Incorporating data analytics, and automation to the change model has been among the most disruptive evolutions to the practice of enterprise transformation that I’ve seen in my 15-year career. Change leaders systematically tell us, “This is just what I have needed all these years. This is a game-changer!” 

If you’re reading this as a transformation leader, change manager, or portfolio manager, you know that typically, business initiatives are initiated by enterprise leadership. Enterprise leaders set the vision and goals and expect organization and portfolio managers to align their own goals and projects to the enterprise objectives. Teams up and down the enterprise hierarchy kick off initiatives to drive solutions to achieve the business outcomes that are expected. 

As a result, a lot of people need to change the way they work, all while they are busy running the business. The trouble is that the people who need to change are traditionally considered during the last mile of the transformation initiative. While the folly of this is obvious since the initiative’s very success depends on the willingness and ability of stakeholders to change, it hasn’t been easy to achieve the necessary paradigm shift to do things differently, especially in a medium- or enterprise-sized company. 

Until now. Artificial intelligence, data analytics, and automation have come a long way in recent years, and offer change leaders phenomenal tools and insights not only for facilitating and accelerating adoption, but also for tangibly demonstrating the value of change management and empowering leadership to consider initiatives from the stakeholder perspective. 

Siloed approaches cloud the stakeholder view

One of the biggest challenges in enterprise change management is understanding change breadth, complexity, and risk across multiple portfolios. Organization A understands what it needs to do, whom it needs to coordinate with and maybe even how much their stakeholders will be impacted. Organization B is implementing initiatives of its own in parallel, some of which may impact the same stakeholder groups as Organization A. 

These different initiatives keep layering on top of each other. Enterprise-sized companies often have dozens of initiatives in flight at any given time. The more organizations are involved, the more saturated the change becomes. Because each organization tends to manage its initiatives in a silo, they don’t necessarily have visibility into what the other organizations are doing, except perhaps for some specific programs. 

Since each organization uses its own change management approaches, stakeholders often have inconsistent experiences with the different initiatives affecting them, which leads to the feeling of activity overload and change fatigue. 

As a result of this siloed approach, a holistic view of the cumulative change breadth, complexity and risk across the enterprise is unavailable. At the same time, change leadership is supposed to be making decisions about what should be done, and when. 

Traditional reporting tools are not scalable

Typically, change managers use tools like templates, assessments, and spreadsheets to gather information about their initiatives. They then manually transform and load the data into visualization tools like Power BI or Tableau to graphically represent what is happening.  

When we consider that nearly half of change managers report* managing at least three change initiatives at a time (and more than 10% manage 10 or more!), all that manual extraction, transformation and loading becomes unfeasible. Many organizations hire contract workers just to keep up with all the data ETL needed to report on their change initiatives, and even then, the information on each report is limited to a specific project. 

Enterprise leaders, then, rely on disparate information from distinct portfolios presented in varying formats to try to piece everything together to understand the big picture of what is changing, who is impacted, and what is being done to get teams ready across the enterprise. With this much complexity and inconsistency in the implementation and reporting of change initiatives, it is no wonder that stakeholders tend to get lost in the shuffle and considered late in the project life cycle! 

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Automation and data analytics enable stakeholder-specific views

Recent developments in data analytics and automation enable change leaders to get actionable insights from data about their projects at every vantage point, from the individual stakeholder level all the way up to the enterprise level. 

Once everyone impacted by your project – whether it be at the -C-suite level, the organization level, the portfolio level, the program level, or the individual end-user level – can clearly see how, when and why your project will affect them specifically, the dialogue changes. It’s no secret that people don’t engage with things they don’t perceive as relevant to them. But when it’s obvious how a project will directly affect them and their teams, engagement comes naturally.  

AI, data analytics and automation also enable you to scale and include information about all of the other initiatives across the enterprise into your model, so that reporting becomes holistic and change plans become coordinated. When your reports are telling you that of the 10 initiatives in flight, six of them affect the marketing department, conversations can happen across programs and portfolios to coordinate the deployment of the action plans so that end users are not overwhelmed. 

