Hype Cycle For Data And Analytics Governance 2022
The market for data governance technologies is maturing quickly with new innovations â and even categories â launching daily. As a data and analytics leader, it can be difficult to keep up the ever-changing ecosystem of data governance tools.
The Gartner Hype Cycle for Data and Analytics Governance, 2022 is here to help. It addresses:
Emerging trends in data governance
Hype and interest around technology innovations
The role of metadata management in data enablement
Gartner, âHype Cycle for Data and Analytics Governance, 2022,â Guido De Simoni, Saul Judah, and Andrew White, June 22, 2022
Gartner and Hype Cycle are registered trademarks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartnerâs Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
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Chief Data Officer Role Shakes Up Traditional Data Governance
The arrival of big data means many things. For one, it has exposed the limits of data stalwarts such as structured query language and relational databases. Sure, an organization’s internal and structured data still matters but its not the only game in town these days. Indeed, the vast majority of what we call big data is both unstructured and lies outside of enterprise walls. Grappling with issues around big data has persuaded many organizations to ink in a new box on the org chart one for the chief data officer role.
Still think big data does not dramatically affect data governance and even basic management on many levels? Think again. Lets take a closer look at what big data specifically, the chief data officer role means for traditional data governance.
Datagovernance: How To Design Deploy And Sustain An Effective Datagovernance Program
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Who Can I Call If I Have More Questions
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A practical guide for todays chief data officers to define and manage data governance programs The relatively new role of chief data officer has been created to address the issue of managing a companys data as a strategic asset, but the problem is that there is no universally accepted playbook for this role. Magnifying the challenge is the rapidly increasing volume and complexity of data, as well as regulatory compliance as it relates to data. In this book, Sunil Soares provides a practical guide for todays chief data officers to manage data as an asset while delivering the trusted data required to power business initiatives, from the tactical to the transformative. The guide describes the relationship between the CDO and the data governance team, whose task is the formulation of policy to optimize, secure, and leverage information as an enterprise asset by aligning the objectives of multiple functions. Soares provides unique insight into the role of the CDO and presents a blueprint for implementing data governance successfully within the context of the position. With practical advice CDOs need, this book helps establish new data governance practices or mature existing practices.
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Streamline The Full Data Management Lifecycle
Data management has been a critical and common practice employed across industries for many years. At its core, data management encompasses all disciplines related to managing data as a strategic and valuable resource, including collecting, processing, governing, sharing and analyzing data and doing it all in a cost-efficient, effective and reliable manner.
But how do organizations truly avoid the data management mess and uplevel this process to more efficiently serve downstream analytics, data science and machine learning? Download this eBook to dive into data management on Databricks: ingestion, transformation, analytics, sharing and governance.
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Career Path And Tenure Info
Research shows that CDOs come from a variety of prior positions within the enterprise, many with data-centric roles that involve business intelligence and analytics. Many have also been promoted to chief data officer after working in IT-related jobs, marketing positions and even finance posts. Today’s CDO in data-driven companies typically possesses a master’s degree in technology or MBA, managerial or supervisory experience, and expertise in data collection, management, analysis and perhaps data science.
NewVantage Partners’ “Big Data and AI Executive Survey 2021″ that included primarily CDOs, as well as CAOs and CIOs, at Fortune 1000 companies and other leading firms provides insight into the evolving role of CDOs in driving digital transformation and the challenges confronting enterprises. Over the past decade, the percentage of mainstream companies adopting the CDO role has increased more than five-fold, from just 12% to 65%. However, the CDO’s role doesn’t necessarily dictate internal C-level status. About 44% of companies appoint an external change agent to handle CDO responsibilities compared to 23% that rely on company insiders, and only one-third of companies acknowledge that the CDO role is successful and established. While nearly half of respondents consider the CDO role “nascent and evolving,” 18% of companies struggle with CDO turnover, although that number decreased from the previous year.
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The Right Way To Measure Roi On Data Quality
To quote a friend, Building your data stack without factoring in data qualityis like buying a Ferrari but keeping it in the garage.
In this article, Francisco Alberini, Product Manager at Monte Carlo, introduces a better way for data teams to measure the financial impact of bad data on your business.
Last week, I was on a Zoom call with Lina, a Data Product Manager at one of our larger customers who oversees their data quality program.
Her team is responsible for maintaining 1000s of data pipelines that populate many of the companys most business critical tables. Reliable and trustworthy data is foundational to the success of their product, yet Lina was struggling to find a clear way to quantify both the importance and scope of the pipelines she owned.
