MODERNIZE YOUR DATA & ANALYTICS STRATEGY

Updated: Feb 11


I remember the days when we had a "BI Team," and IT said "no" to the business or made them wait months for a report or years for a data mart. (If you think you are still part of something that vaguely resembles this, I would love to hear from you; it would be my pleasure to help).


So things have changed. Data and Analytics are now core to how we operate today. It is more than just tracking KPI's; it is about improving the business. Data Leaders have a more strategic visible role in organizations, and they are much closer to the business strategy than ever before.


We hear a lot about digital transformation, and it can't be considered or talked about without incorporating a data strategy. Data & Analytics is fundamentally a set of capabilities for the more extensive digital organization. If we boil it down, to say that an organization is digital means that it must be data-driven. Digital transformation includes becoming data-driven. That is a challenge; those struggling with digital transformation have probably not paid enough attention to their Data Strategy.


Impacts on the Data Strategy


The first impact of the much more strategic nature of Data & Analytics is that the Chief Data Officer (CDO) role is becoming more pervasive and strategic as well. They also report higher up in the organization. The mindset of the traditional BI Team, which I mentioned earlier, has shifted from a service center that delivers projects to that of a competency center that enables every business group in the organization. Executives want to leverage data for business outcomes everywhere. Gartner says that 86% of CDOs report that modernizing Data & Analytics is a top priority, and 71% say they act as strategic advisors to their organization.


Another impact is that data has become part of an ecosystem. Decisions taken about data now have effects on customers and sometimes on society at large. Corporate responsibility is, therefore, increasing, especially around data security and ethics.


We also see that the business units no longer "need" the IT department to get going with Data & Analytics. Twice as many new jobs in data science are in the business units, not IT. IT is still required, but the role has changed.


Data for Everything Requires a Culture


We all know about the proliferation of data. Executives now have been sensitized to its potential value, so they want data to improve products, inform new business models, and help address new markets. So another impact is that it is becoming a question of culture. Ignoring culture, change management, skills and competencies will not lead to good things. And it often starts at the top: many executives are still uncomfortable accessing data directly. I wonder how challenging it is for those leaders to drive for change in a world where data is a core component of how they will deliver their results.


Back to the Data Strategy, it becomes critical that it be tightly aligned with the business strategy. I know we have all heard this before. But traditionally, when we look at Data Strategy documents, there is a very vague link to tangible business outcomes. It typically goes more like "here are a whole bunch of use cases, so we will buy this or that and roll it out for all to use." It has been too IT-focused. It needs to be business-outcomes focused.


Data Strategies tend to be top-down and try to defend some notion of ROI. We often see tallies of mostly IT costs and then say the more people use what we will put in place, the better the return. This method does not consider the capabilities we need to execute or the risks we have that we need to mitigate. For instance, we can't leave out the part about how we will ensure the business uses the capabilities. At best, this Data Strategy is incomplete, at worst, irresponsible. Some industries may require more risks than others.


We need to include the business units in the definition of the problems, the determination of how Data & Analytics will help solve the issues, and how. The Data Strategy needs to be a business strategy infused with Data & Analytics thinking. With data, how can we change what we do? If we don't have the data, how can we get it? How could data help us modify our products or services?


How to Change How We Elaborate the Data Strategy


Given all these changes and how influential culture is to success with Data & Analytics, here are some guiding principles and ideas, courtesy of Moi-même. I am very open to your comments here. 


The Data Strategy should contain the following elements:


  • Each business unit must be involved in its definition. You want to create a shared vision

  • Include an assessment of how you can be successful with data and also assess risks and appetite for risks and investments

  • The operating model of choice is a hybrid of centralization (what benefits all) and decentralization (the leverage of data for specific groups or processes)

  • Identify clear business outcomes and create a clear link between business outcomes and capabilities. Identify mission-critical priorities and value drivers so that the capabilities are delivered incrementally according to a prioritized list that everyone is aware of

  • Name a CDO or, at least, understand the role and assign someone to carry it out. The more decentralized the work gets (a good thing), the more the role of CDO becomes critical. It is an enabler to decentralizing Data & Analytics. The CDO has the accountability for the organization to become data-driven, and she fosters the Data Strategy

  • A central group (call it a Center of Excellence), led by the Chief Data Officer, coordinates the more sophisticated analytics, data governance, competencies, culture building and capabilities that will help all business units

  • Values of collaboration, community enablement, cross-functional teams and coaching should figure prominently

  • Training and data literacy has a lot more impact on ROI than our guts would generally tell us. Include them

  • Walk around the business, use proofs-of-concept to try things out with them. Timebox those efforts focus on clear success criteria

  • Include a section of what is missing to be able to execute on the Data Strategy

  • If you invest in a CDO, make him part of the business and report as high up as possible

  • Identify low-hanging fruits


Communication is Key


The shared vision should be an on-going community conversation. Investments should align with mission-critical priorities and value drivers. Everyone should understand where and why we are making investments.


When there is a success, the CDO should broadcast it. Explaining to someone that has never had chocolate how it tastes is impossible. In the communication strategy, show the examples, the wins. Show successes so others learn and get ideas. Counter-intuitively, the more we explain something, the more conceptual and vague it becomes. When people see and understand data-first approaches, they connect the examples to other ideas, and this could create groundbreaking changes in the industry.


It is also instrumental in knowing who the stakeholders are. They should be explicitly identified in the Data Strategy and leveraged in the communication strategy. 

How can a data-driven organization really label itself as such if it doesn't measure its progress toward that goal? We need metrics and a tracking mechanism to show we are making progress and that the investments are paying off.


Conclusions


Technology has been a massive driver for how profoundly things have changed in Data & Analytics, from cloud platforms to open-source software. What it is doing is blowing up the number of opportunities where data can be leveraged and the number of people that can do so. But if you thought that 20 years ago, having a data warehouse and a rogue business unit with its own independent data mart was a colossal governance problem, I have news for you. The idea of a single version of the truth is no longer possible. What we can do, though, is a single source of truth, but it needs to be managed, not by hope, but by collaboration, a solid CDO, and a modern data strategy.


To sum it up, we need to answer these questions:

  • What is our data-driven vision? How will it change us? How will we make it, and where will we put it?

  • What are the stakeholder outcomes? What unmet needs can we expand into, based on data available? What new data can we collect, and what more could it help us do?

  • What is the value proposition? How are the new capabilities enhancing outcomes with stakeholders? What value can we create? How will we measure it?


Read the second part of this topic here.

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