Introduction to Corus Advisory Services
Tech Development: The technology Corus builds is Commitment to Our Clients
Our proprietary technologies are designed to address many of the deficiencies of the traditional advisory model, including: objectivity, testing of assumptions, purpose-fitted data, and robustness.
How Corus Operates
Corus is more than the sum of its team and technologies. We have a distinct approach to problem solving, which seeks to maximize the advantages of our experience: a judgment, creativity, and advanced analytics. Some of the ways in which this approach gets applied concretely is explained at greater length in the advisory domains described below.
As a boutique firm we offer unparalleled commitment to our clients. Our three service domains provide flexible options when designing the right approach that’s tailored to solve your particular challenges. Our ultimate role is to guide clients towards actionable and achievable outcomes that have predictable results. Where budget constraints, market resistance, or other factors limit our clients’ leeway to maneuver, we’re creative in plotting a new course on-the-fly. Having built our own internal capabilities from scratch, we know how to be lean and fast in helping build our clients’ businesses, too.
Please contact us to learn more.
Market & Customer Insights
The powerful Corus opinion platform reaches our own app panelists in the US, as well as 80m additional respondents worldwide through our integrated partner panels.
Learn how better data, in conjunction with superior analytical methods, can delivers insights your company can confidently act on.
Your company may not have enough analytical resources. Or, you may have a department full of statisticians and data scientists, and yet still lack sufficient bandwidth to address newly emerging challenges.
Our bench of contract analysts can help. We provide a range of support models that will let your company access world-class, on-demand talent.
Where the scope of your business challenge exceeds the domain potential of research and/or data analysis, Corus’ advisors can plot a broader set of a strategic recommendations.
Our collaborative and structured approach to problem solving will give you a clear understanding of how to proceed in the market.
Delivering Analytics as a Service – Not a Solution
“Big data” solutions remain in vogue across the private sector, but for most companies we believe they continue to represent niche tools that should supplement – not stand in for – an “all-purpose” analytics capacity. Big data solutions are undoubtedly well-suited for identifying potential patterns in vast, emerging datasets. Yet even in these scenarios they come with a downside, which is their frequent inability to explain how or why a pattern was discovered in the first place. (Given the corresponding liability to misinterpret the “noise” in a dataset as the “signal”, financial traders who’ve applied machine learning technologies to market data haven’t yet put their systems on autopilot.)
An “all-purpose” analytics capacity, by contrast, entails building a culture and a process around statistical insights, so that they can be used to inform decision-making at any point in an organization where data is stored, and where the outcomes likely outweigh the cost of the analysis. Standardized, big data solutions simply aren’t designed to cope with the multiplicity of data types, business problems, and requisite methodologies that characterize the analytical challenges facing most medium and large enterprises. Above all, big data solutions haven’t yet managed to make the human element redundant from the task of analysis. This is no trivial thing. To creatively apply several different modeling vectors to a single problem, such that conclusions can be independently corroborated, requires that an analyst be able to tie the business context to the state of the underlying data – and moreover to the specific parameters of her chosen methods – so that the results can ultimately be interpretable as an honest appraisal for what’s happening (or for what’s likely to happen) in the real world. A lot can go wrong with this formula, which is where Corus enters in.
Our Client Support Models
In one way, at least, can expert analytics be compared to expert accountancy: both professions maintain an industry-agnostic body of knowledge and practical skills that can be applied, as appropriate, within the particular settings of virtually any business. To get started on your analytical challenges, we therefore only need a relatively clean dataset and – in proportion to the scope of the task at hand – sufficient contextual understanding of the business problem(s) to be solved for. Where we go from there is up to you. Pursuant to our advocacy for building and sustaining an “all-purpose analytics capacity” at your organization, we’re able to offer support under a variety of different partnership models:
Staff augmentation retainers (monthly, quarterly, or annually)
- Tap veteran experience for less than the cost of an employee
- Reserve fractional or full-time equivalent support
- Outsource and resolve complex, multi-stage analytical initiatives
- Minimal supervisory oversight required
On-demand hourly support
- Ideal for adding flex capacity and surge support
- Inexpensive access to peer review
- Refresh old analyses with new data and an outside perspective
Education, evaluation and training
- Define and embed best practices
- Audit veteran analytics departments
- Stand-up new teams and organizational processes from scratch
- Vet potential recruits
- Train junior staff on measurable standards and robust methodologies
- Evaluate the (mis)use of analytics throughout your broader company
How We Work
Our process for analytics was designed to ensure that we can consistently meet our own strict standards for statistical excellence – the criteria for which we capture in the acronym “STARTS’’.
- Beyond the narrow objectives of a given analytical effort, what else might be achievable with this dataset? What questions should the client be asking that they aren’t? How could the findings of an analysis be misapplied by the client?
- For every analytics engagement, Corus brings a multidisciplinary team approach that helps broaden the perspective. Analysts liaise with clients and execute work. Senior leadership then challenges analyst assumptions, probes related strategic considerations, and ensures that the communication of insights is fully responsive to the business needs.
- “Analysis paralysis” stems from lackluster analytic techniques – not from indecision to use results.
- Comprehensive, corroborated analysis should conclude, within a knowable timeframe, with decisive insights.
- Corus relies on our seasoned leadership to accurately scope client work. We candidly apprise clients of how much analysis is the “right” amount, and at what point a dataset is unlikely to divulge any further insights of interest. We furthermore develop automated sub-routines to improve the efficiency of certain labor-intensive data management and formatting tasks.
- Analysis without the intent of acting is academic theorizing at best, and busywork at worst. Defining how a business will operate differently as a consequence of specific findings helps determine what insights need to be included in the final deliverable, and therefore dictates how the data needs to be organized and the analysis itself structured. Before any analytical work is begun, it’s imperative that the fundamental objective(s) of the effort be clearly understood by Corus and client alike. This step also mitigates any time lost to repetitive follow-up conversations.
- Robustness of analysis requires integrity of data. “Dirty data” do not completely prevent real analysis; they just increase the amount of caution one must take when interpreting results.
- Robustness also requires a holistic view, as a simple topline metric devoid of information about variance or outliers prevents meaningful separation of noise from trend. Metrics mean nothing without understanding the underlying drivers. Pivot tables do not suffice, as we must account for incremental, multivariate impacts. Rigor includes post-mortem diagnostics, especially in forecasting, with any meaningful variances leading to investigation.
- It’s not just about “being right”; it’s about “how it’s delivered”. Analytic deliverables given through opaque, black-box mechanisms can create skepticism and reservation on the part of the client to act. Transparency ultimately fosters trust and credibility, and includes explicit sharing of all assumptions, processes, and types and performance of methodologies and models.
- Scalability addresses the “how” of conducting an analysis and about spurring ideas of an initiative’s evolution. Repeating or expanding an analysis implies the opportunity to construct the initial analysis accordingly. This requires efficiency in programming, automation, and an eye for potential. The benefits of setting up an analysis for scalability must outweigh the effort, of course, yet the very mindset may illuminate opportunities to approach the current analysis in new ways.