Every successful strategy (what choice) starts with a goal (what destination).
A CIO or a data leader needs to identify key goals and scope to successfully create a data strategy.
Key goals and objectives
While every tech or IT team has to deal with a long list of goals, the list of demands from the data team can be even longer due to its cross-functional nature. Every team needs data. Hence, a CIO is required to prioritize 1 - 3 key goals and clearly communicate the trade-offs (cover image above).
Scope of data management
The underlying data strategy helps prioritize the scope, that is, types of data that exist in internal sources and the most relevant external sources. Finding most relevant external data sources is critical since the landscape of internal data vs. external data looks like this:
Customer Success Story
“As one of the major US banks, we leveraged a compliance platform to enhance our risk management capabilities. Our key goal was to improve the risk management KPIs by leveraging data. Keeping this goal in mind, we integrated the most relevant three external data sets to enrich asset pricing data and then applied the risk models to achieve the goal.
Plan for the future
The best data strategy includes scope and capabilities to deliver the short term wins and addresses the future plans. The CIO team is required to regularly review and evaluate (at least once a year) the data strategy and make adjustments to ensure alignment with the evolving business needs. As part of this evaluation, a leader needs to conduct a thorough assessment of the current state of data management, and identify gaps and areas for improvement for the next 12 months.
Just like any other business strategy, data strategy must be an integrated strategy across the organization. An integrated strategy has 5 steps that are repeated at least once a year:
Building right capabilities boosts teams’ confidence on data and helps create a data-driven culture
Create a data management roadmap
A data leader needs to first create a data management roadmap based on the strategy. The team needs to conduct a thoughtful analysis of the business goals, requirements and potential data solutions.
As the tech industry moves rapidly, it’s important to continuously evaluate and solutionize your data tech stack. Since the industry is full of technical jargon, a data leader needs to understand the capabilities, benefits and costs of data lake, data warehouses, data marts, data integrations, data quality tools, etc.
In our experience, it’s typically straightforward to use the base or core functionality of a new tool. Then a data team needs to conduct a thoughtful analysis on the business goals, requirements and potential solutions before committing more to a particular tool. Since most of the tools and infrastructure services offer usage based pricing, this way, a data team can truly understand the cost to benefit ratio.
Customer Success Story
“We worked with several companies that have historically used CRM tools like Salesforce or Hubspot as their primary customer data management platform. While these tools are quite powerful, locking your customer data in a SaaS tool designed to automate processes offers low benefit to cost ratio, creates inflexibility, and is hard (or costly) to scale. Alternatively, aggregating customer data through a customer data platform (CDP) in a data warehouse (structured data) has proven to be a more cost effective, flexible and scalable solution.“
Create (or fix) data management processes
Data in an enterprise of any size continues to be dynamic. Hence, a data team is required to create and improve data management processes that can perform real-time data integrations from various sources/tools, support regular data quality check, and ensure data security. A CIO or the data leader is responsible for generating trust in the data.
When teams show a lack of willingness to use the data, it often means: lack of trust in data and/or lack of understanding of data.
Deliver data analytics and visualization
This is where rubber hits the road – gaining business insights and enabling decision making by leveraging the data. A leader can achieve quick wins through some of the data analytics and visualization capabilities and boost business and non-technical teams’ confidence in data.
Customer Success Story
“In companies of size from $25M - $250M in revenue, it is quite possible, within a quarter of execution, to create sales funnel analytics and visualization by leveraging the customer engagement data from CRM, marketing, and advertising tools. Similarly, a product or services delivery team is able to visualize and analyze user journeys in their product through the user experience data streams within a quarter.”
Build a data-driven culture
In modern enterprises, data is the product that generates profitable and sustainable growth. After creating a data solution roadmap, data management processes and delivering data-driven business decisions, a data leader can foster the right data-driven culture. Each team – sales, marketing, customer success, client services, finance, HR, and so on – leverages data differently.
Team-specific education and training on data definitions, data enrichment capabilities, and overall power of data is required to create a data-driven culture.
In order to build a data-driven culture and support these data management capabilities, a CIO needs the following teams and KPIs for each of these teams:
Data architecture: Designing and implementing the overall data architecture and data definitions including capturing the right data for the enterprise.
Data engineering: Implementing the data solution/tech stack, ensuring data is properly labeled & structured and is available for advanced analytics and business intelligence.
Data operations and governance: Establishing clear roles and responsibilities for day to day data management, including data ownership, processes, data quality, and data security. The data team needs to define KPIs and work towards meeting the KPIs.
Security and Compliance: Ensure that the data strategy is in compliance with any relevant laws and regulations, such as General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), Health Insurance Portability and Accountability Act (HIPAA), etc. as per the applicable industry and nature of data. Typically, data compliance should be part of the data solution roadmap so that these requirements are addressed on a timely basis.
Data Security, backup and disaster recovery: Make sure that the data is backed up regularly and have a disaster recovery plan in place in case of any unexpected data loss.
IT ops and project management: Maintaining the data management systems and infrastructure, and supporting the rest of the data management team.