We all know that reliable data is important to the business, she said. But I dont have a great way to measure its ROI of data quality, and from there, justify investment in my team.
Instead of getting the support she required to keep her companys pipelines operational , she found herself working at all hours of the day to personally maintain them. Time and again, budget and resources would be allocated to flashier, more feature-focused data engineering work, leaving her lean team high and dry when quarterly planning came around.
Linas experience so aptly conveys a struggle that many data teams deal with: justifying and measuring the ROI of a data quality program.
The Different Types Of Organizations
Of course, it’s folly to think of all organizations as the same. The business and data challenges facing a midsized American company differ dramatically from those faced by large health care organizations and behemoths like Google. Oh, and legislation differs greatly based on industry and geography. To expect all organizations to manage or govern their data identically or even similarly is just plain silly.
With respect to traditional data governance, my experience shows there are generally three types of organizations:
- Type A. Organizations that have already formalized data governance throughout the enterprise. Data governance here is continuous and systemic. Data governance councils, stewards, sponsors and teams are common.
- Type B. Organizations that informally and invisibly practice data governance. That is, you’re not likely to find any councils at these organizations. Ironically, many individuals and groups may be fulfilling similar and corresponding functions whether they know it or not.
- Type C. Organizations that ask, What’s data governance?
Let’s assume for the sake of argument that Type A and B organizations are doing data governance well. We’ll leave Pandora’s box closed for now.
What happens when an organization appoints its first CDO? Against the backdrop of big data, how can the CDO make data governance sustainable? The short answer is that it depends on the following three factors:
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Why Do Organizations Need A Chief Data Officer
Gartner defines today’s CDO as a “senior executive who bears responsibility for the firm’s enterprise-wide data and information strategy, governance, control, policy development and effective exploitation to create business value.”
The daily amount of data generated by society is estimated at about 2.5 quintillion bytes worldwide and growing. Organizations of all kinds realize they can use business intelligence and analytics tools to mine structured and unstructured data for insights into their business, industry and customers as well as market dynamics and consumer buying patterns.
Leading executives understand that data-driven insights can be monetized to improve their organization’s performance by streamlining business operations, increasing productivity, managing the new reality of remote and hybrid workforces, improving customer experiences and developing new products and services.
More recently, enterprise leaders have come to see data and the ability to mine it for insights and intelligence as critical to driving digital transformation and competing in the digital marketplace.
Digitally savvy enterprises see data as an indispensable asset that requires the management and expertise of a senior-level executive who’s responsible for setting data strategy companywide.
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Chief Data Officer Vs Chief Analytics Officer
Like the CDO and chief digital officer roles, there often is a blurring of lines between the responsibilities of the CDO and chief analytics officer. In fact, some research firms will often provide statistics related to the chief data/analytics officer.
Basically, the CDO’s core responsibilities are the management, flow and strategy of data throughout its lifecycle to meet the company’s business and financial objectives, while the CAO’s core responsibilities focus on the analysis of that data to meet the business, operational and customer analytics needs of the organization. Depending on the company’s size and industry, those roles can command separate titles or be combined into one title.
The CDO is expected to collaborate with other executives on data-driven initiatives and the analysis of data. In some organizations, data analysis is the responsibility of the chief analytics officer, along with other tasks similar to the CDO, including data strategy, treating data as an asset and deriving value from data. The CAO often reports to the CEO, but the position sometimes reports to the CDO. However, many organizations may not have a CAO or lack both a CDO and CAO.
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Future Of The Chief Data Officer Role
While considered a relatively new position and still evolving in corporations and government, CDOs are increasingly being recognized as integral to data-driven companies that are monetizing their collected data to improve business operations, product development, manufacturing, marketing campaigns, employee engagements and customer experiences. But there’s a long way to go.
Many companies are in various stages of digitally transforming or have yet to embark on the journey. One-fourth of organizations reported being at the maturity level of implementing and optimizing several digital transformation initiatives, while nearly three-fourths are in process, just beginning or only planning to digitally transform, according to Enterprise Strategy Group , a division of TechTarget.
As more organizations accelerate their digital transformation initiatives to continue competing at a high level, the evolving role and title of the CDO should achieve greater clarity, importance and C-level acceptance beyond the core responsibilities of creating a data usage strategy, managing the flow of data, liberating data silos and ensuring compliance with regulations. Future CDOs will ideally possess a combination of data management, AI and analytics skills, executive experience and the ability to measure value in business terms.
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