Fly high, fly fast—just don’t fly blind
Data complexities were keeping a global investment management firm from truly understanding its profitability and hampering strategic decision-making.
THE SITUATION
Imagine you’re a 1940s fighter pilot. You have plenty to worry about, but one thing stands out: New technologies have made planes so much faster that just glancing at your instrument panel while in action can put you at risk. This is when the first heads-up displays were developed—technology that’s since made its way into cars and other products, all with a common application: getting information to you more efficiently, thus enhancing reaction time and reducing risk.
Recently, leaders at a leading global investment management firm found themselves contending with a similar problem. They, too, needed crucial information to operate with agility and minimize risk, but they just weren’t getting that information quickly enough.
In this case, the information was data for generating insights about profitability of products, clients, sales channels, regions, and countries. Normally, this data would tell the leaders how market flows and levels, as well as operating costs, affected the firm’s profits, but there was a problem: The data behind the firm’s financial planning and analysis (FP&A) work overall—profitability reporting being just one piece—was uneven, the speed with which it was gathered (too) slow. The data was disaggregated, its sources scattered and unaligned, the rules governing how it was derived nonstandard (all of which, it should be noted, are challenges endemic to the financial services industry overall).
In fact, this firm’s data and analytics were stitched together from more than 50 sources of varying fidelity, with these inconsistencies remedied via manual processing (as in cross-checking via one-on-one interviews with financial managers and normalizing in Excel worksheets). Given this, profitability results, when they came, were historical; actions taken on these results were limited and paper-based, with no flexibility or agility to support evolving business needs like product and pricing changes.
All this meant that while business moved faster and faster, the firm’s profitability information was still taking months and months to collect and reconcile, with the end results, when they finally arrived, simply not useful anymore.
But if firm leaders felt they were flying blind, one thing was clear: Industry trends were pushing them to tighten up operations—to decrease costs while increasing the value of their investment decisions. But how?
To truly understand their profitability (and do so at the speed of business) they’d need to create a new model for how to calculate it and show receipts along the way—demonstrating how each part of the business contributed to the bottom line. Then, they’d need to shift from their existing profitability model to the new one. And to do all this, they’d need to first clean and normalize data sources, then leave behind the paper and the manual processes and migrate to digital platforms.
It was a big lift. For help with that big lift, and buckling down and addressing their own version of a bedrock, industrywide challenge, they called Deloitte.
THESE DATA ISSUES ARE INDUSTRYWIDE. THIS FIRM TOOK THEM ON, HEAD-ON.
THE SOLVE
Deloitte assembled a team with extensive experience in the investment management industry, with specialties in strategy and analytics, and finance and enterprise performance services. Together with company stakeholders, they’d focus on four areas: improving quality of data; developing the revenues and costs allocation model; automating processes to calculate profitability of products, clients, sales channels, regions, and countries; and developing user-friendly dashboards for company leaders to make business decisions based on all this data. And all of it would be in service of strengthening the firm’s operational model.
One team started in on the root issue—the quality and timeliness of data and analytics—with a goal of establishing a single source of truth. In the envisioned future state, data could be trusted; it would come from reliable, controlled sources whose quality aligned with enterprise standards. There would be a major reduction in the effort needed to compile, validate, and scrub it. And of particular importance: analytics would no longer simply describe what had already happened, but new capabilities would be developed within the company to generate predictive analytics—analytics from which, finally, leaders could model future business outcomes.
A thorough assessment of the company’s existing data and technology landscape confirmed the 50+ data sources (of uneven quality, stitched together manually) and the urgent need to design a new, streamlined model (informed by additional data) that integrated with the firm’s profitability framework.
Deliverables for this part of the project included cleansed and normalized data; a net-new, automated profitability model with allocation rules; and interactive dashboards for insights delivery.
Meanwhile, another team tucked in to work on process improvement and automation. Part of this workstream addressed the lack of harmonization for firm allocation rules (rules describing how the firm divides financial data—like costs or revenues—among its different functions). Ideally, these rules ensure that financial information is accurately assigned, in the same way and in a timely manner, to the right areas for further analysis, reporting, and planning. Lack of standardization means data inconsistencies, which means additional work to resolve them. Another part of the workstream addressed a different opportunity for standardization: reviewing, editing, and approving the information being allocated.
And yet another team focused on bringing all this work together into the firm’s version of heads-up displays: on-demand dashboards that shifted the firm from historical analysis to the real-time insights being unlocked by the other workstreams.
To that end, Deloitte professionals provided strategic guidance to affirm that the refreshed profitability model accurately reflected how different internal groups used technology applications, projects, and services. This holistic view enabled the firm to both rationalize technology spending and extract greater value from its investments.
By embracing digital transformation, firm leaders were poised to innovate while optimizing costs, with their heads up (and on a swivel!) in a fast-paced—and rapidly evolving—market.
IT’S A NEW DAY: EFFICIENCY AND AGILITY AT THE SPEED OF BUSINESS
THE IMPACT
The firm’s profitability project has transformed its approach to technology spending, delivering efficiency, transparency, and significant cost savings while supporting strategic business objectives. In detail:
Efficient costing exercises and reporting. The time required for profitability technology costing exercises has been reduced to less than two weeks, making regular monthly and quarterly analyses possible. Meanwhile the cumbersome, manual process of producing profitability reports has been replaced entirely, reducing processing time from months to less than a week. These efficiencies have allowed for more frequent and accurate financial reviews, enhancing the firm’s ability to respond to changes quickly, and enabling real-time data analysis for better decision-making.
Transparent, accountable operations. Today as never before, the firm can quantify, explain, and predict costs for each business area. For example, detailed information about software licenses and applications undergird accountability for investment decisions and ensure that technology assets are used more efficiently. This clarity helps leaders to see exactly where technology spending is going and how it contributes to overall business objectives.
Reduced technology spend, significant cost savings. Identifying redundant applications, eliminating unnecessary expenses, and enabling better contract negotiations with vendors has significantly reduced overall technology spend, resulting in a cost savings of $20 million within six months—savings that have a direct impact on the bottom line.
Real-time cost insights, future flexibility. The revamped profitability model has provided real-time insights into cost drivers, incentivizing value-creating behaviors across the organization, while the solution architecture, designed for future flexibility, allows for easy updates and new product integration—adaptability that will keep the organization agile in responding to business and regulatory changes.
Enhanced business partnerships and strategic decision-making. By creating a shared understanding of how technology resources are consumed, this project has fostered stronger business partnerships between the firm’s technology and business units. Technology investments are now made with a clear understanding—from both parties—of their bottom-line impact.
Profitability insights and strategic planning. Firm leaders have gained a clear view on how technology costs affect the profitability of specific products and services, with additional insights into regional, sales channel, and country profitability making possible comprehensive analyses and strategic decision-making. This holistic view of profitability supports better resource allocation and strategic planning overall.
UNIFORM DATA, REAL-TIME INSIGHTS…YOU CAN SEE (ALMOST) FOREVER
