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12 Min Read

From architect to executive: Seven critical data lessons on the path to leadership

marzo 28, 2024 / Manju Naglapur

Short on time? Read the key takeaways:

  • Data sparked Manju Naglapur’s imagination at a very young age in the form of an interest in trivia about a wide variety of obscure topics.
  • As an adult, the senior vice president and general manager of Unisys Cloud, Applications & Infrastructure Solutions turned his passion for data into a profession, learning several critical lessons along the way as he moved from architecting projects to running business units.
  • One of data’s biggest superpowers is the way it answers business questions. High-quality data can help you better steer your path as an organization.

High-quality data doesn’t lie. It can confirm your organization’s suspicions or hopes. Or it can correct your assumptions so you can adapt your business approach.

This inherent truth of data has been a lifelong passion and the driving force behind my career. As a child, I enjoyed absorbing information about topics that interested me – from the minutiae of game rules to the nuances of interesting articles. This early affinity for details and patterns began a journey that shaped my academic and professional life.

As an engineering student, those retention skills proved invaluable while pursuing my bachelor’s and master’s degrees. My passion for data intensified as a graduate assistant in a school Unix lab, where I dabbled in many projects. However, extracting data then was challenging because of how expensive it was to derive meaningful insights.

Upon entering the professional world, my focus shifted to the application side of data, designing models that ensured accurate data intake from apps. Every position – from my beginnings as a data engineer to my work as a data architect to my transition to a business leader – has reinforced the value of data and the insights it provides.

Along the way, I’ve learned seven critical lessons about data, each a testament to its transformative power. These lessons offer practical wisdom and can guide those navigating the complex and dynamic landscape of data.

Lesson #1: Data answers business questions.

One of my earliest lessons was about data’s biggest superpower: its undeniable ability to shape organizational paths and break assumptions. You can praise a business unit or department for its success, but what does the data say?

Turn to data to answer some of your biggest business questions:

  • Is your organization, business unit or department meeting its business objectives?
  • What KPIs are you achieving, and which are you failing to meet?
  • What are the most promising growth opportunities?
  • Which projects or initiatives require finetuning to yield the best results?

Answers drive action. Answering such questions can unlock significant value and guide future business planning. And it can lead to promising endeavors like optimizing complex logistics.

Lesson #2: Business decision-makers must understand the data.

My first job out of university was as a data engineer/modeler for a federal contractor that managed the data in a real estate portal for several federal agencies. The portal tracked government buildings – critical information for the agencies’ decision-making. The quantity of data was immense, and it was a major undertaking to identify where it was located and extract value from it.

This was about 25 years ago, and reporting tools weren’t nearly as advanced as now. Reporting meant gathering data through disparate queries and exporting it into a spreadsheet. But even then, I knew how important it was for decision-makers to make sense of the data and gain insights from it. So, the team created reports t hat made the data easy to understand and showcased the top data insights for agency executives through reporting dashboards. In the modern era, you can use AI to both collect and report on data.

Lesson #3: A data modernization strategy is a must-have.

Data architecture is about more than interfacing with an application. When I started, the role involved ensuring your architecture could withstand the workload when there was persistence within your applications and if somebody started querying the data. As a result, many design principles outside of data modeling involved data optimization – still a sound principle.

That’s where a comprehensive data modernization strategy becomes vital. Without one, you may end up with duplicate data or other issues that can stall progress. This strategy should answer questions like which data is ripe to modernize and migrate and what steps do I take to secure data.  Given the significant shift in how organizations align data foundations to structured, semi-structured and unstructured data, your strategy should also include training and learning for machine learning models, steps for creating the right data pipeline and data ops.

Generative AI offers unlimited possibilities – and even more of an incentive than ever to prioritize your data. But it’s also making it clear that while data still plays a major role, the infrastructure that supports that data is critical as well. AI-ready enterprises need the right infrastructure to generate insights from data. With it, speed and agility become your allies.

Lesson #4: An early warning system can decrease risk.

With machine learning ops platforms and the challenges of rebuilding data architectures and multi-cloud infrastructure to accommodate generative AI, the landscape keeps shifting at a rapid pace.

If you don’t pay attention to your data from the beginning, you risk having to pay for it later – literally. The 2007-2008 financial crisis demonstrated this clearly. I witnessed this as a data leader overseeing data projects for financial services firms. Consultants were working long hours every day to try to identify why these companies were experiencing substantial financial losses.

The crisis is an example of what can happen when you’re not paying close enough attention to your data. There’s no guarantee that doing so would have prevented all the problems that emerged. However, by establishing mechanisms to detect data anomalies, somebody could have caught it early on and diminished their losses.

Lesson #5: A data lakehouse is a superior storage option.

Many organizations store their data in file-based or image-based relational databases. However, cheap compute created data sprawl and led to challenges, including data governance. That’s where a data lakehouse – cloud-based storage for structured, semi-structured and unstructured data – offers a better option.

Moving your data to a lakehouse enables a smooth flow of information that transforms every aspect of your organization, from personalized customer experiences to predictive maintenance. Data lakehouses are the answer to the data sprawl that is common with traditional storage options like data warehouses.

Lesson #6: Don’t reinvent the wheel.

Before bringing in dozens of engineers and asking them to develop data solutions, perform a cost-benefit analysis of building versus buying. Today, in most cases, opting to buy is the more advantageous route. This approach mitigates the uncertainty associated with the probable success of a newly built solution. Plus, building often requires bringing in numerous data scientists – a challenge given the talent shortage.

With a purchased solution, the cost will likely be less if you have sufficient data storage and a few experienced data engineers available (either in-house or outsourced). For optimal success, consider developing solid partnerships and integrating your data with existing tools that can satisfy your requirements.

Lesson #7: Data is a business asset, not a cost center.

The business and IT sides of an organization may have conflicting objectives. The business side of an organization is often eager for the most information at the lowest cost, while IT wants the best technology available.

As a data leader, I have learned how important it is to respect data’s value to the organization. The business side of an organization must recognize this and invest in technology solutions because data can deliver a huge payoff if appropriately used. This requires looking beyond structured data to semi-structured and unstructured data. The right technology can help you derive value from all types of data.

Advance your data approach with Unisys

One of my favorite things about data is that the learning never stops. While I have collected many lessons during my more than 25-year career, I continue to gain new insights (pun intended). I am excited by what AI has achieved already and eager to witness how its growing use will shape organizations and increase their appreciation for their data – and play a role in driving that.

If you want to optimize your data, maximize its value and prepare it for AI, read more lessons in our “Data Visionaries: Five pivotal strategies and eight transformative stories” eBook and “Data readiness for AI: A practical guide for preparing your data, regardless of your starting point” and explore how Data and Analytics solutions from Unisys can help.