13 benefits of applying data analytics and automation to the process of change management 

Once you can capture and manipulate data about all the change initiatives across the enterprise from multiple perspectives, you can customize your reports to each stakeholder to stimulate engagement, promote adoption, and create a change-embracing culture across the enterprise. As a result: 

1. Your reports focus specifically on information and insights relevant to a specific stakeholder (end user, portfolio, organization, leadership, etc.).

2. Reporting is holistic and includes information about initiatives across portfolios. 

3. Change plans can be coordinated, since information about how the initiatives across the enterprise cumulatively affect each stakeholder.

4. Decisions about what, how and when to deploy are data-driven.

5. Leadership has a holistic understanding of the budget and resources needed to drive change.

6. Reports are generated automatically, saving valuable time and resources.

7. Rather than spending time generating reports, change leaders’ time can be spent on people-first tasks like engaging with leadership and their stakeholders.

8. Holistic views about what is changing, who is impacted and what is being done to get teams ready enable more meaningful dialogue between otherwise siloed portfolios.

9. A culture of change is promoted; people are more ready and willing to adopt change.

10. Change initiatives experience better and faster adoption.

11. Change metrics and ROI are clearly understood.

12. The change model is scalable and can incorporate data from an unlimited number of projects.

13. AI takes insights from past initiatives and intelligently suggests actions for current initiatives, which future-proofs your change model by enabling it to evolve as the enterprise changes and grows. 

If you have incorporated automation and data analytics into your change model, share in the comments what other outcomes you have experienced beyond the ones I’ve listed here! Or, reach out to us at change@italentdigital.com to continue the conversation.

Learn about Chama, our AI-enabled organizational change management tool.

*source: Prosci

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Not too long ago, data and analytics were considered just ordinary parts of larger information-related roles. That’s all changed, though: data and analytics are now table stakes for ensuring competitiveness and innovation. Accordingly, the data and analytics role has been elevated to the C-suite, giving it the prominence it deserves to ensure the success of the executive team and the organization as a whole.

In this article, I describe the role of the chief data and analytics officer (CDAO) in today’s business world. I define the role, contrast it to similar ones in the organization, and explain its context in the C-suite. I then discuss the benefits of having a CDAO in the organization, the roles and responsibilities of a CDAO, and best practices for establishing and staffing the position.

What is a chief data and analytics officer?

The name itself tells much of the story: the CDAO is a senior executive role responsible for leading all of the organization’s data and analytics efforts. This includes establishing and leading the vision for how the company gathers and stores data and transforms it into actionable intelligence.

The CDAO plays a pivotal role in creating and evolving the corporate “data culture” and ensuring that everyone in the organization is focused on using data and analytics to foster innovation and drive profit. The CDAO is also heavily involved in technology efforts, working with the CTO, CIO, and other executives to implement tools and technologies such as AI, machine learning, cloud computing, business intelligence, and much more.

While the CDAO role is still relatively new, it is being rapidly adopted by information-centric firms. Depending on the organization, the CDAO may report to the CEO, COO, chief strategy officer, executive VP, or another senior-level executive.

CDO vs. CAO vs. CDAO

The “alphabet soup” of the modern C-suite can be confusing, and that’s certainly the case when it comes to the data and analytics side. Let’s see if we can make sense of this jumble of acronyms.

The simplest place to start is by comparing the chief data officer (CDO) and chief analytics officer (CAO) roles. While data and analytics are closely related, these roles have important differences:

CDO: The chief data officer is primarily responsible for all the roles associated with managing the organization’s data. This includes establishing the organization’s overall data strategy, overseeing data-gathering activities, defining data quality standards, and establishing policies and procedures for the secure transmission and storage of data.

