Building a Foundation for Enterprise Data Democratization with Kin + Carta

  • By Paige Russo
  • in
  • on December 2, 2022

Contributed by Kin + Carta

Executive Summary  

Most enterprises acknowledge harnessing the potential of data to transform their business as table stakes to competing in the coming decades. They’ve made investments and likely have some signature wins in terms of building data products (e.g. a recommendation algorithm, a customer churn model) or using data analytics to drive strategy. Perhaps they’ve made serious inroads co-locating their data in a lake. But where do they go from here? How do they properly democratize data – in terms of organization, governance, and infrastructure – to scale insights and applications toward true transformation?

Achieving Data Democrazation via Data Products

If you define Data Democratization as the practice of getting data into the hands of key decision makers or applications, you can think of Data Products as the medium or lubrication facilitating this process. On one end of the spectrum, data products can take the form of spreadsheets, powerpoints, or dashboards for business units to make informed decisions regarding their go-forward strategies. Moving down the spectrum, data products can be more interactive interfaces helping business units consume data and syndicate decisions (producing new measurable data!) in one streamlined interface. At the end of the spectrum, algorithms may completely absolve the need for a human in the loop, making customer-facing decisions such as how products or promotions are presented, or making operational decisions such as optimizing how scarce inventory is distributed. Data products can also function on a more meta-level, for example, data transformation pipelines that merge and clean data for dashboards or analyst consumption.

The Tension between Data Democrazation and Data Goverance 

The rapid evolution of big data technologies and cloud platforms, paired with the insatiable demand for data products, has turned old enterprise operating models on their head. To meet demand, artificial silos between data engineers, data scientists, data analysts, and business functions can no longer survive with too much time lost to bureaucratic processes and too many details lost in handoffs and mistranslations.
At Kin + Carta Data Labs, we strongly believe that multidisciplinary individuals and/or teams should be organized to develop data products in support of business domain objectives. This model acknowledges the intense amount of business and operational context necessary to turn messy, raw data into a product with enough integrity to support enterprise decision-making. This model also facilitates the rapid iteration and collaboration necessary to accomplish such ends on a reasonable timescale. But this operating model and focus on rapid data product proliferation will introduce new problems in the realm of data governance if left unchecked. For example, how do you ensure teams aren’t duplicating work? How do you ensure data products treat data consistently e.g. similar KPIs don’t contradict each other? And perhaps most important toward changing culture and gaining trust in business users not used to relying on data: How do you ensure data maintains integrity, and that bugs don’t proliferate across your data ecosystem in unmanageable ways?

Example Data Governance by User:

Data Analysts & Data Scientists

  • How do I know what data’s available for analysis or model development?
  • How do I know the quality of this data, or identify outstanding issues in the
    data?
  • How do I know how metrics are defined? And which data sources are the
    best sources of truth for these metrics?

Data Engineers

  • How do I ensure automated processes are creating quality data for downstream users?
  • When bugs are identified, how do I assess priority or notify consumers of that data that there’s an issue?

Business Users & Product Owners

  • How do I know that data I’m using has integrity?
  • How do I ensure bad data isn’t proliferating into customer facing
    applications?
  • How do I reputably respond to customer data requests under CCPA, or ensure all PII is deleted in requests to be forgotten?

Read the full article here 

About Kin + Carta 

Kin + Carta is a global digital transformation consultancy committed to working alongside our clients to build a world that works better for everyone.

Our 1,700 curious minds make creative connections between people, data and technology to create connected outcomes across the full lifecycle of the product and platform ecosystems.

We’re makers, builders and creators by nature, and we come to work every day to build experiences for some of the world’s most influential companies. We help businesses accelerate their digital roadmap, rapidly innovate, modernize their systems, enable their teams and optimize for continued growth.

We’re a technology business with trust and human connection at its heart. As a Certified B Corp, our triple bottom line focus on people, the planet and profit is at the core of everything we do.

To learn more visit: https://www.kinandcarta.com/en-us/

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PUBLISHED BY Paige Russo

View all posts by Paige Russo

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