Panel discussion about the CDO seat at the cloud table

There was an excellent EDM Webinar with Microsoft discussing the CDO seat at the cloud table with panellist John Bottega (president of the EDM council) , Karthik Ravindran (General Manager, Enterprise Data) and Mike Flasko (General Manager Data governance and privacy platform) on 29 March.

The session discussed some really important topics around, data governance and data management. Most businesses are on a data modernization journey and often the progress with in business is in small steps. This has the advantage to reduce the risk to business and to help move things forward. There are two  important principles to be considered: 

• Modernization not migration. It is that digital transformation focusing on the data estate to accomplish those outcomes. Incrementally modernizing those data estates into an architecture which enables us to consistently manage and govern our data assets, as well as responsibly democratize those data assets 
• There is someone in the business to champion data, solving problems with data across the enterprise and driving new tools to leverage maximising business value. It is important to unlock the potential of data and make it more accessible to users to be able to accomplish those opportunities. Also thinking about how data is proliferating throughout the business and the need to manage and govern it across the enterprise. 

There has been an organic evolution of the data estates, from the paper driven standards defining data management to technology driven outcomes. The implementations of standards is hugely varied and diverse across the organization. Creating benchmarks for core themes accountable to managing data hasn't been consistent or scalable. There has been a focus on making data management, the operations of data management and data governance, consistent and scalable through intelligent automation. When you start democraticing data it is enabling teams across the company to discover the data,  access it and use the data. Knowing that there are guardrails in place to ensure that the foundations of data management and data governance are being done well at scale,  gives confidence in the data. 

There is a question about how does one manage democratization and acceptable data with security and privacy in place. A couple of key characteristics stood out

• the pure volume of data that we were collecting was not going to scale to the ways we approach things originally
• that transformational power of the cloud to governance to data management

Looking at the problem questions they asked

• why can't inventory and classification, be a one click to click problem that scales up and down with your data estate. 
• why can't classification be intelligent with machine learning infused across that journey for a broad and deep understanding of the data assets that are being collected.

This led to core components the should be looked at

• who is the owner 
• who is accountable is useful for managing a data privacy program and trying to facilitate the data subject rights requests or understand where personal data is held about a particular data subject.
• It is important to have a common data mapping to aid understanding so that can be used to drive everything from privacy program to security posture risk analysis.

Most importantly it is to think about data governance differently.  Data governance has been around for a long time. It is often looked at as being a gatekeeper function versus and enabling function. Many companies don't use the word governance as they feel it frightens people. Mike disagreed with that statement where you think of data governance as an impediment to progress. Governance is critical to progress.

A question was asked about how did you sell the governance concept successfully within Microsoft.

There are many opportunities in this space, especially with the advances that are happening in tech and platform with scaling. To achieve this data management excellence from the beginning it is important to standardize, making consistent data, have scalability, be seamless and simple to use. The core things

• be able to discover data assets in a single catalogue
• standardizing data quality management by having consistent way to measure the quality of the data estate across various data quality dimensions, like completeness and accuracy. Be able to see the health of the data estate across those quality dimensions
• privacy, security and compliance. Compliance is especially important for enterprise standards that an organization has to be compliant with but also with the growing and ever evolving set of regulatory standards that are appearing in the industry.

With all of this in mind companies across many industries have come together to create a playbook of best practice for managing data in the cloud. This is the CDMC.  It was built by the community sharing ideas for best practice. 

Data as an asset

I just read an interesting article about the recipe for success handling data assets. It talks about data as an essential factor for business agility and that it enables competitive advantage. Data is an asset in its own right and organizations must change how data is viewed at a strategic level.  Gartner and Accenture talk about data as the essential focus and ingredient.   This assets become valuable once actionable insight can be derived. The article sets out 15 mantras for implementing data as an asset

1. Define your Data Strategy with tangible measurable metrics linked to business outcomes with a data architecture blueprint and executable roadmap.
2. Disrupt business models with AI
3. Establish the right Data Culture and Architecture with accountability, data curation and data quality competency, frictionless trusted data supply with embedded data fluency across the business and a data taxonomy and dictionary.
4. Implement DataOps to infuse life into your data with data acquisition and management connecting data creators with data consumers.
5. Establish Tech Intensity initiatives for Data-Fluency enablement by setting baselines for data literacy skills resulting in data fluency
6. Establish Data Signals and Patterns Repository
7. Establish Data Marketplace – for Data sharing and sourcing across ecosystems reviewing the data supply chain and data monetization strategy
8. Use AI and ML Algorithms
9. Democratize Data – Secure Data Access and the correct type of BI and BI tools and make data visualization more transparent, intuitive and contextualised.
10. Data Governance to produce trusted data with data lineage, managed data quality, business meta data and data profiling with risk and privacy policies for compliance.
11. Establish Data Ethics principles covering things such as transparency, traceability and explainability
12. Data Observability being the understanding of the health of the data in the system. The data observability pillars freshness and velocity, distribution, volume, schemes, lineage and data security & compliance.
13. Define Data security and compliance controls
14. Hire the right Data Engineering and AI talent
15. Establish a Chief Data Officer and office of CDO

The article finishes stating Data Fluency and empowerment will be the determining success factors in a data-literate world.

Data Governance: An Introduction

Initially published on the Coeo blog.  

