DATA MANAGEMENT: the key to greater insight, automation and personalization, but how do we get there?
Businesses around the world are looking to leverage data for value-generating activities more effectively. They are spending a significant amount of time and effort thinking about innovative and strategic approaches to mature their data programs. Many organizations have ambitions to be in the advanced stages—beyond using predictive analytics to optimize data-driven processes and toward using machine learning to actually improve customer journeys. Some may be close. However, the truth is most organizations lack a strong data governance framework, which hampers their ability to use data as a strategic asset and drive value. In this article, Paul Gibson and Nam Tran look at the data management themes challenging an organization’s ability to deliver value to customers and shareholders. Additionally, they outline some next steps to help CDOs effectively establish and sustain their data programs to deliver greater insight, automation and personalization to the business.
At FIMA Europe 2016, the financial data management conference, HSBC Group Chief Data Officer (CDO) Peter Serenita set an interesting tone with his opening remarks discussing how far the industry’s data management capabilities have come and how much further they have to go. The benchmark for this statement comes from the three stages a CDO must work through to implement a fully functional data management structure. These stages include: CDO 1.0, CDO 2.0 and CDO 3.0.
At CDO 1.0, the focus is defining the governance program’s goals and roadmap along with attaining buy-in from stakeholders to successfully engage the organization as a whole. During CDO 2.0, the majority of effort is spent on the implementation of the governance program, including such key activities as identifying the tools that the organization will need to drive the program and defining the target state of the organization’s technology infrastructure. Lastly, CDO 3.0 is where organizations can take advantage of governed data to make informed decisions by using different types of analytics or advanced artificial intelligence (AI) support.
Two Steps Forward, One Step Back
Depending on the size of the organization and its political landscape, CDO 1.0 should take anywhere from three months to one year to implement. Some companies take even longer to advance beyond CDO 1.0. It is easy for companies to want to speed through it or move past it all together before it’s fully ingrained into the organization. Data management functions have a tendency to shift toward investing in the latest governance tools (CDO 2.0) instead of focusing on the basics, such as establishing a sustainable data governance framework (CDO 1.0) where people are responsible for owning the data assets that reside throughout the organization.
Impatience to move to CDO 2.0 or further before establishing a baseline for CDO 1.0 is the fundamental reason why firms are still unable to leverage their data assets to deliver close to their potential value. It stems from the need to prove the value of the governance program, which can be challenging as firms face pressure to compete with new, innovative firms, while juggling greater cost and compliance pressures.
But data isn’t slowing down anytime soon. Over the past two years, more data was created than “in the entire previous history of the human race,” according to Forbes. Firms that are able to organize and govern their ever-increasing data could turn it into a revenue-generating activity. This requires efficient access to clean, accurate data for greater insight generation through a properly governed and centralized organization backed by policies, standards, transparency and collaboration. Those that have begun experimenting with new technologies before fully achieving CDO 1.0 will ultimately struggle to get beyond data quality and compliance issues.
Reestablishing a Foundation
A key initial objective when evaluating the governance framework within an organization (CDO 1.0) is understanding the current state. What is the single source of truth for a particular asset? What does the flow of information from sources and destinations look like for an asset? Who is responsible for the datasets? These fundamental questions take time to answer. Organizations that lose momentum and interest and move onto the next stage of CDO before they are ready will find they have a limited number of datasets they can rely on for value-generating activities.
How can an organization govern an incomplete picture? How can an organization look to improve their technology without fully understanding the current landscape? These are just a couple of questions that can plague an organization should it hastily move past its maturity level.
A big theme across industries today is more efficiently delivering quality customer service. Whether it’s responding to an inquiry or providing an ad hoc report, organizations need the means to meet requests as quickly as possible. Delivering the right information at the most appropriate time only occurs when data governance is properly established because it can identify who owns the data and in which source system it resides.
Imagine a scenario in which a client contacts the organization to seek insight into the types of investments made on their behalf for the year. Will the client representative know exactly where to look and who to ask for this information or will the representative have to recruit multiple employees to analyze and produce that report for the client? In today’s data landscape, it is the latter that tends to happen more frequently.
To continue the example, consider what would happen if the identified subject matter expert is on vacation. How long before a secondary owner is identified? These types of scenarios are playing out across many organizations. To move beyond CDO 1.0 in a maintainable way, organizations must establish a solid governance agenda, including a methodology to identify an owner to every piece of data so that people know the responsible party when they need access or when they need to resolve errors or inconsistencies. This isn’t easy. It’s a challenge for many to achieve data governance buy-in at a time when firms are focused on more direct bottom-line-driven activities to counter shrinking margins and increasing competition. The key is in stories. For the data function to receive the appropriate investment, CDOs have to be able to tell the stories about how their function is moving the organization forward.
CDO 1.0 and Beyond
Given the fundamental importance of data to an organization’s ability to deliver value in this information-driven era, organizations are already asking more of their CDOs. Today’s CDOs need to champion new opportunities, while also staying focused and firm on the governance track. CDOs must work closely with their teams to ensure alignment or else the teams will spend months trying to implement new tools without the proper structure. This process starts by making sure the right story is being communicated and finding the appropriate set of tools for their organization’s data governance maturity level. With them in place, a CDO can show key stakeholders how data can make processes more cost-efficient and customer experiences more personal. Showing incremental value in this way will allow CDOs to maintain support for their program.
CDOs should place the governance program at the front and center of their priorities by holding the team’s direct reports accountable to the program’s deadlines. While CDOs will be pulled in multiple directions, governance must remain their key focus. Those that fail to establish a solid governance foundation and follow the roadmap to CDO 3.0 will not be able to fully maximize the value of the tools that can truly make a difference, such as predictive analytics, AI and whatever comes next.
Paul Gibson is a Business Consultant currently based in New York. As part of the Sapient Consulting strategy team, he conducts research and devises strategies for exploring potential investments and strategic alliance opportunities in the form of partnerships and acquisitions. Paul has extensive experience in how new business and regulatory drivers are impacting the capital markets industry. He has worked with industry participants on products and services strategies, strategic growth areas, digital transformation, data management and quality initiatives. Plus, he has helped organizations understand the impacts of regulatory reform on the industry, designing and implementing solutions to facilitate compliance and more efficient operating models.
Nam Tran is a Data Management Specialist currently based in Boston. Since joining Sapient in 2014, she has worked with multiple clients to mobilize their data governance and quality programs. Her current focus is implementation lead for Sapient Synapse, Sapient’s proprietary data requirements platform, where she advises clients on best data mapping and modelling practices to be implemented in the platform. Prior to joining Sapient, her background was primarily in financial reporting, data analysis, business process redesign and project management.