It’s a known fact that most initiatives in modern businesses increasingly depend on data and technology to bring more innovative products and services to the market. It leads to improved customer experiences as well. However, creating an operational data strategy in real time for achieving business goals and getting the desired outcomes is a big question faced by every organization.
In light of this, Shekhar Jitkar (Senior Director at Quadrant Knowledge Solutions) had a stimulating conversation with Sameer Sharma (Founder & CEO, Datazuum), about operationalizing data strategies for delivering business results. Sameer has been a keynote speaker at various events. He also runs a podcast called Data Strategy Show which has been listed among the top 10 podcasts of 2022.
What are the top priorities while operationalizing the Data Strategy?
Shekhar started the conversation by asking what the most important priority for businesses is while attempting to operationalize data strategy. Sameer answered it by pointing out 5 aspects he always focuses on while creating and operationalizing a data strategy for any organization.
- Set clear and concise strategic objectives to keep business strategy in alignment. Absolute clarity in terms of time and measurability behind those strategic objectives is important.
- Focus on the use cases across the domain and enterprise. The reason is that most organizations duplicate matrices, KPIs, and data in how they measure their business. People tend to duplicate the same data but get different results which is an old adage in the system. According to Sameer, understanding what cuts across the enterprise as the measurements applicable on a broader level (for the C-suite and the company board) along with the exclusive and specific use cases and measurements for different domains such as Finance, HR, etc. is very useful.
- Having a robust prioritization mechanism is the third important priority. It helps get a clear concise view of the use cases. One must discover the data to know what is available, what is not, and what can be availed from external sources to fulfill the use cases. It helps in understanding the prioritization mechanism of the business.
- Undertake a cost-benefit analysis and an approach to ROI for each of the use cases. While executing a business initiative, one must move away from performing it just for the sake of it and understand the facts, figures, and purpose behind it. The way to do it is to analyze how to measure the costs, benefits, and returns in terms of investments for the company over a period of time.
- A pragmatic roadmap is also one of the critical priorities for the business. The problem with very complex roadmaps is that delivering on them is difficult for most. A pragmatic approach to building a roadmap means breaking it into small chunks, simplifying delivery, and understanding each use cases in terms of the assets they use (e.g. people or skills). Building on those assets prevents duplication of the data and efforts, creating efficiency.
Hence, Sameer recommends while delivering a use case to look into its DNA and think about how one can apply data governance in it to reap the benefits of agility in the performance with automation and other capabilities. That is how data should be looked at in any organization.
The Data Team
Building on that, Shekhar mentioned that once a business decides on operationalizing data, the next challenge is team performance. He asked how organizations can organizations build an active data team skilled both qualitatively and quantitatively to help them in effective data-driven decision-making.
Answering the question, Sameer suggested looking at the maturity of the company in terms of skills, capabilities, leadership, processes, and technology as they are critical in deciding business needs. He highlighted the importance of getting the right people for the right things at the right time. A business must understand what it needs and when and building a pragmatic roadmap plays a great role. For example, when it is looking to deliver a specific use case and is sure to be able to do it, the organization may not need a big team of data scientists and engineers. It need not build a team of people it does not know what to do with. Bringing the key data and IT people together to work on an initiative of building an end data product or fulfilling specified strategic objectives could be enough.
He also suggested redeploying key people from some other teams and retraining them on data governance rather than hiring new people. It is better because these people are already business focused and can understand the associated challenges along with what needs to be delivered. Knowing more about the business and the data, they can perform quickly and communicate effectively with the data and IT teams.
Thus while building an active team, Sameer recommends looking at the maturity of the business and retraining people if possible. Of course, a pragmatic roadmap helps map simple steps for medium to long-term hiring needs and actions. Such an iterative view of data and IT teams working together with agility and cohesiveness help organizations deliver data more pragmatically with an idea of where they see the value and how would they drive it out.
Aligning Data Strategy with Business Goals
Moving on, Shekhar pointed to the fact that aligning data and technology strategies with business goals is important to level up data maturity and achieve leapfrogging with the aim of becoming sustainable. He asked Sameer to shed light on his observations and recommendations in this area.
Sameer said he has observed that organizations in many cases do not know how to align these strategies and leave them out while building a data strategy. Then talking about his experience, he said he would rather rename data strategy as ‘business strategy’ since it needs to be weaved into all the other IT or enterprise architecture strategies under the umbrella of business strategy. Businesses fail to realise this or find it difficult to embed. He recommends building something called Data Strategy Canvas to overcome this challenge. It helps the teams understand each other from a business perspective and not merely data or technology point of view.