Data Governance is a core area that businesses need to adopt in the data-driven world. Data has been around since the earliest of times, from the first libraries in the ancient world that started to collect and store information.

The collection of scientific research information, from census information about human populations, weather and spatial data to DNA genetic data, have all been contributing to the need to store data for analysis. The breadth of the information that is available for analysis covers our entire planet and beyond, and the population as well as different species. With our life and environment becoming documented to the finest degree the need for categorisation, data labelling and data management has become engrained into our society. Where research led the way for documentation of classification for data, business is now at a crucial time of growth and expansion to enable innovation.

With all data there becomes a continual need for its management and a core starting place is data governance. The DAMA Dictionary of Data Management defines Data Governance as “The exercise of authority, control and shared decision making (planning, monitoring and enforcement) over the management of data assets".

The goal of data governance is to help an organisation to manage data as an asset efficiently and effectively. It provides the principles, policy, processes, framework, metrics and oversight that are required to drive the most business value. Data governance programs have a goal of creating sustainable data management, good data quality that is measured and defining policies and practices. A much-needed area that needs to be considered is that of culture and embedding that culture of data management into the business.

We start with understanding what data assets a business has from the core known data and dark data; data that is collected but not used. The proliferation of duplicate data around a business is key to document. Often the first thing that comes to mind with data governance these days is compliance with all the data breaches that keep occurring. The areas one thinks of here are:  

• Policies
• Transparency
• Governance
• Regulations, such as GDPR
• Standards
• Rules
• Law

These require data inventories and audits to understand what personal data your organisation collects, where it is stored, how it is protected and who may have access to it.​ This is part of the picture that needs to be considered.

DAMA-DMBOK is an international guiding framework for the management of data. The framework includes areas such as:

• Data Strategy – defining, communicating and driving execution​.
• Policy – metadata management, access, usage, security, quality​
• Standards and quality – data architecture and data quality standards​
• Oversight/audit/stewardship​
• Compliance​
• Data issue management – compliance, ownership, policy, terminology, data quality, data access​
• Data management improvement projects ​
• Data asset valuation constantly define business value of data assets.

Consideration for the allocation of roles and responsibilities within an operating model helps guide the adoption of best practices.

In conclusion, managing data assets within a business requires it to be embedded in the culture of an organisation. Having high quality data leads to better business decisions. Having a core oversight function that is provided by a Chief Data Officer helps with keeping the day to day running of data in the fore front of everyone’s minds and you never know where the next innovation will come from.

More Information

• DAMA-DMBOK
• Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success - Book by Robert S. Seiner A Guide to everything you need to know about dark data
• A brief history of big data everyone should read
• A very short history of big data

The Chief Data Officer and Data Innovation

The chief data officer (CDO) is a new role that has been emerging over the last few years. It sprung into life with businesses realising that every business needs to utilise data to succeed in the age of digital transformation. We have spent the last few years thinking about data culture in organisations and how to enable change to utilise organisational data assets and dark data. There is difficulty in the business change process and the CDO role has seen various iterations, adapting to find its place.

As we have seen, data is core to every business and there is opportunity within business to grow and introduce efficiencies by nurturing data as an asset. The CDO is the champion for data within the business and the voice that always thinks about using data as a strategic asset. Data can be utilised intelligently in advanced analytics to create new business opportunity and increase efficiency.

Cross business team utilisation of data requires a change in culture from hording data to making it assessable and advertising in the business what assets there are. CDOs require budget to be successful and must be able to influence enterprise change. There are restrictions of regulation and compliance that must be adhered to, but the CDO needs to be disruptive and drive innovation through the business. 

The role is still evolving and a blueprint is becoming more established. Having a passion for data and storytelling with data is such an envisioning experience for growth. Erudite CDOs have a passion for exploration of data, just as data scientists do, to find that truth and evangelize the possibilities of data value and find that business opportunity. To enable data within the business to reach its potential use there are some foundations that need to be in place, such as data governance.

Thus a CDO should:

• Understand the data context
• Collaborate with business and IT
• Create a data strategy with best practices on managing data, standards for data sharing, policies and procedures across the entire data lifecycle and identify that data value
• Establish data governance operating models and sit on the data governance council
• Oversee architecture best practices to review the impact of infrastructure change
• Reduce barriers to data accessibility
• Drive a data quality vision
• Support operational efforts with strategic oversight
• Be aware of legislation relating to data oversight
• Data never stops being created and the ongoing role requires evangelizing, finding business value and adapting to changes of use with continuous small steps
• Be a data driven culture leader
• Lead data literacy improvement to help with the data culture adoption.
• Work with IT and the business
• Always know the current state of data maturity
• A CDO needs technical and business acumen
• Lead data transformation projects
• Operationalise data usage

It is not only large corporations that require CDOs. Hiring a CDO for SMEs is a challenge, but will be instrumental in enhancing business value and making sure the data that is used provides that much needed business innovation.

More Information

The Chief Data Officer’s Playbook, Caroline Carruthers and Peter Jackson, second edition (2021)

The CDO Journey, Insights and advice for data leaders, Peter Aiken, Todd Harbour, Kathy Walter, Ed Kelly, and Burt Walsh (2020)

World Economic Forum: 6 data policy issues experts are tracking right now  

Published on the Coeo Blog 26 May 2021