Data Advisory @BigID

About a month ago, I posted on Why I Joined BigID as Director of Data Advisory. As a follow up to that post, I thought I would outline what is Data Advisory at BigID? Included in this post is an overview of the type of work Data Advisory (not meant to be all inclusive) is developing and delivering @BigID. The goal is to collaborate with prospects, customers, and partners to drive focus on the value of data. The role is not just focusing on driving BigID products but to help prospects, customers and partners with their data journeys. 

A good place to start is to understand if a company has a data strategy, components of a data strategy or is just starting on their journey and needs help getting started. There are many definitions and contexts of data strategies depending on perspective so I thought I would set context for this post.

The data strategy context for this post is how to bring together four critical components and resulting outcomes. The four main focus areas include Business Outcomes, Operating Model, Defining Success, and Measuring Success. There can be many debates if these are the right components, what is included in each, and how to execute. This post is intended to get you thinking about what is important to your company’s data strategy, have a basis to begin a conversation across your company and to hopefully encourage you to connect and discuss your challenges across your organization. Connecting the Chief Privacy Officer, The Chief Security Officer and the Chief Data Officer to understand their goals and how each team can help each other is a good place to start. Each company is different and therefore, data strategies can consist of different core components and goals for each of those components. Below is one example to get started for a company that may be at the start of their journey.

Figure 1

Let’s examine each component outlined in figure 1 at a summary level:

Business Outcomes

One of the key principles in any data strategy is to ensure alignment and enablement to the corporate strategy. Having a data strategy that does not directly align to supporting the corporate strategies and/or drive the corporate strategy may not get the appropriate executive support, budget or resources required to deliver measurable results. Having direct linkage from each data outcome to a corporate outcome will ensure one can’t be done without the other, therefore, making data part of the execution and planning vs. an afterthought. 

One concept to think about in aligning data and corporate strategies is data by design. Data by design ensures data is embedded into the corporate strategy planning. Take for example, having a consumer product Go-To-Market strategy. A company wants to develop features and functionality that consumers will pay for and even pay a premium to purchase. What features and functionality can differentiate one company from another in the same space? When designing a new product or a refresh of an existing product, ensuring data is part of that product development program is essential. Data can be used to review customer support calls on existing products to ensure new products do not have the same challenges as existing products. Consumer products that are IoT devices (Internet of Things) can send data on consumer usage patterns to drive usage insights on what features and functions are being used and how they are being used in real time. This type of information can improve consumer experiences driving proactive software updates vs. waiting for a new version of the product. Understanding what data is collected (Discovery), how that data can be used (Policies), what data is critical (Critical Data Elements), is the data fit for purpose (Data Quality) and who has access to that data (Access Management) are all important capabilities to develop to support data execution.

Another concept in driving corporate strategy is by using data as a product. Modern consumer products capture tremendous amounts of data either from the hardware or software on the device itself or through interaction with supporting apps. Data on consumer activities such as how consumers are using their products, what time of day, where they are using them, how they are interacting with other apps and products can be valuable data that can packaged and sold to drive revenue. Understanding privacy concerns, data as a product can be at an aggregated level that does not have any insight into an individual level and personal data concerns.

In the two use cases described above using data to drive product development or packaging data as a product, require Discovery, Privacy, Security, and Governance. Connecting leaders that are accountable for each of these disciplines to align on goals and outcomes is critical.

Operating Model

Once you have figured out how data is needed to drive corporate strategies and have some critical business use cases outlined, it’s time to figure out how to execute. 

Some key areas to consider:

• Organizational model - centralize, decentralized or hybrid model
• Communication plan - clear, transparent communications on progress, dependencies, road blocks, and other key factors to drive engagement, reduce surprises and ensure alignment.
• Change Management - understanding promoters and detractors, assessing maturity of understanding of the data strategy to ensure it is at the right level for the right people to get the right support.
• Roles and responsibilities - documented, understood, and transparent outline of who is accountable for ownership of data, parts of the strategy, roadmaps and deliverables. This piece is not just about head nodding or hand raising but about ensuring acceptance of accountability with clearly defined deliverables.

Defining and Measuring Success

Ok, you have data and corporate strategy alignment and everyone knows what is being delivered, by whom, with an ongoing plan to ensure transparency, alignment and accountability. How do you know when things are delivering value? How do you know when to quit something because expected value is not being delivered? How do you know when you are done? How do you know when to invest more because more value is being delivered then expected?

Setting measurements are key. Key performance indicators (KPIs) or Objectives and Key Results (OKRs) need to be defined and agreed to by executives and key stakeholders. These measurements can be extremely complex when trying to measure the true value of return on investment (ROI) or risk measurements. Advice is to start simple and then mature success measurements as Data Literacy maturity increases. Data Literacy can be defined in many ways - for context Data Literacy is defined as “the ability to understand data related concepts and how to apply those concepts to drive business outcomes”. As Data Literacy increases, complexity of success measurements can become more complex. This is due to key stakeholders having an increased understanding of the value of the data in supporting corporate strategies with the ability to connect the dots across data and corporate strategies.

Some examples defining success include:

• Low Data Literacy Maturity - instead of measuring ROI on data programs, use simple measurements such as data inventory coverage. Measuring what data across the organization has a data catalog (inventoried) created vs. what percentage is dark data. Completeness of a business glossary that is defined with ownership. Evaluating how long it takes to fulfill Data Subject Access Requests (DSARs)
• High Data Literacy Maturing - calculating data contribution to the P&L can only be possible with high data literacy maturity. Developing contribution calculations for data to directly drive data or reduce cost requires the business owners, financial analysts, and data stakeholders to be able to clearly define and agree to ROI contributions. Website purchase conversion is increasing due to personalization of a customer model. The ability to attribute conversions that drive website models directly to a machine learning personalization model takes complete understanding and alignment from various functions.

In measuring success, it is important to have transparent and consistent report outs measuring progress. Assessing where you start, where you are and where you want to be using a Maturity Matrix (see figure 2) can provide a clear path for executives and stakeholders to track progress (or sometimes lack of progress). Measuring success ties all of the core strategy components together and ensures transparent accountability, aligns stakeholders on when to stop or make more investment on programs and have a clear picture of where the organization is on the data journey.

Figure 2

Conclusion 

While there is no one answer to a company's data journey, having a framework to start your journey along with key partners can help organizations avoid mistakes, utilize best practices and frameworks and provide advisory during the journey. The goal is to use data as a competitive differentiator while ensuring Privacy, Security and Governance. Some companies will start with Privacy driven by Legal. Others will start with Security driven by Information Security team. While others will start with the business trying to use the data driving Governance. Regardless of where you start, Discovery is a foundation capability that must be driven by automation to be successful. Where you may start will depend on your most pressing business challenges or where you get the most support. Regardless of where your organization begins, it is vital to understand how to get started ensuring short term value while mapping out a long term vision aligning and driving corporate strategies.

I am looking forward to seeing comments on this post as there are many ways to drive a data journey and hope some of you will share your journey. Would love to connect and discuss your data journey! If you happen to be at MIT CDOIQ Symposium the week of July 18, 2022, let me know and we can connect in person. Otherwise feel free to shoot me a message @ sgatchell@bigid.com. Appreciate you reading this post and hope you found it valuable.