Q&A with Data Consultant, Karim Jessani

Over the past 12 months, Kainos’ Canadian office has had considerable growth with Karim leading the data team as a Data Consultant. 

What are your main priorities as a Data Consultant at Kainos? 

Leading and coordinating data and analytics governance efforts, as well as playing a key role in fostering a culture that values data as a strategic asset. I am tasked with providing strategy and domain expertise in the essential aspects of Ddata Mmanagement / Ggovernance, as well as being a valued partner in other key areas. 

Moving to the cloud, robust enterprise data governance, master data management, self-service analytics, AI, data lakes, and a data warehouse were all part of a data strategy ten years ago. While these are still relevant, I don’t believe they should not be the primary drivers for a Data Strategy.  Growth, innovation, and M&A, as well as themes including business process optimization, data monetization, data markets, regulatory compliance, and data privacy, should be the primary focus. This involves mapping out the data strategy, implementing a data-focused technology landscape, becoming a change agent in making the organization data-literate and constantly realigning the information inventory based on business outcomes and needs.  

My goal is to use information as an asset and find new ways to use that data to improve the bottom line of the organization. 

Tell us a bit about your background: 

I have experience throughout the entire systems development life cycle; covering data management and information architecture, project and program management, software engineering, and development.  

Over my twenty-five-year career, I have held leadership roles in numerous data management assignments in private companies as well as government agencies. My consulting assignments have provided opportunities to observe examples of effective data governance, as well as situations where effective data governance has seemed an elusive goal. 

I've worked with cross-functional IT teams on business strategies, needs, product roadmaps, services, products, and features. Creating actionable recommendations and setting goals and measurements that are aligned with the service transformation goals. One responsibility was to analyze technology goals and target adoption potential/timing in order to determine product development targets that are aligned with defined customer journey maps. 

What do you get up to in your spare time? 

I am very particular about my health, I love going on long walks with my dog when I have time to spare. This allows me to get some fresh air, take a break from looking at my monitor, and time for reflection.  

Going out for a cup of coffee or dinner with my friends and family is my idea of fun.  

I read current blogs and catch up on current events. If not for the current health situation, I’d be travelling on my time off. Travelling gives me a chance to meet people from different nationalities, to learn about traditions, customs and culture. It helps me realize some of what is essential when I can talk to people, live alongside them, shop in the same places they do, and feel out of my comfort zone in their environment.  

One of my favourite quotes is “Travel is fatal to prejudice, bigotry, and narrow-mindedness”. 

What are common misconceptions about data governance in business?  

1. Treating data governance as a technology project run by IT.   

The truth is, given the inherently variable nature of data governance, policy creation should not be considered as a project that can be planned and released. A data governance policy that does not adapt to changing requirements will eventually collapse. Worse yet, such a guideline may be perceived as an inconvenient stumbling block to getting work done, prompting teams to devise their solutions. 

Data governance should be treated as a business challenge that IT has a partnership in.  With more people relying on high-quality data to accomplish their jobs well, everyone has a stake in figuring out the best way to validate data. Business units can no longer assume IT knows the business requirements for which quality must be validated, and it should not be forced upon them to leverage data integration technologies solely to handle quality in today's data-driven world. In the future, business users will need to build their quality procedures since they already know what they want, where they want, and how they want it. In addition, getting more involved in actually executing quality checks will make life much easier for the organization and speed up the process. 

2. Data governance is extremely costly and time-consuming 

The truth is: Data is today's most valuable asset for any company. Data will deliver the ability to derive insights across all business units, allowing for better decision-making throughout the company. Users will adapt more readily a simple data governance programme and structure that integrates organically into business processes, resulting in better decision-making across the enterprise. Creating self-sustaining Data Quality environments and solving overarching problems makes it possible to drive value and eliminate the process of continuously acquiring funding. 

An organization has to look at it from the other side of the coin and consider the financial benefits of implementing a Data Governance Strategy, some of which include: 

• Elimination of time spent by different knowledge workers hunting for the same or comparable information. 
• Due to poor data quality, this results in more rework and rationalization. 

• Data is better understood and more easily used, resulting in increased productivity. 
• Fines are being reduced or eliminated as data is better supported by regulatory compliance. 
• The return on investment (ROI) associated with various analytics activities can be calculated, assuming that better data availability and quality support them. 
• Improvements are eliminated due to financial restatements. 
• Organizations which do not have strong data governance programs miss out on data-driven possibilities and squander money. Data processing and cleanup can take up to half of an analytics team's work, even for highly compensated data scientists, limiting scalability and frustrating personnel. This results in employee productivity being harmed across the board. 

What are the biggest opportunities our customers can leverage by proactively implementing a data strategy? 

Data governance should be treated as an “accelerant” to all data-driven initiatives.  For all projects that I have been involved with, introducing DG into the project has enabled the project to be delivered ahead of schedule. 

Effective data management policies and processes improve business outcomes and drive business growth, which is a prerequisite for enterprises in today's competitive market. Currently, businesses acquire an enormous amount of internal and external data. Effective data governance is required to efficiently use that data, control risks, and cut costs.   

Last, data governance is essential for gaining value from analytics, digital transformation, and other revolutionary prospects. While many firms struggle to get it right, every company can succeed if it shifts its perspective from thinking of data governance as rules and policies to thinking of it as strategic integration into how the organization operates every day.