Business Outcomes-Driven Data Governance to Maximize Value of Transformation Investments

In today's information-driven digital economy, data drives competitive advantage. Customer-centricity and product-centricity are on every CEO's mind to improve top-line growth and drive operational efficiencies. The current pandemic has created urgency for companies to focus on what’s really important: providing value to customers and accelerate digital and data transformation initiatives. This is leading them to invest significant dollars to establish capabilities in cloud infrastructure, customer relationship management (CRM), master data management (MDM), data lakes, and buying off-the-shelf advanced analytics solutions. However, many of these investments fail to bring the significant gains that executives expect and don’t deliver on the promise of improved customer segmentation, deepened customer relationships, and an optimized end-to-end customer journey. Often, a key reason is that transformation programs fail to address data governance and data quality issues. For these programs to be successful, it's important to understand where the data is sourced, establish data ownership, an assessment of data quality, and restrictions related to data privacy and usage. Data Governance, a topic which many of us have been involved with for decades, is as important today in the age of artificial intelligence, machine learning, and advanced analytics, as ever. Data Governance enables companies in achieving defensive goals (e.g., securing client sensitive data) as well as offensive goals (e.g., cross-selling/up-selling opportunities) through improved data quality, data accessibility, ownership, and users’ trust.

Maximize Business Value of your Data Governance program

Let's look at a few practical insights for the successful roll-out of the Data Governance program and to maximize the business value of transformation related investments:

Connect data governance outcomes to tangible business goals and objectives

While the benefits of a data governance program go way beyond cost and efficiencies, identifying tangible cost savings gives impetus and executive buy-in to launch the program. Estimate the time spent and cost of data wrangling, reconciliations, shadow-IT processes, data troubleshooting, and defects upfront in the program. Many of our clients are creating sound business cases to articulate how the program can be self-funded through incremental cost and efficiency benefits.

Give ownership of the data to the business

IT cannot own master data (e.g., customer, products) and transactional data (e.g., ecommerce, customer support) that is crucial for enabling business operations and identifying revenue opportunities. Our clients nominate data-savvy individuals from business units as data owners and stewards and engage them early to define standards, ingestion rules, standardize definitions and usability of data. The program's success hinges upon business stakeholders getting their hands dirty and helping to drive data-related decisions.

Control cost and effort with an iterative, minimal viable product (MVP) approach

Agility and adaptability are critical if you want to avoid costly development and diminishing returns. Start small by selecting a small subset of data elements and strive to create a use-case driven MVP to achieve a discrete goal like an individual report, KPI, or process/system improvement. This approach gives users time to adopt change and delivers tangible benefits through small improvements. It also allows for the flexibility needed to adjust to changing business needs.

Create a strong data management foundation before tackling other initiatives

We caution clients against starting an MDM, CRM, or ERP transformation program without first investing in data governance and data quality. Data Governance lays out the groundwork by defining the operating model, roadmap, execution processes, and technology infrastructure for other initiatives. It gives companies a toolkit for identification of value-driven use-cases, prioritization guidelines and guardrails for change management.

Invest in data leadership

Data governance is not a part-time job. Pragmatic data-focused leadership and a collaborative approach is imperative to building and scaling successful programs. Sometimes, hiring an interim leader to fill a critical data leadership gap is a great way to jump start progress while searching for the ideal permanent candidate.

Secure enabling tools early in the process

Enable teams working on data governance program to work efficiently by giving them the right tools for business terms definitions, data profiling, and data lineage documentation early on. Cutting corners by using SharePoint and Excel Files that poorly fit to tasks will slow implementation and compromise results. Fit-for-purpose data governance toolsets ensure measures of integrity and security right from data gathering to data profiling. It enables better decision making, operational efficiency, understanding of data flows, improved data compliance, and increased revenues.

Conclusion

Achieving results at scale requires executive leadership who advocate for the importance of data as a strategic asset and critical enabler to enterprise change, and who commit to employing data governance best practices. The road to recovery from the current pandemic is paved with data, and companies that embrace data governance and data quality in their transformation journey will be competitively positioned in a post-pandemic economy.

Vipul Parekh is a Senior Director with Alvarez & Marsal Corporate Performance Improvement in New York. He has 18+ years of experience in financial services, insurance, and healthcare industries leading digital, data, cloud, and infrastructure transformations to improve organization performance and effectiveness.

Articles are author’s own.