CAO: While the CDO deals with data in its various forms, the chief analytics officer is responsible for leading organizational efforts to transform that data into information and intelligence to enable sound decision-making. The CAO sets up and manages data science and data analysis operations, working with other departments within the organization to discover and define opportunities for enhancing the business through data-driven analytics.

So, how does a chief data and analytics officer differ from a chief data officer or a chief analytics officer? Unfortunately, there’s no single clear answer to this. These terms may even be used interchangeably, which can be confusing; for example, the well-known research and consulting firm Gartner Group says that the CAO, CDO, and CDAO roles are “equivalent.” This is understandable, but as mentioned above, there are differences between the CDO and CAO functions. Accordingly, a more useful definition basically says that the CDAO role encompasses both the CDO and CAO functions, and that’s the one I use in this article.

The idea of combining the two roles under the CDAO umbrella is to make clear that the CDO and CAO functions are different but work hand in hand. Having a single CDAO oversee both functions can help optimize the resources in each department, allowing the firm to exploit the obvious synergies between the data management and analytics functions. Merging the CDO and CAO roles can also be useful in other areas, such as enhancing regulatory compliance. In larger data-driven organizations, it’s also possible that there might be a separate CDO and CAO reporting to a CDAO.

Getting back to the “alphabet soup” issue mentioned earlier, be aware of the following:

• CDO can also stand for “chief digital officer.” 
• CAO may also mean “chief administrative officer.”
• CDAO can also stand for “chief digital and artificial intelligence officer”; this is especially common in defense and military circles. 

While some of these functions are clearly related to the ones we’re discussing in this article, they are distinct roles.

Key business benefits of the CDAO role

Hiring a CDAO puts a champion for data and analytics at the highest levels of the corporate structure. Data by itself is useful, but to unlock its potential it must be applied to produce value. One of the most essential advantages of formalizing the data and analytics role is that it puts corporate emphasis on the transformation of data into information to drive action.

Here are some other important potential benefits of the CDAO role:

• Sending a clear signal to employees, customers, and suppliers that the company understands the importance of data and analytics
• Helping to marshal funding for data-related projects and initiatives
• Enhancing the quality of data
• Driving innovation through the application of data-based insight
• Improving the data literacy of the organization
• Supporting the monetization of insights derived from data
• Ensuring the efficient flow of data between organizations
• Breaking down silos between computer systems and departments
• Enhancing security by increasing understanding of the role of data and the importance of its safety

Key specific responsibilities

The duties of a chief data and analytics officer vary depending on how each organization defines and implements the role. However, while the details may differ, these individuals are all C-suite executives, which means a core part of their role is leading, not just managing. A CDAO must focus on establishing a vision related to data and analytics, inspiring the organization to take steps to achieve that vision, and revising it as the business changes.

Specific CDAO responsibilities generally span the range of tasks that a CDO and CAO must do, and may include any or all of the following:

• Creating and implementing a general data strategy for the organization
• Setting up data flows into, within, and out of the business
• Managing the data lifecycle
• Defining policies for organizing data
• Analyzing data to generate insights
• Working with other executives to support change management
• Defining and upholding data quality standards through policies and specific initiatives
• Creating mechanisms to assure the safety and trustworthiness of data
• Assessing and improving user access to critical data sets
• Identifying business opportunities based on the results of data analysis
• Collaborating with other parts of the business to develop data insights
• Designing, building, and maintaining data repositories
• Crafting business intelligence systems
• Documenting procedures to ensure compliance with data privacy regulations
• Identifying and breaking down data silos
• Supporting the creation of new products and services
• Preparing to implement the CDAO role

CDAO success factors

The CDAO role is relatively new, and many companies are eager to jump into creating and staffing it. However, moving too fast can backfire. Without the right plan and infrastructure in place, the organization risks expending a great deal of time and money finding a great candidate, only to have the person quickly move on.

