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.

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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.

Brought to you by

AWS
More 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.