FeedStock
This white paper draws on discussions we have had with enterprise leaders across the financial services, recruitment and real estate sectors to assess the requirements of a successful digital transformation and identify two key outcomes from its implementation.
“In the next five years, every successful company will become an AI company. It is now the next level of competitive differentiation.”
An empirical study on AI adoption across 151 organisations in the financial services industry found that over three-quarters of all respondents expect AI to form an integral part of their business within two years.*
*Cambridge Centre for Alternative Finance and World Economic Forum, Transforming Paradigms: A Global AI in Financial Services Survey, Q3 2019
Currently, the most frequent use of AI is in risk management (54%), with the generation of new revenue potential the second most frequent use-case (52%); however, an overwhelming 95% of survey respondents expect to be using AI technologies to drive revenue-generation in the next two years. We are experiencing a shift in perspective among global enterprises to utilise AI technology to drive increased revenue generation, improved decision making and to empower front-office employees with instant, actionable insights so they can outperform. This white paper draws on discussions we have had with enterprise leaders across the financial services, recruitment and real estate sectors to assess the requirements of a successful digital transformation and will identify two key outcomes from its implementation.
Our findings from discussions with C-Suite executives in the financial services industry have been supported by a research report carried out by the Cambridge Centre for Alternative Finance and the World Economic Forum which found that 85% of organisations are currently using some form of AI within the business. We very rarely have discussions with business leaders in which the value and opportunities presented by AI are not appreciated. However, we believe that 2020 will be the year to progress digital transformation strategies from “explore” to “action”. With that in mind, this white paper aims to present a framework for a strategic approach to successful digital transformations and to identify two specific outcomes which should result from its implementation.
72% of CDO respondents are now business-goal-driven in the implementation of digital transformation strategies. This represents a shift away from being technology-driven (21%) towards a more strategic, objective-driven approach.*
(Gartner, Fourth Annual Chief Data Officer Study, 2018)
The UK government has shown its intention to promote and rapidly progress the AI industry in the form of a £506 million investment in AI training at graduate level.
Artificial Intelligence is expected to turn into an essential business driver across the Financial Services industry, with 77% of all respondents anticipating AI to possess “high” or “very high” overall importance to their businesses within two years.*
(Cambridge Centre for Alternative Finance and World Economic Forum, Transforming Paradigms: A Global AI in Financial Services Survey, Q3 2019)
Gartner’s Fourth Annual Chief Data Officer Study found that 72% of CDO respondents are now business-goal-driven in the implementation of digital transformation strategies. This represents a shift away from being technology-driven (21%) towards a more strategic, objective-driven approach. CDOs are also prioritising internal data and analytics use (63%) versus the monetization of data and analytics information (31%).
Regardless of industry, sector or vertical, the successful implementation of a digital transformation strategy lies primarily with its alignment to business goals. We have moved away from implementing AI on a technology-based initiative level, towards building it in to the wider enterprise strategy. In Boston Consulting Group and MIT Sloane Management Review’s white paper Winning with AI, CIO of Roche Pharmaceuticals, Steve Guise, lays out the argument that organisations need to keep the focus on strategy, and executives need to appreciate the ways in which “AI can influence entire business models”. This aligns directly with FeedStock’s approach. Delivering solutions that drive tangible business outcomes and bottom line growth must always remain the focus for any successful SaaS vendor.
FeedStock’s previous quick-read article “Capitalising on your digital transformation: converting data into value” fully details the challenges and opportunities of aligning business strategy with digital transformations. Research into transformation strategies in over 1,000 large UK organisations (+500 employees) has found that organisations already making progress on their journey to implementing AI at scale are performing 11.5% better than those who are not, a figure that has more than doubled since 2018. In particular, ‘AI Leaders’ are more productive (11%), show higher profitability (12%) and experience better business outcomes (11%). This is an extraordinary increase and clear evidence of the significant benefits awaiting companies who act now to embed AI across their business.
98% of survey respondents consider data governance important or critically important to business operations.*
(DATAVERSITY and erwin, 2020 State of Data Governance and Automation, 2019)
Only 12% of organisations have fully implemented data governance programmes, this marks a regression since 2018 when it was 31%. This reflects the growing understanding of what full data governance procedures involve.
(DATAVERSITY and erwin, 2020 State of Data Governance and Automation, 2019)
62% of survey respondents (260 business technology professionals across North America) said that the key driver of improving data governance in the enterprise was improved decision making, followed by improved analytics (51%) and regulatory compliance (48%). This is a direct inversion from 2 years ago.
(DATAVERSITY and erwin, 2020 State of Data Governance and Automation, 2019)
Many organizations have started their data governance journeys to achieve data intelligence, but they have not automated their data operations to create sustainable and repeatable practices. Without an accurate, high-quality, real-time enterprise data pipeline, it can be difficult to uncover the necessary insights to make optimal business decisions.