This phenomenon is so widespread that Harvard Business Review published an article on the topic a couple of years ago; it focuses on CDOs, but the same concerns apply to the CDAO role. The study says that the number-one reason for problems is simply that the role is not properly defined. Organizations also often have unrealistic expectations of the person brought in or are unwilling to make the necessary organizational and cultural changes required for a CDO/CAO/CDAO to succeed and help the business. Other studies indicate that up to half of CDOs believe that the value of the role isn’t yet properly recognized among their C-suite peers.

It’s clearly essential to “get your ducks in a row” before hiring. Here are some specific steps the organization can take in advance to ensure that a new CDAO thrives:

• Define the role clearly, so everyone knows what its responsibilities are
• Be sure everyone understands how the CDAO fits into the C-suite and its interfaces with other departments and functions
• Clearly define, write down, and get buy-in on the goals and expectations for the role
• Ensure that there’s an adequate support structure in place for the data and analytics function
• Do a survey of the company’s culture. Is everyone ready for data and analytics to play a major role in the company? If not, how can that be addressed?
• Assess the technological infrastructure. Be sure the technology stack is current enough to support modern data and analytics; if not, be prepared for the CDAO to work with the CIO, CTO, and other leaders to bring it up to speed
• Set aside adequate funding for the data/analytics function

Staffing the CDAO position

Finding the right candidate for the CDAO is challenging due to the complexity of the role and the many areas of the business it touches. The ideal person for the CDAO position will have experience and aptitude in many areas, including the following:

• Leadership
• Organizational technology
• Interpersonal relations
• Data management and processing
• Data science and analysis
• Change management
• People management
• Problem-solving 
• Interdepartmental collaboration

Here are a few specific tips for hiring a CDAO:

Properly define the scope and position of the role in the organization: I mentioned this before, but it’s important enough to warrant emphasizing.

Hire at the right time: Be sure the preparations discussed previously are all in place before starting to recruit. Beyond that, make sure that hiring into the role occurs when the company is in the right place in terms of its growth cycle.

Look for applicants with a balance of applicable skills: While obviously focused on data/analytics, this is more of a broad role than a narrowly focused one.

Consider applicants from non-technical backgrounds: Because of the many different skills required to be a good CDAO, it’s not always the case that a “techie” will be the right candidate; it’s worth keeping an open mind.

Consider an AI-enabled candidate matching platform like MojoHire. Finding the right person using conventional methods can be like finding a needle in a haystack. Let AI get you to the best candidate faster.

How iTalent can help

iTalent Digital’s Data Transformation and Business Intelligence Practice can support your enterprise CDAO role, from strategy to implementation. 

• We can help you choose an optimal data analytics strategy to create an explicit link to value creation and business outcomes and guide you on designing, developing, implementing, and improving your existing data analytics landscape.

• We can help turn that strategy into action by choosing the right D&A leader(s) who best fit(s) your organizational profile. 
• With our flexible service approach, we can also bring in the right experts to carry out many of the same functions that a typical CDAO would do.

Contact us at itbi@italentdigital.com to explore how we can help you achieve your specific data management and transformation goals.

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]]> https://newdev.iappease.com/blog/how-generative-ai-will-impact-enterprise-community-user-engagement/ Wed, 26 Apr 2023 14:40:00 +0000 http://dev.iappease.com/?p=7255

Reid Hoffman, co-founder of LinkedIn, former board member and original investor of OpenAI, and active board member of Microsoft, recently co-authored a book called Impromptu: Amplifying Our Humanity through AI. His co-author? GPT-4. In his book, Mr. Hoffman suggests the impact of generative AI across several industries and professions.

Being the enterprise community professional and overall technology nerd that I am, my attention naturally turns to asking questions about how AI will impact the online enterprise community category. But rather than make predictions – and even worse, assumptions – as to the overall reach AI will undoubtedly have in the space, I thought I might present a list of questions we enterprise community professionals should be asking as they relate to our roles.

