Point of View

Focus more on data-centric new ways of working and decision making

August 24, 2021

HFS defines the importance and maturity of data in the HFS OneOffice™ mindset. The OneOffice is where automation becomes a native competency, human performance is augmented by unleashing creativity and personal interaction, and the immediacy of data creates insights to support critical decision cycles. Data management is at the heart of the OneOffice’s success.

“OneOffice is where data drives everything. It is your strategy.
Your core processes must be redesigned to get this data you have to have.
They must be automated and run in the cloud. Then you can apply many flavors of AI to get data at a speed and precision you never thought possible.”
— Phil Fersht, February 2021

When a recent HFS survey asked 800 business and IT executives worldwide about investment changes for smart analytics (predictive and prescriptive analytics), 36% of the leaders said that investments would increase significantly in the next 12 to 18 months.

Exhibit 1: OneOffice: Where data is the strategy, automation the discipline, and AI the refinement

Source: HFS Research, 2021

The recent HFS Symposium panel Discovering Data underscored these sentiments. Participants from HFS, Infosys, Citibank Capital Markets, Lincoln Financial, Wolters Kluwer, M&T Bank, and Soroco discussed the central role of data in driving enterprise transformation across of wide array of industry settings:

  • Reetika Fleming, Research VP, HFS (Moderator)
  • David Cushman, Director, HFS
  • Mohit Joshi, President, Infosys
  • Abhishek Mittal, Head, Data & Operational Excellence, Wolters Kluwer
  • Rohan Murty, Founder, Soroco
  • Ben Rayner, Head of Innovation and Productivity, Citigroup Global Capital Markets Operations and Technology
  • Allison Sagraves, Chief Data Officer, M&T Bank
  • Jamie Warner, Assistant Vice President, Data Science, Lincoln Financial Group
Exhibit 2: The panel discussed the central role of data in driving enterprise transformation

Source: HFS Digital Symposium, June 8-9, 2021

The pandemic created an opportunity to work with new data models and workflows

IT and business executives have always understood data’s important role in enterprise-wide transformation programs. However, there is always a need for exogenous events to get the world to move to new levels, and no change management project could rival the level of attitude change that the pandemic provoked. If nothing else, the pandemic was a digital literacy event in which the importance of data was paramount, noted Allison Sagraves (M&T Bank).

Every action associated with addressing the pandemic required access to and analysis of enormous amounts of data in a short ramp-up period. AI becomes critical to this endeavor, noted Mohit Joshi (Infosys). The pandemic resulted in a step-change in how Infosys thought about using digital AI and analytics in its financial services business. Driving this movement even further and faster was the overnight change in mindset to working from home, even in regulated financial markets.

A serendipitous event occurred for Citibank when innovation systems put in place ostensibly for business continuity became imperative for operating in a “business as usual” scenario during the pandemic. Ben Rayner explained that Citibank gained a competitive advantage by leveraging investments it had already made in data and workforce management. A corollary benefit is that aggressive use of AI and workflow has lessened the staff’s isolation while working remotely. A key change in working has been to use AI and workflow to present work to people to help automate decision-making processes, thus easing the burden of isolated decision making.

Rohan Murty at Soroco underscored this point further by noting that the breakthrough in thinking came from leveraging data to understand how work happens, which is more than just disassembling processes into steps. Can you use data generated from a team’s experience to understand broader patterns?

The panel further discussed how to build trust in new workflows, which Abhishek Mittal at Wolters Kluwer highlighted. A key requirement is to bring in subject matter experts (SMEs) to highlight the advantages and limitations of new ways of working while simultaneously using technology like AI in the background to bring forth insights and inconsistencies. In other words, building trust from the ground up is the key aspect.

Ultimately, we may want to demonstrate the advantages of new workflow models by taking some percentage of transactions (possibly 30%) and executing them end-to-end with automation. These themes  underscore the importance of resiliency as part of AI enablement and build a foundation for trust in technology and new ways of working.

Improving data integrity and ethical data usage and developing the ability to embed data in all key activities will drive successful data deployment

The group delineated the following actions to ensure successful enterprise-wide data deployment:

  • Make ethical use of data paramount. For example, evaluate what COVID-19 data to use and the impacts of smart devices and the data they generate.
  • Eliminate bias at all costs. A key initiative for many companies regarding data is to root out biases. Developing a baseline model to eliminate bias is a first step. As we move to less reliance on intuition and experience in decision making, we must establish baselines with proven results and outcomes.
  • Focus on business metrics to understand the value of new data architectures. So many data lake initiatives have failed to deliver on ROI because they were not rooted in business-centric use cases and goals.
  • Create environments where data needs to be embedded in how things get done. Creating a mobile workforce requires a level of flexibility that could not have been imagined in the past.
  • Data needs to be accessible and the vehicle for automation. There’s a logical linkage between data and automation, and we must internally align along these capabilities to make progress.
  • Use data to break old ways of decision making. Organizations will have to drive new behaviors and cultures that don’t rely solely on gut-based decision making, and start to weave in data and insights. It all comes down to change management.
  • Map and store relationships with obligation graphs as a foundational step for effective data use. Data flows must be mapped to how we run our processes, with meaningful new approaches to metadata management.
The Bottom line: C-level decision makers should repurpose their investments around the primacy of data.

There is a general agreement on the growing importance of data in transformation activities. This reliance on data will expand beyond the walls of the single enterprise, and data will be the primary currency of enterprises’ relationships. Companies are not built around data flows today, but many are moving rapidly in this direction. The use of “everything as a service” delivery models will only accelerate this trend.

In the end, Jamie Warner (Lincoln) reminded us that the transformative use of data needs to be rooted in a narrative, and we all must encourage our colleagues to reiterate stories about the importance of data to get C-level decision makers to repurpose investment strategies around the primacy of data.

Watch the Discovering Data panel

You can read other POVs and a comprehensive ebook about the Symposium, plus watch video highlights of the two-day event, here.

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