Research done on 151 fintech and financial services organisations across 33 countries found that leveraging alternative datasets to generate hidden business insights is a key part of harnessing the benefits of AI. The survey found that 60% of all respondents utilise new or alternative forms of data in AI applications.*
*Cambridge Centre for Alternative Finance and World Economic Forum, Transforming Paradigms: A Global AI in Financial Services Survey, Q3 2019
A survey of 260 North American technology professionals found that 70% of respondents spend an average of 10 or more hours per week on data-related activities, and most of that time is spent searching for and preparing data.* There may well be a connection between the long hours worked on data-related activities and the fact that the majority of respondents’ data operations are not automated or only mildly automated. FeedStock’s automated data-capture system has proven to collect 10 times more client engagement data and four times the number of unique client contacts than a market leading CRM.
*DATAVERSITY and erwin, 2020 State of Data Governance and Automation, 2019
FeedStock has found that in order to fully realise the advantages of data-driven business intelligence, data operations must be automated across the board. Without automated data capture, the governance housekeeping load on the business is so great that data quality will inevitably suffer. In today’s increasingly complex digital environment, being able to account for all enterprise data and resolve disparity in data sources and silos using manual approaches is wishful thinking.
According to research carried out by Avaloq, wealth managers use only about 35% of their available data.* When done properly, we have found that data integration can lead to increased efficiencies, business growth, cost reduction and risk reduction. The automation of data capture and the application of AI to extract business intelligence from alternative data sources has enabled our clients on the buy-side to reduce their risk of financial research inducement by over 90%.
*Avaloq, Wealth Management redefined using Artificial Intelligence, 2019
However, the benefits of future-proofing an enterprise’s data collection do not stop with compliance risk mitigation. A white paper by erwin, Inc. and DATAVERSITY states that better decision-making is now the main priority when it comes to data governance decisions, rather than compliance which was the main driver 2 years ago. 62% of survey respondents said that the key driver of improving data governance in the enterprise was improved decision making, followed by improved analytics (51%) and regulatory compliance (48%).*
*DATAVERSITY and erwin, 2020 State of Data Governance and Automation, 2019
AI-driven data capture technology is essential to minimise the cost and risk of compliance requirements in today’s increasingly stringent and turbulent regulatory environment. As we progress into the next decade, we will see AI technology being deployed to enable a more collaborative, open and data-driven enterprise. Those firms that are proactively managing the challenge of regulatory change today and embracing the benefits to the entire enterprise that AI technology presents, will be the ones to emerge on top in tomorrow’s marketplace.
Businesses still depend too much on manual approaches to data management. Without an accurate, high-quality, real-time data pipeline, it will be difficult to uncover the information necessary for making the best decisions. Automating data operations creates sustainable and repeatable practices that reduce errors, improve analytics and increase speed to insights.
63% of CEOs said they are likely to change their business models to address the growing importance of data and analytics in driving value across the enterprise.
(Gartner, Data & Analytics Leadership and Vision for 2020, January 2020)
78% of executives see AI driving a high degree of performance improvement in their overall business as well as their teams (75%), their value chains (72%) and individuals (66%).
(Forbes Insights, Everyday AI: Harnessing Artificial Intelligence to Empower the Knowledge Worker, 2019)
Use of data analytics in sales teams has 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%.*
(McKinsey & Company, The five lessons B2B sales leaders should learn to make analytics work, December 2019)
Data-literacy is a term that has emerged relatively recently to describe an enterprise’s ability to read, write and communicate data in context. Data-literacy includes the understanding of data sources and constructs; analytical methods and techniques applied; and the ability to describe the use-case application and resulting value.
By 2023, data-literacy will become an explicit and necessary driver of business value, demonstrated by its formal inclusion in over 80% of data and analytics strategies and change management programs.*
FeedStock has repeatedly seen the value that data-literacy initiatives can deliver:
*Gartner, Data & Analytics Leadership and Vision for 2020, January 2020
The world’s leading organisations are looking at ways to improve data-literacy across their organisation in order to reveal hidden opportunities and provide answers to questions that weren’t being answered before. The challenge surrounding effective data-literacy is understanding the business decisions that need to be made and making the relevant datasets available to assist in those decisions.
One example of this is the use of data to improve customer retention and minimise churn. It is a well-known fact that it is cheaper to retain customers than it is to gain new ones. If you don’t have accurate customer data, you don’t know what they want, when they want it, or through what channel. Real-time, automated data capture not only removes the error-prone requirement of manual data inputs, it also empowers client-facing employees with the intelligence they need to refine and optimise their sales activities.
Gartner Research’s Distinguished VP Analyst, Debra Logan, points out that “data-driven decisions” can seem like an unfamiliar and concerning concept.* Humans tend to want to use the data they have to support decisions they have already made. At FeedStock we have seen how iterative change, along with the close alignment of business goals with a digital transformation strategy, leads to successful implementation of a culture of data-literacy. Enterprises operating in today’s increasingly digital environment are generating data at an exponential rate. However, big rewards await those who embrace the challenge of unlocking the insights within that data to drive better decision making, operational efficiencies across the business, and improved performance for the “knowledge workers”.
*Gartner, Data & Analytics Leadership and Vision for 2020, January 2020
