Last Wednesday I presented at an event organized by IBM and FordhamUniversity
Through surveys that were recently completed by business and IT leaders around the world, IBM has determined that the corporate interest in business analytics and information-based (rather than gut-based) decisions is increasing, while the number of qualified candidates who can help such organizations remains small. Similar conclusions have been reached by researchers at Villanova University's School of Business in a study found here. The Fordham program will educate individuals on how to blend business with quantitative analysis skills. Today universities graduate “pure quants” that find their way to many corporations from Wall Street investment banks, to internet social networks like Facebook. Pure quants have obviously strong quantitative analysis skills but relatively weak business skills.
I was asked to talk about the drivers for business analytics and their impact on investments in new analytics companies. For several years Trident realized the importance of analytics in business decisions and has invested in several software companies that either develop applications and platforms to support the creation of business intelligence and analytics or their business is mainly driven by analytics. Today almost 20% of our active portfolio companies belong to these two categories. Moreover, we continue to look for additional investment opportunities in these areas here and abroad.
I see three drivers for today’s growing investor and corporate interest around business analytics:
- Big data. Data is becoming strategically important to enterprises of any size and type. It is also being generated in unprecedented volumes. This is particularly the case with Internet companies. Yahoo, Fox, AOL and some of the larger ad networks routinely generate upwards of 100 TB of data per year each. Facebook is generating an order of magnitude more data than that. Once prepared for analysis, the size of each such data set can triple in size. By comparison, in the 90s, when I was running IBM’s BI Solutions organization and later when I was the CEO of Customer Analytics, we were dealing with data warehouses that contained, at most, a few terabytes of data and most frequently only a several hundreds of gigabytes. To analyze big data in an impactful and timely manner, we need new data management paradigms (e.g., we are starting to see the broad use of column-based databases, and the emerging use of Hadoop by a variety of organizations across several industries) and new analysis paradigms that efficiently combine solid analytic techniques with business/industry knowledge, practices, etc.
- The quest for performance-driven decisions. Corporations are moving from report-driven decisions (where business intelligence was used only to passively present historical data so that a business executive can make decisions) to performance-driven decisions where analytics are used to, oftentimes automatically make decisions that impact corporate performance. For example, ad networks combine sophisticated analytics with novel data management to determine within a few milliseconds which ad to show an Internet user. Sophisticated optimization techniques are used by ecommerce companies to determine the price of keywords used in search advertising or the price they will pay for a new customer lead.
- The need for broadening the corporate use of analytics. In their quest to achieve analytics-driven decision-making, corporations are accelerating the use SaaS BI and cloud-based analytic solutions because of their lower total cost of ownership, speedier implementation compared to their on-premise equivalent solutions, and support of community-based and collaborative problem-solving.
Realizing the impact of analytics-driven decision-making, corporations are now thinking about analytics while designing new business processes and the associated applications, rather than after the fact, as was happening to date. By comparison, we used to build data warehouses and BI applications by extracting and reporting on data from systems that had been around and were never designed for analytics-driven decision-making, e.g., ERP systems, or supply chain management systems. Today, most state of the art applications are designed with an analytics component instead of being fitted with one.
Over the next couple of years we will be able to assess whether Fordham’s business analytics curriculum will have the desired impact and provide IBM with a rich pipeline of analysts that combine the right mix of the business and quantitative skills needed to satisfy the corporate demand for analytics-driven decision-making.