A strong plurality of corporate executives (48.4%) are for the first time reporting that their investments in Big Data are yielding measurable business results, according to NewVantage Partners annual Big Data executive survey, released earlier this year. Respondents comprised C-executives (92%) for leading mainstream corporations ranging from AIG to Wells Fargo in financial services and from Bayer to United Healthcare in the life sciences. The survey suggests that several factors are attributing to this newfound success:
- Data volumes continue to proliferate at geometrical rates, driven by new sources of data, including sensor devices;
- The pervasiveness of digital services and the Internet of Things (IOT) is making data more accessible and available for immediate action;
- In contrast to traditional batch-oriented data management approaches, which could be cumbersome and expensive, Big Data is iterative and nimble;
- Big Data is empowering Artificial Intelligence (AI) and Machine Learning applications by providing large data sets that are accessible and operate as scale.
While the impact of the first three factors has been established for several years now, Artificial Intelligence is emerging as perhaps the most powerful factor yet in enabling Big Data initiatives to operate at greater scale. Let’s examine the evolution of Big Data in context.
Failing Fast and Learning Faster
Big Data approaches differ from traditional data management approaches in providing a foundation that is particularly well-suited to discovery, experimentation, and iterative learning. To take advantage of this flexibility, a growing number of corporations have invested in Big Data labs, analytical sandboxes, Centers of Excellence, and other “experimental” testing environments. These firms are leveraging the agility of Big Data approaches to streamline and refine their data and analytics processes to eliminate redundant and non-indicative data, and focus on highly predictive elements that facilitate rapid, flexible decision-making. The chief benefit from this agile approach is an ability to act quicker, make decisions faster, and introduce new products and services to market with greater speed. Data agility enables more dexterous and innovative business practices, creating opportunities to disrupt traditional ways of doing business. Simply put, firms can fail fast, learn faster, and innovate with greater speed. In dynamic, rapidly evolving markets, this advantage becomes essential.
Leveraging “Quick Wins” to Build Business Momentum
Big Data is following a predictable business arch as adoption expands. Initial Big Data efforts focused primarily on expense reduction – the “low-hanging fruit” for demonstrating proof of business value. These cost-savings initiatives focused on “defensive” measures to reduce expenses through process improvement, elimination of operational redundancies, and by transitioning many data processing functions from expensive data warehouse environments to lower-cost Big Data environments. Nearly one-half (49.2%) of all executives indicate that they have now decreased expenses through operational cost-efficiencies from their investments in Big Data. As a result, firms have been able to demonstrate “quick wins” that build credibility and establish momentum for more ambitious Big Data initiatives that are tied directly to innovation and disruption.
Big Data as Fuel for Innovation
Increasingly, businesses are seizing the initiative and are aggressively moving to undertake “offensive” efforts explicitly intended to change how they do business. Executives are turning their attention to new ways to innovate and adapt their businesses in an information-rich world. Over half of executives report their firms have now launched innovation initiatives in several key areas:
- Establish a data-driven culture (69.4%)
- Create new avenues for innovation and disruption (64.5%)
- Accelerate the speed with which new capabilities and services are deployed (64.5%)
- Launch new product and service offerings (62.9%)
- Monetize Big Data through increased revenues and new revenue sources (54.8%)
- Transform and reposition your business for the future (51.6%).
What stands out is the disruptive potential of Big Data, and the zeal with which major companies are looking to leverage Big Data capabilities to drive transformative change within their organizations.
Leveraging AI at Scale
In a recent MIT Sloan Management Review article, I discussed How Big Data is Empowering AI and Machine Learning at Scale. Access to more data, in larger quantities than previously available, are enabling AI and machine learning by providing greater sample sizes, and through Big Data approaches, are enabling companies to use “all the data” to enrich AI and machine learning algorithms. A majority of the firms surveyed (68.9%) are now employing AI and machine learning capabilities within their business, with a vast majority (88.5%) anticipating that AI and machine learning will have an impact on their business during the next decade.
Big Data has moved from big idea to operational reality for most firms. The business impact is just beginning to be felt.