and
ensure development teams receive regular feedback and insights on usage,
performance, security, and other metrics?
Development teams that include updating the
during their app release management process helps to maintain documentation
and inform analysts.
It’s a data governance force multiplier
because it enables
an agile collaboration between developers and analysts
and promotes creating data-driven feedback loops when DevOps teams release new
app versions and features.
3. Decide When to Source New Data and When to Consolidate
The first two examples focus on consuming and producing internal data sources.
The third force multiplier comes from how organizations identify, procure,
utilize, support, and manage third-party data sources.
Analysts often integrate and use third-party data sources to enrich internal
data on companies, people, and products. In addition, weather, economic,
government, and other contextual information are vital to load into analytical
and machine learning models to identify correlations and causalities.
Procuring a data source is only one step in the sourcing lifecycle. We also
want analysts to find and review existing data sources before seeking new
Support practices to monitor usage patterns and data source changes are also
needed. For example, many organizations end up with duplicate or
near-identical data sources, and consolidating the usage to one primary source
can often yield cost and quality benefits. In addition, when third-party data
sources are underutilized, there may be a business rationale to unsubscribe
and reduce costs.
Data catalogs can be even more beneficial when procuring new data sources, and
the data governance team establishes a selection process. With a data catalog
in place, they can easily collaborate with analysts and other data consumers
to identify requirements, pilot analytics, and select winning solutions. Once
selected, they can update the data catalog and enable more consumers to
leverage sources in their analytics.
The force multiplier is that the catalog encourages reusing third-party data
sources and reducing underutilized ones. The process creates another feedback
loop where the most important third-party data sources are
To summarize, a key to digital transformations is running experiments,
capturing customer feedback, and leveraging data to realign programs.
Enterprises using a data catalog have a game-changing tool and practice that
enables more analysts, subject matter experts, developers, and decision-makers
to collaborate with higher quality data and analytics.
This post is brought to you by Boomi
The views and opinions expressed herein are those of the author and do not
necessarily represent the views and opinions of Boomi.