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Why exploiting your data is essential to thrive

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We talk about being data-driven, but how does data and analytics enable an enterprise to thrive? We share our insider knowledge at FeedStock, alongside insights from our discussions with data and information leaders. Drawing on research from Gartner, McKinsey and Deloitte we look at why data and analytics strategy is essential for every enterprise to thrive in today’s complex, digital and disparate working era.

  • Importance of data and analytics to navigate change
  • The pitfalls of traditional data-management
  • Why you need to start building data capital now

“In these uncharted waters, where the tides continue to shift, it’s not surprising that analytics, widely recognized for its problem-solving and predictive prowess, has become an essential navigational tool.*”

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Why 65% of companies are increasing spend on data

Sisense’s “State of BI and Analytics Report 2020” surveyed 500 data professionals across multiple industries in the US and found that 50% or respondents feel that data and analytics has become “much more important” than before COVID-19. Only 14% of companies are cutting back their data and analytics spend, whilst 65% are increasing spend or staying level. In times of crisis, businesses turn to data to navigate change and uncertainty.

Where should the data and analytics focus lie?

Discussions we have had with data and information leaders across the financial services sector demonstrates that there has been a shift in priorities among the large enterprises regarding the type of BI they are looking to extract from their data. The focus has moved away from identifying new revenue-generating opportunities and driving productivity, towards cost-management, streamlining and revenue protection. We are talking to an increasing number of C-Suite executives who are looking to use data to identify where the cost-control opportunities and operational risks lie. They want answer questions such as: which clients are we over or under servicing? Who in the organisation holds the key relationships with our top accounts? Can we leverage those relationships for other clients? What is the risk of personnel changes? What are our clients’ topic preferences over time, and are there gaps or duplications which could be streamlined?

The answers lie in the data, if you can extract it in real-time.

In these uncharted waters, where the tides continue to shift, it’s not surprising that analytics, widely recognized for its problem-solving and predictive prowess, has become an essential navigational tool. Leaders who embed AI data-capture and analytics enterprise-wide will be in a stronger position to tap deeper into the value waiting to be unlocked.

How can enterprises do more, with less?

Empower, don’t stifle. Equip front-office sales professionals with the data and analytics they need to outperform. Put them in a position to make value-generative decisions, in real-time. The best performing sales teams are 1.5 times more likely to base sales forecasts on data-driven insights rather than intuition. A McKinsey report found that the implementation of data analytics has been proven to increase the speed of initiation of first sales by 50%, reduce churn by 25%, increase sales from new accounts by 10% and improve return on sales through pricing by 2 – 5%.

In today’s rapidly changing business environment, it is more important than ever that sales professionals have the intelligence they need to adapt and respond to their clients’ changing requirements, in real-time. Building a bottom-up culture of data-literacy has enabled the enterprises we work with to capitalise on new opportunities and identify clients at risk. This has enabled them to protect revenues effectively and minimise the impact of the contraction in demand that has been experienced in the past quarter.

The pitfalls of traditional data-management?

Manually input data is simply not able to address the quantity of enterprise data that is being generated every day. A FeedStock case study found that a typical sell-side enterprise with 2,500 capital market professionals could generate 2.5 million action records per day, with 33.6 attributes, leading to 21 billion data points annually. Deployment of NLP & NER algorithms on this data would bring further datapoints and intelligence to the dataset and deliver a whole new layer of transparency for the business.

Manual data logging is completely ineffective in handling this kind of scale. It can also never be in real-time and causes unnecessary drag to workflows across the business.

Looking at the benefits of implementing data-driven practices across the enterprise, it becomes clear that transitioning away from traditional, manual data management systems towards an automated AI-driven data analytics platform needs to be an urgent priority in today’s environment.

Why you need to start building your data capital now?

Even before the COVID-19 pandemic kick-started a focus on leanness and resiliency across every organisation, a Gartner webcast from January 2020 found that 63% of CEOs were already planning to change their business models to address the growing importance of data and analytics in driving value across the enterprise.

The pandemic environment has only accelerated the need to use data and analytics in daily business practices. It is more critical than ever that enterprise leaders make the correct strategic decisions to navigate successfully through the pandemic environment. As we identified earlier, there is a vast bank of data that is available to enterprises if they deploy the correct data-capture and deep learning NLP technologies. The untapped cost-management and revenue-generating insights which lie hidden in this data are key to moving from surviving to thriving in whatever the new normal might be.



*Nicolaus Henke, Ankur Puri, and Tamim Saleh, McKinsey Analytics, Accelerating analytics to navigate COVID-19 and the new normal, May 2020



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