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Data Leadership Book Review and Interview

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Key Takeaways

  • Many data management programs fail to connect their efforts to meaningful and measurable business impacts.
  • Data Leadership is how the organizations choose to apply the resources toward creating data capabilities to influence their business.
  • Nothing built with data matters at all if it’s not improving the business in terms of revenue, cost, or risk management.
  • Simple Virtuous Cycle process which includes three activities (measure, identify improvements, and improve), helps to get started with data governance.
  • The Data Leadership Framework (DLF) consists of five categories and 25 disciplines.

Data Leadership book, authored by Anthony Algmin and published by DATAVERSITY Press, covers the data leadership topic and how data leaders should manage and govern the data management programs in their organizations.

Algmin defines Data Leadership as how the organizations choose to apply their energy and resources toward creating data capabilities to influence their business. It’s important to not just create data capabilities that have potential value, but to actually apply those capabilities to realize the actual value. Data Value can be measured in three ways:

  • Increasing revenue
  • Decreasing cost, and
  • Managing risk

Author discusses data leadership topic in the book in three different parts.

  • Part 1: Data Leadership Foundations: This part covers the topics data value, the importance of communication between business and technology personnel, and how to bridge the divide between IT and the business teams. Author also discusses the Data Governance which should help the organizations operate better through the effective use of data. Simple Virtuous Cycle process which includes three activities (measure, identify improvements, and improve), helps to get started with data governance.
  • Part 2: Data Leadership Framework (DLF): The DLF framework consists of five categories (Access, Refine, Adopt, Impact and Align) and 25 disciplines. These are all action-oriented activities that help with establishing and sustaining the data management programs in organizations. These five categories cover all the aspects of data management lifecycle, including data security, data architecture, metadata, curation, data modeling and warehousing, emerging data technologies, business process automation, data monetization, stakeholder engagement, and regulatory compliance.
  • Part 3: Data Leadership in Action: In this last part, Algmin discusses how to execute the data leadership framework. It includes how to build data leadership teams, and various data related technologies to be familiar with, like big data, cloud computing, serverless & event-driven and data lakes.

Data Leadership Framework provides a structured approach in organizing the data related questions to navigate the data leadership journey. Senior leadership support and executive visibility are critical for a successful data leadership program.

Author also discusses various emerging technologies and tools in the data management space, that data leaders should have some familiarity because technologies are also an important part of the overall data strategy. Some of the technologies discussed in the book are: Big Data, Public Cloud, Internet of Things, Smart Data Lakes, and Serverless Computing and Event-Driven architectures and languages like Python and R.

InfoQ spoke with Algmin about data leadership and how business and IT leaders can better understand the data management and establish the Data Leadership Framework discussed in the book.

InfoQ: Who are the target audience for your book?

Anthony Algmin: The goal for the book is that it would be something that people who attend a DATAVERSITY conference would use themselves and then share with the folks back at their office to help everyone understand why data is so important, and what should be done to help our businesses make the most of it.

InfoQ: What are some current challenges in data management programs in organizations?

Algmin: So often it seems like the biggest challenges are around building data management capabilities in the first place, and then keeping the momentum building over time instead of fizzling out. My hypothesis on this is that many data management programs fail to connect their efforts to meaningful and measurable business impacts – which is why the business eventually loses interest in supporting these data management efforts.

InfoQ: Data management is one area that's more critical than any other that needs business and IT collaboration. What do you advise the data architects, developers, and DBA's to keep in mind when working with business teams?

Algmin: First is to remember that nothing we build with data matters at all if we aren’t somehow improving the business in terms of revenue, cost, or risk management. Find a way to work with the business where those of us on the IT side can use our knowledge and skills to help the business carry out data-driven decisions and activities in the most productive ways. We are change agents, and we are going to make a much bigger difference when we pull the rope in the same direction as the business.

InfoQ: DevOps in data management area has been getting more attention lately. Can you talk about this and how DevOps practices, applied in the data space, would help the organizations?

Algmin: The first decade of my career was building data and technology systems, and I always had to manage the operations of what I built. It’s really the only way I’d ever gain enough insight into how to make it better. So, I think it’s about time DevOps became the norm in data management as well! I also think focusing on more frequent, incremental changes is a far less risky approach compared to large releases. Beyond that, I think it is important for data management and governance folks to be directly and intimately involved in data systems-building. I’m always baffled when they aren’t. There’s only so much we can accomplish if we aren’t rolling up our sleeves and working where the data lives.

InfoQ: Chapter 5 title in the book is "Stop Talking About Working and Start Working", very powerful statement and representation of current affairs in most of the organizations when it comes to innovative ideas that never see the light of implementation. Can you talk more about this and what can teams do to "start working?"

Algmin: This goes back to the challenge so many data management efforts seem to have in remaining relevant. I can’t count how many times the decision gets made to do data governance, for example, and the first thing an organization does is have a meeting to talk about data. They focus so much effort on meetings and talking that they forget that none of it matters until it helps the business do business things better. We need to stop all the talking and focus more on making a real impact in business terms.

InfoQ: Can you discuss the Data Leadership Framework you wrote about in the book and how it helps with data architecture and data management goals?

Algmin: The Data Leadership Framework is first about acknowledging that there is a whole bunch of stuff an organization needs to do to make the most of data. The five DLF Categories are where we evaluate an organization’s data capabilities and figure out where they are struggling most among the complexity. The twenty-five DLF Disciplines are where we then focus energy (i.e., invest our limited resources) to make the biggest outcomes. By creating relative balance across the DLF Categories, we maximize the overall impact of our data efforts.

This is what we need to be doing all the time with data, but without something like the Data Leadership Framework, the problems can feel overwhelming and people have trouble figuring out where to start, or what to do next. This is true of everybody, from data architects and developers to the CEO. If we can use the Data Leadership Framework to make sense amidst the chaos, the individual steps themselves are much less daunting.

InfoQ: What new developments and innovations do you see coming up in the data space in next few years?

Algmin: Data competency is no longer a “nice-to-have” item. From data breaches to analytics-driven disruptors in every industry, this is as big of a deal to businesses as cash flow. I think we’re going to see a renewed interest in the building blocks, things like data management and data quality – because things like machine learning and the cloud are not going to solve the basic, everyday data challenges for us. They will amplify our abilities, but if we have bad fundamental practices they will just create ever-bigger problems and more missed opportunities.

I feel so strongly about the topic of Data Leadership because for our businesses to survive past the next few years, we must succeed with data. It is time to wake up from our dreams of quick-fixes and realize that the alarm has been going off for a while and now we’d better hurry up and get to work – before it’s too late.

If you are more interested in Data Leadership topic, checkout the Podcasts page or the Blog section on the book author’s website.

About the Interviewee

Anthony J. Algmin is the Founder and CEO of Algmin Data Leadership, a company helping business and technology leaders transform their future with data. With decades of experience in business and hands-on technology roles, Anthony brings better data management to organizations of all kinds. He has led data change initiatives in many industries, serving as a project manager, data architect, and Chief Data Officer.

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