On a broader level, this canvas means knowing business questions that need to be answered to drive the strategic objectives, decisions that need to be made based on those insights, and actions that need to be taken to achieve those values and results. Additionally, it means knowing where to find the systems (such as CRM, ERP, finance, HR, etc.) holding data on how things are set up, locating them accurately, and whether there is enough data or not. Another layer of cogitation here is about the business processes involved in achieving the goals and whether they cut across the organizations or not. People forget while building the strategies how these things embed into the business processes. But it is important to know these trigger points in the processes.
Considering whether the business has skills and capabilities, change management practices, and a culture capable of absorbing what the business is trying to achieve is also important.On top of all these questions, businesses can also contemplate whether it can monetize this data. Looking at all the supply chain partners could present some monetization activities that a business can leverage. So, the data strategy canvas helps think about these internal aspects along with the external factors while working on business initiatives. This thought process, according to Sameer, is the best way to reverse engineer a strategic objective rather than taking a tech-first approach.
Will the roles of CDOs and CIOs change and how?
Next, Shekhar inquired about the challenges the CIOs and CDOs face and how to address them. Adding to it, he asked whether Sameer saw the roles of these officers being changed in the coming years.
Talking about the CIOs, Sameer said that the increasing proliferation of technology in an organization bring new kind of challenges for them. For example, building a business data model should be the first notion while re-architecting the business for data; and CIOs should be involved in that process. This change in the job role for CIOs will be challenging looking at the complex layers of tooling and technologies. They will have to think about how to navigate the stack and walk through all the layers of systems that do not interact with each other in the organization. It will require them to work with the CDOs to re-architect their business through data.
When it comes to the CDOs, they seem to be stuck with long drawn-out governance programs as per Sameer. It seems that the businesses are not succeeding in putting the CDOs in the right places. There is a need for realignment in their position, job roles and reporting structures. According to him, there needs to be a better view of how to drive value in terms of revenue and growth. That is where these officials should focus – working with the CIOs to get the infrastructure in place and driving the teams toward realizing the expected business results. This will also give CEOs the luxury of getting a full enterprise view looking across the business and give them the autonomy in working with the different lines of business to drive the desired value.
All in all, he is of the opinion that businesses need to build value incrementally on top of the foundation work rather than just focusing on the foundation forever. The businesses should drive value and returns from the simple use cases first and then move on to more complex use cases.
Roadmap for Data Monetization
Building on the point of data monetization, Shekhar asked Sameer to highlight challenges in the area and to suggest a possible roadmap for nurturing and transforming data into a strategic business asset. Responding to the question, Sameer explained a five-point framework.
- Create a data strategy for your business. This involves a clear understanding of what data you have, where they are located, and what you intend to do with it along with a ‘why’ behind it.
- Create an inventory for your data. It helps to gauge how your available data can help internally and externally.
- Consider all the potential stakeholders. It means taking into account the unique pain points and challenges of all the value chain partners such as customers, suppliers, and distributors.
- Understand key challenges the business aims to address by building different data and analytics products. It could be a monetizable opportunity or a barter value exchange proposition between similar organizations.
- Understand relevant security, privacy, and regulatory laws. It is important to be secure and stay within the regulatory framework.
This five-point framework is key to understanding the monetizable effect of your data along with ensuring that you are involved in ethical practices throughout.
How does data enhance CX?
The last question Shekhar asked was how the effective utilization of data can help in enhancing the customer experience. Sameer answered this with the help of the concept of Customer Journey. Through data, businesses get a clear picture of where the customers are in their customer journey, the value of each interaction point, and of what value exchange is happening at each point. Measuring these points and applying business questions to them becomes very easy with the help of data. It brings a much-needed realization that the customer journey is not a single-line process but works more like a web of interaction points. Hence, the first value addition is that data helps redefine customer journeys.
Another advantage is that it helps in segmenting different customers in different journeys requiring different deliverables based on their needs and characteristics. Businesses can locate customers in their respective journeys and put them on another journey based on their behavior. Data plays the most important role in this process. A side benefit of this is the ability to get to the bottom of why people are not buying the product and overcome those bottlenecks by re-assessing and re-parameterizing the customer journeys.
Moreover, organizations can also redefine the concept of NPS (Net Promotor Score) by pondering over how it helps in the experience factors. It also helps understand what needs to be done when and how and not let a customer go off on the customer journey without it. That is where the value of data lies in the context of personalizing the customer experience.
This brought an end to the conversation. There is a long way to go for organizations to monetize on the tremendously valuable asset called data. Realizing this fact and operationalizing a strong and effective data strategy will be the right way to go for them in the coming years. The good news is that it is being talked about increasingly which will eventually pave the way for proper execution in the future. Sounds exciting, right? Quadrant Knowledge Solutions is actively tracking the movements, trends, and updates in the area of data strategy.
Stay tuned to find out more!
Author
Vaishnavi Dave is a Content Writer at Quadrant Knowledge Solutions.