If your professional role is even remotely connected to technology, then without a doubt, you are already part of this conversation taking place with respect to artificial intelligence and GPT (generative pre-trained transformer) technology breakthroughs. So it shouldn’t be surprising that we are seeing books co-authored (and soon to be authored) by AI.

Mr. Hoffman, no doubt, has had a front row seat to the transformative landscape that is artificial intelligence. In this book, he breaks down the potential impacts that generative AI technologies will have in education, creativity, justice, journalism, social media and the general transformation of work as we know it. It’s exciting, it’s scary and it’s already here. It’s a good read and I recommend it for anyone looking to better understand the potential in front of us.

The implications of generative AI for enterprise communities

If you have used ChatGPT or GPT-4, then you have most likely learned that the better the prompt, the better the output. In this spirit, let’s consider our prompts and questions that will help us best leverage AI for building and developing world-class enterprise communities.

First, a question for all of us to consider: Is generative artificial intelligence a threat to my job or an opportunity in my career? I ask this question as a starting prompt because it will likely dictate how you ask other subsequent questions around AI. It also might reveal whether you are of a fixed mindset or a growth mindset.

Questions for community technology builders and product managers

For people who build enterprise community platforms, there are going to be important questions to consider around AI. We already know that it is already being used to a small degree in community platforms. The areas that are prominent (as of this writing):

• Back-end workflows centered around moderation
• Guided onboarding and navigation with AI assistants and chatbots
• Personalization and search

The above represents more of a machine learning model and not specifically a large language model (LLM) that GPT technologies represent.

Questions for community developers and builders to consider include:

1. What is the impact and threat of LLMs and GPT (generative pre-trained transformer) technologies to communities centered around knowledge transfer? Will people forgo participation in online community in favor of interacting with an automated LLM chat interface?
2. What opportunities do LLMs present for enterprise online communities?
3. Are there immediate opportunities to integrate with LLMs and GPT technologies through an API layer to aid community users with contributing knowledge (think Microsoft Copilot)?
4. Can content contained in communities be made available to LLMs as a source of knowledge and truth? How do we ensure that we are not feeding an LLM false or misleading content that might lead to incorrect ‘facts’?
5.  How much of my R&D budget should be applied to AI?

Questions for community managers

For those leading large enterprise communities, there are also some important considerations and rules of engagement that should be developed. Just as academic institutions are putting in place policies and procedures around the use of GPT technology, community managers will need to grapple with many of the same questions.

1. Do we allow non-human registration to participate in online communities? If not, how do we verify human versus non-human registrants? This could also be a question for community builders in developing tools and technologies to identify non-human participants.
2.  What policies and procedures need to be developed as a terms-of-use agreement between our community members and use of generative AI?
3. What platforms are category-leading in terms of their R&D investment into AI?

Questions for community moderators

Moderation is an important function of communities and sometimes overlooked as the gatekeeper. Reducing the number of bad actors inside a community is an ever-evolving practice.

1. Can GPT technologies aid in identifying bad actors in my community and provide a level of automation that is not possible with human moderators?
2. How do human moderators and non-human moderators co-exist in my community workflows and practice?

Questions for community consultants and influencers

An emerging category in the space, community consultants represent a growing influence among global enterprises who are looking for expertise to help guide their own community strategy. These subject matter experts should be well educated and knowledgeable about generative AI to help keep their clients out in front of this tectonic paradigm shift.

1. How am I keeping abreast of the rapidly changing environment of generative AI and LLM technologies?
2. What should be developed in terms of a framework and operating model in how I make recommendations to my clients around generative AI technologies?

Final thoughts

 We are on the cusp of huge advancements in AI for the benefit of humanity, as Reid Hoffman argues in his book. How will the enterprise community segment respond? Well, that is up to us to consider, ponder, and more importantly, act with intention. Let’s shape AI for the overall good of community, not its demise.

Learn more about iTalent Digital’s enterprise community practice.

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