In the late 90s, Oakland Athletics general manager Billy Beane used data to discover undervalued talent and assemble a perennial playoff-caliber team, and he did so on a shoestring budget compared to Major League Baseball’s heavy hitters. Beane’s pioneering use of data analytics became the subject of the bestselling book Moneyball.
Today, whether you’re a small business or global enterprise, you need to play “moneyball” or be left behind.
Analytics and Revenue Growth
Industry research and real-world success stories demonstrate that data-driven decisions are the key to improving revenue, reducing operating expenses, and maximizing profit.
In late 2019, Accenture surveyed more than 8,300 companies to assess technology adoption across industries. It found that the top 10% “leader” companies grow revenue at more than twice the rate of the bottom 25% “laggard” companies. Leaders are data-driven, with 90% ensuring data quality, while only 40% of laggards do so.
Furthermore, 90% of leaders are continuing to enhance their data, compared to just 54% of laggards. Leaders have also adopted automation and artificial intelligence five times as fast as laggards.
Meanwhile, the McKinsey Global Institute reports that data-driven organizations are 23 times more likely to outperform the competition in terms of new customer acquisition, and 19 times more likely to achieve above-average profitability.
Analytics Adoption and Competitive Pressure
Whether leaders or laggards, businesses increasingly understand the advantages of analytics and the ability to make data-backed decisions. In a June 2020 survey, Dimensional Research found that 98% of companies are using business intelligence today, and that 71% of companies plan to hire more data analysts over the next 12 months.
The trend will only grow as industries across the global economy race to adopt modern data infrastructure and analytics. According to a 2019 IDC report, spending on the technologies and services that enable data-based insights is forecast to reach $2.3 trillion in 2023.
In May 2020, IDC introduced the idea of “Generation Data,” or “Gen D.” Gen D is a new generation of workers who were born into a data-rich world. Whether it’s data analysts, data scientists, data engineers or data architects delivering daily insights for lines of business, or a chief data officer making recommendations in the boardroom, Gen D is well on its way to flourishing in digital-driven organizations.
The Value of Analytics in Uncertain Times
Not only is the value of data analytics increasing over time, it’s even more valuable in times of uncertainty. A recent Gartner report, “Top 10 Trends in Data and Analytics, 2020,” asserts:
Massive disruption, crisis and the ensuing economic downturn are forcing companies to respond to previously unimaginable demands to resource optimize, reinvent processes, and rethink products, business models and even their very purpose. Only resilient, nimble and creative organizations will survive and thrive.
Additional research from Gartner found that when companies made a “big bet” on analytics during a crisis period, they realized a 32% greater average return on assets and a 10% greater return on their investment.
The Analytics Paradox: Even Savvy Businesses Struggle
While many organizations recognize analytics as a competitive imperative and growth driver, many have struggled to do it successfully. New Vantage Partners recently surveyed more than 60 of the biggest brands in the world — American Express, General Motors and Johnson & Johnson, to name a few — and found that:
- 69% have not created a data-driven organization
- 53% are not yet treating data as a business asset
- 52% are not competing on data and analytics
Why do so many organizations — from global brands to nimble, midsize companies — struggle to establish successful analytics programs, especially when most recognize the competitive importance of analytics? There’s a simple answer: data integration.
Data now originates from hundreds or thousands of sources across organizations, and businesses increasingly rely on SaaS applications to handle operations like customer relationship management, billing and customer service. Each app features a web of complex APIs and data models that can change at a moment’s notice. Traditional data integration methodologies are simply not equipped to handle this type of data volume and complexity, and many businesses find themselves saddled with outmoded technology and uncertain how to modernize their analytics programs.
In our blog post “Why ELT Is the Future of Data Integration,” we take a closer look at why data integration is so challenging. We then explore how more modern, automated methods of data integration can easily handle the ever-expanding universe of digital data.