The Culture of Data Leaders

 

The Culture of data leaders report identified four key cultural themes from conversations with a subset of digital leaders and laggards. The themes:

Adopt a learning mindset towards risk and continuous learning.

Create shared context through data with collaboration which promotes transparency.

Measure everything and empower the teams to examine data.

Connect data and metrics to align the organisation. Using Objectives and Key Results (OKRs) with team and employee buy in. 

Data culture can align an organisation both horizontally and vertically. Culture as a digital transformation agent comes from that learning mindset, transparent access to data an clear metrics. 

Using data to make decisions empowers organisations and objective measures helps drive success. 

AI Maturity Model and building your AI Strategy

Reimagine your data strategy and become an intelligence driven organization requires a change in mindset. There are many opportunities within many industry verticals for AI growth, a few examples below. 

• Retail - know your customer, personalisation, omnichannel optimisation, supply chain resilience and sustainability
• Manufacturing - Agile factories, remote monitoring, predictive maintenance, field logistics
• sustainability, food production and distribution, water, energy supply, transportation, ecosystems
• Health - clinical analytics, faster diagnosis, intelligent operations, patient journey
• Banking and insurance - fraud and risk, innovative insurance

AI is a disruptive technology and has various difference scopes. Using the  McKinsey’s Three Horizons Model of innovation first described in Baghai, Coley, and White in 2000, in The Alchemy of Growth it provided a useful taxonomy although it no longer applies with the advent of speed of new technological rollout. The horizon growth model is split over time 1 year, 2-3 years, 5+ years strategy. However it has some interesting things to consider 

• Operational (what must be done to stay in business) - reduce costs, optimise operations, maximize revenue streams, defend market share, improve customer satisfaction and automate processes. 
• Innovative  (what is a must do to remain current in a fast moving market) innovate within traditional industry boundaries, create new digital products and services , create new customer experiences and innovate for cloud.
• Transformational (How to define a new market) innovate across boundaries, redefine industry definition, create new customer needs  and grow through network effects.

So how do we think about measuring where we are. An AI Maturity Model model from a Microsoft presentation gives an idea on how progress is described. 

Creating patterns and knowing anti-patterns can help with building an AI strategy for customers. Considerations to also include are those for set up of an AI ethics committees. There is plenty to consider when building the disruptive agile AI approach and these are just a few areas to consider.

Podcast: A Recap of SQLBits 2022

Excited to be a part of a podcast with Jessica Healy and Niall Quinn, hosted by Justin Langford chatting about SQLBits 2022 highlights and benefits. It was amazing to hear the various view points of others who attended the event.  Coeo have been an amazing sponsor of the event for many years.  Tune in an have a listen.

Monitor scan runs in Microsoft Purview

In Microsoft Purview the monitoring of scan performance is now in the data map menu hierarchy. 

Microsoft Purview account -> open Microsoft Purview Studio -> Data map -> Monitoring.

You can now register, scan data sources and view the scan status history in one place.  This is in Preview.

Azure Purview becomes Microsoft Purview

 A major change was announce yesterday with the future of compliance and data governance combined. The naming has changed from Azure Purview to Microsoft Purview. Watch the announcement here. 

The announcement can be read here . Microsoft talk about the lines between risk roles are blurring. 

• The pandemic 
• Nation-state attacks  
• Remote work 
• Evolving regulations 
• Data sharing 
• Growing CDO responsibilities 
• Governance and compliance

It is interesting that Microsoft mention that the CDO role may go beyond data management and protection to include business intelligence, AI and machine learning. I am not sure how that would work with the chief analytics officer (CAO) and CISO and overlapping roles.

The new Microsoft Purview is explained in the article as it:

• Helps you gain visibility into assets across your entire data estate.
• Enables easy access to all your data, security, and risk solutions. 
• Helps safeguard and manage sensitive data across clouds, apps, and endpoints.
• Manages end-to-end data risks and regulatory compliance.
• Empowers your organization to govern, protect, and manage data in new, comprehensive ways.

The article also talk about enhancing data governance with Microsoft Priva.

Read more about Azure Purview is now Microsoft Purview where it is shared the Microsoft Purview is a comprehensive set of solutions to help you govern, protect, and manage your entire data estate. They have brought together  Azure Purview and Microsoft 365 Compliance portfolio under one brand.

Watch Go Beyond with Microsoft Purview

 

The SQLBits Buddies at SQLBits

I am part of an amazing new venture  - SQLBits buddies. An initiative to help people feel more comfortable and have a friend when they are attending the conference. 

The SQLBits Buddies was a new idea that was trialled at SQLBits Arcade!

They were there to help you have a fantastic experience at SQLBits and give you that person to talk to and ask about all things SQLBits! Did you need someone you can talk to about the social side of SQLBits? Did you know about the game’s night, pub quiz and party? Ask a SQLBits Buddy!

You can read more here

There are interviews with a few of the Bits buddies

Angela Henry 

Malcolm Smith

Steph Martin

Victoria Holt

We had lots of fun at the event and spoke to lots of people first timers, people who had been before but work colleagues were online or simply they had come to the conference on their own this time. We shall see you all at the next event.

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. 

How to get started with Data Governance

This is a question that is being asked by many at the moment. The term data governance still comes with the old stigma of control and costly and requiring teams of people. There are those that still believe that going in under the radar and growing once value is shown is a way to start. Then there are those that think we should utilise tools to help with data governance to enable speed of adoption. Myself I think considering business value must be the place to start to have great data and business buy in. After all why do we want to collect the data in the first place. So don't talk about governance but about understanding that data, data erudition, and knowing your current data quality state.  Once the business is in place, embed the process for each use case.

Data governance needs to be simple clear messaging to the business based on an agile method. The benefits of each use case must be measurable and bring value. To be successful it relies on cross team communication and diversity in views. That strong cross functional collaboration, ethical understanding which is sustainable. Data governance underpins all data tasks.

Start with an agenda item in an existing meeting and the program will grow over time. 

Don't follow a framework as every business is different but pull from DAMA (body of knowledge) and CDMC (playbook of best practice )

Start with a data readiness assessment with four areas that looks at the current usage, data quality, how data is managed and business readiness.

Think about the two core questions that are trying to be solved are 

1.             what business problem are you trying to solve

2.             What tooling would help solve the business problem

Start small and create an iterative roadmap based on business value

Reading 

A Guide to Data Governance – Microsoft   https://bit.ly/3hDe5Sn

Chief Data Officer (CDO) Seat at the Cloud Table    https://bit.ly/375px7e

Get started with Azure Purview   aka.ms/azurepurview/getstarted

Microsoft Learn: introduction to Azure Purview   aka.ms/intro-to-azure-purview

Azure Purview Documentation   https://docs.microsoft.com/en-us/azure/purview/

Azure Purview Readiness   Checklist  https://bit.ly/3HIytMy