How Organizations Can Build Analytics Agility

Companies must increase their analytical fitness and develop strong muscle memory when they are tested by disruptive events.

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Building a Winning Data Strategy

Building a winning data strategy requires bold moves and new ideas. Creating a strong data foundation within the organization and putting a premium on nontechnical factors such as analytical agility and culture can help companies stay ahead. This MIT SMR Executive Guide, published as a series over three weeks, offers insights on how companies can move forward with data in an era of constant change.

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In an era of constant change, companies’ data and analytics capabilities must rapidly adapt to ensure that the business survives, never mind competes. Organizations seek insights from their data to inform strategic priorities in real time, yet much of the historical data and modeling formerly applied to predict future behavior and guide actions are proving to be far less predictive, or even irrelevant, in our current normal with COVID-19.

In order to survive through crises, proactively detect trends, and respond to new challenges, companies need to develop greater analytical agility. This agility comes from three areas: improving the quality and connections of the data itself, augmenting analytical “horsepower” at the organization level, and leveraging talent that is capable of bridging business needs with analytics to find opportunity in the data.

The Answers Are in the Data

The quest for better data is not new, but the cost of not having it is easier to substantiate and understand in a time of crisis. Gaps in data quality — whether it’s time-lagged, disconnected, insufficient in granularity, or poorly curated (rendering analysis slow or impossible) — become intolerable amid chaos when companies must act quickly. Crises can be opportunities to augment data quality and further enrich the data to better serve customers and the company.

Making the business case for data investments suddenly makes sense as business leaders live through data gap implications in real time. Monetizing data typically comes from four sources:

  • Connecting data with other data differently than before.
  • Getting new data sources, or more specific levels of the same data you already had.
  • Putting data to better or faster use than the competition.
  • Getting data faster.

Data and analytics leaders must frame investments in the current context and prioritize data investments wisely by taking a complete view of what is happening to the business across a number of functions. For example, customers bank very differently in a time of crisis, and this requires banks to change how they operate in order to accommodate them. The COVID-19 pandemic forced banks to take another look at the multiple channels their customers traverse — branches, mobile, online banking, ATMs — and how their comfort levels with each shifted. How customers bank, and what journeys they engage in at what times and in what sequence, are all highly relevant to helping them achieve their financial goals. The rapid collection and analysis of data from across channels, paired with key economic factors, provided context that allowed banks to better serve customers in the moment. New and different sources of information — be it transaction-level data, payment behaviors, or real-time credit bureau information — can help ensure that customer credit is protected and that fraudulent activity is kept at bay.

Making the business case for data investments suddenly makes sense as business leaders live through data gap implications in real time.

Every data and analytics team has a roster of data demands that outpace budgets. Foundational data investments often languish and are perpetually underfunded because their value is difficult to isolate and describe to others. Events such as COVID-19 pinpoint investments that are likely already on the list but lack sufficient organizational buy-in to propel them forward. As the adage goes, never let a good crisis go to waste — use it to enrich your data and customer understanding.

Augment Analytical Horsepower With Business Parameters

Experienced analysts recognize that all analysis requires a blend of art and science. However, the nature of crises and unusual events means that analysts simply do not have the standard observation windows on which to build their typical projections and formulate baselines. History is no longer as useful. Moreover, rapidly changing market conditions require teams to constantly readjust models and analytical approaches to stay current. This requires adequate datasense (a blend of analytical facts and business intuition) in order to set the right parameters and optimize the business.

We often think of business rules as the antithesis of data-driven decisions or models, because they introduce a layer of subjectivity into a world that thrives on objectivity. In an unrecognizable crisis, however, establishing these parameters is important for a business to function practically and make basic analytical decisions. In our banking example, consider the customer conversations happening within a branch network throughout COVID-19. Historically, there has been a set capacity available to handle and fulfill requests, and the nature of conversations — whether financial check-ins, mortgage renewals, or efforts to ensure that customers have the right financial solutions to meet their immediate needs based on emerging life events — has been known and model-driven.

In the current reality, that constraint-based optimization problem is turned on its head because customer needs are vastly different, staff availability is in flux, branch hours are changing, and orchestrating effective customer conversations requires new analytics. Augmenting the process with business rules helps define the parameters of what can be done. Next, the whole approach to customer conversation optimization needs to be recalibrated (across branches and every channel) based on circumstances that are changing daily because what you are optimizing for has changed. And although machine learning and automation can help, the vast aberration in the data from the pandemic means that it will take time for such solutions to adapt and become relevant.

This reveals a need for a different and augmented approach to analytics that suits the time. It necessitates a greater blend of “art and science,” of “(wo)man and machine,” and of business rules and models in order to navigate gray areas.

Business-Analytics Hybrids

Navigating uncertainty and responding to change requires an exceptional translation layer — a team of individuals whose skill sets blend a superior understanding of the business with the technical acumen to transform data into insight. The world is always hungry for these skills, and in the future it will make the difference between brands that are nimble and thrive and those that languish.

The COVID-19 pandemic has been characterized by rapid unpredictability and never-before-seen trends. For businesses to adapt and make key strategic changes in short order, they need teams with hybrid skills, capable of both finding opportunity in data and executing quickly and accurately when the business knows what it wants to do.

In a crisis, data and analytics can overwhelm leaders and prevent them from acting quickly on account of the sheer volume, pace, and continuous shifting of data. In order to break down emergent trends and properly contextualize them, organizational functions must come together in new ways. While few businesses ever anticipated needing to transform into operating as fully remote workforces, many are seeing that the intense collaboration and connectedness of their people have formed strong virtual networks that are at the heart of their survival.

A Culture of Analytics and Business Collaboration

When teams come together to interrogate new, shifting data from multiple perspectives, they begin to gain comfort in establishing more “knowns” in an unknown world. The result is that leaders can make the best possible decisions in the most rapid time frame. Done well, this will help companies thrive in disruption and gain competitive advantage, but it requires a high level of analytical literacy throughout the business and, most important, a culture of collaboration.

Facilitating these ongoing exchanges can happen in a variety of forums, ranging from the formal (such as customer optimization forums and risk exchanges) to the informal (such as real-time dashboards and views of computations on the fly). It’s important that this collaboration is continuous, interactive, and inclusive, with both business and analytical teams present so that the data is properly interpreted and all stakeholders understand any actions that are required.

In order to detect and respond to disruptive events with agility, companies must increase their analytical fitness and develop strong muscle memory when they are put to the test. Navigating the pandemic often feels like running a marathon at sprint speed into the dark, and an agile approach to data and analytics will be the headlamp that companies cannot do without.

Topics

Building a Winning Data Strategy

Building a winning data strategy requires bold moves and new ideas. Creating a strong data foundation within the organization and putting a premium on nontechnical factors such as analytical agility and culture can help companies stay ahead. This MIT SMR Executive Guide, published as a series over three weeks, offers insights on how companies can move forward with data in an era of constant change.

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AWS
See All Articles in This Series

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Comment (1)
Xose Xavier Rodriguez Rivera
Thanks a lot  for this interesting article.In the last 5 years I've seen C-level executives obsessed with diferent models of data governance but the future is governing the data and you need these hybrid profiles the translators  that they can modify  assumptions,strategies, KPIs and take decissions on the fly if it is needed with the best BA/IA support available. How important are these profiles (sometimes in no man's land) to fit the pieces within a company.