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Author's profile photo Tina ROSARIO

A Data & Analytics Roadmap for Less Complexity and More Agility in 2022


Data and analytics champions will face greater expectations and a din of data-centric noise in the coming year. Meeting expectations and accelerating your business strategy depends on adding agility and reducing complexity.  

Tina Rosario, Head of Data Innovation and Chief Data Officer, SAP Europe

Looking at 2022, you’ve probably already identified your main objectives for yourself and your team. Maybe you are combining data from a recent M&A, expanding into a new global market, launching a product line or tackling an existing data silo in your organization. Whatever your data challenges are, they are thorny, complex problems.

According to analyst technology 2022 predictions, data and analytics (D&A) leaders will also face increased interest and demand for data fabrics, generative AI, and cloud native platforms. For example, generative AI holds the potential of providing new visuals in medical scans that could improve diagnoses and treatments. Marketing teams could use generative AI to automate promotions based on best performing copy and images. These new technologies are exciting, but they add to the data complexity that we already face.

While it can be overwhelming to look ahead at all that is coming in 2022, developing an agile approach for your data reduces complexities, cuts through the noise, and keeps you focused. To stay on track, here are five tips to help you realize agility and reduce complexity. They are about taking a step back, reviewing business objectives, and prioritizing what is possible and will deliver the highest value for your organization.

1) Define a plan for data. When customers describe their data landscapes, I often hear about multiple data lakes, data that’s not orchestrated or structured, and data still residing in spreadsheets and on local servers. Corralling that data is only a portion of creating a plan. You need an agile data framework that is grounded by the business’ goals.

The framework I created has three easy to follow phases that will accelerate your D&A program in a scaled, agile way. It helps you identify the patterns most relevant to your organization, prioritize your data and analytics requirements, and develop a fast path to success. (There are lots of nuances in this framework. For a deep dive, watch my Masterclass videos on Agile Data and Analytics.)

When you follow this framework, you can identify challenges and opportunities while realizing business outcomes along the way.

2) Be bold and willing to take risks.  As your company’s D&A leader, you need to move your plan forward without hesitation. You may need to take risks. Some actions to consider:

  • Re-prioritize the data projects in flight to ensure they’re tightly aligned with business goals
  • Bring fresh ideas to line-of-business leaders and executives that push them to think creatively about data and the insights it holds
  • Don’t wait for the perfect time to introduce self-service options
  • Start adding data that will supplement and enhance your data sets

3) Surround yourself with friends of data. It’s easy to feel isolated in this role, but you’re not on an island. Block out time to build relationships with others in your organization who you can count on for smart insights and who want to be a part of your success. You are likely to find support from:

  • Chief Financial Officer: These executives need data that supports the financial processes. They want data that validates their financial projections and helps steer the organization from a financial point of view.
  • Supply Chain Strategist, VP: These business leaders want data that helps improve and automate planning processes. Predictive analytics can help them better understand where and why the supply chain fails and how they can adjust for the future.
  • Operations or Manufacturing Director: Data can help these leaders improve processes with real-time insight into operational breakdowns that identify where they have staff or materials outages.
  • Chief Risk Officer: These executives want data that helps ensure compliance with rules and regulations. They want reports that monitor risks and the ability to drill down into possible compliance issues.
  • Sustainability Officer: These leaders rely on high-quality data for reports to track sustainability goals. They may also want to integrate external data with internal data to track carbon footprint or emissions.

4) Avoid making technology your strategy. Create a comprehensive approach that encompasses the enterprise and identifies how your team will contribute to the organization’s business goals. Adding new technologies or more technical staff can be a part of your strategy but talk about them in how they will benefit the organization:

  • The new analytics platform will identify which markets offer the most potential for our new product line.
  • The data modeling program will integrate our CRM data and manufacturing data to help us understand supply and ensure we can meet demand.
  • A strong data science team and data quality experts will deliver insights and learnings across business units.

5) Look at every business problem from a data lens. Not everyone will understand data the way you do, so don’t hesitate to be a data evangelist. You can create an environment where every question or problem starts with a visit to the data program.

A Fortune 500 company, for example, was struggling with an acquisition. The acquisition company had sent all its data in one huge file, resulting in dirty data that was hurting the business processes. Customers were complaining about receiving multiple mailings and marketing materials with wrong or misspelled names. Addressing data concerns early in the M&A would have prevented this problem.

The Fortune 500 company realized that it needed to define data processes, rules, and standards in the early stage of an M&A. This led to the creation of a D&A operational unit responsible for all data decisions around governance, integration, quality management, and process design. Now the company is completely data driven and relies on its D&A group to help out on both large and small problems.

Ultimately, that’s also your end-goal: leading a team that addresses business problems and opportunities through its D&A. These five tips are a good starting point for reaching that goal, and you can find more in-depth information in “Playbook for Agile Data and Analytics.” In the coming months, I’ll share more best practices, tips, and resources in this blog to help you meet and exceed your 2022 goals and objectives.

Looking for viewpoints and perspectives from other CDOs and D&A executives? Read the white paper, “The Role of Chief Data Officers” to understand how responsibilities are expanding beyond compliance and governance to driving business outcomes.  

In the coming months, I’ll share more best practices, tips, and resources in this blog to help you meet and exceed your data and analytics goals and objectives. Follow the tag and my profile to stay up to date for future blogs.


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